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One of the next big shifts in patient care will be precision medicine will be "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person," as the Precision Medicine Initiative describes it.

For physicians and researchers this means predicting more accurately which treatment and prevention strategies for a particular disease will work in particular groups of people.

This is completely different from the traditional one-size-fits-all approach, in which treatment and prevention strategies are developed for the average person, with less consideration for the differences between individuals.

What does this mean for healthcare and health IT? A lot of new challenges. Because precision medicine and genomics generate massive volumes of varied and granular data, new approaches to data storage and exchange and new designs for electronic health records, for example, may be required. Physician education and patient communication are two other areas that will demand attention

Some advanced healthcare provider organizations, such as large academic medical centers, are already well-advanced in their precision medicine efforts. But most providers are still early in the journey, if they're attempting it at all. But many are preparing today for what many think will be the next step in the evolution of healthcare.

This story, focused on precision medicine and other emerging technologies, is the sixth and final installment in Healthcare IT News' feature series, "Health IT Investment: The Next Five Years."

The series offers interviews with primarily CIOs to learn from them the path forward through the priorities they set with their investments in six categories: AI and machine learning; interoperability; telehealth, connected health and remote patient monitoring; cybersecurity; electronic health records and population health; and precision medicine and other emerging technologies. Click here to access all the features.

The six health IT leaders discussing their plans for the next five years in this sixth and final installment in the series include:

Cara Babachicos, senior vice president and CIO at South Shore Health, a health system based in Weymouth, Massachusetts

Dr. Shaun Grannis, vice president for data and analytics at Regenstrief Institute in Indianapolis

Matt Hocks, COO at Sioux Falls, South Dakota-based Sanford Health, a $6 billion health system serving a predominantly rural population over a four-state footprint, with both payer and provider arms

Dr. Matthias J. Kleinz, senior vice president and head of translational sciences at UPMC Enterprises, the innovation, commercialization and venture capital arm of UPMC

Mike Mistretta, vice president and CIO at Virginia Hospital Center in Arlington

B.J. Moore, CIO of Providence, a health system that operates 52 hospitals across seven states - Alaska, Montana, Oregon, Washington, California, New Mexico and Texas

Big investments in precision medicine

Precision medicine has been an organizational priority for UPMC for more than a decade, and it has an ambitious vision of using it to provide better, more personalized care and improved outcomes for patients.

"Through these efforts, we aim to create new insights into the drivers of health and disease to allow the discovery of innovative therapies and models of care, while also lowering the cost of care by avoiding diagnostic delays and therapeutic dead ends," said Kleinz of UPMC.

"As one of the largest integrated healthcare delivery and insurance organizations, UPMC has the scale, capabilities and ambition to lead the discovery, assessment and clinical deployment of impactful precision medicine approaches," he continued.

"Our efforts are led by the Institute for Precision Medicine, which was established in 2014 in collaboration with our academic research partner, the University of Pittsburgh."

The mission for the institute is to accelerate translational and clinical research in precision medicine and to deliver the most advanced prediction and treatment of disease, tailored to an individual's unique circumstances, history and condition.

"In this context, we have and will continue to make significant investments in established molecular and genomic tests [and] emerging proteomic, metabolomic, and microbiome assay technology, and drive the discovery of highly personalized precision therapeutic approaches, including cell, gene and regenerative medicines," Kleinz explained.

The most important technologies

Investment in deployment and development of novel technologies is an important pillar in unlocking the value of precision medicine.

UPMC has made a number of significant initial investments in the following areas, Kleinz noted, and is continuing to evaluate new opportunities:

Clinical next-generation sequencing through the establishment of the UPMC Genome Center, a high-throughput CAP/CLIA genome sequencing facility that supports clinical genomics in a number of key areas

Pharmacogenomics in a range of populations at high risk of adverse events or undesirable drug-drug interactions

Innovative precision therapeutics. Through UPMC Enterprises, UPMC provides financial and strategic support to a group of innovative life sciences startups in Pittsburgh and around the globe

Data infrastructure for the streamlined delivery of results, and provider and patient education to maximize the impact of precision medicine

"UPMC's leadership strongly supports this vision and already has invested heavily in the implementation of precision medicine," he said. "The appropriate use of precision medicine approaches benefits first and foremost our patients, but also supports our providers as they deliver care across the UPMC system.

"The tangible benefits are streamlined clinical workflows, improved patient outcomes, and the potential to optimize resource allocation and reduce the long-term cost of care," he continued.

"We are dedicated to continuing the aggressive rollout of precision medicine, both through internal efforts and increasingly through creative partnerships with industry, such as our partnership with proteomics company Somalogic."

Another org diving into precision medicine

Sanford Health believes precision medicine will be the future of healthcare, so it continues to make significant investments in this space.

"Leveraging machine learning and high computational power to analyze data sets containing genetic, clinical and socioeconomic data will not only help design the best personalized treatment for our patients, but also will help identify those patients or patient populations that would benefit most from early screening and interventions to prevent disease," said Hocks of Sanford Health.

"Precision medicine will allow us to concentrate our efforts on prevention and early screening, diagnosis, and care that will help keep our patients healthy and thriving for generations to come," he added. "Cancer care and chronic disease management are burdensome to patients, communities and health systems. Concentrating resources to prevent these conditions will benefit us all."

Remote patient monitoring and mobility

Mobile health is an area of health IT that has been emerging in recent years. The same with remote patient monitoring, which has especially gained ground during the COVID-19 pandemic. Virginia Hospital Center is on top of both.

"Virginia Hospital Center does not view itself as cutting-edge when it comes to technology," Mistretta said. "It considers itself more of a fast, early adopter of new technology it believes may provide an advantage to its patients.

"We are extremely patient-focused, so many of our investments moving forward are going to be in that realm," he continued. "We will be investing in hospital-at-home and remote patient monitoring features in depth, along with other patient engagement functions to empower our population and maintain low-touch care to minimize costs."

Mobility is in demand by patients, so connecting through web and app technologies will be a high priority, he added.

"We need to make care convenient for patients and provide care on their terms," he observed. "In our Northern Virginia/D.C. market, we hear about this frequently due to traffic and distance considerations."

Thus the development of pilot programs like the organization's OB Connect, where patients followed for maternity care are issued home equipment, post results and are able to skip the office if everything is within expected limits. Mistretta believes this kind of technology will permeate the market.

"These types of technologies will be required to sustain significant growth for health systems," he said. "Combined with the effective use of data to produce appropriate metrics, we should be able to pinpoint more specific markets and what treatments produce more effective outcomes.

"It also is the only way we will be able to meet the significant demands that will be placed on the care system with the shortage of nursing and primary care resources predicted to hit in the coming years," he added. "We simply will not be able to continue to experience the same results and levels of treatment enjoyed today as the population grows and ages without providing increased care outside the walls of our traditional organizational structures."

Leadership buy-in on a different approach will take some time, but with successes along the way (and supporting data to reinforce), healthcare organizations will be able to achieve what will be needed, he said.

The internet of things

Providence pledged to invest $50 million over five years in health equity. Here is a recap of how it invested in year one.

Elsewhere, Moore is concerned with the internet of things.

"The internet of things is it - it's smart devices," he said. "We may give our patients smart devices that sit in our care delivery environments, and have the telemetry information, and go into our big data model. Because that's how we're really going to make these machine learning and artificial intelligence models shine.

"We in healthcare say big data, but until you're working with streams of data, it's not really big data, it's just large data warehouses," he continued. "So getting that remote care delivery data is important, like a temperature four times a day, or real-time streaming of oxygen or heart data."

Moore believes the IoT and the streams of data it can provide are things healthcare executives should be talking about more. "It's all about data volumes: The bigger the volume, the better," he said.

AI bias and health equity

Regenstrief is in the process of developing tools and processes to identify bias in algorithms to improve health equity, said Grannis of Regenstrief Institute.

"As AI becomes more ubiquitous, researchers, clinicians, health systems, industry, government and others must be wary of unintended consequences," he stated. "Our research scientists are working on best practices as well as novel analytical tools to regularly monitor for bias in algorithms, a process Regenstrief and CEO Dr. Peter Embí have coined "algorithmovigilance."

"Over the next five years, Regenstrief will be working with individuals and organizations around the world to implement," he added.

Regenstrief also is investing in the broader ecosystem required to sustain advanced AI and machine learning methods. In the same way that clinical decision-makers, including physicians and other care providers, undergo regular training updates and certification due to healthcare's evolving nature and potential for bias, advanced algorithms will need frequent updates and certification to minimize bias and or errors, Grannis said.

Frameworks for overseeing algorithms and analytics are nascent. Developing and evaluating approaches to accurately and efficiently monitor AI and machine learning will become increasingly important in the future of healthcare analytics, he added.

"We also are investing in patient ergonomics - the application of human factors, engineering and psychology to the design and evaluation of patient-facing technology to enhance delivery of healthcare," he explained.

"Institute scientists are using user-centered design to create apps that help informal caregivers provide care for their loved ones with Alzheimer's and other chronic conditions. Other apps are exploring the benefits of specific diets and brain-stimulating games."

Patient access centers

Babachicos of South Shore Health believes tools that assist patients with care navigation will allow for a more improved and directed patient experience.

"These tools combined with the next-generation call centers - also known as patient access centers - can be accessed 24/7 by patients looking for care options and direct patients to the right place at the right time for their care needs," she explained. "These patient access centers will use multichannel options such as text, voice and chat while allowing patients to perform many self-service functions, as well.

"These patient access centers might also be staffed by care navigators for a more human connection when necessary," she concluded. "The same centers could potentially deliver virtual visits/consults, as well as potentially manage patient medications and vitals for subscribed patients in the community."
Twitter: @SiwickiHealthIT
Email the writer:bsiwicki@himss.org
Healthcare IT News is a HIMSS Media publication.

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CIOs' 5-year plans for precision medicine and emerging technologies

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One of the next big shifts in patient care will be precision medicine will be "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person," as the Precision Medicine Initiative describes it.

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Anna Schoenbaum received the HIMSS-ANI Nursing Informatics Changemaker Award. Her latest achievements focused on the digital transformation of Penn Medicine’s newest state-of-the-art hospital and integrating genomic results into its electronic health record.

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HIMSS members whose courage, curiosity and determination are helping transform the global health ecosystem have been recognized as 2023 HIMSS Changemaker in Health Award Recipients.

The Changemaker in Health Awards celebrate inspirational and innovative healthcare leaders for their relentless pursuit to improve care by harnessing the power of information and technology.

Changemaker Award categories:

HIMSS Changemakers are inspiring leaders who rigorously challenge the status quo in their journey to build a brighter health future. Congratulations to all Changemaker Award recipients.

HIMSS-ACCE Excellence in Clinical Engineering and Information Technology Changemaker Award

Paul Sherman, CCE, FACCE

President, Sherman Engineering LLC
Hammond, Oregon, United States

For more than 30 years, Paul Sherman has been an active member of the healthcare technology and clinical engineering community. During this time, he served in various biomedical engineering roles at the Department of Veterans. In 2012, he founded Sherman Engineering LLC, where he helped create a support program teaching returning veterans and displaced workers about electronic medical records and managed Integrating the Healthcare Enterprise's Patient Care Device Domain.

Sherman is an advocate of the clinical engineering discipline, which helps to define clinical workflows so clinicians can use ever-increasing medical device data. Through his active engagement at HIMSS, he has resolved several challenges surrounding interoperability and improving user requirements for its adoption.

HIMSS-AMDIS Physician Executive Leader Changemaker Award

Margaret Lozovatsky, MD, FAMIA

Senior Vice President | Chief Health Informatics Officer, Novant Health
Charlotte, North Carolina, United States

As senior vice president and chief health informatics officer for Novant Health, Margaret Lozovatsky leads a clinical informatics team across a large integrated health system. She is passionate about optimizing the use of technology to improve patient outcomes, lower the cost of healthcare, enhance clinician wellness and advance patient experience. Lozovatsky is also a pediatric hospitalist and continues to practice medicine.

HIMSS-ANI Nursing Informatics Changemaker Award

Anna Schoenbaum, DNP, MS, RN-BC, FHIMSS

Vice President of Applications and Digital Health, Penn Medicine
Philadelphia, Pennsylvania, United States

Anna Schoenbaum leverages her advanced nursing and informatics background to improve healthcare delivery and outcomes across the healthcare industry. Her role in academic medicine has led to the transformative integration of healthcare information technology and created practice and policy standards at a local and national level. Schoenbaum's efforts work to ensure new technologies accelerate research and improve patient care outcomes. Her latest achievements focused on the digital transformation of Penn Medicine's newest state-of-the-art hospital and integrating genomic results into its electronic health record.

HIMSS Chapter Advocate Changemaker Award

A. Lee Lavergne, CPHIMS, FHIMSS

Vice President, Advocacy HIMSS-DFW Chapter
Dallas, Texas, United States

A. Lee Lavergne has worked in healthcare IT for more than 35 years and held various HIMSS Dallas-Fort Worth Chapter board positions. A change leader in healthcare transformation, he has led numerous EHR implementations and process improvement efforts for several organizations. As vice president of advocacy for the HIMSS Dallas-Fort Worth Chapter, Lavergne is currently leading efforts in HIT advocacy for telehealth, maternal health and health equity at the Texas state legislature. He also serves as co-vice chair for the National Chapter Advocacy Task Force. In addition to his advocacy work, Lavergne assists with policies on the Texas Interoperability Committee.

HIMSS Chapter Leader of the Year Changemaker Award

Deepak Chaudhry, MSIS, CPHIMS, FHIMSS

Digital Health Practice Leader, FTI Consulting
Houston, Texas, United States

Deepak Chaudhry has been an active member of the HIMSS Houston Chapter board for almost 10 years and spent the last six years serving as president. He has steered several local and regional initiatives to help educate and advocate for health IT, collaborate on events with regional HIMSS chapters and the Texas Medical Center. He led the HIMSS Houston Chapter in 2019 to be the recipient of the HIMSS Chapter of the Year Award.

HIMSS Global Patient Innovator Changemaker Award

Founder, Blickwinkel Diabetes; Insulea.de
Kiel, Germany

Lea Raak is a patient expert and advocate for people with chronic conditions and disabilities. Diagnosed at a young age with Diabetes Type 1, asthma and endometriosis, she has been involved in a variety of international projects focusing on psychological aspects, peer support and raising awareness to fight stigma surrounding chronic conditions and disabilities. Insulea.de was one of the first German blogs about living with Type 1 Diabetes. In June 2021, Lea initiated Blickwinkel Diabetes, a German volunteer organization for people living with diabetes that strengthens peer support and educational work through online and offline events.

HIMSS Health Equity Changemaker Award

Fran Ayalasomayajula, MPH, MSMIS, PMP, HMCT, CD (DONA)

President; CEO, REACH; RTL Innovation
San Diego, California, United States

Fran Ayalasomayajula serves populations around the world, improving quality of care, increasing access to healthcare and raising health literacy and patient engagement. From the Tiwi Islands to Dakar, migratory farming communities, to vulnerable populations and beyond, she advocates universal parity, inclusion, and representation. Her mission is to enable communities and healthcare professionals to live healthy and thrive. Ayalasomayajula focuses on leading significant and meaningful large-scale initiatives that have a far-reaching, sustainable and positive impact.

Grady Burrows, MBA

Executive Director, HIT in the CLE
Cleveland, Ohio, United States

Grady Burrows is passionate about the growth and diversification of the health IT talent pipeline in northeast Ohio and beyond. Since 2015, Burrows has served as executive director of HIT in the CLE, a grassroots organization created with the mission of organically growing and diversifying the talent pipeline of health IT software developers and data scientists for Cleveland and the surrounding region. His work is dramatically changing the trajectory for Black and brown residents of the city of Cleveland by creating better access and awareness of health IT training and opportunities.

John Casillas

Executive Director, GlobalRise
Franklin, Tennessee, United States

John Casillas is the executive director of GlobalRise, the mission of which is a world of healthy kids. Casillas has the vision to deploy drones to connect mountain farmers with formal markets to create a thriving mountain economy that lifts families out of disease and poverty. A HIMSS nonprofit member, GlobalRise, hosted HIMSS members in Uganda for two weeks, where they connected with community leaders, farmers and villagers, orphanages, hospitals and universities to learn what is required to scale digital health solutions in the poorest regions of the world.

Michael "Chris" Gibbons, MD, MPH

Founder & CEO, The Greystone Group
Baltimore, Maryland, United States

Michael Gibbons is a digital health innovator, entrepreneur, physician and scientist. His career has been dedicated to solving some of life's most challenging health problems including improving health among racial and ethnic minorities and using digital technologies to improve health beyond the walls of the hospital. His vision is to enable any city, community, home or other environment, including those in underserved communities, to become spaces that enable healthcare delivery and promote health, wellbeing, education and economic development, through new digital ecosystems.

HIMSS John A. Page Distinguished Fellows Service Changemaker Award

Donna Roach, MS, CHCIO, CDH-E, FCHIME, LFHIMSS

CIO, University of Utah Health
Salt Lake City, Utah, United States

With more than 35 years' experience in the healthcare industry, Donna Roach has focused on healthcare information technology for the past 30 years. Previously, she was the president of the HIMSS Michigan Chapter and chair of the Michigan Hospital Association Technology Board. She has taught at the University of Illinois - Circle Campus, Rush University Medical College, Department of Health Systems Management; and UCONN, Health Administration. She has served in numerous volunteer roles and currently serves on the HIMSS Executive Summit Advisory Work Group providing valuable insights and feedback.

HIMSS Most Influential Women in Health IT Changemaker Award

Janis Curtis, MSPH, MA

Associate Director, Clinical Data Research Networks, Duke Health
School of Medicine, Clinical Translational Science Institute
Durham, North Carolina, United States

In her current role as chair of the HIMSS Global Health Conference Education Committee and previous role as chair of the Davies Committee, Janis Curtis consistently champions the effective use of technology and health data to aid administrative, clinical and policy decisions. In her current position managing the Duke Clinical Data Research Networks Program, Janis exemplifies her commitment to collaborate with and provide health data that enables clinicians to conduct multisite clinical research.

Anca del Rio, MSPH

President, EIT Alumni, European Institute of Innovation and Technology
Zurich, Switzerland

Anca del Rio is a purpose-driven health economist and thought leader in global health, innovation, governance and digital public health strategy. Throughout her global career across Europe, the U.S., the Middle East and Australia, her bold contributions to repurpose information and technology for public good and social impact represent the highest standards of healthcare leadership. Currently, she is on a mission to make a difference at scale and globally innovate the future of health by building the largest multistakeholder community for healthcare innovation from the heart of the life sciences ecosystem, Basel, Switzerland, where it all began centuries ago.

Joy Rios, MBA

Founder | CEO | Host, HIT Like a Girl Podcast
Baja California, Mexico

During the past 10 years, Joy Rios has been a passionate ambassador for value-based care and serves as a mentor to many women in the field of health information. She is the founder, CEO and host of the popular podcast HIT Like a Girl. Rios encourages women in health IT to share their ideas and express commitment to improving patient care by tapping into the power of information to connect the dots of a fragmented health ecosystem. Rios is not only a champion for women, but for all. She takes bold actions and inspires the health IT community to positively transform.

Victoria Tiase, PhD, RN-BC, FAMIA, FNAP, FAAN

Strategic Director Digital Health, Assistant Professor of Biomedical Informatics
University of Utah, School of Medicine
Salt Lake City, Utah, United States

Victoria Tiase is an active HIMSS member for over 15 years, including as chair of the Nursing Informatics Committee, member of SDOH Task Force and member of mHealth App Guidelines workgroup. Her expertise ranges from leveraging patient-generated health data to mentoring digital health startups. Most recently, she was the informatics lead on the National Academy of Medicine's Future of Nursing 2030 Committee to envision the nurse's role in using technology to promote health equity and create healthier communities.

Gabriela Mustata Wilson, PhD, MSc, FHIMSS, SNAI, FIAHSI

Professor and Founding Co-Director, Multi-Interprofessional Center for Health Informatics (MICHI), University of Texas at Arlington (UTA)
Arlington, Texas, United States

Gabriela Wilson is a health informatics wizard with bold ideas to improve people's lives. She is a firm believer in the power of many, nurtured independence and mission-first focus, with a strong desire to serve and inspire others. She is passionate about using health informatics tools to bridge the knowledge-to-action gap related to integrating public and ecological health to foster resilience. An expert in multidisciplinary research collaborations, her current focus is on addressing racial injustice and health inequity.

HIMSS Policy Influencer Changemaker Award

Zahid Butt, MD, FACG

CEO, Medisolv
Columbia, Maryland, United States

Zahid Butt is an industry leader and pioneer in digital quality measurement. He founded Medisolv, a Best in KLAS software company, recognized for implementation and testing of digital measures in national quality reporting programs. He served on many technical expert panels with CMS, NCQA and TJC. As a HIMSS member for over 20 years, he has served on committees and taskforces, and he currently chairs the HIMSS Quality and Safety Taskforce that provides input to the HIMSS Public Affairs Committee.

Daniel Jernigan, MD, MPH

Deputy Director for Public Health Science and Surveillance
U.S. Centers for Disease Control and Prevention
Atlanta, Georgia, United States

Daniel Jernigan is responsible for strengthening CDC's scientific foundation by working across the Office of Science, the Office of Laboratory Science and Safety, the Center for Surveillance, Epidemiology, and Laboratory Services, and the National Center for Health Statistics. His focus has been on identifying innovative ways to support the state, tribal, local and territorial public health departments in their digital health transformation, while leading the CDC's internal data modernization efforts. He has been outspoken in his work on standards-based interoperability, and the need to incorporate public health infrastructure modernization into the overall health IT plans for the country.

Hans Henri Marcel Paul Kluge, MD

European Regional Director, World Health Organization
Copenhagen, Denmark

Hans Henri Marcel Paul Kluge strives to support governments in their digital transformation journeys to produce better population health outcomes and to allow every individual to make safe, informed choices on their health and well-being. His dream is a society where no one is left behind. A world where health is incorporated in government policies to enable all people of all ages to live healthy lives, and where people-centered public health and healthcare services are universally available.

Micky Tripathi, PhD

National Coordinator for Health IT
Office of the National Coordinator for Health IT
Washington, D.C., United States

As the current National Coordinator for Health IT for the United States government, Micky Tripathi focuses ONC efforts on real world solutions to advance interoperability. Tripathi made his priority incorporating health equity into ONC's strategies and health IT-related programs across the government. Tripathi is taking ONC in a new direction to fulfill the expectations of the 21st Century Cures Act to reap the full potential of truly digital healthcare system.

HIMSS Senior Executive Changemaker Award

Yanwar Hadiyanto, MD

Doctor, Pondok Indah Hospital Group
Jakarta, Indonesia

"Extraordinary patient experience." Yanwar Hadiyanto enthusiastically believed this could be achievable since he joined Pondok Indah Hospital Group. He believes digital transformation is one way to achieve that. In 2008, Hadiyanto began his first attempt of digitizing patient data into an EMR, not a common practice at that time. Eventually, under Hadiyanto's encouragement and leadership, all three hospitals within the hospital group became entirely connected in real time. In April 2022, the three hospitals were first in Indonesia to achieve Electronic Medical Record Adoption Model (EMRAM) Stage 6.

Ray Lowe, MS

Senior Vice President / CIO, AltaMed Health Services
Commerce, California, United States

With over 25 years of healthcare experience, Ray Lowe is a strong advocate of health equity and culturally competent care. Lowe delivers quality-based care and health equity initiatives for socially challenged economic areas to more than 400,000 people. He has an instrumental role in transforming and providing healthcare to the Southern California Medicaid patient population, particularly for low-income, Latino, multiethnic and underserved communities. As an innovator, he adopted a mobile-first strategy and led a holistic digital framework transformation, which included remote patient monitoring and care transitions focused on value-based care outcome.

Kathleen McGrow, DNP, MS, RN, PMP

Chief Nursing Information Officer and Industry Executive, Microsoft
Baltimore, Maryland, United States

Kathleen McGrow leverages her expertise at the intersection of clinical knowledge and information technology to drive digital transformation imperatives such as workforce engagement, provider enablement, patient engagement and operational efficiencies. Her expertise in data, analytics and artificial intelligence is used to identify areas for organizational improvements. McGrow's current focus is on workforce crisis initiatives including the discovery of solutions that identify employees at risk for moral distress and to promote and address employee well-being.

Srinivasan Suresh, MD, MBA, FAAP

Vice-President, CIO & CMIO, UPMC Children's Hospital of Pittsburgh
Pittsburgh, Pennsylvania, United States

Srinivasan Suresh is a board-certified informatician, professor of pediatrics, vice president, chief information officer and chief medical information officer of a leading children's hospital, and a practicing pediatric emergency physician. He was instrumental in his organization winning the HIMSS Davies Award of Excellence and maintaining HIMSS Stage 7 recognition on EMRAM and O-EMRAM. An informatics researcher, Suresh leads multidisciplinary teams in applying business intelligence (measurably enhance healthcare for children), and in implementing robust clinical decision support tools (improve quality outcomes and control costs).

Catherine Turner, BSN, MBA, RN-BC

Associate Vice President, MEDITECH
Boston, Massachusetts, United States

Recognizing the value that technology can bring to patients, care teams and healthcare organizations, Catherine Turner has lent her passion and expertise to the implementation, development and marketing of MEDITECH's EHR. Embracing the future vision of health information technology, Turner presents this vision at national and international conferences, as adjunct professor at Northeastern University and as a reviewer for HIT publications and chapter author for Colibri Healthcare.

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Celebrating the 2023 HIMSS Changemaker in Health Award Recipients

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HIMSS members whose courage, curiosity and determination are helping transform the global health ecosystem have been recognized as 2023 HIMSS Changemaker in Health Award Recipients. The Changemaker in Health Awards celebrate inspirational and innovative healthcare leaders for their relentless pursuit to improve care by harnessing the power of information and technology.

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BI Blog | Data Visualization & Analytics Blog | datapine

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Big data has changed how we manage, analyze, and leverage data across industries. One of the most notable areas where data analysis is making big changes is healthcare.

In fact, healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve the quality of life in general. The average human lifespan is increasing across the world population, which poses new challenges to today's treatment delivery methods. Health professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and looking for the best strategies to use these numbers.

In this article, we're going to address the need for big data in healthcare and hospital big data: why and how can it help? What are the obstacles to its adoption? We will then look at 24 big data examples in healthcare that already exist and that medical-based institutions can benefit from.

But first, let's examine the core concept and role of big data in healthcare.

Big data in healthcare is a term used to describe massive volumes of information created by the adoption of digital technologies that collect patients' records and help in managing hospital performance; otherwise too large and complex for traditional technologies.

The application of big data analytics in healthcare has a lot of positive and also life-saving outcomes. In essence, big-style data refers to the vast quantities of information created by the digitization of everything that gets consolidated and analyzed by specific technologies. Applied to healthcare, it will use specific health data of a population (or of a particular individual) and potentially help to prevent epidemics, cure diseases, cut down costs, etc.

Now that we live longer, treatment models have changed, and many of these changes are namely driven by data. Doctors want to understand as much as they can about a person and, as early in their life as possible, to pick up warning signs of serious illness as they arise - treating any disease at an early stage is far more simple and less expensive. By utilizing key performance indicators in healthcare and healthcare data analytics, prevention is better than cure, and managing to draw a comprehensive picture of someone will let insurance provide a tailored package. This is the industry's attempt to tackle the siloes problems a patient's data has: everywhere are collected bits and bites of it and archived in hospitals, clinics, surgeries, etc., with the impossibility of communicating properly.

That said, the amount of sources from which health professionals can gain insights from their patients keeps growing. This data is normally coming in different formats and sizes, which presents a challenge to the user. However, the current focus is no longer on how "big" the data is but on how smartly it is managed. With the help of the right technology, data can be extracted from the following sources of the healthcare industry in a smart and fast way:

Patients portals

Research studies

EHRs

Wearable devices

Search engines

Generic databases

Government agencies

Payer records

Staffing schedules

Patient waiting room

Indeed, for years gathering huge amounts of data for medical use has been costly and time-consuming. With today's always-improving technologies, it becomes easier not only to collect such data but also to create comprehensive healthcare reports and convert them into relevant critical insights that can then be used to provide better care. This is the purpose of healthcare data analysis: using data-driven findings to predict and solve a problem before it is too late, but also assess methods and treatments faster, keep better track of inventory, involve patients more in their own health, and empower them with the tools to do so.

Now that you understand the importance of big data in the healthcare industry let's explore 24 real-world applications that demonstrate how an analytical approach can improve processes, enhance patient care, and, ultimately, save lives.

1) Patients Predictions For Improved Staffing

For our first example of big data in healthcare, we will look at one classic problem that any shift manager faces: how many people do I put on staff at any given period? If you put on too many workers, you run the risk of having unnecessary labor costs add up. With too few workers, you can have poor customer service outcomes - which can be fatal for patients in that industry.

Big data is helping to solve this problem, at least at a few hospitals in Paris. A white paper by Intel details how four hospitals that are part of the Assistance Publique-Hôpitaux de Paris have been using data from a variety of sources to come up with daily and hourly predictions of how many patients are expected to be at each facility.

One of the key data sets is 10 years' worth of hospital admissions records, which data scientists crunched using "time series analysis" techniques. These analyses allowed the researchers to see relevant patterns in admission rates. Then, they could use machine learning to find the most accurate algorithms that predicted future admissions trends.

Summing up the product of all this work, the data science team developed a web-based user interface that forecasts patient loads and helps in planning resource allocation by utilizing online data visualization that reaches the goal of improving the overall patients' care.

2) Electronic Health Records (EHRs)

It's the most widespread application of big data in medicine. Every person has their own digital record, which includes demographics, medical history, allergies, laboratory test results, etc. Records are shared via secure information systems and are available for providers from both the public and private sectors. Every record is comprised of one modifiable file, which means that doctors can implement changes over time with no paperwork and no danger of data replication.

EHRs can also trigger warnings and reminders when a patient should get a new lab test or track prescriptions to see if he or she has been following doctors' orders.

Although EHR is a great idea, many countries still struggle to implement them fully. U.S. has made a major leap, with 94% of hospitals adopting EHRs according to this HITECH research, but the EU still lags behind. However, an ambitious directive drafted by the European Commission is supposed to change it.

Kaiser Permanente is leading the way in the U.S. and could provide a model for the EU to follow. They've fully implemented a system called HealthConnect that shares data across all of their facilities and makes it easier to use EHRs. A McKinsey report on big data healthcare analytics states that "The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests."

3) Real-Time Alerting

Other examples of data analytics in healthcare share one crucial functionality - real-time alerting. In hospitals, Clinical Decision Support (CDS) software analyzes medical data on the spot, providing health practitioners with advice as they make prescriptive decisions.

However, doctors want patients to stay away from hospitals to avoid costly in-house treatments. This is already trending as one of the business intelligence buzzwords in 2021 and has the potential to become part of a new strategy. Wearables will collect patients' health data continuously and send this data to the cloud.

Additionally, this information will be accessed to the database on the state of health of the general public, which will allow doctors to compare this data in a socio-economic context and modify the delivery strategies accordingly. Institutions and care managers will use sophisticated tools to monitor this massive data stream and react every time the results will be disturbing.

For example, if a patient's blood pressure increases alarmingly, the system will send a live alert to the doctor, who will then take action to reach the patient and administer measures to lower the pressure.

Another example is that of Asthmapolis, which has started to use inhalers with GPS-enabled trackers in order to identify asthma trends both on an individual level and looking at larger populations. This data is being used in conjunction with data from the CDC in order to develop better treatment plans for asthmatics.

4) Enhancing Patient Engagement

Many consumers - and hence, potential patients - already have an interest in smart devices that record every step they take, their heart rates, sleeping habits, etc., on a permanent basis. All this vital information can be coupled with other trackable data to identify potential health risks lurking. Chronic insomnia and an elevated heart rate can signal a risk for future heart disease, for instance. Patients are directly involved in the monitoring of their own health, and incentives from health insurance can push them to lead a healthy lifestyle (e.g., giving money back to people using smartwatches).

Another way to do so comes with new wearables under development, tracking specific health trends and relaying them to the cloud where physicians can monitor them. Patients suffering from asthma or blood pressure could benefit from it, become a bit more independent and reduce unnecessary visits to the doctor.

5) Prevent Opioid Abuse In The US

Our fifth example of big data healthcare is tackling a serious problem in the US. Here's a sobering fact: as of this year, overdoses from misused opioids have caused more accidental deaths in the U.S. than road accidents, which were previously the most common cause of accidental death.

Analysis expert Bernard Marr writes about the problem in a Forbes article. The situation has gotten so dire that Canada has declared opioid abuse to be a "national health crisis," and President Obama earmarked $1.1 billion dollars for developing solutions to the issue while he was in office.

Once again, an application of big data analytics in healthcare might be the answer everyone is looking for: data scientists at Blue Cross Blue Shield have started working with analytical experts at Fuzzy Logix to tackle the problem. Using years of insurance and pharmacy data, Fuzzy Logix analysts have been able to identify 742 risk factors that predict with a high degree of accuracy whether someone is at risk for abusing opioids.

To be fair, reaching out to people identified as "high risk" and preventing them from developing a drug issue is a delicate undertaking. However, this project still offers a lot of hope for mitigating an issue that is destroying the lives of many people and costing the system a lot of money.

6) Using Health Data For Informed Strategic Planning

The use of big data in healthcare allows for strategic planning thanks to better insights into people's motivations. Care managers can analyze check-up results among people in different demographic groups and identify what factors discourage people from taking up treatment.

The University of Florida made use of Google Maps and free public health data to prepare heat maps targeted at multiple issues, such as population growth and chronic diseases. Subsequently, academics compared this data with the availability of medical services in most heated areas. The insights gleaned from this allowed them to review their delivery strategy and add more care units to the most problematic areas.

7) Big Data Might Just Cure Cancer

Another interesting example of the use of big data in healthcare is the Cancer Moonshot program. Before the end of his second term, President Obama came up with this program that had the goal of accomplishing 10 years' worth of progress toward curing cancer in half that time.

Medical researchers can use large amounts of data on treatment plans and recovery rates of cancer patients in order to find trends and treatments that have the highest rates of success in the real world. For example, researchers can examine tumor samples in biobanks that are linked up with patient treatment records. Using this data, researchers can see things like how certain mutations and cancer proteins interact with different treatments and find trends that will lead to better outcomes.

This data can also lead to unexpected benefits, such as finding that Desipramine, which is an antidepressant, has the ability to help cure certain types of lung cancer.

However, in order to make these kinds of insights more available, patient databases from different institutions, such as hospitals, universities, and nonprofits, need to be linked up. Then, for example, researchers could access patient biopsy reports from other institutions. One of the potential big data use cases in healthcare would be genetically sequencing cancer tissue samples from clinical trial patients and making these data available to the wider cancer database.

But, there are a lot of obstacles in the way, including:

Incompatible data systems. This is perhaps the biggest technical challenge, as making these data sets able to interface with each other is quite a feat.

Patient confidentiality issues. There are differing laws state by state which govern what patient information can be released with or without consent, and all of these would have to be navigated.

Simply put, institutions that have put a lot of resources and money into developing their own cancer dataset may not be eager to share it with others, even though it could lead to a cure much more quickly.

However, as an article by Fast Company states, there are precedents to navigating these types of problems and roadblocks while accelerating progress toward curing cancer using the strength of data analysis.

8) Predictive Analytics In Healthcare

We have already recognized predictive analysis as one of the biggest business intelligence trends for two years in a row, but the potential applications reach far beyond business and much further into the future. Optum Labs, a US research collaborative, has collected EHRs of over 30 million patients to create a database for predictive analysis tools that will improve the delivery of care.

The goal of healthcare online business intelligence is to help doctors make data-driven decisions within seconds and improve patients' treatment. This is particularly useful in the case of patients with complex medical histories suffering from multiple conditions. New BI solutions and tools would also be able to predict, for example, who is at risk of diabetes and thereby be advised to make use of additional screenings or weight management.

9) Reduce Fraud And Enhance Security

Some studies have shown that 93% of healthcare organizations have experienced a data breach. The reason is simple: personal data is extremely valuable and profitable on the black market. And any breach would have dramatic consequences. With that in mind, many organizations started to use analytics to help prevent security threats by identifying changes in network traffic or any other behavior that reflects a cyber-attack. Of course, big data has inherent security issues, and many think that using it will make organizations more vulnerable than they already are. But advances in security, such as encryption technology, firewalls, anti-virus software, etc., answer the need for more security, and the benefits brought largely overtake the risks.

Likewise, it can help prevent fraud and inaccurate claims in a systemic, repeatable way. Analytical tools help to streamline the processing of insurance claims, enabling patients to get better returns on their claims and caregivers to be paid faster. For instance, the Centers for Medicare and Medicaid Services said they saved over $210.7 million in fraud in just a year.

10) Telemedicine

Telemedicine has been present on the market for over 40 years, but only today, with the arrival of online video conferences, smartphones, wireless devices, and wearables, has it been able to come into full bloom. The term refers to the delivery of remote clinical services using technology.

It is used for primary consultations and initial diagnosis, remote patient monitoring, and medical education for health professionals. Some more specific uses include telesurgery - doctors can perform operations with the use of robots and high-speed real-time data delivery without physically being in the same location as a patient.

Clinicians use telemedicine to provide personalized treatment plans and prevent hospitalization or re-admission. Such use of healthcare data analytics can be linked to the use of predictive analytics, as seen previously. It allows clinicians to predict acute medical events in advance and prevent the deterioration of patients' conditions.

By keeping patients away from hospitals, telemedicine helps to reduce costs and improve the quality of service. Patients can avoid waiting in lines, and doctors don't waste time on unnecessary consultations and paperwork. Telemedicine also improves the availability of care as patients' states can be monitored and consulted anywhere and anytime.

11) Integrating Big-Style Data With Medical Imaging

Medical imaging is vital, and each year in the US, about 600 million imaging procedures are performed. Analyzing and storing these images manually is expensive both in terms of time and money, as radiologists need to examine each image individually, while hospitals need to store them for several years.

Medical imaging provider Carestream explains how big data analytics for healthcare could change how images are read: algorithms developed analyzing hundreds of thousands of images could identify specific patterns in the pixels and convert them into a number to help the physician with the diagnosis. They even go further, saying that it could be possible that radiologists will no longer need to look at the images but instead analyze the outcomes of the algorithms that will inevitably study and remember more images than they could in a lifetime. This would undoubtedly impact the role of radiologists, their education, and the required skillset.

12) A Way To Prevent Unnecessary ER Visits

Saving time, money, and energy using big data analytics for healthcare is necessary. What if we told you that over the course of 3 years, one woman visited the ER on more than 900 occasions? That situation is a reality in Oakland, California, where a woman who suffers from mental illness and substance abuse went to a variety of local hospitals on an almost daily basis.

This woman's issues were exacerbated by the lack of shared medical records between local emergency rooms, increasing the cost to taxpayers and hospitals and making it harder for this woman to get good care. As Tracy Schrider, who coordinates the care management program at Alta Bates Summit Medical Center in Oakland, stated in a Kaiser Health News article:
"Everybody meant well. But she was being referred to three different substance abuse clinics and two different mental health clinics, and she had two case management workers both working on housing. It was not only bad for the patient, it was also a waste of precious resources for both hospitals."
In order to prevent future situations like this from happening, Alameda County hospitals came together to create a program called PreManage ED, which shares patient records between emergency departments.

This system lets the ER staff know things like:

If the patient they are treating has already had certain tests done at other hospitals, what the results of those tests are.

If the patient in question already has a case manager at another hospital, prevent unnecessary assignments.

What advice has already been given to the patient so that a coherent message to the patient can be maintained by providers?

This is another great example where the application of healthcare data analysis is useful and needed. In the past, hospitals without PreManage ED would repeat tests over and over, and even if they could see that a test had been done at another hospital, they would have to go old school and request or send long fax just to get the information they needed.

13) Smart Staffing & Personnel Management

Without a cohesive, engaged workforce, patient care will dwindle, service rates will drop, and mistakes will happen. But with big data tools in healthcare, it's possible to streamline your staff administration activities in a wealth of key areas. By working with the right HR analytics, it's possible for time-stretched medical institutions to optimize staffing while forecasting operating room demands, streamlining patient care as a result.

Too often, there is a significant lack of fluidity in healthcare institutions, with staff distributed in the wrong areas at the wrong time. This imbalance of personnel administration could mean a particular department is either too overcrowded with staff or lacking staff when it matters most, which can develop risks of lower motivation for work and increases the absenteeism rate. An HR dashboard, in this case, may help:

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Through data-driven analytics, it's possible to predict when you might need staff in particular departments at peak times while distributing skilled personnel to other areas within the institution during quieter periods.

Moreover, medical data analysis will empower senior staff or operatives to offer the right level of support when needed, improve strategic planning, and make vital staff and personnel management processes as efficient as possible.

14) Learning & Development

Expanding on our previous point, in a hospital or medical institution, the skills, confidence, and abilities of your staff can mean the difference between life and death. Naturally, doctors and surgeons are highly skilled in their areas of expertise. But most medical institutions have a range of people working under one roof, from porters and admin clerks to cardiac specialists and brain surgeons.

In healthcare, soft skills are almost important as certifications. To keep the institution running at optimum capacity, you have to encourage continual learning and development. By keeping track of employee performance across the board while keeping a note of training data, you can use healthcare data analysis to gain insight into who needs support or training and when. If everyone is able to evolve with the changes around them, you will save more lives - and medical data analytics will help you do just that.

15) Advanced Risk & Disease Control

Big data and healthcare analytics are essential for tackling the hospitalization risk for specific patients with chronic diseases. It can also help prevent deterioration.

By drilling down into insights such as medication type, symptoms, and the frequency of medical visits, among many others, it's possible for healthcare institutions to provide accurate preventative care and, ultimately, reduce hospital admissions. Not only will this level of risk calculation result in reduced spending on in-house patient care, but it will also ensure that space and resources are available for those who need it most. This is a clearcut example of how analytics in healthcare can improve and save people's lives.

As a result, big data for healthcare can improve the quality of patient care while making the organization more economically streamlined in every key area.

16) Suicide & Self-Harm Prevention

Globally, almost 800,000 people die from suicide every year. Plus, 17% of the world's population will self-harm during their lifetime. These numbers are alarming. But while this is a very difficult area to tackle, big data uses in healthcare are helping to make a positive change concerning suicide and self-harm. As entities that see a wealth of patients every single day, healthcare institutions can use data analysis to identify individuals that might be likely to harm themselves.

In a 2018 study from KP and the Mental Health Research Network, a mix of EHR data and a standard depression questionnaire identified individuals who had an enhanced risk of a suicide attempt with great accuracy. Utilizing a predictions algorithm, the team found that suicide attempts and successes were 200 times more likely among the top 1% of patients flagged according to specific datasets. Speaking on the subject, Gregory E. Simon, MD, MPH, a senior investigator at Kaiser Permanente Washington Health Research Institute, explained:
"We demonstrated that we can use electronic health record data in combination with other tools to accurately identify people at high risk for suicide attempt or suicide death."
This essential use case for big data in the healthcare industry really is a testament to the fact that medical analytics can save lives.
"If somebody tortures the data enough (open or not), it will confess anything." -Paolo Magrassi, former vice president, research director, Gartner.
17) Improved Supply Chain Management

If a medical institution's supply chain is weakened or fragmented, everything else is likely to suffer, from patient care and treatment to long-term finances and beyond. That said, the next in our big data in healthcare examples focuses on the value of analytics to keep the supply chain fluent and efficient from end to end.

Leveraging analytical tools to track the supply chain performance metrics and make accurate, data-driven decisions concerning operations as well as spending can save hospitals up to $10 million per year.

Both descriptive and predictive models can enhance decisions for negotiating prices, reducing the variation in supplies, and optimizing the ordering process as a whole. By doing so, medical institutions can thrive in the long term while delivering vital treatment to patients without potentially disastrous delays, snags, or bottlenecks.

18) Financial facility management

As you've learned by now, data plays a crucial role in today's society. It has permeated industries across the world to assist in the development of new technologies and innovations. This is especially true in the business landscape, where data-driven initiatives have become a requirement rather than a choice. This is no different in the healthcare industry, which leads us to our next big data application.

So far, we've covered multiple applications related to patients and medical innovation. That said, all of these breakthroughs would not be possible if the finances of the different health organizations wouldn't be on check. Big data in healthcare provides the necessary tools and knowledge to ensure optimal financial health across facilities, especially for big hospital networks that manage multiple facilities simultaneously.

When we talk about the financial side of the healthcare industry, we'll encounter similar KPIs as any other organization, such as net profit, cash flow, etc. However, there are some financial indicators that are unique to this sector. We are talking about drug costs, claims denial rates, reimbursement rates, and much more. Keeping track of all those indicators is not an easy task; however, organizations that invest in software to collect, arrange and analyze their data will ensure their finances are in check and the facility remains profitable while providing the best care.

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The example below is a hospital network dashboard generated with datapine's dashboard designer. The template provides a strategic overview of a private hospital network with insights into multiple facilities. With these insights in hand, decision-makers can make informed decisions and spot improvement opportunities to boost their financial efficiency.

19) Developing New Therapies & Innovations

The next in our healthcare analytics examples centers on working for a brighter, bolder future in the medical industry. Big data analysis in healthcare has the power to assist in new therapy and innovative drug discoveries. By utilizing a mix of historical, real-time, and predictive metrics as well as a cohesive mix of data visualization techniques, healthcare experts can identify potential strengths and weaknesses in trials or processes.

Moreover, through data-driven genetic information analysis as well as reactionary predictions in patients, big data analytics in healthcare can play a pivotal role in the development of groundbreaking new drugs and forward-thinking therapies. Data analytics in healthcare can streamline, innovate, provide security, and save lives. It gives confidence and clarity, and it is the way forward.

20) Help To Manage And Track Mass Diseases

Since it started at the beginning of the year 2020, the COVID-19 pandemic has impacted millions and millions of people around the world. The widespread nature of this virus presented a challenge to the health industry, which found itself trying to learn from it and control it at the same time. In that context, big data played a fundamental role in the response given to this growing disease that made the world stop for years.

Supported by advanced data management technologies, health experts were able to track in real-time how COVID was spreading, how fast it mutated under different conditions, as well as the effect it was having on the different world economies. This is done by analyzing massive data sets coming from diverse sources, such as medical records as well as individual human behaviors. For instance, looking at how many of them are staying at home, getting on the train, or going to school, as this highly influences how fast the virus spreads.

Paired to this, technologies such as AI allowed various medical imaging modalities, such as X-rays, tomography, ultrasounds, and others, to provide an earlier diagnosis to patients and prevent the spread of the disease. In fact, in 2020, the EU backed a software called InferRead to facilitate this process. Essentially, this AI-powered software "analyses images of the lungs taken by a CT scanner, identifies the signs of coronavirus, and assesses the lesions. The process would typically require a careful study by an experienced doctor, yet the machine needs only a few seconds". This level of technology allowed hospitals around Europe to prevent the spread of the virus and, consequently, flatten the curve.

21) Improved Drug Prescription Processes

As seen throughout our list of examples, the vast amount of data that care professionals have at their fingertips has helped revolutionize patient care, disease control, and many others. That said, it is not a surprise that this trend has also spread into the pharmaceutical industry, where it promises to maximize the value of medicines and improve the quality of prescribing. This leads us to our next big data application in healthcare.

One of the companies applying it is Express Scripts, a third-party organization that manages medicines coverage for clients who provide health insurance plans. According to a publication from the Pharmaceutical Journal, the US-based company collected data from 83 million patients, which included everything from patients' clinical and behavioral characteristics, such as someone picking up a prescription, to Tweets directed at the company. With this data in hand, Express Scripts can predict important scenarios, such as who is likely to become addicted to a certain medication or who is not adhering to its treatment. If the last happens, the company immediately sends personalized interventions and support to make sure people adhere to their prescribed treatment. As a result, Express Scripts stated that they cut the non-adherence rate of hepatitis C patients from 8,3% to 4,8%.

22) Prevent Human Error

Fraud in the health industry can be considered anything from erroneous billings to inefficiencies that result in wasteful tests or even adding incorrect information to a person's medical record. In fact, the National Health Care Anti-Fraud Association estimates that the financial losses due to healthcare fraud could reach $300 billion in the US alone. This means that around 10% of the total health spending is wasted due to fraud or human error. However, money is not even the biggest concern when it comes to human error and fraud, it's patients' lives that are put at risk. Prescribing someone with the wrong medicine or treatment can result in long-lasting consequences or even death.

To avoid this, companies leverage big data and forecasting to identify and prevent fraud or human error quickly. For example, by analyzing massive amounts of prescription patterns, experts are able to detect prescription errors before they occur. The same happens with dosage ranges, tests, and other procedures. In time, this not only allows doctors and caregivers to trust in technology for their decision-making but also saves facilities a big amount of money while providing the best care.

23) Alerting heart problems through personal devices

In 2017, Apple and Stanford Medicine joined forces to carry out one-of-a-kind research called "Apple Heart Study." They studied the heart rate sensor data of 400.000 Apple Watch users to see if wearable devices could help in detecting irregular heart rates to prevent fatal complications, especially atrial fibrillation (AFib).

AFib is among the leading causes of strokes across the world. Costing the life of approximately 130.000 people in the US every year, with another 750.000 hospitalizations. That's because patients that are suffering from AFib often don't experience any symptoms, so the condition can go unnoticed until it is fatal.

According to Jeff Williams, Apple's COO, the motivation to carry out this study was their own customer's testimonies. At the time, he said: "Every week, we receive incredible customer letters about how Apple Watch has affected their lives, including learning that they have AFib. These stories inspire us, and we're determined to do more to help people understand their health"

Apple and Standford Medicine created an app where any Apple Watch users in the US that were over the age of 22 could join the study. If an irregular heart rate were identified, the user would get a notification and a free doctor consultation with an electrocardiogram (ECG) patch for additional monitoring. After 8 months of testing using top-notch technology, in 2019, the results of the study were published.

Stanford Medicine found that only 0.5% of participants had irregular heart rate notifications, a positive result overall. Of the participants that did receive the notification, 84% were diagnosed with atrial fibrillation at the time of the notification. 34% of the participants that received a notification and followed up with an ECG patch were finally diagnosed with AFib. This last result was not surprising, as AFib can often go undetected.

Lastly, the study showed that comparisons between Apple Watch pulse detection and ECG patches pulse detection have a 71% positive predictive value. Showing that these devices can help people take charge of their health and, in some cases, save lives. A great testament to the power of big data in healthcare.

24) Bluetooth helps asthma patients

According to the WHO, Asthma affected an estimated 262 million people in 2019 and caused 455 000 deaths globally. People who suffer from asthma can be triggered even by invisible particles in the air, making it a complicated disease to carry.

With those concerning numbers in mind, a Wisconsin-based digital health company called Propeller Health launched a Bluetooth-powered inhaler that can be connected to a person's smartphone to collect data about the frequency of asthma attacks and the environmental conditions in which the person was located.

Once the inhaler is pressed, data about the time and location of the patient's asthma attack is sent to an app on the phone via a Bluetooth sensor. Doctors can use this data later on to analyze the frequency of the attacks as well as any other factors such as location and weather. Plus, the inhaler is also set to help patients with their medication schedules since taking medication correctly and when expected can significantly decrease the chances of ending up in the emergency room for an attack.

In fact, research carried out by Propeller Health showed that 330 patients who had attached the Bluetooth sensor to their inhaler experienced 60% fewer asthma-related emergency hospital visits. The company also found that collecting data like this for patients that live in the same town or region can prove even more valuable. Another test of their Bluetooth inhaler sensor showed that in Louisville, Kentucky, asthma attacks were mostly caused by proximity to railroads and utilities. Plus, they found multiple asthma triggers in public areas. The data collected by the sensor is highly valuable to the city as health authorities can use it to prevent asthma from becoming a major issue for their population.

All in all, we've noticed three key trends through these 24 examples of healthcare analytics: the patient experience will improve dramatically, including quality of treatment and satisfaction levels; the overall health of the population can also be enhanced on a sustainable basis, and operational costs can be reduced significantly.

Let's have a look now at concrete examples of big data in healthcare:

a) Big Data In Healthcare Applied On A Hospital Dashboard

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This healthcare dashboard below provides you with the overview needed as a hospital director or as a facility manager. Gathering in one central point all the data on every division of the hospital, the attendance, its nature, the costs incurred, etc., you have the big picture of your facility, which will be of great help to run it smoothly.

You can see here the most important metrics concerning various aspects: the number of patients that were welcomed in your facility, how long they stayed and where, how much it cost to treat them, and the average waiting time in emergency rooms. Such a holistic view helps top administrators to identify potential bottlenecks, spot trends and patterns over time, and in general, assess the situation. This is key in order to make better-informed decisions that will improve the overall operations performance, with the goal of treating patients better and having the right staffing resources.

b) Big Data Healthcare Application On Patients' Care

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Another real-world application of healthcare big data analytics, our dynamic patient KPI dashboard, is a visually-balanced tool designed to enhance service levels as well as treatment accuracy across departments.

By offering a perfect storm or patience-centric information in one central location, medical institutions can create harmony between departments while streamlining care processes in a wealth of vital areas. For instance, bed occupancy rate metrics offer a window of insight into where resources might be required while tracking canceled or missed appointments will give senior executives the data they need to reduce costly patient no-shows.

Here, you will find everything you need to enhance your level of patient care both in real-time and in the long term. This is a visual innovation that has the power to improve every type of medical institution, big or small.

By looking at our list of most insightful medical big data applications, you should have a notion of how positive the use of analytics can be for this industry. If this is not clear yet, here we will summarize the main points of importance by listing a few benefits of big data in healthcare.
Reducing costs
As mentioned, there's a huge need for big data in healthcare, especially due to rising costs in nations like the United States. As a McKinsey report states: "After more than 20 years of steady increases, healthcare expenses now represent 17.6 percent of GDP - nearly $600 billion more than the expected benchmark for a nation of the United States's size and wealth." This quote leads us to our first benefit.

As stated above, costs are much higher than they should be, and they have been rising for the past 20 years. Clearly, we are in need of some smart, data-driven thinking in this area. And current incentives are changing as well: many insurance companies are switching from fee-for-service plans (which reward using expensive and sometimes unnecessary treatments and treating large amounts of patients quickly) to plans that prioritize patient outcomes

As the authors of the popular Freakonomics books have argued, financial incentives matter - and incentives that prioritize patients' health over treating large amounts of patients are a good thing. Why does this matter?
Reducing medical errors
Well, in the previous scheme, healthcare providers had no direct incentive to share patient information with one another, which made it harder to utilize the power of analytics. Now that more of them are getting paid based on patient outcomes, they have a financial incentive to share data that can be used to improve the lives of patients while cutting costs for insurance companies.

Physician decisions are becoming more and more evidence-based, meaning that they rely on large swathes of research and clinical data as opposed to solely their schooling and professional opinion. That said, the risk of human error is always a latent threat. Even though doctors are highly trained professionals, they are still human, and the risk of selecting the wrong medication or treatment can potentially risk a person's life. With the use of big data and the tools that we mentioned throughout this post, professionals can be easily alerted when the wrong medication, test, treatment, or other has been provided and remediate it immediately. In time, this can significantly reduce the rates of medical errors and improve the facility's reputation.
Optimizing organizational and personnel management
As in many other industries, data gathering and management are getting bigger, and professionals need help in the matter. This new treatment attitude means there is a greater demand for big data analytics in healthcare facilities than ever before, and the rise of SaaS BI tools is also answering that need.

While using data to ensure you are providing the best care to patients is fundamental, there are also other operational areas in which it can assist the health industry. Part of providing quality care is ensuring the facility works optimally, and this can also be achieved with the help of big data.

By using the right BI software, professionals can gather and analyze real-time data about the performance of their organization in areas such as operations and finances, as well as personnel management. For instance, predictive analytics technologies can provide relevant information regarding admission rates. These insights can help define staffing schedules to cover demand as well as inventory for medical supplies. This way, care facilities can stay one step ahead and ensure that patients are getting the best experience possible.
Drive innovation and growth
Getting this level of insight in such an intuitive way allows managers to redirect resources where they are most needed and optimize areas that are not performing well to ensure the best return on investment possible.

Our last benefit is one that should be the clearest from the list of applications we provided earlier. The use of big data in the care industry enables professionals to test new technologies, drugs, and treatments to improve the quality of care given to patients and battle diseases that were once thought of as unbeatable.

Thanks to wearable devices that can tell your heart rate, Bluetooth asthma inhalers that gather insights to prevent attacks, and much more, doctors are able to use data to understand how common diseases work and how certain external factors might be affecting entire communities. Through that, they are able to provide personalized quality care to each and every person that goes into a hospital.

There is no denying that the power of big data analytics is saving lives. That being said, the process of managing data requires a lot of effort, and with that comes challenges, which we will discuss below.

The points mentioned above are just a few of the countless benefits of big data in healthcare. That said, with benefits also come limitations. In order to provide a full picture of the topic, we will now list a few obstacles and healthcare data challenges that organizations can face when implementing analytics into their processes.

Data integration and storage: One of the biggest hurdles standing in the way of using big data in medicine is how medical data is spread across many sources governed by different states, hospitals, and administrative departments. The integration of these data sources would require developing a new infrastructure where all data providers collaborate with each other.

Data sharing: Equally important is implementing new online reporting software and business intelligence strategy that will allow all relevant users to be connected with the data. Healthcare needs to catch up with other industries that have already moved from standard regression-based methods to more future-oriented ones like predictive analytics, machine learning, and graph analytics. This is done with the help of modern reporting tools such as a dashboard creator that allows anyone to perform advanced analytics with just a few clicks easily.

Security and privacy: Security and privacy are constant concerns are one of the biggest challenges of big data in healthcare. Daily, hospitals and care centers deal with sensitive patient data that needs to be carefully protected. Taking that into consideration with the fact that the data is coming from many different sources, security can present a challenge for these types of organizations. To avoid this, it is critical to follow law regulations, conduct regular audits to ensure everything is going well, and train employees on data protection best practices.

Data literacy: Using big data and analytics in healthcare involves many processes and tools to collect, clean, process, manage, and analyze the huge amounts of data available. This requires a level of knowledge and skills that can present a limitation for average users that are not acquainted with these processes. However, while data literacy might have been one of the big disadvantages of big data in healthcare, it is no longer the case. In the past years, various self service BI tools have been developed with the purpose of opening the analytical doors to any type of use without the need for any technical knowledge.

So, even if these analytical services are not your cup of tea, you are a potential patient, and so you should care about new healthcare analytics applications. Besides, it's good to take a look around sometimes and see how other industries cope with it. They can inspire you to adapt and adopt some good ideas.

24 Big Data Examples In Healthcare - A Summary

Predict the daily patients' income to tailor staffing accordingly

Use Electronic Health Records (EHRs)

Use real-time alerting for instant care

Help in preventing opioid abuse in the US

Enhance patient engagement in their own health

Use health data for a better-informed strategic planning

Research more extensively to cure cancer

Use predictive analytics

Reduce fraud and enhance data security

Practice telemedicine

Integrate medical imaging for a broader diagnosis

Prevent unnecessary ER visits

Smart staffing & personnel management

Learning & development

Advanced risk & disease management

Suicide & self-harm prevention

Improved supply chain management

Financial facility management

Developing new therapies & innovations

Track and control mass diseases

Improve the prescription process

Mitigate human error

Apple can alert people about heart problems

Bluetooth helps asthma patients

"Most of the world will make decisions by either guessing or using their gut. They will be either lucky or wrong." - Suhail Doshi, chief executive officer, Mixpanel.
The industry is changing, and like any other, big-style data is starting to transform it - but there is still a lot of work to be done. The sector slowly adopts the new technologies that will push it into the future, helping it to make better-informed decisions, improve operations, etc. In a nutshell, here's a short list of the examples we have gone over in this article. With healthcare data analytics, you can:

These 24 real-world examples of data analytics in healthcare prove that medical applications can save lives and should be a top priority of experts across the field. Even now, data-driven analytics facilitates early identification as well as intervention in illnesses while streamlining institutions for swifter, safer, and more accurate patient care. As technology evolves, these invaluable functions can only get stronger - the future of healthcare is here, and it lies in data.

Want to take your healthcare institution to the next level? Start building your own analysis and reports, and improve your healthcare data management with datapine's 14-day free trial!

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24 Real Life Examples of Big Data In Healthcare Analytics

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Big data in healthcare saves lives, but also money and time. See 24 real-world applications & examples of big data analytics in healthcare here!

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Roberta Pastorino, 1, ⁴ Corrado De Vito, ² Giuseppe Migliara, ² Katrin Glocker, ³ Ilona Binenbaum, 3, ⁴ Walter Ricciardi, 1, ⁵ and Stefania Boccia 1, ⁵

Abstract

Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen's expectations. Improving health outcomes while containing costs acts as a stumbling block. In this context, Big Data can help healthcare providers meet these goals in unprecedented ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers. In several contexts, the use of Big Data in healthcare is already offering solutions for the improvement of patient care and the generation of value in healthcare organizations. This approach requires, however, that all the relevant stakeholders collaborate and adapt the design and performance of their systems. They must build the technological infrastructure to house and converge the massive volume of healthcare data, and to invest in the human capital to guide citizens into this new frontier of human health and well-being. The present work reports an overview of best practice initiatives in Europe related to Big Data analytics in public health and oncology sectors, aimed to generate new knowledge, improve clinical care and streamline public health surveillance.

Introduction

Data have become an omnipresent concept in our daily lives with the routine collection, storage, processing and analysis of immense amount of data. This characteristic is cross-sectorial, ranging from the domain of machine learning and engineering, to economics and medicine.

Over the last decades, there has been growing enthusiasm of the potential usefulness of these massive quantities of data, called Big Data, in transforming personal care, clinical care and public health.1

An overview of Big Data definitions

Despite the term Big Data having become ubiquitous, there is no universal definition until now on the use of this term. According to McKinsey the term Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyse.2 Gartner proposed the popular definition of Big Data with the '3V': Big Data is volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.3 According to other definitions, instead, Big Data is also characterized by a fourth dimension: Veracity, concerning the quality, authenticity, 'trustworthiness' of data. 4

Furthermore, there is an emergent discussion that 'Big' is no longer the defining parameter, but rather how 'smart' the data are, focusing on the insights that the volume of data can reasonably provide.5 This aspect is fundamental in the health sector. The potential of Big Data in improving health is enormous. However, its potential value is unlocked only when leveraged to drive decision making and, to enable such evidence-based decision making, it is necessary to have efficient processes to analyse and turn high volumes of data into meaningful insights.

A specific definition of what Big Data means for health research was proposed by the Health Directorate of the Directorate-General for Research and Innovation of the European Commission: Big Data in health encompasses high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.6

Big Data analytics for health systems

The complexity of Big Data analysis arises from combining different types of information, which are electronically captured. The last years have seen an explosion of new platforms, tools and methodologies in storing, and structuring such data, followed by a growth of publications on Big Data and Health ( figure 1 ). To date, we can collect data from electronic healthcare records, social media, patient summaries, genomic and pharmaceutical data, clinical trials, telemedicine, mobile apps, sensors and information on well-being, behaviour and socio-economic indicators.

Healthcare professionals can, therefore, benefit from an incredibly large amount of data. Recent reports suggest that US healthcare system alone stored around a total of 150 exabytes of data in 2011 with the perspective to reach the yottabyte.7

Starting with the collection of individual data elements and moving to the fusion of heterogeneous data coming from different sources, can reveal entirely new approaches to improve health by providing insights into the causes and outcomes of disease, better drug targets for precision medicine, and enhanced disease prediction and prevention.

In light of this, opportunities and potential are enormous for the benefit of patients and, in general, of the healthcare system.

The potential benefits of Big Data for healthcare in the European Union

Big Data is a sensitive issue for European Union (EU) institutions: the availability of health-related Big Data can have a positive impact on medical and healthcare functions. EU is faced with several changes that may affect the sustainability of its healthcare system. By 2025 life expectancy is expected to further increase, and this may result in more people living longer, but not necessarily in a healthy and active condition. This will put further pressure on Europe's healthcare costs and economic productivity.

In this context, the data sharing approach can improve outcomes for patients and evidence-based healthcare decision making as reported during the workshop on 'Digitalisation and Big Data: implications for the health sector', held on 19 June 2018 at the European Parliament.8

The use of Big Data in healthcare, in fact, can contribute at different levels as reported by the Study on Big Data in Public Health, Telemedicine and Healthcare of the European Commission:9 (i) increasing earlier diagnosis and the effectiveness and quality of treatments by the discovery of early signals and disease intervention, reduced probability of adverse reactions, etc. (sector 1); (ii) widening possibilities for prevention of diseases by identification of risk factors for disease (sector 2); (iii) improvement of pharmacovigilance and patient safety through the ability to make more informed medical decisions based on directly delivered information to the patients (sector 3); (iv) prediction of outcomes (sector 4).

In the next paragraphs, examples of EU initiatives in the four macro sectors are listed.

Big Data have the potential to yield new insights into risk factors that lead to disease. There is the possibility to engage with the individual patient more closely and import data from mobile health applications or connected devices. These data have the potential to be analysed and used in real-time to prompt changes in behaviours that can reduce health risks, reduce harmful environmental exposures or optimize health outcomes.

Finally, Big Data can help identify and promptly intervene on high-risk and high-cost patients.10 Effective ways of managing these data can therefore facilitate precision medicine by enabling detection of heterogeneity in patient responses to treatments and tailoring of healthcare to the specific needs of individuals.11 All these aspects should eventually lead to a reduction in inefficiency and improvement in cost containment for the healthcare system.

Examples of Big Data analytics for new knowledge generation, improved clinical care and streamlined public health surveillance are already available. Below we report a selection of best practices in Europe in the public health and oncology fields.

Big Data in public health

Efforts to improve the availability and accessibility of data in the EU appear to be driven mainly by socio-economic purposes. However, great importance is placed on the need of using data and new information and communication technology (ICT) in public health to improve quality of prevention and care.

In the next years, European health systems must respond more efficiently to the exponential increase of chronic patients identifying the most efficient interventions and releasing the full potential of ICT. The e-health platforms that many European governments are trying to implement can be effective in improving the management of chronic patients in the community setting by interfacing between different health professionals and specialists and with the patients. Furthermore, a large part of EU citizens uses the internet looking for information on health and access to health services.

Moreover, Big Data and predictive analytics can contribute to precision public health by improving public health surveillance and assessment, therefore, in a public health perspective, the gathering of a very large amount of data, constitute an inestimable resource to be used in epidemiological research, analysis of the health needs of the population, evaluation of population-based intervention and informed policy making.9

Many projects across the EU are exploring the potential of available Big Data in a wide range of fields. A systematic review published in 2016 from the European Commission identified at that time 10 priority projects on Big Data implemented in Europe that fall in the four macro sectors described above and are aimed to support the sustainability of health systems by addressing the improvement of the quality and effectiveness of treatment, fighting chronic disease and supporting healthy lifestyles.9 Some of these projects focussed on gathering a very wide range of data types, from GP records, hospitalizations, drug prescription and laboratory and radiology analyses in order to create comprehensive national data warehouses. Among them, the 'Decision Support for Health Policy and Planning: Methods, Models and Technologies based on Existing Health Care Data' (DEXHELPP), the eHealth project in Estonia, the ARNO observatory in Italy and the Hospital Episode Statistics in the United Kingdom. The DEXHELPP project (mainly regarding sectors 1 and 4) used routinely collected health data sources to analyse the performance of the health system, to forecast future changes and to simulate the application of policy and interventions. The Estonian eHealth project (mainly regarding sectors 1, 2 and 3) was more oriented toward the improvement of the quality and efficiency of health services, aiming to digitalize all the information and prescription of each patient. Furthermore, in order to facilitate data collection, they provide an environment called X-Road to which all healthcare providers can link while using their own ICT solutions. The ARNO project (mainly regarding sector 4), was committed to epidemiological research, giving the possibility of deep stratification of the general population. The Hospital Episode Statistics (mainly regarding sector 4) was in charge of the Secondary Uses Service that publishes reports and analyses to support the National Health Service in the delivery of healthcare services.

Beside these projects characterized by a comprehensive approach, other initiatives focused on specific conditions (e.g. chronic conditions, rare diseases and psychiatric disorders) are also available in the EU.9 Among them, the PASSI surveillance project (mainly regarding sectors 1 and 2) in Italy provides large amount of information on the life style of almost the 90% of the population, enabling to individuate specific targets to implement and assess public health actions. The Shared Care Platform (mainly regarding sectors 1 and 3) in Denmark is focused on chronic patients, aiming to harmonize the course of treatment among health and social care providers. The Spanish Rare Diseases Registries Research Network (SpainRDR) (mainly regarding sector 1) focuses on the development of clinical research on rare diseases, providing the harmonization and unification into one comprehensive platform of pre-existing databases and registries of rare diseases. CEPHOS-LINK (mainly regarding sectors 1, 2 and 4), is a platform dedicated to mental health that involves six EU countries. It is committed to collect data on psychiatric hospital admissions and re-admissions, with the aim of finding determinants of re-admissions and to harmonize the psychiatric care pathways across the EU.

In addition to the projects reported above, the EU's framework programmes for research and innovation funded a large number of initiatives on Big Data in public health. In table 1 , we list 11 projects funded from the EU between 2012 and 2018 with a contribution over €499.999 that are captured from the Cordis website (source: cordis.europa.eu).

Table 1

Project acronym

Full title

Coordinator country

Start date

End date

Big Data in oncology

Cancer is one of the major health problems affecting our society, a situation that is set to deteriorate globally as the population grows and ages. According to the State of Health in the EU reports, cancer is recognized as one of the major contributors to premature deaths in the EU. It also has an impact on the economy in terms of lower labour market participation and productivity. Advances in Big Data analytics are given cancer researchers powerful new ways to extract value from diverse sources of data.

These diverse sources include a huge amount of data for one patient. As cancer is a molecularly highly complex disease with an enormous intra- and intertumoral heterogeneity among different cancer types and even patients, the collection of various different types of omics data can provide a unique molecular profile for each patient and significantly aid oncologists in their effort for personalized therapy approaches.12

The approach of combining these sources of data is implemented in Comprehensive Cancer Centres (CCCs).13 One of 13 CCCs in Germany is the National Center of Tumor Diseases, where the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) trial is conducted (mainly regarding sector 1, 2 and 3). In a recent review article,14 this trial was illustrated as an example of a highly successful programme addressing the molecular profiling in cancer patients. Within the MASTER trial data relevant to diagnostic information of young patients with advanced-stage cancer diseases is collected by performance of whole exome or whole genome sequencing and RNA sequencing, analysed and discussed.

Another example for a success story given in the review is the INdividualized therapy FOr Relapsed Malignancies in children (INFORM) (mainly regarding sector 1, 2 and 3) registry which aims to address relapses of high-risk tumours in paediatric patients. Data from whole-exome, low-coverage whole-genome, RNA sequencing and microarray-based DNA methylation profiling are utilized to identify patient-specific therapeutic targets. The INFORM registry started as a national effort in Germany and has been extended with the participation of eight European countries, as well as Australia.

Next to the described projects there are many other initiatives which focus on the value of Big Data in oncology, the EU alone funds more than 90 projects working on this topic (projects with a funding over €499.999 are listed in table 2 ). A potential umbrella for bringing together national efforts such as those mentioned above at the European level is Cancer Core Europe.15

Table 2

Project acronym

Full title

Coordinator country

Start date

End date

Collaborations are of extremely high importance especially in the case of paediatric or other rare types of cancer, where the data collected for one patient is indeed enormous, however the number of patients a single centre can have access to is too low to obtain statistical power high enough to reach meaningful results. One of the main challenges of these collaborations is the access to the data as well as the opportunity to analyse the huge amount of data in an efficient way. Physicians, researchers and informatics experts can only benefit from collected data and expert knowledge when they get easy and intuitive access to own data or data of partners. For example, at the German Cancer Research Center, tools are developed to grant ways to access and analyse own data together with data from partners.

Additionally, international exemplary approaches of sharing data among partners or the public are done by The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) which provide researchers with access to thousands of sequenced patients with different types of cancer. The availability of these data jointly with data by other partners has enabled large meta-analyses and machine learning algorithms, integrating different types of cancer that led to the identification of novel cancer driver genes that belong to specific pathways and can be possible therapy targets. Furthermore, there are a number of public databases that provide access to catalogues of mutations found to be involved in cancer, such as the Catalogue of Somatic Mutations in Cancer (COSMIC).

All these multiple sources of information combined and the establishment and support of CCCs across Europe offer the potential to increase the number of patients that can be offered molecular profiling and individualized treatment based on Big Data analysis.

Ethical and legal issues for the effective use of Big Data in healthcare

The use of Big Data in healthcare poses new ethical and legal challenges because of the personal nature of the information enclosed.

Ethical and legal challenges include the risk to compromise privacy, personal autonomy, as well as effects on public demand for transparency, trust and fairness while using Big Data.16

Data heterogeneity, data protection, analytical flows in analysing data and the lack of appropriate infrastructures for data storage emerged as critical technical and infrastructural issues that might endanger a Big-Data-driven healthcare.

Recently, Skovgaard et al.17 explored attitudes among people living in the EU toward the reuse of health data. The review indicates that the use of health data for purposes other than treatment enjoys support among people, as long as the data are expected to further the common good. In this context, the recent call reported in Science from a number of eminent scientists worldwide, for the unrestricted use of public genomic data, finds a fertile ground from the public.18 Concerns evolve around the commercialization of data, data security and the use of data against the interests of the people providing the data.

The recent EU Data Protection Regulation (GDPR) tries to balance patients' privacy while ensuring patient's data can be shared for healthcare and research purposes.

On 23 January 2017 the Consultative Committee of the Council of Europe's data protection convention adopted 'Guidelines on the protection of individuals with regard to the processing of personal data in a world of Big Data',19 the first document on privacy and Big Data which provides suggested measures for preventing any possible negative effects of the use of Big Data on human rights and freedoms.

Therefore, any government that uses Big Data in the health sector needs to establish affirmative policies to protect the health data of individuals, in terms of confidentiality, privacy and security, while ensuring that advancements in science can take advantage from the open use of data for the community well-being.

Conclusions

Big Data is beginning to revolutionize healthcare in Europe as it offers paths and solutions to improve health of individual persons as well as to improve the performance and outcomes of healthcare systems.

The implementation of precision medicine remains contingent on significant data acquisition and timely analysis to determine the most appropriate basis on which to tailor health optimization for individual prevention, diagnosis and disease treatment. Achieving effective and proportionate governance of health-related data will be essential for the future healthcare systems, and it requires that stakeholders collaborate and adapt the design and performance of their systems to reach the maximum innovative potential of information and innovation technology on health in the EU.

In this context, EU Member States should agree on international technical standards, taking also into account openness that is considered as the basic paradigm for digital transformation. Additionally, new approaches must be found for translating the vast amount of data into meaningful information that healthcare professionals can use. Further efforts must be made to make information for doctors and health professionals more accessible and understandable.

To achieve this, existing training and education programmes for healthcare professionals should integrate the issues of data handling in the curricula to ensure the development of the necessary skills and competencies. This is one of the objectives of the 'European network staff eXchange for integrAting precision health in the health Care sysTems' consortium (ExACT)20 project that aims to integrate precision health in European health systems by training a new generation of healthcare professionals across and outside of the EU.

In conclusion, we are living in fast-moving times, not least in terms of healthcare innovation. Whilst there are pressing needs for more personalized and sustainable health services, science and technology are offering a host of potentially invaluable new tools to deliver them. A cooperation at the EU level is needed to facilitate investments both in new technology and in the human capital, in order to guide citizens into this new frontier of human health and well-being where data are becoming a significant corporate asset, a vital economic input and the foundation of new business models.

Conflicts of interest: None declared.

References

2. Manyika J, Chui M, Brown B, et al. Big data: The next frontier for innovation, competition, and productivity. 2011. www.mckinsey.com/mgi (12 September 2019, date last accessed).

3. Beyer M, Laney D The Importance of 'Big Data': A Definition. Gartner report. 2012, 1-9.

5. George G, Haas MR, Pentland A. Big Data and management. AMJ 2014;57:321-6. [ Google Scholar]

11. Hopp WJ, Li J, Wang G. Big Data and the precision medicine revolution. Prod Oper Manag 2018;27:1647-64. [ Google Scholar]

15. Eggermont AMM, Apolone G, Baumann M, et al. Cancer core Europe: a translational research infrastructure for a European mission on cancer. [[ Mol Oncol2019;13:521-7. PMC free article] [ PubMed] Google Scholar]

19. Guidelines on the protection of individuals with regard to the processing of personal data in a world of Big Data Consultative Committee of the Convention for the Protection of Individuals with Regard to Automatic Processing of Personal Data (T-PD) Guidelines 1 on the Protection of Individuals with Regard to the Processing of Personal Data in a World of Big Data 2 Directorate General of Human Rights and Rule of Law. www.coe.int/data-protection (12 September 2019, date last accessed).

20. Boccia S, Pastorino R, Mariani M, Ricciardi W. The European network staff eXchange for integrAting precision health in the health Care sysTems (ExACT): a Marie Curie Research and Innovation Staff Exchange (RISE) project. Epidemiol Biostat Public Heal 2019;16. doi: 10.2427/13122. [ Google Scholar]

Articles from The European Journal of Public Health are provided here courtesy of Oxford University Press

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Benefits and challenges of Big Data in healthcare: an overview of the European initiatives

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Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen's expectations. Improving health outcomes while containing costs acts ...

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Using Big Data to Improve Healthcare Services | Tiranee Achalakul | TEDxChiangMai

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Electronic health record (EHR) systems are essential to provide the highest quality care while maximizing profits.

The implementation process can take time, but the benefits of electronic medical records (EMR) over paper records far outweigh the costs.

Including your staff in the decision-making process can help your organization adopt an EHR system quickly and efficiently.

This article is for healthcare administrators who want to know each step of the EHR implementation process.

The healthcare industry's transition from paper records to electronic medical records (EMR) has been a rocky, winding road. While electronic health records (EHR) systems have been the bane of some healthcare providers' existence, their implementation is essential to maximizing profits and delivering a modern standard of care to your patients.

Fewer stages of the EMR adoption journey are more arduous than the implementation phase. This phase is where the most problems occur and where shortcuts could lead to catastrophic issues later on. A botched EHR implementation can damage your revenue cycle for months, reduce patient confidence and harm your relationships with other healthcare providers. Moreover, a failing implementation might cause you to incur penalties from the federal government if your interim recordkeeping doesn't meet regulatory standards.

Here's how to make sure your medical practice avoids a crisis scenario when transitioning to a new EHR system.
Editor's note: Looking for the right EMR system for your business? Fill out the below questionnaire to have our vendor partners contact you about your needs.
What is EMR implementation?

EMR implementation refers to the integration of software into a healthcare organization's workflow. Steps in this process include selecting and preparing the EMR software, ensuring compliance with privacy and security, training the care team, and troubleshooting any issues that arise. Taking the time to implement EMR thoughtfully will help smooth the transition and benefit your practice, your patients and your staff.

While some providers may be concerned about the costs and time associated with EMR systems, their implementation is vital to improving both patient care and efficiency within a practice. EMRs have been shown to increase quality of care, patient outcomes and safety, as well as enhance communication between patients and providers. EMR systems also streamline scheduling and patient data management processes, thus freeing valuable time for healthcare providers.
Key takeaway: Preparing and implementing EMR software takes time and money, but it can vastly improve an organization's efficiency and patient care outcomes.
How long is the implementation process?
Key takeaway: The size of your organization and the complexity of your existing system are two primary indicators of the time commitment required to implement an EMR solution. Generally speaking, most facilities will be up and running in six to nine months.
The EMR implementation timeline will vary based on several factors, including the complexity of the system, the size of your organization or practice, and the location of the server (cloud-based versus on-site). As a general estimate, a healthcare organization can set up a novel EMR system within a year. EMR implementation for smaller practices (or modifications of an existing system) may take six to nine months, and cloud-based systems may take weeks rather than months.

Steps of implementation

1. Plan your implementation EMR road map.

The first step of implementing an EMR system is creating a checklist of all the steps you anticipate will need to be completed. Consider carefully who will need to complete which task, such as your healthcare providers, managers, IT staff and patients.

Critical tasks include stakeholder recruitment, budget planning, scheduling implementation, transferring data, providing training and live testing, and establishing go-live activities and metrics for success. Collaborative project management software, such as Google Sheets and Trello, can be an especially helpful tool in developing a road map.

2. Define your budget.

The exact cost of EMR implementation will depend on both the software's features and the size of your organization. Hardware and network upgrades, vendor training and consultant fees, data backups and storage, and cost per employee can all affect your costs.

You also should consider the initial loss in productivity that comes in the early stages of EMR implementation. Providers and staff will need dedicated time for training, which may come out of their patient care hours, and they may require time additional time beyond that to adjust to the new system. Once the system is fully implemented, however, your organization can benefit from a more streamlined workflow and overall increased productivity.

3. Configure the EMR system.

The first step in setting up a new EHR is doing a system configuration, which is essential to accurately represent the details of your medical practice and your patients' information within the software. That means programming in the location(s) of your practice and the providers who will be using the system. You also need to design your practice's clinical workflow, which includes the creation of clinical templates. Templates are required to meet Meaningful Use and Physician Quality Reporting System standards outlined by federal regulations. Following those rules is critical to ensure your practice does not incur Medicare payment penalties.

During the system-configuration phase, your EHR will be integrated with your practice management (PM) system if it is not already. Also, any data stored within your old system will be migrated to the new one in this phase.

"An important component of putting a complete PM and EHR system in place is conducting a data import from the legacy system to the new system," Adam Siegel, an EHR consultant, told Business News Daily in an earlier interview. "A new software system will not automatically convert patients from one system to another. This is a complex process that must be facilitated by technical experts."

4. Begin enrollment and credentialing.

Enrollment and credentialing, which details how you are paid, depends to some extent on the responsiveness of the payers. At best, changing payer enrollment from one PM system to another will take a couple of weeks, and at worst, the process can drag on for a few months. It's imperative that you include enough time in the implementation process in case the enrollment and credentialing step takes longer than expected.

"Enrollment and credentialing is critical for accurate and consistent payment from insurance companies," Siegel said. "The process can be drawn out depending on payer responsiveness and if the practice is represented as a group or individual providers."

Getting this step right is crucial to ensuring smooth operations for your future revenue-cycle management, including the receipt of accurate, consistent and timely payments from insurance companies. Still, you won't want it pushing back your "go live" date, which would hinder your medical practice's operations.

5. Optimize the workflow.

Outline each step of your organization's current workflow, then ask the following questions:

Is it necessary?

Does it add value for the patient?

Is it being done in the right order and by the right person?

For certain processes that are done by a physician, ask the following questions:

Does this require a physician's skills or training?

If not, can someone else complete this task?

By optimizing your workflows before EMR implementation, you can increase the likelihood of a smooth rollout and minimize any inefficiencies in the use of EMR.

6. Train your team.

Even the best software on the market is useless if staff isn't trained on how to properly use it. Training will vary based on your practice's specific workflow, the different roles of your staff members and their preferences on how to engage with the new system. There are several different ways to bring staff members up to speed, but the key is to begin training a short time before the new EHR goes online so staff members retain as much of the information as possible.

"Training is best done within a couple weeks of the go live date," Siegel said. "This ensures that new workflow and process will be fresh in the minds of the staff. ... Staff interact with their [practice management] and EHR systems constantly, and it's important that they adopt the new system and look for ways to use it as efficiently as possible."

Training options range from self-paced distance learning, which is the most affordable and simplest, to on-site, instructor-led training, which is a comprehensive, in-person program. Self-paced distance learning involves reading user guides and watching instructional videos. Of course, on-site training costs more, since a professional integrator must travel to your practice to train your staff, but in-person training can provide your staff with one-on-one time with an experienced instructor who knows the ins and outs of the new system. Siegel recommended choosing less-robust training methods for practices with a smaller staff and going with on-site training for large practices or hospitals.

7. Troubleshoot the system and mitigate risk.

EHR software is complex and far-reaching, so be prepared to engage in troubleshooting and risk mitigation. As Siegel puts it, "The implementation process as a core principle involves stopping revenue through one channel and restarting it through another."

Naturally, this is a delicate and risky thing to do. Here are a few suggestions to protect yourself against some of the potential issues surrounding implementation:

Choose a system with a friendly user interface. Any EHR system can either streamline or hinder operations in your medical practice. To ensure the new system has the desired effect, make sure the user interface of the system you choose is intuitive and simple to learn. This will help make the transition easier for your staff and increase the effectiveness of your system, all without depressing productivity.

Include your staff in the decision-making process. The best way to determine how prepared your staff is to adapt to the new system is by including them in the decision-making process. Do they find certain interfaces friendlier than others? What does their preferred workflow look like? What kind of training would they find most helpful? These questions can help you choose a system with your staff in mind and make adoption of the new system much easier for them in the end. Involving each of the different departments in your practice during the decision-making process provides invaluable insight when making a final choice.

Integrate all software with your new PM/EHR system. "Many practices use additional software products to provide the full spectrum of solutions needed to run an efficient modern medical practice," Siegel said. "This starts with integrating any additional systems with the new PM and EHR software. Products like appointment-reminder systems and interfaces with lab and imaging systems are critical to practice efficiency and shouldn't be overlooked during the conversion process."

Honestly assess your practice before implementation begins. Providing an honest and accurate assessment of your medical practice's current finances, workflow and capabilities is a major part of devising a successful implementation. Through this assessment, and during the development of clearly stated future goals, a practice's administration can more effectively set and track metrics of success for the new system.

"A practice should have a clear understanding of how their complete practice will operate on the new system by the time they sign the contract and begin the implementation process," Siegel said.

The payoff of a fully implemented EHR system

At the end of the implementation process, your practice will have a fully functioning, modern EHR system that will hopefully improve both the quality of healthcare services you provide and your practice's profitability. Most practices implement new software only once or twice, and if it's done right, the rewards are exceptional.

Some of the benefits your practice should realize are:
Key takeaway: Additional reporting by Sean Peek. Some source interviews were conducted for a previous version of this article. Editor's note: If you need help choosing the EMR system that's right for you, use the questionnaire below to receive more information from our vendor partners. Although EMR implementation requires a time, energy and monetary commitment that may seem daunting, the many benefits of these systems far outweigh the costs.

A more optimized day-to-day workflow and friendlier user experience

More efficient, timely and comprehensive medical care for patients

More complete documentation of patient visits and rendered services

Greater patient satisfaction due to decreased wait times and simplified billing

More efficient scheduling and increased appointments, as well as reductions in no-shows and cancellations

Increased revenue and claims accepted by payers on the first submission, as well as a reduction in the amount of aging accounts-receivable in collections

Improved connectivity with labs, hospitals and specialists that work with your practice

"By visualizing a modern medical practice and putting in place the right tools to manage it, a practice can put themselves in a grounded position of system optimization and excellence in performance poised for future growth," Siegel said.

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How to Implement an Electronic Health Records System - businessnewsdaily.com

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Electronic health record (EHR) systems are essential to provide the highest quality care while maximizing profits. The implementation process can take time, but the benefits of electronic medical records (EMR) over paper records far outweigh the costs. Including your staff in the decision-making process can help your organization adopt an EHR system quickly and efficiently.

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"Understanding nurses’ attitudes toward healthcare technology may help drive acceptance, as well as maximize the inherent potential of the new technologies toward improving patient care."

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"I think a lot of clinicians feel they have had this burden of EHRs put on them for the benefit of bureaucracies and scientists and accountants."

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MIT News | Massachusetts Institute of Technology

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Electronic health records have been widely adopted with the hope they would save time and improve the quality of patient care. But due to fragmented interfaces and tedious data entry procedures, physicians often spend more time navigating these systems than they do interacting with patients.

Researchers at MIT and the Beth Israel Deaconess Medical Center are combining machine learning and human-computer interaction to create a better electronic health record (EHR). They developed MedKnowts, a system that unifies the processes of looking up medical records and documenting patient information into a single, interactive interface.

Driven by artificial intelligence, this "smart" EHR automatically displays customized, patient-specific medical records when a clinician needs them. MedKnowts also provides autocomplete for clinical terms and auto-populates fields with patient information to help doctors work more efficiently.

"In the origins of EHRs, there was this tremendous enthusiasm that getting all this information organized would be helpful to be able to track billing records, report statistics to the government, and provide data for scientific research. But few stopped to ask the deep questions around whether they would be of use for the clinician. I think a lot of clinicians feel they have had this burden of EHRs put on them for the benefit of bureaucracies and scientists and accountants. We came into this project asking how EHRs might actually benefit clinicians," says David Karger, professor of computer science in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and senior author of the paper.

The research was co-authored by CSAIL graduate students Luke Murray, who is the lead author, Divya Gopinath, and Monica Agrawal. Other authors include Steven Horng, an emergency medicine attending physician and clinical lead for machine learning at the Center for Healthcare Delivery Science of Beth Israel Deaconess Medical Center, and David Sontag, associate professor of electrical engineering and computer science at MIT and a member of CSAIL and the Institute for Medical Engineering and Science. It will be presented at the Association for Computing Machinery Symposium on User Interface Software and Technology next month.
A problem-oriented tool
To design an EHR that would benefit doctors, the researchers had to think like doctors.

They created a note-taking editor with a side panel that displays relevant information from the patient's medical history. That historical information appears in the form of cards that are focused on particular problems or concepts.

For instance, if MedKnowts identifies the clinical term "diabetes" in the text as a clinician types, the system automatically displays a "diabetes card" containing medications, lab values, and snippets from past records that are relevant to diabetes treatment.

Most EHRs store historical information on separate pages and list medications or lab values alphabetically or chronologically, forcing the clinician to search through data to find the information they need, Murray says. MedKnowts only displays information relevant to the particular concept the clinician is writing about.

"This is a closer match to the way doctors think about information. A lot of times, doctors will do this subconsciously. They will look through a medications page and only focus on the medications that are relevant to the current conditions. We are helping to do that process automatically and hopefully move some things out of the doctor's head so they have more time to think about the complex part, which is determining what is wrong with the patient and coming up with a treatment plan," Murray says.

Pieces of interactive text called chips serve as links to related cards. As a physician types a note, the autocomplete system recognizes clinical terms, such as medications, lab values, or conditions, and transforms them into chips. Each chip is displayed as a word or phrase that has been highlighted in a certain color depending on its category (red for a medical condition, green for a medication, yellow for a procedure, etc.)

Through the use of autocomplete, structured data on the patient's conditions, symptoms, and medication usage is collected with no additional effort from the physician.

Sontag says he hopes the advance will "change the paradigm of how to create large-scale health datasets for studying disease progression and assessing the real-world effectiveness of treatments."
In practice
After a year-long iterative design process, the researchers tested MedKnowts by deploying the software in the emergency department at Beth Israel Deaconess Medical Center in Boston. They worked with an emergency physician and four hospital scribes who enter notes into the electronic health record.

Deploying the software in an emergency department, where doctors operate in a high-stress environment, involved a delicate balancing act, Agrawal says.

"One of the biggest challenges we faced was trying to get people to shift what they currently do. Doctors who have used the same system, and done the same dance of clicks so many times, form a sort of muscle memory. Whenever you are going to make a change, there is a question of is this worth it? And we definitely found that some features had greater usage than others," she says.

The Covid-19 pandemic complicated the deployment, too. The researchers had been visiting the emergency department to get a sense of the workflow, but were forced to end those visits due to Covid-19 and were unable to be in the hospital while the system was being deployed.

Despite those initial challenges, MedKnowts became popular with the scribes over the course of the one-month deployment. They gave the system an average rating of 83.75 (out of 100) for usability.

Scribes found the autocomplete function especially useful for speeding up their work, according to survey results. Also, the color-coded chips helped them quickly scan notes for relevant information.

Those initial results are promising, but as the researchers consider the feedback and work on future iterations of MedKnowts, they plan to proceed with caution.

"What we are trying to do here is smooth the pathway for doctors and let them accelerate. There is some risk there. Part of the purpose of bureaucracy is to slow things down and make sure all the i's are dotted and all the t's are crossed. And if we have a computer dotting the i's and crossing the t's for doctors, that may actually be countering the goals of the bureaucracy, which is to force doctors to think twice before they make a decision. We have to be thinking about how to protect doctors and patients from the consequences of making the doctors more efficient," Karger says.
A longer-term vision
The researchers plan to improve the machine learning algorithms that drive MedKnowts so the system can more effectively highlight parts of the medical record that are most relevant, Agrawal says.

They also want to consider the needs of different medical users. The researchers designed MedKnowts with an emergency department in mind - a setting where doctors are typically seeing patients for the first time. A primary care physician who knows their patients much better would likely have some different needs.

In the longer-term, the researchers envision creating an adaptive system that clinicians can contribute to. For example, perhaps a doctor realizes a certain cardiology term is missing from MedKnowts and adds that information to a card, which would update the system for all users.

The team is exploring commercialization as an avenue for further deployment.

"We want to build tools that let doctors create their own tools. We don't expect doctors to learn to be programmers, but with the right support they might be able to radically customize whatever medical applications they are using to really suit their own needs and preferences," Karger says.

This research was funded by the MIT Abdul Latif Jameel Clinic for Machine Learning in Health.

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Toward a smarter electronic health record

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MedKnowts, a "smart" electronic health record system, can help doctors work more efficiently by presenting relevant information from a patient's medical history, autocompleting medical terms as a clinician types, and auto-populating repetitive fields. The system was developed by MIT and Beth Israel Deaconess Medical Center researchers.

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" integrating PDMPs into electronic health record (EHR) systems, aim to improve prescribing practices by increasing the utility of information contained in PDMPs and decreasing prescription diversion and doctor shopping."

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Chelsea Richwine and Jordan Everson | January 19, 2023

In the midst of a growing drug overdose crisis exacerbated by the COVID-19 pandemic, clinicians' use of prescription drug monitoring programs (PDMPs) and electronic prescribing of controlled substances (EPCS) technology is critical to improving opioid prescribing practices, informing treatment decisions, and supporting safe and effective patient care. Recent efforts, such as mandating use of EPCS technology and integrating PDMPs into electronic health record (EHR) systems, aim to improve prescribing practices by increasing the utility of information contained in PDMPs and decreasing prescription diversion and doctor shopping.

Tracking Progress towards Universal, High-Value Use of PDMPs and EPCS

ONC engages on several research projects to track progress and assess benefits from PDMP and EPCS use. Recently, two of these studies leveraged publicly available data from the National Electronic Health Records Survey (NEHRS) - a nationally representative survey of U.S. office-based physicians - to characterize physicians' use of EPCS and PDMPs, and the benefits associated with their use. An ONC data brief released in June 2022 showed substantial growth in the use of EPCS among physician prescribers of controlled substances between 2017 and 2019, where adoption likely grew in response to state mandated EPCS laws and in anticipation of the federal Medicare Part D mandate that was announced in 2018 and would become effective in 2021.

A newly released ONC data brief shows that nearly three-quarters (71%) of physician prescribers used EPCS in 2021 with 62 percent reporting they used EPCS "often" - a significant increase from just over one-third of prescribers that reported using EPCS "often" in 2019 (37%). The new data brief covers the post-mandate period and shows continued progress in the use of EPCS among physician prescribers.

In addition to marked growth in the use of EPCS following the federal mandate, the data brief highlights widespread use of PDMPs among office-based physicians in 2021, with more than three-quarters (78%) of physician prescribers indicating they checked their state's PDMP prior to prescribing controlled substances to a patient for the first time. While PDMP use remained relatively steady between 2019 and 2021, one-third of prescribers now access the PDMP through their EHR (compared with one-fifth in 2019).

Identifying Benefits of PDMP and EPCS

Identifying and measuring the benefits of PDMPs is an important step towards increasing their utility. A recent ONC study, using data from the 2019 NEHRS, added to the growing literature demonstrating benefits of PDMPs by showing greater frequency and ease of use among physicians with PDMPs integrated with their EHR. PDMP-EHR integration is beneficial to clinicians as it increases the accessibility of PDMP data at the point of care, making that information more actionable for clinical decision-making. Physicians who frequently checked their state's PDMP and reported it was easy to use were more likely to report perceived benefits such as reduced prescribing of controlled substances, confirming the appropriateness of treatment, and improved care coordination. In 2021, a majority of physicians who checked their state's PDMP reported experiencing at least one benefit associated with use.

Because EPCS only began to be adopted at scale a few years ago, evidence on the benefits of using EPCS has so far been scarce, with some mixed evidence. Earlier this year, an ONC study examining the impact of policy that mandated EPCS use in New York state showed that opioid prescribing fell by almost six percentage points following EPCS adoption. We're hopeful that additional analyses focused on the Part D mandate will further confirm the impact of EPCS on opioid prescribing.

Looking Ahead

While the progress in physicians' use of EPCS and PDMPs is promising, our recent analyses underscore important areas for improvement. First, despite high rates of EPCS and PDMP use at the national level, rates of use varied by specialty, practice size, ownership, and EHR developer market share, suggesting targeted support may be needed to increase access and use in lower-resource settings. Second, despite physicians' widespread access to data from their state's PDMP, the electronic exchange of PDMP data across state lines remains limited, restricting physicians' ability to capture complete prescription histories of patients who received care in different geographic locations. Finally, while the share of physicians who reported accessing the PDMP through their EHR increased significantly between 2019 and 2021, over two-thirds of physicians still access the PDMP through an outside system, which may limit the use of PDMP data that are not well-integrated into clinical workflows.

This finding highlights the importance of continuing efforts to achieve PDMP-EHR integration, including the development of technical resources to guide organizations through the different phases of integration. Moreover, it is important to ensure that integration drives value by embedding data in clinician workflows and powering actionable decision support tools to combat the opioid crisis.

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Evidence on the Growing Use of Health IT to Address the Opioid Epidemic - Health IT Buzz

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In the midst of a growing drug overdose crisis exacerbated by the COVID-19 pandemic, clinicians' use of prescription drug monitoring programs (PDMPs) and electronic prescribing of controlled substances (EPCS) technology is critical to improving opioid prescribing practices, informing treatment decisions, and supporting safe and effective patient care.

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https://medicine.yale.edu/intmed/genmed/games/

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The Yale Center for Health & Learning Games develops and evaluates videogame-based interventions with the goal of reducing risk and promoting healthy behaviors,

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https://www.fastcompany.com/90867065/future-of-telehealth-public-health-emergency-ending

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https://health.usnews.com/health-care/patient-advice/slideshows/ways-to-prevent-medical-errors

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The U.S. News Health team delivers accurate information about health, nutrition and fitness, as well as in-depth medical condition guides. All of our stories rely on multiple, independent sources and experts in the field, such as medical doctors and licensed nutritionists. To learn more about how we keep our content accurate and trustworthy, read our editorial guidelines.

Jeff Brady, MD, MPH

Brady is director of the Agency for Healthcare Research and Quality's Center for Quality Improvement and Patient Safety in the Washington, D.C., area.

David Newman-Toker, MD, PhD

Newman-Toker is director of the division of neuro-visual and vestibular disorders in the Department of Neurology and director of the Armstrong Institute Center for Diagnostic Excellence at Johns Hopkins Medicine in Baltimore.

Ada Stewart, MD, FAAFP

Stewart is president of the American Academy of Family Physicians and a practicing family physician in Columbia, South Carolina.

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Medical Mistakes Can Happen Even at Your Doctor's Office or Pharmacy. Here's How to Prevent Errors That Can Impact Your Health

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The U.S. News Health team delivers accurate information about health, nutrition and fitness, as well as in-depth medical condition guides. All of our stories rely on multiple, independent sources and experts in the field, such as medical doctors and licensed nutritionists. To learn more about how we keep our content accurate and trustworthy, read our editorial guidelines.

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"In the discussion over the ethics of such computer use in health care, we see a recurring theme of tensions among the principles of respect for autonomy, beneficence, and nonmaleficence."

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Health informatics studies health care information and communication, both of which have been greatly affected in recent years by the use of computers and the web by both clinicians and patients. Many practices now use electronic medical records (EMRs) and computerized provider order entry (CPOE). Patients often surf the web seeking health information. But some uses of computers in health care are controversial and raise ethical concerns, especially when patient safety and outcomes are at stake.

For guidance about ethics, health care often relies on the principles of respect for autonomy, nonmaleficence, beneficence, and justice.1 Respecting autonomy means allowing individuals control over their lives. Nonmaleficence requires refraining from harming patients, while beneficence entails providing beneficial care. (Beneficence also includes preventing harm - as opposed to refraining from harm, which is nonmaleficence.) Justice seeks a fair distribution of resources and responsibilities.

These principles have relevance to health informatics practices. In the discussion over the ethics of such computer use in health care, we see a recurring theme of tensions among the principles of respect for autonomy, beneficence, and nonmaleficence.

For example, EMR systems can benefit patients by improving care access, quality, coordination, and cost.2 If the ideal use of EMR and CPOE systems provides significant patient benefit, there would seem to be a moral obligation for clinicians to use them. But some clinicians find the inconvenience of such systems to be a burden, and patients can experience clinician use of such systems during medical encounters as intrusive. Furthermore, what if clinicians using such systems in actual practice engage in documentation techniques that may be dangerous to patients? Would that call into question that use of such systems is a moral obligation? Or only that if used, such systems must be used in a morally responsible fashion? Tension among the ethical principles also occurs in the area of patient use of the web and social media. How can we respect the autonomy of patients to use these technologies while at the same time preventing possible patient harm from inaccurate information and illicit prescription medication purchases?

Confidentiality and Privacy in a Wired World

Clinicians and institutions using EMR systems must ensure patient health information confidentiality and protect patient privacy. With computer systems this seems a difficult task because records are easily shared with more people: physicians, nurses, other clinicians, clerks, coders, managers, information technology staff, insurance personnel, the government, patients, vendor staff, and even patients themselves. Health information exists not only in an institution's electronic medical records but is also made available through health information exchanges, personal health records, and patient portals.

Those working in health care have a moral (and legal) obligation to protect information confidentiality and patient privacy. Electronic records and associated systems are thus monitored and secured to prevent privacy breaches. The moral basis of such an obligation is in respecting patient autonomy (keeping patients in control of their health information), and refraining from harming and preventing harm to patients (nonmaleficence and beneficence), as might occur, for example, through disclosure of embarrassing information. Patients who feel their health information will be kept confidential will more likely share it willingly with providers, which is important for optimal care.

Yet despite the existence of the HIPAA (Health Insurance Portability and Accountability Act) regulations and institutional monitoring of records, snooping, hacking, public breaches, and health care identify thefts occur.3, 4 Perhaps even greater security technologies and policies are warranted. But strict security measures are often in tension with clinician needs for convenient and timely access to such information. Clinicians already chafe at what they see as overly strict and burdensome security requirements such as having to constantly log in after a short auto-log out, change passwords at regular time intervals (and meet technical requirements for password acceptability, making them difficult to recall, and be unable to reuse previous passwords), remember multiple user names and passwords for different systems, be unable to store chart notes on computers that leave the facility, establish VPN (Virtual Private Network) connections to access hospital records from home, and authenticate/justify one's role before accessing particular patient records. Information technology staff need to work with clinicians and seek their input in security techniques, otherwise they risk clinicians trying to bypass security measures out of frustration.

Respecting patient autonomy may even entail allowing patients to weigh in on some policy decisions. Hospitals debate whether patient consent to Health Information Exchange (HIE) participation should be in the form of an opt-out or opt-in policy. Opt-out policies may increase patient participation. But allowing patients to opt-out of participation in a Health Information Exchange may not safeguard patient autonomy as much as would requiring patients to opt-in. How much information needs to be provided to patients about the HIE for an opt-out policy to constitute consent that is truly informed? From reading a technical paragraph in a consent form presented by a receptionist, do patients fully understand what information is being made available where? These questions will be even more relevant if a nationwide HIE (a National Health Information Network (NHIN) or Nationwide Health Information Network (NwHIN)) is realized because patients may object to having to opt-out to prevent their personal health information being made available practically anywhere to any provider at any institution. Some patients are so apprehensive about loss of control they worry about health information stored in an EMR quite apart from an HIE - some desire an opt-out policy for certain information in the EMR.5

Clinical Documentation Practices

Not only are confidentiality, privacy, and security requirements sometimes controversial, so too is the trade-off between documentation accuracy and convenience. Clinicians have a moral responsibility to make sure medical records are accurate, but accuracy is sometimes sacrificed to convenience through questionable EMR documentation practices.6

Copy and Paste

One area of concern is the use of what has been termed "content importing technology" (CIT).7 EMR systems commonly allow copy and paste techniques from one note to another, even between different patients, and they allow copying from the notes of other providers. Many EMR systems allow the clinician to import almost all of the content of a previous note (a note documenting a past encounter) into a new note (a note for a different encounter). This particular functionality may be called "pull previous" or "copy forward." Identical sections appearing in completely different notes created using such techniques may be considered instances of "cloned documentation." EMR systems also often feature templates, or preconfigured note structures that may already be prepopulated with section contents for a specific type of procedure or even for a specific disease. Other common shortcuts are capabilities to automatically include a complete and normal review of systems and to import lab results directly into a chart note. Providers insist on such techniques because they can be tremendous documentation time-savers.

But used carelessly such shortcuts can result in inaccurate notes and may create legal liability problems.8 When copying, importing, or using templates, the provider is supposed to carefully review the note for faithfulness to what actually happened in the current encounter. Medical decisions made on the basis of inaccurate notes may compromise patient safety or outcomes. Copying and pasting another clinician's content without proper attribution might be considered plagiarism or fraud.

Such documentation problems may be the result of not just carelessness but unfamiliarity with the system. To be used optimally, EMR systems require user training.9 It is often difficult for providers to fit training into a busy patient schedule, but institutions have a moral responsibility to provide clinicians with adequate training and providers have the responsibility to avail themselves of it.

Computer in the Exam Room

Another controversy is over the presence of the computer in the exam room, and this too can become an ethics matter if it affects what occurs during the clinician-patient encounter. Some providers interact with EMR and CPOE systems while at the same time trying to engage the patient. A computer being used for documentation in the exam room can function as a physical or psychological barrier between provider and patient. Patients who feel physicians interact more with the computer than with them may feel alienated and consequently decline to share important concerns.10

Structured Data

A final documentation issue concerns structured data. Increasingly, the federal government (meaningful use requirements) is requiring at least some of the information in a note be structured data. Structured data exists when such information is in a discrete form that can be optimally stored in the fields of a database. In such a form, and aggregated, patient information can be used for secondary data analysis. Data querying and data mining could yield important previously undetected patterns about patient symptoms, diseases, treatments, and complications. Such information could also be used for public health surveillance.11 Structured data is also often needed to trigger decision-support features in the system.

Because computers cannot yet fully "understand" natural language sentences, the traditional narrative text of a chart note does not result in structured data in the database. Current natural language processing programs may be able to convert some of narrative text to structured data, but by and large to get structured data into a database the clinician must enter it that way. Hence the existence of drop-down menus, data entry boxes, and the like for entering vitals and creating problem lists and medication lists, for example.

Some providers find entering data by such techniques saves time, while others find it tedious. Unfortunately, though, it is the provider who bears the burden of having to enter data in a way the computer requires, not in a way that they might find more convenient (narrative dictation). Completeness may also suffer. Chart notes created by structured data entry techniques may resemble an outline more than a detailed story, which may make it difficult for other clinicians reading the note to get an adequate sense of the "patient's story." Can one tell the unique story of a particular patient by using drop down menus without something being left out? Will the provider then resort to using both structured data entry and accompanying narrative text, which would seem to take longer than narrative text alone?

Secondary data analysis made possible by structured data raises questions about privacy and consent and thus ultimately patient autonomy.12 Some seek to justify use of patient information in secondary data analysis on the basic of serving the public good.13 But a patient may object that their consent to use of personal health information for treatment, payment, or operations (TPO) does not automatically include consent to use for scientific research, no matter how noble; they never agreed to details of their clinical encounters being used for that, even in aggregated, de-identified form.

Decision Support

EMR and CPOE systems often include decision-support features, such as diagnosis aids, medication recommendations, customized information displays (such as graphs showing trends), clinical calculators (for example, for dosing), available guidelines and protocols, and warning messages and alerts (for example, about possible drug-drug interactions). As mentioned, these functions sometimes key off of structured data. For example, dosing recommendation depend on the patient's weight being entered in a special data-entry box rather than being buried in a narrative paragraph. Drug-drug interaction problem detection depends on a medication list completed by drop-down menu choices rather than a narrative paragraph listing medications.

Ethical

Several ethical issues arise about the development and use of decision-support technologies. If such a technology is available, and might help prevent patient harm or improve patient outcomes, is the provider morally obligated to use it? Since the provider is the ultimate decision-maker, what if the provider relies on decision-support recommendations that prove incorrect? Is the provider morally (or legally) at fault? Does the vendor bear any moral responsibility for incorrect recommendations or faulty software? What if the problem is not faulty information in the system, but rather a poorly designed display that misleads or is easily misinterpreted by the provider?

Legal

On the other hand, there may be legal liability issues if the provider decides not to make use of available decision-support recommendations.14 Government regulations and the courts would decide whether particular software programs could be held legally at fault under standards of ordinary negligence or strict product liability. Vendors commonly write "indemnify and hold harmless" clauses into contracts with purchasers to avoid such responsibility.16 But one could argue that vendors nevertheless bear moral responsibility for using best practices to develop and test their software systems.

Functionality

In some cases, decision support functionality can become intrusive and counterproductive. For example, too many warnings can cause "alert fatigue."16 Furthermore, if the provider cannot override an alert when legitimately needed, the system may hinder, rather than enhance, proper patient care.17

Social Media

Information and Advice

The explosion of social media during the last decade has created many opportunities in health care for using the web and mobile devices; some of these are characterized as "consumer informatics." Associated informatics-related fields or subfields are mHealth (mobile devices) and eHealth (electronic delivery, such as online eVisits). On the web patients can obtain health-related information, interact in blogs and support groups, purchase health-related products (such as exercise equipment, monitoring devices, and nutritional supplements), engage in eVisits, access medical records and pay hospital bills through patient portals, maintain personal health records, and even buy prescription medications.

Web sources have a moral obligation to ensure health information provided is accurate, since to do otherwise may harm patients. It is not easy for the patient to discern what is accurate, and much health information on the web is inaccurate.18 Online blogs, forums, and support groups may spread incorrect information or imprudent health advice that may be physically or psychologically harmful.

Prescription Medication

Of questionable morality is the alleged practice of some pharmaceutical companies of operating support group sites whose real purpose is clandestine market research. Also controversial is pharmaceutical companies engaging in online direct-to-consumer (DTC) advertisement of drugs in an attempt to have patients "pressure" physicians to prescribe them and perhaps also to subtly advertise to physicians directly. Such advertisements may appear to patients searching for information about associated diseases or participating in related online forums. Patients may be unaware they are being targeted.

Patients use the web for more than information - they can buy prescription medications from online pharmacies. While some legitimate pharmacies have an online sales presence and require prescriptions for purchases, others are from unknown businesses and may provide prescription medications to patients without prescriptions. In such cases the patient has no way of knowing what he or she is really buying. It may be illegal for those in the United States to buy from online pharmacies located outside the country, but the fact that some consumers buy medications from such sources raises possible ethical concerns in itself. Patients may think they are getting a better price. Are American drug prices so high some patients cannot afford them? Or they may think their provider will not meet their medication needs. Do patients have trouble obtaining needed medications from their local physician and pharmacy? In cases where cost or access is an issue the principle of justice, interpreted as fairness to all patients in provision of health care resources, may be relevant.

The ethical issue for health care professionals is about what guidance to give patients about use of sites dispensing information, advice, or medications. Steering patients away from all such sites seems unrealistic. Recommending specific reputable sites may be more helpful, but obviously self-diagnosis from web information is no substitute for seeing a provider. Some hospitals and practices themselves make health information available on the web in an attempt to provide a reliable source of information for patients. Cautioning patients about online prescription purchases from questionable sites would also seem important. Again, though, sometimes here we see a tension between following the obligation to respect patient autonomy and preventing harm to patients (beneficence).

Patient Portals

To further engage patients in responsibility for their health, patient portals are now available for viewing and paying on financial accounts, as well as accessing limited clinical data. Some facilities allow patients to access their own medical records or lab test results via a patient portal. Critics contend lab results could be easily misinterpreted by a patient. Some hospitals offer personal health records that download information from the hospital records. Obviously if data must be entered into PHRs by patients a concern is possible inaccurate or missing information. Other patient delivery modalities are eVisits, telehealth, and email communication. eVisits have found limited use to help with monitoring and management of chronic conditions for existing patients but it would be arguably irresponsible to attempt to provide extensive management of complex conditions or seeing new patient via eVisits. Telehealth, similar to eVisits but often for monitoring purposes only or making more extensive use of video interaction, also is better suited to some usages (emergency trauma transport) than others (more complicated initial diagnoses). Some physicians are willing to engage in email exchanges with patients but for only limited purposes. Confidentiality concerns, unrealistic patient expectations of service or for immediate replies, time constraints, and reimbursement issues make email of limited usefulness.

Clinician Use

Clinicians themselves need to exercise caution on the web. They should never post patient pictures or protected health information (PHI) on social media sites, such as Facebook, even if the patient is not easily identifiable. Griping about patients, other clinicians, or health care management should also be avoided. Some physicians feel the same should hold for patient behavior and so refuse to take patients who will not sign a pledge to refrain from negative reviews of the physician on websites. While the desire to avoid unfair damage to one's reputation is understandable, a coercive stipulation like this seems difficult to square with the moral obligation to always place the patient's interests above one's own.

The use of EMRs and CPOEs is seemingly becoming a standard of care.19 The challenge will be to use this new standard of care in a morally responsible manner.

Biography

Win Phillips, PhD, is Associate Teaching Professor in the Department of Health Management and Informatics at the University of Missouri School of Medicine.

Contact: ude.iruossim@niwspillihp

References

1. Beauchamp TL, Childress JF. Principles of biomedical ethics. 6th edition. Oxford University Press; 2009. [ Google Scholar]

4. Sittig DF, Singh H. Legal, ethical, and financial dilemmas in electronic health record adoption and use. Pediatrics. 2011;127(4):e1042-e1047. [[ PMC free article] [ PubMed] Google Scholar]

5. Sittig and Singh. 2011

8. Sittig and Singh. 2011

9. Sittig DF, Singh H. Rights and responsibilities of user of electronic health records. Canadian Medical Association Journal. 2012;184(13):1479-1483. [[ PMC free article] [ PubMed] Google Scholar]

14. Sittig and Singh. 2011

15. Sittig and Singh. 2011

16. Sittig and Singh. 2011

17. Sittig and Singh. 2012

Articles from Missouri Medicine are provided here courtesy of Missouri State Medical Association

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Ethical Controversies About Proper Health Informatics Practices

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Health informatics studies health care information and communication, both of which have been greatly affected in recent years by the use of computers and the web by both clinicians and patients. Many practices now use electronic medical records (EMRs) and computerized provider order entry (CPOE). Patients often surf the web seeking health information.

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free resources to increase health education

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Health, United States 2020-2021 Infographics

Twitter Graphics

Health, United States, 2020-2021

Health, United States, 2019

Spotlight Infographics

The Health, United States Spotlight is an infographic series that explores topics of public health importance using data from the Health, United States annual report. Spotlights are published throughout the year and feature the most current data available at the time of publication. Consistent with the annual report, the Spotlight focuses on trends over time and features data from both NCHS and non-NCHS (government or private) sources. Health, United States data cover four subject areas: health status and determinants, utilization of health resources, health care resources, and health care expenditures and payers.

Methodology

Where possible, changes over time and differences across groups are tested for statistical significance. Differences were assessed for statistical significance using statistical testing methods recommended by the individual data systems. Statistical significance is assessed at the 0.05 level without correction for multiple comparisons.

To describe differences across groups, terms such as "similar" and "no difference" are used to indicate that the estimates being compared were not found to be significantly different, while terms such as "higher" or "lower" indicate that a significant difference was detected. Lack of comment regarding the difference between estimates does not necessarily mean that differences were tested and found not to be significant.

To describe trends, terms such as "stable" and "no clear trend" are used to indicate that the slope of the segment was not statistically different from 0, while terms such as "increased" and "decreased" indicate that a significant slope was detected. Since the 2018 publication of the National Center for Health Statistics (NCHS) Guidelines for Analysis of Trends, the Health, United States infographics have adhered to these guidelines in order to analyze trends from NCHS complex sample surveys and the National Vital Statistics System.

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Health, United States provides a wide array of trends in health statistics to policymakers, public health professionals, and the public. It presents statistics in four overarching areas: health status and determinants, health care utilization, health care resources, and health expenditures and payers.

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Abstract

Background

Understanding how people use infographics and their opinion on them has important implications for the design of infographics but has not been investigated. The aim of this study was to describe people's use of and opinions about infographics summarising health and medical research, preferences for information to include in infographics, and barriers to reading full-text articles.

Methods

We conducted an online cross-sectional survey of consumers of infographics that summarise health or medical research. Demographic and outcome data were collected and summarised using descriptive statistics. A sensitivity analysis explored whether being a researcher/academic influenced the findings.

Results

Two hundred fifty-four participants completed the survey (88% completion rate). Participants included health professionals (66%), researchers (34%), academics (24%), and patients/the public (13%). Most used Twitter (67%) and smartphones (89%) to access and view infographics, and thought infographics were useful tools to communicate research (92%) and increase the attention research receives (95%). Although most participants were somewhat/extremely likely (76%) to read the full-text article after viewing an infographic, some used infographics as a substitute for the full text at least half of the time (41%), thought infographics should be detailed enough so they do not have to read the full text (55%), and viewed infographics as tools to reduce the time burden of reading the full text (64%). Researchers/academics were less likely to report behaviours/beliefs suggesting infographics can reduce the need to read the full-text article.

Conclusions

Given many people use infographics as a substitute for reading the full-text article and want infographics to be detailed enough so they don't have to read the full text, a checklist to facilitate clear, transparent, and sufficiently detailed infographics summarising some types of health and medical research may be useful.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12909-022-03744-6.

Keywords: Infographics, Visual abstract, Graphical abstract, Health, Medicine, Cross-sectional, Survey

Introduction

'Infographic' is an abbreviated term for an information graphic [ 1]. Infographics generally combine text, images and data visualisations to present information visually, increase the attention it receives, and to improve comprehension and recall [ 1- 3]. Infographics are becoming increasingly popular as a method for summarising research findings [ 3- 5], although they often have other uses in research (e.g. present information more visually or interactively, highlight certain information from a research article). Many health and medical journals now use infographics, such as visual abstracts, graphical abstracts and interactive graphics, to boost the visibility and dissemination of the research they publish [ 6] (e.g. New England Journal of Medicine, The BMJ, JAMA Oncology, British Journal of Sports Medicine).

Infographics appear to be increasing in popularity [ 1] and evidence suggests some infographics increase the attention an article receives on social media [ 6- 8]. However, it is unknown whether people use different types of infographics more commonly as stand-alone resources to interpret research findings or to decide whether to seek more information about a study. Either approach could explain why some infographics decrease or have no effect on the attention full-text articles receive in some cases [ 3, 8- 10]. There is evidence of harmful misuse of research [ 11] when clinicians only read the abstract of an article. For example, the President of Global Strategies for HIV Prevention (Arthur Amman) tells a story of a physician engaged in perinatal HIV prevention in southern Africa who started delivering a less effective preventative treatment because they only had access to the abstract of an article, which spun the study's results. The physician's decision to alter their prevention approach based on the article's abstract may have harmed many people [ 11]. We are concerned a similar scenario could occur if health professionals, researchers, or patients view infographics as stand-alone resources. These concerns are compounded by our recent work showing that most infographics from health and medical journals do not report enough information to allow readers to adequately interpret and apply study findings (e.g. key study characteristics, limitations, effect sizes) [ 1]. Conversely, if research is not accessible because it takes too long to read for overwhelmingly busy clinicians or is too challenging for many patients to understand, valuable health innovations could be under-utilised.

Understanding people's use of and opinions about infographics has important implications for their design but has not been investigated. Infographics won't ever be able to replace the full-text article. However, if most people use infographics as stand-alone resources or substitutes for reading the full-text article, there may be a need to ensure information presented in infographics is more comprehensive (i.e. through enhanced visuals or text) and reflects important features of the full-text article. People may also have different opinions about the functions of infographics, the type of information they expect to see, and experience barriers to reading full-text articles which should be considered when designing infographics. The aims of this study were to describe how people use infographics that summarise health and medical research, their opinions about these infographics, their preferences for what information to include in these infographics, and barriers to reading full-text articles that might lead them to solely rely on an infographic.

Methods

Study design, participants, and recruitment

We conducted an online cross-sectional survey, with data collected between August and December 2021. We posted a link advertising our study on Twitter and Facebook to recruit people who self-identified as consumers of infographics summarising health or medical research (e.g. health professionals, researchers, patients or members of the public). People had to be 18 years or older to participate and could be living in any country. Study advertisements were posted from the Institute for Musculoskeletal Health Twitter (@msk_health; 4500 followers) and Facebook (@IMHSydney1; 150 followers) accounts, which reach a mix of researchers, academics, health professionals, and members of the public. These accounts mostly post, retweet or share the findings of interesting musculoskeletal research. They were not posting infographics produced by the Institute for Musculoskeletal Health at the time this survey was conducted, although infographics from journals are occasionally retweeted or shared.

Data collection

The Twitter and Facebook post included a link to complete an online survey hosted in Qualtrics survey software. The first page of the survey briefly described the study, provided a link to read the Participant Information Statement (which defined infographics as per the opening sentences of this manuscript), and included a Participant Consent Form. There was no financial incentive for participants. All recruitment and data collection procedures were approved by the University of Sydney Human Research Ethics Committee (Reference number: 2021/542). All methods were carried out in accordance with relevant guidelines and regulations. Before starting the survey, all participants provided consent by checking a box that confirmed they had read the Participant Information Statement and Consent Form and agreed to participate. Participants' rights were protected. Informed consent was obtained from each participant.

Participants were asked to provide demographic data such as age, gender, educational attainment, employment status, and background (researcher, health professional, academic, patient or member of the public, other). Participants were asked: "When did you last come across an infographic that summarised research you were interested in (e.g. on social media, in a journal)?" Response options included: "In the past week"; "In the past month"; "In the past 6 months"; "In the past 12 months"; "I have never come across an infographic that summarised research I was interested in". Participants who selected the last option were excluded from the study. The full survey is in Additional file 1.

Outcomes

Use of infographics

This included questions on how likely participants are to find and read the full text article after viewing an infographic (5-point Likert scale from 'Extremely unlikely' to 'Extremely likely'), how often they use infographics as a substitute for reading the full text article (5-point Likert scale from 'Never' to 'Always'), and how participants access (e.g. social media, journals) and view infographics (e.g. smart phone, laptop).

Opinions about infographics

Participants were asked whether they think infographics should be detailed enough so readers can translate the findings to their context without having to read the full-text article (5-point Likert scale from 'Definitely not' to 'Definitely yes'), whether they think infographics are useful tools for communicating research and increasing the attention research receives (5-point Likert scale from 'Definitely not' to 'Definitely yes'), and what they think the functions of an infographic should be. This last question included pre-specified options (e.g. 'reduce the time burden of reading the full-text article', 'entice people to read the full-text article') and a free-text box for participants to list other functions.

Preferences for information to include in infographics

Participants were asked to indicate what information from the full-text article should be included in an infographic (e.g. 'conclusion or 'take away' message', 'description of the population, intervention(s), comparison(s) and outcome(s)', 'sample size').

Barriers to reading full-text articles

Participants were asked to indicate what barriers they experience when trying to read full-text articles.

The survey finished with a free-text response question where participants could add any other comments that would help the researchers understand how they use or view infographics (Additional file 2).

Data analysis

All data were summarised descriptively using counts and percentages, means and standard deviations (SD), and median and interquartile ranges (IQR), as appropriate. We performed a sensitivity analysis summarising outcome data for participants involved (vs. not involved) in research and/or academia since these groups may use infographics for different reasons (e.g. due to a lack of access or time to read full-text articles). We used a Chi ² test to investigate differences in outcomes between these groups and conducted analyses using Stata BE Version 17.1 (StataCorp LLC, College Station, Texas, USA). Responses to the free-text question at the end of the survey were grouped into themes (Additional file 2).

Results

Sample characteristics

88% of those who commenced the survey completed it ( n = 254/289). Table 1 reports the characteristics of participants who completed the survey (n = 254). These participants completed the survey in a median time of 7 minutes (IQR 5 to 10).

Table 1

Demographics

Descriptive statistics

Female, n (%)
116 (46%)

Age (years), mean (SD)
37 (12)

Education, n (%)

High school (completed)

12 (5%)

Non-university tertiary education

6 (2%)

University

236 (93%)

Employment, n (%)

Employed

217 (85%)

Student

32 (13%)

Unemployed or retired

5 (2%)

Background, n (%)a

Researcher

85 (33%)

Academic

62 (24%)

Health professional

174 (69%)

Patient or member of the public

25 (10%)

Researcher or academic

116 (46%)

Not involved in research or academia

138 (54%)

Last come across an infographic, n (%)

In the past week

193 (76%)

In the past month

47 (19%)

In the past 6 months

13 (5%)

In the past 12 months

1 (< 1%)

Use of infographics

Most participants were somewhat/extremely likely to find and read the full-text article after viewing an infographic (76%). Some used infographics as a substitute for reading the full-text article at least half of the time (41%), and this was more common among those not involved (vs. involved) in research or academia (53% vs. 29%; p < 0.001). Most access infographics via Twitter (67%) and view infographics on a smartphone (89%). Participants not involved (vs. involved) in research or academia were less likely to access infographics via Twitter (58% vs. 78%, p = 0.001), more likely to access infographics via Instagram (57% vs. 29%, p < 0.001), and less likely to use a laptop to view infographics (47% vs. 62%, p = 0.017) (Table 2 ).

Table 2

Total sample

Not involved in research/ academia

Involved in research/ academia

Chi2, p-value**

Use of infographics (primary outcomes)

Likely to read full text after viewing an infographic, n (%)

Use infographics as a substitute for reading full text, n (%)

Accessing infographics, n (%)*

Device used to view infographics, n (%)*

Opinions about infographics

Infographics should be detailed enough so readers don't have to read the full-text, n (%)

Infographics are useful tools to communicate research, n (%)

Infographics increase the attention research receives, n (%)

Functions of an infographic, n (%)*

Preferences for information to include in infographics

Information expected to see in an infographic, n (%)*

Barriers to reading full-text articles, n (%)*

Opinions about infographics

Many participants thought infographics should probably/definitely be detailed enough so readers can translate the findings to their context without having to read the full-text article (55%), and this was more common among those not involved (vs. involved) in research or academia (65% vs. 43%; p = 0.007). Most thought infographics probably/definitely were useful tools for communicating research (92%), and this was more common among those not involved (vs. involved) in research or academia (96% vs. 87%, p = 0.022). Most thought infographics probably/definitely were useful tools for increasing the attention research receives (95%) and viewed infographics as a way to communicate research in a more user-friendly way (89%). Most viewed infographics as a way to reduce the time burden of reading the full-text article (64%), and this was more common among those not involved (vs. involved) in research or academia (76% vs. 49%, p < 0.001) (Table ​(Table2). 2 ). Other functions of infographics included delivering evidence-based, accessible information to patients and the public (including those with low health literacy), reaching new audiences, and using infographics as a recap after reading the full-text article.

Preferences for information to include in infographics

Most participants expected to see a conclusion or 'take away' message in an infographic (95%), a description of the four PICO elements - population (81%), intervention(s) (92%), outcome(s) (87%) and comparison(s) (74%) - the sample size (65%), and statistics summarising the effects of an intervention (58%). Fewer participants expected to see study limitations (39%) and conflicts of interest (26%). Participants not involved (vs. involved) in research or academia were less likely to want a description of the population in an infographic (76% vs .87%, p = 0.026) (Table ​(Table2 2 ).

Barriers to reading full text articles

The most common barriers to reading full-text articles were lack of time (77%) and access (71%). Less frequently reported barriers included being unsure how to determine study quality (26%), interpret the methods (23%), and interpret the results (24%). Participants not involved (vs. involved) in research or academia were more likely to report lack of access (83% vs. 56%, p < 0.001) and being unsure how to determine study quality as barriers (35% vs. 16%, p = 0.001) (Table ​(Table2 2 ).

Other comments about use or views on infographics

The most common themes from free-text responses about use of or views about infographics were that they help communicate research in user-friendly way, inform people and support decision making, and are not all equal in quality (Additional file 2).

Discussion

Summary of main findings

Although most participants were somewhat/extremely likely to read the full-text article after viewing an infographic, some used infographics as a substitute for reading the full-text article at least half of the time, thought infographics should be detailed enough so they don't have to read the full-text article, and viewed infographics as tools to reduce the time burden of reading the full-text article. Most of these behaviours and beliefs were more common among those who were not involved in research or academia. Most participants used Twitter and smartphones to access and view infographics, and thought infographics were useful tools to communicate research and increase the attention research receives. A large majority expected to see the conclusion or 'take away' message and the four PICO elements in infographics. Lack of time was the most common barrier to reading full-text articles in the total sample, while lack of access was the most common barrier for those not involved in research or academia.

Strengths and weaknesses of the study

We recruited participants from a range of backgrounds (e.g. researchers, health professionals, patients, members of the public) and had a high completion rate (88%). The main limitation of this study is that data on people's use of infographics is self-reported and may be different in real life. Another limitation is only recruiting through Twitter and Facebook, which may have led to a biased sample. For example, the high proportion of health professionals, academics, and researchers likely reflects the composition of people who follow the Institute for Musculoskeletal Health Twitter and Facebook accounts, and the type of people who were liking, re-tweeting, or sharing our study advertisement. Future research should consider ways to reach a broader audience in the medical and research community.

Meaning of the study

Participants who were not involved in research or academia were more likely to use infographics as a substitute for reading the full-text article, think infographics should be detailed enough so they don't have to read the full-text article, and view infographics as tools to reduce the time burden of reading the full-text article. Given that most infographics don't allow readers to adequately interpret and apply study findings [ 1], these findings confirm our initial concern that some health professionals may be making poor preventative or treatment decisions because they use infographics as a substitute for reading the full-text article [ 11]. Nearly one-third of researchers and academics use infographics as a substitute for reading the full-text article at least half of the time, which might be due to a lack of time (reported by 4 in 5 researchers and academics). However, there could be other explanations. Some researchers and academics may use infographics to quickly scan for studies that align with their research interests or for studies that have features indicating low risk of bias (e.g. randomisation, low loss to follow-up).

Participants who were not involved in research or academia were more likely to report lack of access as a barrier to reading the full-text article, which may explain why they appear more reliant on the content of infographics. However, they were not less likely to read the full-text article after viewing an infographic compared to researchers and academics. One explanation is that participants may have interpreted our question about their likelihood of reading a full-text article after viewing an infographic in the context of having access to the full-text article. In addition, the question about barriers was framed in a way that anyone who has been unable to access a full-text article on at least one occasion would have reported 'lack of access' as a barrier. This may have inflated the prevalence of this barrier in our sample, particularly among those not involved in research or academia.

Another interesting finding is that participants not involved in research or academia were more likely to access infographics via Instagram (nearly twice as much) and less likely to access them via Twitter. This should be considered by journal editors and researchers trying to increase the reach of their research, particularly among non-academic audiences. In addition, a similar proportion of people involved vs. not involved in research or academia access infographics on journal websites (37% vs. 32%) which might be explained by some publishers making their infographics open access even when the full-text article is behind a paywall [ 12].

Comparison to existing literature

Our participants' view that infographics can be useful tools to communicate research is consistent with previous research highlighting that visual abstracts (a type of infographic) require less cognitive load and are more preferred over traditional abstract summarises [ 13] and are rated as more user-friendly [ 14] (without negatively impacting knowledge [ 14] or information retention [ 13]). However, it needs to be acknowledged that considerable care is required when examining research on the benefits and limitations of infographics since there is substantial variation in the design of infographics and the quality and impact of the research they summarise.

Participants' view that infographics increase the attention research receives is also consistent with several studies that have found some infographics increased the number of impressions and re-tweets Twitter posts receive [ 6, 8] and increased abstract views [ 3, 10]. However, there are some infographics that have reduced impressions, re-tweets, abstract views and citations [ 9, 15]. There are also examples of some infographics that may not be superior to other research communication tools. For example, a survey of 58 physicians and nurses who were shown a one-page text summary and an infographic of a systematic review on pain medication for acute migraine found the infographic had substantially greater visual appeal, but lower clarity and comprehensibility [ 2].

Many participants in our study reported regularly using infographics as a substitute for reading the full-text article, thought infographics should be detailed enough so readers can translate the findings to their context without having to read the full-text article, and viewed infographics as tools to reduce the time burden of reading the full-text article. This appears to be consistent with previous research showing that some infographics either have no effect or a negative effect on article visits and full-text downloads [ 3, 8- 10]. However, in some cases a lack of effect may be explained by low article views overall. For example, a prospective, case-control crossover study took 40 articles published over a 11-month period in American Journal of Nephrology and tweeted each article in three formats (citation only vs. citation plus key figure from the article vs. citation plus visual abstract) [ 8]. Tweets including visual abstracts received similar article visits compared to the other formats (9.0 for visual abstracts vs. 8.1 for citation only vs. 6.7 for citation plus key figure). Another study took 12 articles published in the Canadian Journal of Emergency Medicine that were promoted on Facebook and Twitter without infographics over a 3-month period in 2015/16 and compared these to 11 articles published in this journal and additionally promoted using infographics over the same time [ 3]. Articles promoted using infographics received slightly less full-text views compared to articles promoted without infographics (65 vs. 73).

In The BMJ between June and December 2018, tweets including visual abstracts received fewer URL clicks (29 vs. 60) compared to the average of all tweets from The BMJ. However, this finding was only specific to visual abstracts. Other types of infographics performed better (e.g. more detailed full page visual summaries, interactive graphics) [ 15]. Over the past 3 years, The BMJ has found that 64.9% of their tweets including visual abstracts (87.5% in 2021) have been at least 15% above the average tweet in most metrics (unpublished data). This suggests improving the quality of visual abstracts may improve their impact. Other studies have also found a positive effect of infographics on article visits [ 6]. Nevertheless, the total available evidence may challenge the assumption that infographics increase the attention research receives and may suggest many people are solely relying on the infographic summary of some studies to inform their research, clinical practice or healthcare choices.

Unanswered questions and future research

There are limited data on people's preference for what information to include in infographics and why people have certain preferences. When The BMJ were developing their template for visual abstracts, they sought feedback on a visual abstract of tai chi for fibromyalgia from 77 health professionals who treat patients with fibromyalgia (e.g. primary care physicians) [ 15]. Participants wanted more detailed statistics so they could quickly judge how effective tai chi was and for the conclusion to be at the top of the visual abstract. Possible explanations for this feedback include that these health professionals wanted infographics to reduce the time burden of reading the full-text article or that more detailed statistics may have helped them decide whether they need to read the full-text article. Our study adds further data to what people want to see in infographics, but it does not explain why people have certain preferences. Future qualitative research could be useful to explore this issue.

Given that most people sometimes use infographics as a substitute for reading the full-text article and that most broadly agree that infographics should be detailed enough so they do not have to read the full-text article, a checklist or 'minimum standard' for certain types of infographics may facilitate clear, transparent, and sufficiently detailed infographics summarising health and medical research. We are currently conducting a Delphi study to develop a checklist to improve the reporting of infographics summarising the main findings of comparative studies of health and medical research (e.g. randomised controlled trials, systematic reviews) [ 16]. Such a checklist will likely outline the minimum items necessary to include in these types of infographics (e.g. PICO items, effect size, 95% CIs) but give designers flexibility on how to present these items visually or through text. A checklist could improve the accuracy with which research findings are communicated and avoid research findings being mis-interpreted if consumers (e.g. health professionals, researchers) do not refer to the full-text article. Once developed, we will evaluate the acceptability of the checklist and whether infographics developed according to the checklist are more acceptable and lead to wiser healthcare choices. These findings will determine whether checklists for other types of infographics could be useful, but we acknowledge that checklists may be inappropriate for some types of infographics (e.g. an infographic focusing on the interaction of different participant characteristics on a secondary outcome).

Conclusion

Although most participants were somewhat/extremely likely (76%) to read the full-text article after viewing an infographic, some used infographics as a substitute for reading the full-text article at least half of the time (41%), thought infographics should be detailed enough so they don't have to read the full-text article (55%), and viewed infographics as tools to reduce the time burden of reading the full-text article (64%). These behaviours and beliefs were more common among those who were not involved in research or academia. A checklist to facilitate clear, transparent, and sufficiently detailed infographics summarising some types of health and medical research could improve the accuracy with which research findings are communicated and avoid research findings being mis-interpreted if readers do not refer to the full-text article.

Supplementary Information

Authors' contributions

All authors critically revised the manuscript for important intellectual content and approved the final manuscript. Please find below a detailed description of the role of each author: Joshua R Zadro: conception and design, analysis and interpretation of data, drafting and revision of the manuscript, and final approval of the version to be published. Giovanni Ferreira: conception and design, interpretation of data, drafting and revision of the manuscript and final approval of the version to be published. Mary O'Keeffe: conception and design, interpretation of data, drafting and revision of the manuscript and final approval of the version to be published. Will Stahl-Timmins: conception and design, interpretation of data, drafting and revision of the manuscript and final approval of the version to be published. Mark R Elkins: conception and design, interpretation of data, drafting and revision of the manuscript and final approval of the version to be published. Christopher G Maher: conception and design, interpretation of data, drafting and revision of the manuscript and final approval of the version to be published. The Corresponding Author (JZ) attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding

This study did not receive any funding.

Availability of data and materials

All data relevant to the study are available upon reasonable request to the corresponding author.

Declarations

Ethics approval and consent to participate

All recruitment and data collection procedures were approved by the University of Sydney Human Research Ethics Committee (Reference number: 2021/542) and all methods were carried out in accordance with relevant guidelines and regulations. Before starting the survey, all participants provided consent by checking a box that confirmed they had read the Participant Information Statement and Consent Form and agreed to participate. Participants' rights were protected. Informed consent was obtained from each participant.

Consent for publication

Not applicable.

Competing interests

All authors declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work; no other relationships or activities that could appear to have influenced the submitted work.

Footnotes

Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

13. Martin LJ, Turnquist A, Groot B, Huang SYM, Kok E, Thoma B, et al. Exploring the role of Infographics for summarizing medical literature. Health Prof Educ. 2019;5(1):48-57. [ Google Scholar]

15. Stahl-Timmins W, Black J, Simpson P. Pragmatic evaluation of The BMJ's visual abstracts. Inf Des J. 2019;25(1):101-109. [ Google Scholar]

Articles from BMC Medical Education are provided here courtesy of BioMed Central

# data[].attributes.attachment_link.title
How do people use and view infographics that summarise health and medical research? A cross-sectional survey

# data[].attributes.attachment_link.description
Understanding how people use infographics and their opinion on them has important implications for the design of infographics but has not been investigated. The aim of this study was to describe people's use of and opinions about infographics summarising health and medical research, preferences for information to include in infographics, and barriers to reading full-text articles.

# data[].attributes.headline
"Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers."

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"Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers."

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Roberta Pastorino, 1, ⁴ Corrado De Vito, ² Giuseppe Migliara, ² Katrin Glocker, ³ Ilona Binenbaum, 3, ⁴ Walter Ricciardi, 1, ⁵ and Stefania Boccia 1, ⁵

Abstract

Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen's expectations. Improving health outcomes while containing costs acts as a stumbling block. In this context, Big Data can help healthcare providers meet these goals in unprecedented ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers. In several contexts, the use of Big Data in healthcare is already offering solutions for the improvement of patient care and the generation of value in healthcare organizations. This approach requires, however, that all the relevant stakeholders collaborate and adapt the design and performance of their systems. They must build the technological infrastructure to house and converge the massive volume of healthcare data, and to invest in the human capital to guide citizens into this new frontier of human health and well-being. The present work reports an overview of best practice initiatives in Europe related to Big Data analytics in public health and oncology sectors, aimed to generate new knowledge, improve clinical care and streamline public health surveillance.

Introduction

Data have become an omnipresent concept in our daily lives with the routine collection, storage, processing and analysis of immense amount of data. This characteristic is cross-sectorial, ranging from the domain of machine learning and engineering, to economics and medicine.

Over the last decades, there has been growing enthusiasm of the potential usefulness of these massive quantities of data, called Big Data, in transforming personal care, clinical care and public health.1

An overview of Big Data definitions

Despite the term Big Data having become ubiquitous, there is no universal definition until now on the use of this term. According to McKinsey the term Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyse.2 Gartner proposed the popular definition of Big Data with the '3V': Big Data is volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.3 According to other definitions, instead, Big Data is also characterized by a fourth dimension: Veracity, concerning the quality, authenticity, 'trustworthiness' of data. 4

Furthermore, there is an emergent discussion that 'Big' is no longer the defining parameter, but rather how 'smart' the data are, focusing on the insights that the volume of data can reasonably provide.5 This aspect is fundamental in the health sector. The potential of Big Data in improving health is enormous. However, its potential value is unlocked only when leveraged to drive decision making and, to enable such evidence-based decision making, it is necessary to have efficient processes to analyse and turn high volumes of data into meaningful insights.

A specific definition of what Big Data means for health research was proposed by the Health Directorate of the Directorate-General for Research and Innovation of the European Commission: Big Data in health encompasses high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.6

Big Data analytics for health systems

The complexity of Big Data analysis arises from combining different types of information, which are electronically captured. The last years have seen an explosion of new platforms, tools and methodologies in storing, and structuring such data, followed by a growth of publications on Big Data and Health ( figure 1 ). To date, we can collect data from electronic healthcare records, social media, patient summaries, genomic and pharmaceutical data, clinical trials, telemedicine, mobile apps, sensors and information on well-being, behaviour and socio-economic indicators.

Healthcare professionals can, therefore, benefit from an incredibly large amount of data. Recent reports suggest that US healthcare system alone stored around a total of 150 exabytes of data in 2011 with the perspective to reach the yottabyte.7

Starting with the collection of individual data elements and moving to the fusion of heterogeneous data coming from different sources, can reveal entirely new approaches to improve health by providing insights into the causes and outcomes of disease, better drug targets for precision medicine, and enhanced disease prediction and prevention.

In light of this, opportunities and potential are enormous for the benefit of patients and, in general, of the healthcare system.

The potential benefits of Big Data for healthcare in the European Union

Big Data is a sensitive issue for European Union (EU) institutions: the availability of health-related Big Data can have a positive impact on medical and healthcare functions. EU is faced with several changes that may affect the sustainability of its healthcare system. By 2025 life expectancy is expected to further increase, and this may result in more people living longer, but not necessarily in a healthy and active condition. This will put further pressure on Europe's healthcare costs and economic productivity.

In this context, the data sharing approach can improve outcomes for patients and evidence-based healthcare decision making as reported during the workshop on 'Digitalisation and Big Data: implications for the health sector', held on 19 June 2018 at the European Parliament.8

The use of Big Data in healthcare, in fact, can contribute at different levels as reported by the Study on Big Data in Public Health, Telemedicine and Healthcare of the European Commission:9 (i) increasing earlier diagnosis and the effectiveness and quality of treatments by the discovery of early signals and disease intervention, reduced probability of adverse reactions, etc. (sector 1); (ii) widening possibilities for prevention of diseases by identification of risk factors for disease (sector 2); (iii) improvement of pharmacovigilance and patient safety through the ability to make more informed medical decisions based on directly delivered information to the patients (sector 3); (iv) prediction of outcomes (sector 4).

In the next paragraphs, examples of EU initiatives in the four macro sectors are listed.

Big Data have the potential to yield new insights into risk factors that lead to disease. There is the possibility to engage with the individual patient more closely and import data from mobile health applications or connected devices. These data have the potential to be analysed and used in real-time to prompt changes in behaviours that can reduce health risks, reduce harmful environmental exposures or optimize health outcomes.

Finally, Big Data can help identify and promptly intervene on high-risk and high-cost patients.10 Effective ways of managing these data can therefore facilitate precision medicine by enabling detection of heterogeneity in patient responses to treatments and tailoring of healthcare to the specific needs of individuals.11 All these aspects should eventually lead to a reduction in inefficiency and improvement in cost containment for the healthcare system.

Examples of Big Data analytics for new knowledge generation, improved clinical care and streamlined public health surveillance are already available. Below we report a selection of best practices in Europe in the public health and oncology fields.

Big Data in public health

Efforts to improve the availability and accessibility of data in the EU appear to be driven mainly by socio-economic purposes. However, great importance is placed on the need of using data and new information and communication technology (ICT) in public health to improve quality of prevention and care.

In the next years, European health systems must respond more efficiently to the exponential increase of chronic patients identifying the most efficient interventions and releasing the full potential of ICT. The e-health platforms that many European governments are trying to implement can be effective in improving the management of chronic patients in the community setting by interfacing between different health professionals and specialists and with the patients. Furthermore, a large part of EU citizens uses the internet looking for information on health and access to health services.

Moreover, Big Data and predictive analytics can contribute to precision public health by improving public health surveillance and assessment, therefore, in a public health perspective, the gathering of a very large amount of data, constitute an inestimable resource to be used in epidemiological research, analysis of the health needs of the population, evaluation of population-based intervention and informed policy making.9

Many projects across the EU are exploring the potential of available Big Data in a wide range of fields. A systematic review published in 2016 from the European Commission identified at that time 10 priority projects on Big Data implemented in Europe that fall in the four macro sectors described above and are aimed to support the sustainability of health systems by addressing the improvement of the quality and effectiveness of treatment, fighting chronic disease and supporting healthy lifestyles.9 Some of these projects focussed on gathering a very wide range of data types, from GP records, hospitalizations, drug prescription and laboratory and radiology analyses in order to create comprehensive national data warehouses. Among them, the 'Decision Support for Health Policy and Planning: Methods, Models and Technologies based on Existing Health Care Data' (DEXHELPP), the eHealth project in Estonia, the ARNO observatory in Italy and the Hospital Episode Statistics in the United Kingdom. The DEXHELPP project (mainly regarding sectors 1 and 4) used routinely collected health data sources to analyse the performance of the health system, to forecast future changes and to simulate the application of policy and interventions. The Estonian eHealth project (mainly regarding sectors 1, 2 and 3) was more oriented toward the improvement of the quality and efficiency of health services, aiming to digitalize all the information and prescription of each patient. Furthermore, in order to facilitate data collection, they provide an environment called X-Road to which all healthcare providers can link while using their own ICT solutions. The ARNO project (mainly regarding sector 4), was committed to epidemiological research, giving the possibility of deep stratification of the general population. The Hospital Episode Statistics (mainly regarding sector 4) was in charge of the Secondary Uses Service that publishes reports and analyses to support the National Health Service in the delivery of healthcare services.

Beside these projects characterized by a comprehensive approach, other initiatives focused on specific conditions (e.g. chronic conditions, rare diseases and psychiatric disorders) are also available in the EU.9 Among them, the PASSI surveillance project (mainly regarding sectors 1 and 2) in Italy provides large amount of information on the life style of almost the 90% of the population, enabling to individuate specific targets to implement and assess public health actions. The Shared Care Platform (mainly regarding sectors 1 and 3) in Denmark is focused on chronic patients, aiming to harmonize the course of treatment among health and social care providers. The Spanish Rare Diseases Registries Research Network (SpainRDR) (mainly regarding sector 1) focuses on the development of clinical research on rare diseases, providing the harmonization and unification into one comprehensive platform of pre-existing databases and registries of rare diseases. CEPHOS-LINK (mainly regarding sectors 1, 2 and 4), is a platform dedicated to mental health that involves six EU countries. It is committed to collect data on psychiatric hospital admissions and re-admissions, with the aim of finding determinants of re-admissions and to harmonize the psychiatric care pathways across the EU.

In addition to the projects reported above, the EU's framework programmes for research and innovation funded a large number of initiatives on Big Data in public health. In table 1 , we list 11 projects funded from the EU between 2012 and 2018 with a contribution over €499.999 that are captured from the Cordis website (source: cordis.europa.eu).

Table 1

Project acronym

Full title

Coordinator country

Start date

End date

Big Data in oncology

Cancer is one of the major health problems affecting our society, a situation that is set to deteriorate globally as the population grows and ages. According to the State of Health in the EU reports, cancer is recognized as one of the major contributors to premature deaths in the EU. It also has an impact on the economy in terms of lower labour market participation and productivity. Advances in Big Data analytics are given cancer researchers powerful new ways to extract value from diverse sources of data.

These diverse sources include a huge amount of data for one patient. As cancer is a molecularly highly complex disease with an enormous intra- and intertumoral heterogeneity among different cancer types and even patients, the collection of various different types of omics data can provide a unique molecular profile for each patient and significantly aid oncologists in their effort for personalized therapy approaches.12

The approach of combining these sources of data is implemented in Comprehensive Cancer Centres (CCCs).13 One of 13 CCCs in Germany is the National Center of Tumor Diseases, where the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) trial is conducted (mainly regarding sector 1, 2 and 3). In a recent review article,14 this trial was illustrated as an example of a highly successful programme addressing the molecular profiling in cancer patients. Within the MASTER trial data relevant to diagnostic information of young patients with advanced-stage cancer diseases is collected by performance of whole exome or whole genome sequencing and RNA sequencing, analysed and discussed.

Another example for a success story given in the review is the INdividualized therapy FOr Relapsed Malignancies in children (INFORM) (mainly regarding sector 1, 2 and 3) registry which aims to address relapses of high-risk tumours in paediatric patients. Data from whole-exome, low-coverage whole-genome, RNA sequencing and microarray-based DNA methylation profiling are utilized to identify patient-specific therapeutic targets. The INFORM registry started as a national effort in Germany and has been extended with the participation of eight European countries, as well as Australia.

Next to the described projects there are many other initiatives which focus on the value of Big Data in oncology, the EU alone funds more than 90 projects working on this topic (projects with a funding over €499.999 are listed in table 2 ). A potential umbrella for bringing together national efforts such as those mentioned above at the European level is Cancer Core Europe.15

Table 2

Project acronym

Full title

Coordinator country

Start date

End date

Collaborations are of extremely high importance especially in the case of paediatric or other rare types of cancer, where the data collected for one patient is indeed enormous, however the number of patients a single centre can have access to is too low to obtain statistical power high enough to reach meaningful results. One of the main challenges of these collaborations is the access to the data as well as the opportunity to analyse the huge amount of data in an efficient way. Physicians, researchers and informatics experts can only benefit from collected data and expert knowledge when they get easy and intuitive access to own data or data of partners. For example, at the German Cancer Research Center, tools are developed to grant ways to access and analyse own data together with data from partners.

Additionally, international exemplary approaches of sharing data among partners or the public are done by The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) which provide researchers with access to thousands of sequenced patients with different types of cancer. The availability of these data jointly with data by other partners has enabled large meta-analyses and machine learning algorithms, integrating different types of cancer that led to the identification of novel cancer driver genes that belong to specific pathways and can be possible therapy targets. Furthermore, there are a number of public databases that provide access to catalogues of mutations found to be involved in cancer, such as the Catalogue of Somatic Mutations in Cancer (COSMIC).

All these multiple sources of information combined and the establishment and support of CCCs across Europe offer the potential to increase the number of patients that can be offered molecular profiling and individualized treatment based on Big Data analysis.

Ethical and legal issues for the effective use of Big Data in healthcare

The use of Big Data in healthcare poses new ethical and legal challenges because of the personal nature of the information enclosed.

Ethical and legal challenges include the risk to compromise privacy, personal autonomy, as well as effects on public demand for transparency, trust and fairness while using Big Data.16

Data heterogeneity, data protection, analytical flows in analysing data and the lack of appropriate infrastructures for data storage emerged as critical technical and infrastructural issues that might endanger a Big-Data-driven healthcare.

Recently, Skovgaard et al.17 explored attitudes among people living in the EU toward the reuse of health data. The review indicates that the use of health data for purposes other than treatment enjoys support among people, as long as the data are expected to further the common good. In this context, the recent call reported in Science from a number of eminent scientists worldwide, for the unrestricted use of public genomic data, finds a fertile ground from the public.18 Concerns evolve around the commercialization of data, data security and the use of data against the interests of the people providing the data.

The recent EU Data Protection Regulation (GDPR) tries to balance patients' privacy while ensuring patient's data can be shared for healthcare and research purposes.

On 23 January 2017 the Consultative Committee of the Council of Europe's data protection convention adopted 'Guidelines on the protection of individuals with regard to the processing of personal data in a world of Big Data',19 the first document on privacy and Big Data which provides suggested measures for preventing any possible negative effects of the use of Big Data on human rights and freedoms.

Therefore, any government that uses Big Data in the health sector needs to establish affirmative policies to protect the health data of individuals, in terms of confidentiality, privacy and security, while ensuring that advancements in science can take advantage from the open use of data for the community well-being.

Conclusions

Big Data is beginning to revolutionize healthcare in Europe as it offers paths and solutions to improve health of individual persons as well as to improve the performance and outcomes of healthcare systems.

The implementation of precision medicine remains contingent on significant data acquisition and timely analysis to determine the most appropriate basis on which to tailor health optimization for individual prevention, diagnosis and disease treatment. Achieving effective and proportionate governance of health-related data will be essential for the future healthcare systems, and it requires that stakeholders collaborate and adapt the design and performance of their systems to reach the maximum innovative potential of information and innovation technology on health in the EU.

In this context, EU Member States should agree on international technical standards, taking also into account openness that is considered as the basic paradigm for digital transformation. Additionally, new approaches must be found for translating the vast amount of data into meaningful information that healthcare professionals can use. Further efforts must be made to make information for doctors and health professionals more accessible and understandable.

To achieve this, existing training and education programmes for healthcare professionals should integrate the issues of data handling in the curricula to ensure the development of the necessary skills and competencies. This is one of the objectives of the 'European network staff eXchange for integrAting precision health in the health Care sysTems' consortium (ExACT)20 project that aims to integrate precision health in European health systems by training a new generation of healthcare professionals across and outside of the EU.

In conclusion, we are living in fast-moving times, not least in terms of healthcare innovation. Whilst there are pressing needs for more personalized and sustainable health services, science and technology are offering a host of potentially invaluable new tools to deliver them. A cooperation at the EU level is needed to facilitate investments both in new technology and in the human capital, in order to guide citizens into this new frontier of human health and well-being where data are becoming a significant corporate asset, a vital economic input and the foundation of new business models.

Conflicts of interest: None declared.

References

2. Manyika J, Chui M, Brown B, et al. Big data: The next frontier for innovation, competition, and productivity. 2011. www.mckinsey.com/mgi (12 September 2019, date last accessed).

3. Beyer M, Laney D The Importance of 'Big Data': A Definition. Gartner report. 2012, 1-9.

5. George G, Haas MR, Pentland A. Big Data and management. AMJ 2014;57:321-6. [ Google Scholar]

11. Hopp WJ, Li J, Wang G. Big Data and the precision medicine revolution. Prod Oper Manag 2018;27:1647-64. [ Google Scholar]

15. Eggermont AMM, Apolone G, Baumann M, et al. Cancer core Europe: a translational research infrastructure for a European mission on cancer. [[ Mol Oncol2019;13:521-7. PMC free article] [ PubMed] Google Scholar]

19. Guidelines on the protection of individuals with regard to the processing of personal data in a world of Big Data Consultative Committee of the Convention for the Protection of Individuals with Regard to Automatic Processing of Personal Data (T-PD) Guidelines 1 on the Protection of Individuals with Regard to the Processing of Personal Data in a World of Big Data 2 Directorate General of Human Rights and Rule of Law. www.coe.int/data-protection (12 September 2019, date last accessed).

20. Boccia S, Pastorino R, Mariani M, Ricciardi W. The European network staff eXchange for integrAting precision health in the health Care sysTems (ExACT): a Marie Curie Research and Innovation Staff Exchange (RISE) project. Epidemiol Biostat Public Heal 2019;16. doi: 10.2427/13122. [ Google Scholar]

Articles from The European Journal of Public Health are provided here courtesy of Oxford University Press

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Benefits and challenges of Big Data in healthcare: an overview of the European initiatives

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Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen's expectations. Improving health outcomes while containing costs acts ...

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Privacy-preserving data sharing infrastructures for medical research: systematization and comparison - BMC Medical Informatics and Decision Making

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Background Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research…

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This past June, a John Doe plaintiff who was a patient at Baltimore-based Medstar Health System filed a class-action complaint against Meta Platforms in the U.S. District Court for the Northern District of California.

The plaintiff alleged that Meta, the parent company of Facebook, was using its Pixel tracking technology to access patient information from websites and portals of hospitals and health systems for targeted marketing.

Since then, at least two other class-action suits have been filed against Meta alleging illegal information harvesting. And several major U.S. health systems have either been named as co-defendants (Dignity Health, UCSF) or faced lawsuits themselves (Northwestern Memorial Hospital) for alleged misuse or misconfiguration of the Pixel tool.

Meanwhile, in August, Novant Health disclosed a data breach related to the Java tracking script that may have affected 1.3 million individuals. And in October, Advocate Aurora Health said as many as 3 million users of its MyChart patient portal and LiveWell website and app may have had their data transmitted via Pixel technology.

In addition to the class-action suits, legislators on Capitol Hill are taking notice. On October 20, U.S. Sen. Mark R. Warner, D-Virginia, sent a letter to Meta CEO Mark Zuckerberg with a series of pointed questions about the company's data tracking policies.

Hospitals and health systems now have some questions to ask of themselves about how their websites and apps are built, and how third parties may, inadvertently or not, be putting patients' protected health information at risk.

We asked Andrew Mahler, a former investigator with HHS Office for Civil Rights and now VP of Privacy and Compliance at CynergisTek, what he thought about these and other issues: how healthcare organizations should be thinking about privacy laws and different data types, best practices for educating staff and patients, laws and regulations on how and when data can be shared - and how these privacy challenges may have added salience in a post-Dobbs decision world.
Q. What do you make of the Advocate Aurora "pixel" breach, which has now led to a lawsuit?
A. People are not only becoming more aware of how their data could and should be shared, but we are becoming more knowledgeable about the vast implications of data sharing, more broadly. I think what concerns many about the disclosures to Meta/Facebook is not simply that their data is being shared, but that their data may be being shared broadly and for advertising and tracking purposes without their consent or knowledge.

HIPAA governs how protected health information may be used and disclosed by covered entities - including certain providers, plans - and certain business associates/vendors, and it also provides certain requirements related to the privacy and security of data, as well as individual rights.

Those standards include requirements to provide notice to patients about how PHI may be used and disclosed by the organization, the requirement to obtain valid authorization for certain types of uses and disclosures, and the requirement to obtain certain assurances before disclosing PHI to vendors.

What makes this situation especially complex and troubling is that the healthcare organizations themselves may not have been aware that the Meta Pixel Tool had been embedded in its website and/or that it was tracking, comparing and receiving data about patients, including PHI. This underscores the importance of performing thorough risk analyses, proper training and education, as well as independent third-party reviews of policies, processes and systems to highlight potential gaps and risks.

Organizations should be carefully considering all the ways PHI may be used, disclosed and accessed, and the use - whether inadvertent or purposeful - of Meta Pixel has highlighted the need for organizations to think beyond the routine and usual, and consider innovative (or insidious) ways data can be accessed.
Q. What about the ways companies like Meta are working with hospitals and health systems, more generally?
A. Just as there is an app for everything, there is a vendor for everything. Many companies provide valuable applications and technology services to hospitals and health systems. However, as data (even de-identified data) becomes more valuable, companies are going to logically look for ways to monetize and further use data, and we hope that if they are doing so, they are doing it in a way that is compliant, secure, and ethical.

While HIPAA-covered entities and business associates have control over how they use and disclose information, they may determine that there is an organizational need to utilize tracking services that could make certain web-based experiences easier or more efficient for patients, prospective patients, or the organization itself. In one situation, an organization determined that the use of Meta Pixel supported consumer experience and helped encourage the scheduling of preventive care.

However, when making business decisions like this, organizations must weigh the benefit to the organization/patient with the risk to the patient/organization. When making or reviewing these determinations, it is imperative that a thorough risk analysis is not performed in a vacuum but includes as much key staff as possible to provide insights not only about the business need or benefit, but also about risk (past, present, and future) and potential ethical concerns.
Q. How concerned should patients be from a privacy perspective, even if they're not affected by a breach, per se?
A. HIPAA permits covered entities to use and share protected health information for certain purposes. Some of these purposes are only permitted if the patient authorizes the disclosure, but not all. HIPAA expressly permits certain uses and disclosures that do not require authorization or an opportunity to agree or object. This includes disclosures for certain, permitted law enforcement purposes, research purposes and business associates.

When thinking about concerns about our PHI, it is important to keep in mind that the enforcement of the HIPAA Privacy and Security Rules was initiated almost 20 years ago. Healthcare providers and insurers subject to HIPAA have had a lot of time to understand how to protect and secure health information and implement good practices related to data sharing and maintenance.

However, we should all be somewhat skeptical about how our data is used and shared, and that includes asking questions when information about privacy or security practices is unclear or unknown. Organizations will need to adjust practices or implement new ones as technologies improve and risks become apparent. In addition, the U.S. Department of Health and Human Services, Office for Civil Rights, is responsible for enforcing HIPAA and we have seen consistent, active enforcement over the years.

The financial penalties are not small, and the repercussions, including loss of trust, can be difficult to overcome. Of course, this isn't to say patients shouldn't be concerned about how their data is managed, but HIPAA-covered entities and business associates are subject to scrutiny, fines and penalties when they do not comply with regulatory requirements related to how our data is used, shared and managed.
Q. What does U.S. privacy law - HIPAA, HITECH, CCPA, etc. - say about data collection like the kind Meta and Google are doing? What about GDPR?
A. While HIPAA protects certain information, it does not protect all of the data about us. We are constantly disclosing data, whether through applications, our phones, using credit cards, browsing the web, making an airline reservation for a partner, or through any number of the many ways organizations may be accessing and acquiring our data.

Most of us have received dozens, if not more, notifications over the past few years that our data may have been accessed or breached. Some of these notifications are required by a rule or law, but not all. It's difficult to keep up, and it's even more difficult to know what, if anything, we should or could do about it.

Most, if not all, privacy and data protection laws, whether HIPAA, GDPR, CCPA or others, include similar requirements: provide appropriate privacy and security safeguards, provide individuals with certain rights related to their data and provide notification when there has been a breach.

Each regulation defines the type of information that must be protected, and there is often no overlap between data protected by one rule or law or another. The patchwork of privacy and breach notification laws requires organizations to understand all types of data maintained, received and transmitted, and to provide adequate protections around regulated data, and assess incidents when there may have been an improper use or disclosure.
Q. What are some privacy best practices for staff at healthcare providers, in a digital age where websites of all shapes and sizes are tracking users' online behavior?
A. Staff must receive appropriate training and education about how to protect and secure information. This training should include information about PHI, including the types of identifiers about patients, their relatives, employers or household members that could be used to identify patients.

For example, some of the information collected by Meta Pixel includes IP addresses; dates, times, and/or locations of scheduled appointments; proximity to an office or clinic; information about the provider; type of appointment or procedure, among others. While many will (accurately) assume names, addresses and phone numbers could be PHI, not all understand that IP addresses and dates, and times can also be PHI.

This particular issue highlights the importance that even those involved in designing the website, reviewing code and maintaining the organization's social media sites receive regular and effective training and education.

From both a privacy and security perspective, this issue underscores the importance of performing an accurate and thorough assessment of the potential risks and vulnerabilities to the confidentiality, integrity and availability of protected health information. This risk analysis will support the organization in reviewing risks and making certain business and operational decisions to either mitigate risk through controls or discontinue a particular practice.

As part of this analysis, organizations must consider whether and how access to data is being monitored and assessed. If your organization is not aware of how patient data may be used or disclosed, how will you assure patients that their data is being protected?
Q. How should employees be trained to keep their data - and perhaps that of patients - safe?
A. From an organizational perspective, training and education must be effective. That often means finding creative ways to encourage interaction and facilitate understanding. It's important to understand that not only do we learn differently but employees need to fit training into their already busy schedules. Sometimes, this means creating training that is unique to specific staff/tasks and using a combination of training methods.

In addition, I always encourage friends, family and clients to be thoughtful about the applications and devices they plan to use - perform their mini, personal "risk analysis" - to have an informed idea of how their data could be used and disclosed to others (OCR has provided helpful guidance about protecting the privacy and security of personal data on personal devices as well). I believe that as we begin to think more critically about how our data is shared and monetized, we will be more likely to raise questions and creative solutions within our workplaces.
Q. In your experience, how aware are healthcare organizations of the ins and outs of how and what data can be shared with third parties, law enforcement, etc.?
A. While many organizations may have strong processes in place for routine disclosures (for example, sending prescription information to a patient's pharmacy), I find that organizations have challenges operationalizing the less-routine disclosures.

While some hospitals may receive frequent requests from law enforcement, not all do, which may result in an unprepared or untrained staff member disclosing information improperly. On the other hand, larger hospitals may work with thousands of third-party vendors, and thus, may have difficulties understanding risks involving all vendors, while smaller clinics may disclose data to a much smaller group of vendors.

The ins and outs of data sharing can be very complicated, which is why I believe effective policies, procedures and training are vital to protecting both the organization and the patient. Training and education, based on roles and job descriptions, should equip each employee to understand whether, when and how data may be shared with others.
Q. These issues have added salience in a post-Dobbs world. What might this privacy landscape look like for reproductive health?
A. While the Dobbs decision has not changed how health information must be protected and secured under HIPAA, it has created substantial anxiety about how data could be used and shared, especially related to reproductive health data and possible state law enforcement actions. While we have certainly been desensitized in many ways over the years regarding the collection of our data, many of us are for the first time considering all of the downstream repercussions of sharing data.

Our phones and apps (and thus, the organizations designing, updating and operating those services) likely know more about our habits than we do. They store data about places we've been, people we've called and photos that we've taken after we have long forgotten about what we did that day. Each company may store a massive amount of data that could be used to prosecute someone seeking reproductive healthcare. For example, a law enforcement official in a state that bans abortion may issue a subpoena to a company for information about an individual's web searches and geolocation data, to build a criminal case against that person.

This and similar concerns have created a sense of urgency about how to best protect data, and we have seen discussions about data protection escalate within state legislatures, as well as at the federal level.

In addition, an Executive Order, issued this summer, directs HHS to consider actions and guidance to strengthen the security and privacy protections specifically related to reproductive healthcare. Organizations using, disclosing and maintaining health information should focus on the rules, laws and risks that currently apply, but they should also pay close attention to legislative discussions and enforcement actions.

We have seen a shift in priorities and expectations related to personal data over the past few years, and organizations will want to be in the best possible position to quickly address and manage new requirements and risks.
The HIMSS 2022 Healthcare Cybersecurity Forum takes place Dec. 5 and 6 at the Renaissance Boston Waterfront Hotel. Register here. Twitter: @MikeMiliardHITN
Email the writer:mike.miliard@himssmedia.com
Healthcare IT News is a HIMSS publication.

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New healthcare privacy challenges as online data tracking, sharing methods evolve

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This past June, a John Doe plaintiff who was a patient at Baltimore-based Medstar Health System filed a class-action complaint against Meta Platforms in the U.S. District Court for the Northern District of California. The plaintiff alleged that Meta, the parent company of Facebook, was using its Pixel tracking technology to access patient information from websites and portals of hospitals and health systems for targeted marketing.