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The HIMSS Big Data & Healthcare Analytics Forum brings together leaders from across the nation to guide senior healthcare leaders through the steps needed to maximize their data's value and make a real impact on costs and care.

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About HIMSS

HIMSS is a global voice, advisor and thought leader of health transformation through health information and technology with a unique breadth and depth of expertise and capabilities to improve the quality, safety, and efficiency of health, healthcare and care outcomes. HIMSS designs and leverages key data assets, predictive models and tools to advise global leaders, stakeholders and influencers of best practices in health information and technology, so they have the right information at the point of decision.

HIMSS drives innovative, forward thinking around best uses of information and technology in support of better connected care, improved population health and low cost of care. HIMSS is a not-for-profit, headquartered in Chicago, Illinois, with additional offices in North America, Europe, United Kingdom and Asia.

HIMSS Vision

Better health through information and technology.

HIMSS Mission

Globally, lead endeavors optimizing health engagements and care outcomes through information and technology.

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HIMSS is a global voice, advisor and thought leader of health transformation through health information and technology with a unique breadth and depth of expertise and capabilities to improve the quality, safety, and efficiency of health, healthcare and care outcomes.

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This final installment in the Big Data series addresses how population health information can be leveraged to improve both clinical and business outcomes. Initiatives such as predictive analytics and personalized medicine have the potential to improve the quality and efficacy of care programs, while interventions such as preventative medicine and clinical decision support have been proven to significantly reduce the overall cost of care delivery.

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This is Part 1 of a six part series that discusses how to leverage big data in healthcare. This educational video discusses the benefits of analyzing clinical and business outcomes to improve patient care.

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Types of data: Yang and Veltri (2015) discuss that "big data" goes far beyond the electronic health records we nurses are used to seeing in the hospital (or clinic) setting. They list sources including:

electronic health records and associated clinical text

biomedical imaging

healthcare sensor data (like monitors for VS)

genomic and pharmaceutical databases

spontaneous reporting systems (like adverse drug reactions)

biomedical and healthcare literature

social media

Because the data is so vast, the new methods of analyzing data can sometimes help us see connections between things we wouldn't otherwise see. Big data can also be useful in situations where an RCT is not possible. However, we have to be careful not to confuse correlation with causation (Frakt & Pizer, 2016).

Ties with meaningful use: ability of big data to help with clinical decision support, disease surveillance, population health management via activities like early detection, determining who is likely to benefit from a treatment vs who is not likely to benefit, improved analysis of disease patterns/tracking, etc. (Raghupathi & Raghupathi, 2014).

Ethics: Ajunwa, Crawford, and Ford (2016) discuss ethics in the context of the use of big data by corporate wellness programs. However, many of the concerns apply in other contexts as well. For example:

Loss of privacy (e.g., employers using big data to predict which employees may be pregnant or planning a pregnancy soon)

Possible discrimination (e.g., towards employees who have/are at risk for DM2)

Is consent truly informed? If patients don’t truly understand the vast array of data that is being collected about them, and how it may be used, is their giving consent for this information to be shared with their employer truly an informed consent?

Who owns the data? (e.g., Fitbit data about sleep and exercise habits being transmitted to a corporate wellness program)

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Implications for advanced nursing practice and research

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Implications for advanced nursing practice and research

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Presently we are able to access very large, massive data sets and computer resources such as EHRs more than ever before. We can look at patterns and meanings in ways we couldn’t see before. In the nursing profession we focus on the human response to disease, illness, injury, developmental challenges, family dynamics, community issues, patient experiences and we can use big data to help us understand. As nurses we have the opportunity and responsibility to turn data information into best practices. We also have a responsibility to use what we know to continue to inform the big data initiative. We need to bring a culture of engagement and empowerment. The amount of data doubles every 18 months to two years, we need to keep in mind that clinical information is more than EHRs. Big data gives us stories of peoples lives and it gives us indicators of how they are managing in the world.

Large data sets can be useful in the research community because it can guide the questions we are seeking answers to. What do we want to know from big data? We need to be asking questions to link the data to the questions we are after. Data is meaningless unless we interpret the data. We connect data together not only to make sense of it but to also generate answers to help solve problems. Data is derived for a purpose and it is our professional knowledge and judgment that helps to create the questions that leads to answers from data sets.

We can apply big data to help us educate our patients on their risks and promote individualized behavior change that can help promote better outcomes. For example, we have data that tells us who is more at risk for developing breast cancer, hepatitis, and many other diseases. Looking at trends and data we have learned that antibiotic resistance is a big problem right now and it is shaping the way we mange infections.

Meaningful data and meaningful use is data that has a meaning to humans and data that is happening in real time. Why is this important? It helps us look at what’s happening to look at trends and patterns and gives us the opportunity to prevent and intervene. Using standard terms in nursing and being mindful of how we label our information so our data can be useful to others, is one way we can help bring meaningful data. One of the biggest projects taking place that utilizes big data is the human genome project. Looking at the make up of humans is giving large insight to the health of the individual and risk for illnesses and disease.  Knowing this data can help guide proper treatments and prevention.

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With the rapid emergence of digital devices, an unstoppable, invisible force is changing human lives in incredible ways. Every two days the human race is now generating as much data as was generated from the dawn of humanity through the year 2003.

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Big Data Background Information

·  The term “Big Data” has been around for nearly 20 years. Originally meant a lot of data but has evolved to mean more. The term now refers to data management and analysis of large and complex data sets, integrating and mining internal and external data and using tools to analyze and view these data.

·  The term “Big Data” now has many different working definitions that can be useful for many different fields. In relation to nursing, one of those definitions is “large amounts of patient data that can give us insights about the patient experience.” It has also been called a “tsunami of data” and “Extremely large data volumes at high velocities.”

·  Originally started being used by the banking and retail industry but healthcare is one of the latest areas to catch on with respect to leveraging the concepts and tools of big data.

·   Can be traced back to the Human Genome Project in 1990’s as the launch of Big Data in healthcare.

·   Now being used by pharmaceutical industry, providers, insurance companies and more to analyze the data in order to address problems with healthcare, quality and spending and to determine which treatments and drugs are the most beneficial. The aims are to slow increasing costs, guide providers with practicing more effective medicine, empower patients and caregivers, support fitness and preventive self-care, and to create more personalized medicine.

·   The data that is now being used in healthcare includes things like health records, clinical trial data, risk analysis data, genomics, device readings and population and disease tracking.

·   In the healthcare field, in particular nursing, Big Data is being used to make discoveries that enhance nursing care.  Big Data is being used to advance nursing science that will drive the delivery of patient care.

Increasing number of companies using Big Data in healthcare:                                              * Genome Health Solutions - personalized genomic medicine                                    * GNS Healthcare – outcomes management                                                                * DNAnexus - data management and analysis                                                              * Appistry Inc. - easy-to-read reports that can be used by doctors,                              patients, pathologists and hospitals

* NextBio – mines data focusing on focus on oncology and
metabolic and autoimmune diseases.

* Health Fidelity – changes narrative medical records) into structured
data that can be used for computer management, to address needs
in revenue cycle management, compliance, and analytics

Increasing number of companies that support patients and self-care

* Humetrix’s iBlueButton – pools data and provides device-to-device                         communication and data exchange at the point of care between
patients and providers

* Ginger.io - collecting real-time passive and active behavioral data
from patients’ cell phones focusing on mental health conditions

* Welldoc - automated, real-time coaching using behavioral and
clinical messaging, as one element of its FDA Class II medical
device platform, helps patients manage chronic diseases, such as
diabetes

·  With the use of Big Data, issues have emerged that may challenge the successful use of healthcare Big Data, including privacy and security, who owns the data, regulations over the use of Big Data and many others.

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What is “Big Data”?

Big data is defined as large volumes of high velocity, complex, and variable data that requires advanced technology to capture, store, distribute, manage and analyze the information.  Big data is a relatively new concept in healthcare and it’s being used in many ways to interpret data in hopes of improving health outcomes and reducing healthcare costs.  This rapid digitization of massive amounts of data is being driven by mandatory regulations in the hopes of improving clinical decision making, disease surveillance and population management.  Big data comes from many types of information including:
·      Clinical data

·      Activity and cost data

·      Pharmaceutical research data

·      Patient behavior data

In the future, data will no longer be gathered from individuals and applied to the population, but vast amounts of population data will be used to predict what happens to an individual.

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www.siemens.com/big-data-in-healthcare A major trend will influence the way healthcare will be delivered around the world in the coming years: Big Data. This video points out the opportunities in making the data usable which could transform approaches in healthcare that have long defined the industry.

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                              References

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                              References

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Ajunwa, I., Crawford, K., Ford, J.S. (2016). Health and big data: an Ethical framework for health information collection by corporate wellness programs. Journal of Law, Medicine & Ethics, 44, 474-480.

Brennan, P., & Bakken, S. (2015). Nursing needs big data and big data needs nursing. Journal  of  Nursing Scholarship, 47(5), 477-484.

Feldman, B., Martin, E., & Skotnes, T. (2012). Big data in healthcare: Hype and hope. Dr. Bonnie, 360, 1-56.

Frakt, A.B., & Pizer, S.D. (2016). The promise and perils of big data in healthcare. American Journal of Managed Care, 22, 98 - 99.

Groves, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013). The big-data revolution in US healthcare: Accelerating value and innovation. Center for US Health System Reform Business Technology Office. Retrieved from http://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-big-data-revolution-in-us health-care

NIH. (2015) What is Big Data? Retrieved from https://datascience.nih.gov/bd2k/about/what

Raghupathi, W., and Raghupathi, V.  (2014).  Big data analytics in healthcare:  Promise and potential.  Health Information Science and Systems.  doi: 10.1186/2047-2501-2-3

Siemens Healthineers.  (2015).  Healthcare dives into big city data.  Retrieved from https://www.healthcare.siemens.com/news-and-events/mso-big-data-and-healthcare-1

Spencer, G. (2016). Big data: More than just big and more than just data. Frontiers of Health    Services Management, 32(4), 27-33.

Yang, C.C., & Veltri, P. (2015). Intelligent healthcare informatics in big data era. Artificial intelligence in medicine, 65, 75 - 77.