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Week 3: Using Big Data to Decrease Hospital Re-Admissions 

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Week 3: Using Big Data to Decrease Hospital Re-Admissions 

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This video discusses how big data can be used to decrease re-admissions to the hospital. Through embedded charting systems, in electronic health records, nurses are prompted to ask patients questions or record data in the flowsheet which determines whether or not the patient is ready for discharge. To me this is just one great example of how big data has and will continue to improve our healthcare system.

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Improving health outcomes with big data | Ronda Hughes | TEDxUofSC

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This is a quick overview of interoperability in healthcare. Being a visual person, I liked this video because it was simple and to the point. For me, knowing the basic overview and visualizing information, before I get down to the fine detail, helps me to have a greater understanding of the topic.

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This video is a brief overview of the rules of HIPPA. I found this video helpful because it discusses the HIPPA guidelines in a concise and easy to understand way. Among the HIPPA rules or guidelines, the video goes into an explanation of the HIPPA privacy rule, security rule, Omnibus rule and breach notifications. All of which are important to know and understand as a medical professional.

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This is a brief video explaining the connection between improving patient care and technology by way of nursing informatics. The video does a nice job of demonstrating how a nurse can assist in healthcare informatics through first hand knowledge acquired at the bedside. With the foundation a bedside nurse has, combined with healthcare informatics, the options to assist in improving the quality of patient care through more efficient technology and utilization of resources are vast.

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University of Maryland School of Nursing

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Nursing Informatics: Connecting Patient Care and Technology

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Week 1: Improving Healthcare with Health Informatics

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Week 1: Improving Healthcare with Health Informatics

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In this Ted Talk, Dr. Tiranee Achalakul discusses some of the technologies that have been developed to gather data and improve patient care. One of the fascinating developments is an inhaler with a tracking system, storing data for providers to individualize patient care and improve asthma control. I like this Ted Talk because it provides a window into the endless ways to advance healthcare, reducing costs, and providing safer, better quality patient centered care.

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Dr. Tiranee Achalakul: Using Big Data to Improve Healthcare Services

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This is an article outlining the TIGER initiative and its evolution since 2006. I found this article to be clear and concise, allowing me to understand the different phases the TIGER initiative has gone through over the years. Until this class I was unaware of the TIGER initiative and realize how important this work is for nursing and the way our current system operates.

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Shaw, Toria MSW; Sensmeier, Joyce MS, RN-BC, CPHIMS, FHIMSS, FAAN; Anderson, Christel MA

Editor(s): Carter-Templeton, Heather PhD, RN-BC

Manager, Clinical Informatics (Ms Shaw), Vice President, Informatics (Ms Sensmeier), and Senior Director, Interoperability Initiatives (Ms Anderson), HIMSS North America, Chicago, IL.

doi: 10.1097/CIN.0000000000000369

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Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.

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Week 2: HIMSS and The Future of Healthcare  

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Week 2: HIMSS and The Future of Healthcare  

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At the HIMSS 2021 Global health Conference, Darryl Gibbings-Isaac, from Accenture Digital Health Technology, discusses the future of technology in the healthcare setting. From the start of the pandemic, patients have been able to receive care in settings other than a hospital or office, improving convenience for patients and clinicians. However, research has proven that clinicians are weary of the use of technology going forward, and will require a detailed implementation plan to integrate technology into the healthcare setting as we move out of these unprecedented times. Some of the key take aways from of the implementation plan would be to establish trust and promote transparency among healthcare clinicians.

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Week 4: Electronic Health Record Implementation 

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Week 4: Electronic Health Record Implementation 

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As we learned this week, implementing an electronic health record (EHR) requires an organized plan for the success of the system. The research in this article was done in hopes to find a publication outlining an EHR rollout strategy from beginning to end. Because the process of transitioning to an EHR must follow a certain criteria, it would be of use to have the important steps within the process within one study. This is valid research and is useful to clinicians across the healthcare system.

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Your access to the NCBI website at www.ncbi.nlm.nih.gov has been temporarily blocked due to a possible misuse/abuse situation involving your site. This is not an indication of a security issue such as a virus or attack. It could be something as simple as a run away script or learning how to better use E-utilities, http://www.ncbi.nlm.nih.gov/books/NBK25497/, for more efficient work such that your work does not impact the ability of other researchers to also use our site. To restore access and understand how to better interact with our site to avoid this in the future, please have your system administrator contact info@ncbi.nlm.nih.gov.

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Your access to the NCBI website at www.ncbi.nlm.nih.gov has been temporarily blocked due to a possible misuse/abuse situation involving your site. This is not an indication of a security issue such as a virus or attack.

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In this short video, the DIKW Hierarchy is described in detail using helpful examples. Through these examples, the viewer is able to gain a clear understanding of the relationship between each piece of the framework. Each research starts with data and moves through a process from data to information to knowledge and eventually this information evolves to wisdom. This hierarchy provides a visual understanding and organization to the wisdom of healthcare practice.

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This video details the requirements of stage 3 of meaningful use. These details further describe the provider requirements, interoperability requirements and patient requirements of stage 3 in the application of stage 3 of the electronic health record roll out. This video is quick, easy and gives examples as to why requirements are in place.

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This article was recently published by the California Health Care Foundation on the adoption of electronic health records over the years. The article goes on to discuss the role of the Health IT Landscape Assessment and how it has been used to assess Health IT in California. This assessment allows researchers in the state to evaluate the transition into using electronic health system across California and meeting the goals of full interoperability. I liked that this article outlines challenges met along the way in this transition because it allows me to understand the gaps in the system, a way to learn where improvements can be made.

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Week 6: How Advancements in Technology Can Improve Clinical Decision Making

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Week 6: How Advancements in Technology Can Improve Clinical Decision Making

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In this Ted Talk, Chief Data Scientist, Dr. Atul Butte, discusses concepts on how health data can be used to assist with clinical support and decision making. With the use of electronic health records to gather large amounts of data, research is advancing rapidly. Among the vast opportunities these advancements have allowed, Dr. Butte goes on to discuss how providers will now have evidence to show what is working and what is not in when creating a treatment plan for a specific disease. In addition, health apps are now being created to allow patient's to view their own data. These are just a few examples of how advancements in technology are being used to help support clinical decisions among patients and their providers.

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What Happens When We Mix Real Doctors, Big Data, and AI? | Atul Butte | TEDxHarkerSchool

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Week 6: Opportunities and Pitfalls of EHR Data for Clinical Decision Research

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Week 6: Opportunities and Pitfalls of EHR Data for Clinical Decision Research

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I found this article helpful in discussing the alternative views of gathering large amounts of data for clinical research. Among the benefits with gathering data from the electronic health record (EHR), this article discusses some of the pitfalls in this type of data collection. The article demonstrates examples of how gathering data can go wrong using EHRs and ways to avoid this from happening.

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. 2021 Feb;41(2):133-142.

doi: 10.1177/0272989X20954403.

Affiliations

¹ Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.

² Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA.

³ Center for Health Care Research and Policy, The MetroHealth System and Case Western Reserve University, Cleveland, OH, USA.

⁴ Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.

Free PMC article

Glen B Taksler et al. Med Decis Making.

Free PMC article

. 2021 Feb;41(2):133-142.

doi: 10.1177/0272989X20954403.

Affiliations

¹ Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.

² Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA.

³ Center for Health Care Research and Policy, The MetroHealth System and Case Western Reserve University, Cleveland, OH, USA.

⁴ Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.

Abstract

Electronic health records (EHRs) offer the potential to study large numbers of patients but are designed for clinical practice, not research. Despite the increasing availability of EHR data, their use in research comes with its own set of challenges. In this article, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be nonrepresentative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building 2 large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets.

Keywords: data science; electronic health records; health services research; registries.

Conflict of interest statement

Financial conflicts: Dr. Perzynski reports book royalties from Springer Nature and Taylor Francis and is co-founder of Global Health Metrics. No other financial disclosures were reported.

Figures

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Week 7: Patient Data Exchange Through Wearable Devices

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Week 7: Patient Data Exchange Through Wearable Devices

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This article discusses the potential to use patient data collected through wearable devices, such as the Apple Watch, to further improve patient care. The article goes on to discuss how the innovation of these devices have allowed patients to better manage their chronic disease from home and the software that is being developed to turn this data into a more reliable source of patient information. Though there are are many advantages presented in this article, there are also disadvantages to ensuring the accuracy of this data collected. However, new software is being developed to advanced the use of these health technologies for better patient care everyday.

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Review

¹ Department of Population Health, New York University School of Medicine, New York, NY, United States

² Accenture, New York, NY, United States

*all authors contributed equally

Corresponding Author:

Devin Mann, MS, MD

Department of Population Health

New York University School of Medicine

227 E 30th Street

New York, NY, 10016

United States

Phone: 1 6465012503

Email: devin.mann@nyulangone.org

Background: Due to the adoption of electronic health records (EHRs) and legislation on meaningful use in recent decades, health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data. A novel capability offered by health systems encompasses the integration between EHRs and wearable health technology. Although wearables have the potential to transform patient care, issues such as concerns with patient privacy, system interoperability, and patient data overload pose a challenge to the adoption of wearables by providers.

Objective: This study aimed to review the landscape of wearable health technology and data integration to provider EHRs, specifically Epic, because of its prevalence among health systems. The objectives of the study were to (1) identify the current innovations and new directions in the field across start-ups, health systems, and insurance companies and (2) understand the associated challenges to inform future wearable health technology projects at other health organizations.

Methods: We used a scoping process to survey existing efforts through Epic's Web-based hub and discussion forum, UserWeb, and on the general Web, PubMed, and Google Scholar. We contacted Epic, because of their position as the largest commercial EHR system, for information on published client work in the integration of patient-collected data. Results from our searches had to meet criteria such as publication date and matching relevant search terms.

Results: Numerous health institutions have started to integrate device data into patient portals. We identified the following 10 start-up organizations that have developed, or are in the process of developing, technology to enhance wearable health technology and enable EHR integration for health systems: Overlap, Royal Philips, Vivify Health, Validic, Doximity Dialer, Xealth, Redox, Conversa, Human API, and Glooko. We reported sample start-up partnerships with a total of 16 health systems in addressing challenges of the meaningful use of device data and streamlining provider workflows. We also found 4 insurance companies that encourage the growth and uptake of wearables through health tracking and incentive programs: Oscar Health, United Healthcare, Humana, and John Hancock.

Conclusions: The future design and development of digital technology in this space will rely on continued analysis of best practices, pain points, and potential solutions to mitigate existing challenges. Although this study does not provide a full comprehensive catalog of all wearable health technology initiatives, it is representative of trends and implications for the integration of patient data into the EHR. Our work serves as an initial foundation to provide resources on implementation and workflows around wearable health technology for organizations across the health care industry.

JMIR Mhealth Uhealth 2019;7(9):e12861

doi:10.2196/12861

Keywords

Electronic Health Record Adoption and Expanded Access to Patient-Collected Data

Although electronic health records (EHRs) date back to the 1960s, widespread adoption was stagnant until the more recent passage of the Health Information Technology for Economic and Clinical Health Act in 2009 [-]. Between 2001 and 2011, the number of physicians using EHR systems increased from 18% to 57% []. Policies such as Meaningful Use (which prioritized quality, care coordination, and security of personal health information) incentivized the continued adoption of EHRs. By 2015, nearly 9 in 10 (87%) of office-based physicians adopted an EHR system [-]. Of all EHR vendors, Epic, Cerner, and Meditech are the most prevalent among health care systems [].

In addition to driving EHR adoption among providers and health systems, legislation supporting meaningful use also paved the way for continued development of EHR capabilities to enhance the patient experience. Health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data []. Features include secure messaging with patients and features to view, download, and transmit their EHR. Such capabilities are becoming more prevalent to facilitate streamlined patient data exchanges with their provider [].

Availability and Adoption of Wearable Devices

Wearable devices include wristbands, smartwatches, wearable mobile sensors, and other mobile hub medical devices that collect a large range of data from blood sugar and exercise routines to sleep and mood. Patient data are collected either through consumer reporting or passively through sensors in apps that communicate with devices through application programming interfaces (APIs); these data are then shared through data aggregators such as Apple's HealthKit that pools data from multiple health apps [].

According to a recent consumer survey on digital health by Accenture, a significant percentage of US adults were willing to wear technology that tracks their health statistics (see ) []. Due to mobile integration platforms such as Google Fit and Apple HealthKit, we can expect to see an increase in the number of health-wearable users over the next few years [,]. The upward trend in device usage to monitor health-related data additionally suggests there will be a correlated rise in patient data available for health management []. Large health systems are likely to trend toward larger rollouts of wearable technology in the next few years, potentially incorporating wearables as part of their preventative care strategy by monitoring heart rate, blood pressure, and other information [,]. There are currently more than 400 EHR-compatible devices on the market, a number that is expected to rise exponentially in the coming years [].

‎
Figure 1. Percentage of US adults who were willing to wear technology that tracks select health statistics as of 2018. Screenshot from www.statista.com [].
View this figure

Clinical Impact of Wearable Devices

The purpose of this paper was to conduct a scoping review of the wearable health technology field to provide an overview of current wearable innovations in the EHR. Similar to a number of existing scoping reviews, we used internet search engines in addition to our database searches to capture the rapid updates in the area of health system integration of remotely collected patient data []. We used these sources to generate a targeted list of organizations that are leaders in the overall field of wearable health technology, along with their partnerships.

This paper provides an overview of (1) our process in determining the current landscape of wearable health technology and (2) descriptions of some leading innovations and partnerships by start-ups, providers, and insurance companies. By sharing our results, we hope to create a process to identify relevant organizations in this field and provide resources for organizations that are interested in joining or learning more about implementation and workflows around wearable health technology and patient data integration to EHRs. This study is specific to integration into the Epic portal and is not a comprehensive search; however, results are representative of the field because of Epic's prominence in the US acute care hospital market (25.8%) [].

Search Process

To better understand the scope of wearables and other health tracking devices and the resulting impact on EHRs, we used a scoping process to survey existing efforts on the Web. Although not directly relevant to a scoping review, we reviewed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to enhance the quality of our search. In our search, to identify the leaders in the field, we contacted the largest commercial EHR system (Epic) for information on client work in the integration of patient-collected data. We used this information to further inform our search terms in Epic's UserWeb portal, the primary platform for Epic users to share and discuss topics such as innovative idea generation and event postings, and the general Web, PubMed, and Google Scholar database searches from July 2018 to January 2019.

We recognize the risk of bias in this study, as our search process was limited to Epic clients and the information publicly available on the World Wide Web.

Inclusion Criteria

We used a set of inclusion criteria in UserWeb to ensure that postings were accurate and up to date. Results had to meet the following standards: (1) be posted after June 2017 and (2) have responses to topic threads. Key search terms included Apple HealthKit, Patient remote data integration, Fitbit integration, Withings integration, and Wearables.

Similarly, our findings on wearable technology companies and initiatives from the general Web, PubMed, and Google Scholar database searches had to meet the following criteria: (1) be posted after June 2017 and (2) match search terms including but not limited to Device integration, EHR data integration, Epic MyChart integration, Patient MyChart integration, Patient remote data integration, Patient data access, Wearables, Provider wearables, Hospital wearables, Hospitals AND Apple HealthKit device integration, Apple HealthKit device integration AND Epic, Start-ups AND EHR integration, Insurance companies AND device integration, APIs AND device integration.

Challenges of Wearable Device Integration

Although wearable health technology has the potential to transform patient care, issues such as concerns with patient privacy, system interoperability, and the immense amount of patient data pose a challenge to the adoption of wearables by providers [,]. Such challenges are critical to consider for future wearable use to deliver safe and quality care for patients. Although there are potential solutions for these implementation issues, more innovative work is required for wide-scale adoption of wearable health technology.

Protecting the Confidentiality and Privacy of Patients

The HIPAA is a US legislation that protects the privacy of individuals' medical records and applies to health providers and plans []. With the continuous stream of data from personal devices, data privacy and security for health information must be addressed as to meet HIPAA standards and not impede patients' willingness to share their data []. demonstrates that patients have some concerns about the electronic exchange of data between providers; the percentage of individuals expressing these concerns has remained relatively the same since 2011 []. To protect against potential cybersecurity attacks and missing or stolen patient records through the implementation of wearable health technologies, hospitals must ensure that devices are connected to a secure network and monitor the hospital data network continuously []. To prioritize data privacy, health systems are likely to be required to set up another secure network for wearable devices, separate from the main network [].

‎
Figure 2. Individuals' perceptions of the privacy and security of medical records and health information exchange in 2017. Screenshot from https://dashboard.healthit.gov/quickstats/quickstats.php [].
View this figure

Lack of System Interoperability and Connectivity

As a result, this barrier has created subsets of data collected from patients that become secondary in value because they cannot be easily integrated into patient historical data [,]. Researchers have recently looked to achieve plug-and-play interoperability to standardize platforms and integrate these information islands, a standard that already exists in the world of consumer electronics as consumers demand simple and seamless functionality []. Plug-and-play standards require ease of use, device compatibility, and streamlined scalability and reconfigurability between different vendors; systems must be able to detect new devices, negotiate communication, and allow devices to synchronize and work with each other [].

As the need for system interoperability grows, third-party applications aimed to address interoperability issues have become more prominent []. Increased partnerships and opportunities between makers of these applications and health systems are necessary to reach high interoperability and streamlined communication between EHR platforms, patient devices, and providers. Improving these relationships can improve health care efficiency, provider safer transitions of care, and help lower health care costs [].

Patient Information and Data Overload

In addition, a strong user interface (UI) for providers is important for provider buy-in and engagement during implementation. As a result, there has been an increasing trend within health care organizations to incorporate user experience and UI designers into a cross-functional information technology (IT) team to address this need []. The multidisciplinary skills of such teams can offer improved UIs combined with IT expertise and enhance the ability to comprehend wearable patient data. These improvements in provider engagement and workflow could improve overall time efficiency for providers and quality of care for patients.

Innovations in Wearable Health Technology

However, EHRs still cannot connect to many other devices and require the development of new solutions to address challenges such as interoperability and visualization for the information they are currently collecting []. The wearable health technology space features numerous start-up partnerships with health care providers and insurance company innovations that are working to address these key challenges and promote growth in wearable usage and EHR integration capabilities.

The overall themes that we used to describe the different focus areas of each partnership included personalized patient experience, rewards program, data analytics, remote monitoring, access to patient records, and AI technology. A summary of key organizations working in wearable health technology compiled from the general Web search and Epic's UserWeb portal (as of May 2018) is presented in and , respectively.

Start-Up Partnerships

As listed in below, we identified the following 10 start-up organizations that have developed or are in the process of developing technology to improve wearable health technology and/or patient data integration to EHRs: Overlap, Royal Philips, Vivify Health, Validic, Doximity Dialer, Xealth, Redox, Conversa, Human API, and Glooko. We reported sample start-up partnerships with a total of 16 health systems in addressing challenges of meaningful use of device data and streamlining provider workflows. The partnerships between these start-ups and health systems serve to improve the data collection process, synthesize actionable information for providers to review, and create a more personalized experience between patients and providers. Due to the rapidly moving field of wearables, our research represents a snapshot in time of wearable health technologies and is not meant to be a fully exhaustive list.

Table 1. Wearable health technology start-up partnerships.

Start-up organizations

Select hospital partnership(s)

Theme(s)

Technology overview

Overlap 2019 []

Columbia University Medical Center and UC Davis Health

Data analytics and remote monitoring

Collects patient data through a customizable Overlap app that integrates with EHRs a and various wearable devices

Doximity Dialer 2018 []

Johns Hopkins Hospital

Access to patient records and personalized patient experience

Allows providers to access their patients' records and make patient calls on the go from their personal cell phones, using the office as the caller ID b while on personal phones

a EHR: electronic health data.

b ID: identification.

Table 2. Insurance companies.

Organization

Theme(s)

Technology overview

Insurance Companies

In addition, we compiled a number of insurance companies that encourage the growth and uptake of wearable health technology through incentive programs: Oscar Health, United Healthcare, Humana, and John Hancock (see ). These companies all offer health tracking through devices and promote the use of remote patient data to improve patient engagement and health. Key focus areas included patient data tracking and rewards programs for customers who use devices to track their health and achieve milestones. These rewards programs gamify health goals into point systems and offer incentives for customers, including gift cards, electronic devices, and travel. These initiatives by insurance companies support the uptake of wearable health technologies and expand the use of patient-collected data to improve patient health.

Data Analysis

On the basis of the information collected from our survey sample of 10 start-up organizations and 4 insurance companies, the most common themes included a personalized patient experience based on health goals and past medical history, gamification through a rewards program, and data analytics capabilities (see ).

We also recorded several key observations based on analyzed data:

1. Current rewards programs are strongly linked with wearable devices. Of the identified organizations, all rewards programs relied on the use of wearable devices to track data that could be used for patient incentives. The most common data point was step tracking; a patient could earn money or points to be traded in for prizes when they walked a certain number of steps each day.

2. AI capabilities are still limited. AI has yet to become fully established in the field of wearable health technology. A limited number of organizations are leveraging these digital capabilities to collect, analyze, and integrate patient data and monitoring and creating ongoing dialog about patient health activities.

3. There are varied approaches for personalization of patient information. Personalization of a patient's experience was a prevalent theme across several of the surveyed organizations. The personalized experience was created through various approaches, including recommending health apps, facilitating ongoing conversations with a doctor or AI bot, or providing assessments so that a patient could better understand their health.

4. There are challenges and risks to all aspects of wearable health technology. Addressing system interoperability, patient privacy, and data overload risks will be critical to the use of wearable health technology. We mapped out the previously discussed challenges for each of the 6 themes in .

Table 3. Prevalence of wearable health technology themes across surveyed start-ups and insurance companies.

Theme

Number of surveyed organizations addressing themes

Personalized patient experience

4

Rewards program

4

Data analytics

3

Remote monitoring

2

Access to patient records

1

AI a technology

1

a AI: artificial intelligence.

Table 4. Challenges and risks associated with wearable health technology.

Theme

Challenges

System interoperability

Patient privacy

Data overload

Personalized patient experience

- a

X b

-

Rewards program

-

X

-

Data analytics

-

-

X

Remote monitoring

X

X

X

Access to patient records

X

X

X

AI c technology

-

X

-

a No expected challenge or risk associated with wearable technology theme.

b X: challenge or risk associated with wearable technology theme.

c AI: artificial intelligence.

Principal Findings

This scoping study reviewed current innovations of wearable health technology and EHRs across health care systems, start-ups, and insurance companies and documented key innovation trends, partnerships, and incentives, along with challenges of wearables. Our findings reflect the movement toward the adoption of mobile health devices through the availability of digital tools and gamification of health data collection. However, numerous barriers to the efficient implementation of wearable health technology exist and are likely to hinder widespread adoption across health systems. Our report presents several current approaches to addressing wearable health technology and EHR integration barriers; these findings highlight the direction of wearable health innovation and serve to identify potential partnerships for future wearable adoption.

The development of technologies by start-ups outside of EHR systems highlights the interest in solving challenges in wearable health technology, such as information overload and system interoperability. Companies such as Redox are addressing interoperability issues by creating the technology to link hospitals' EHR systems to outside applications regardless of software vendor. Others, such as Validic and Human API, are working to improve the workload for providers by simplifying the data collection from devices and outputting processed and easily understandable results.

Across the field of wearable health technology, maintaining patient privacy with the expanding use of wearables, rewards programs, remote monitoring, and AI continues to pose the greatest challenge to the growth of wearable health technology. Obtaining informed patient consent will be critical to provide clarity regarding what data are collected and which third parties can access patient data; this will continue to be a key discussion topic, as organizations seek to create a personalized patient experience based on patient-collected data. For example, companies such as Conversa allow for automated and personalized virtual care using conversational AI technology and patient remote data.

The implementation of health tracking rewards programs by insurance companies additionally signifies the interest and direction in which wearable health technology is moving to improve consumer health. These health insurance companies' decisions to engage in wearables through rewards programs can offer increased opportunities in data collection and need for the above start-up technologies to provide a seamless experience for both providers and consumers. As wearable health technology becomes linked to gamification and rewards program initiatives for insurance companies, patient data integration across other platforms is also likely to become more commonplace.

This report serves as a starting point for those interested in wearable innovations rather than a comprehensive summary because of the rapidly changing nature of wearable health technology. As more institutions share their work in this area, address challenges, and create more efficient workflows/processes, the ability to transform patient care and streamline the integration of mobile health devices will improve the health outcomes and quality of care for patients.

Limitations

Although we developed a detailed process to search and document the current state of wearables through our study, several challenges exist to create a comprehensive list. The nature of this report is Epic centric, as we were not able to access other internal EHR portals. There were a limited number of health systems actively publicizing or publishing their work on new integration methods through the general Web. Those that did also used different names (ie, remote data integration and device integration) that may not have been included in our search terms. Furthermore, based on the growing adoption of wearable health technology in health systems over the past few years, we anticipate that new names would have been added to this list since our search.

Conclusions

Wearable health technology will play a critical role in greater transparency between patients and providers and chronic condition management. Devices and technologies that enable the streamlined movement of data from patients to providers are key to improving a patient's care journey and empowering them to manage their own health. The future design and development of digital technology in this space will rely on continued analysis of best practices, pain points, and potential solutions to mitigate existing challenges.

By sharing our results, we have presented key challenges and emerging solutions to this rapidly evolving field. Our work serves as an initial foundation to the creation of a streamlined process to identify relevant entities in this field and provide resources on the implementation of and workflows around wearable health technology and EHR integration for organizations across the health care industry. As much of this work is still ongoing, we anticipate that these findings will serve as the foundation for future studies on wearable health technology.

Conflicts of Interest

None declared.

‎

AI: artificial intelligence

API: application programming interface

EHR: electronic health record

HIPAA: Health Insurance Portability and Accountability Act of 1996

IT: information technology

UI: user interface

Edited by G Eysenbach; submitted 19.11.18; peer-reviewed by V Mishra, C Fernández; comments to author 31.03.19; revised version received 26.05.19; accepted 21.07.19; published 11.09.19

©Catherine Dinh-Le, Rachel Chuang, Sara Chokshi, Devin Mann. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 11.09.2019.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

Background: Due to the adoption of electronic health records (EHRs) and legislation on meaningful use in recent decades, health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data. A novel capability offered by health systems encompasses the integration between EHRs and wearable health technology. Although wearables have the potential to transform patient care, issues such as concerns with patient privacy, system interoperability, and patient data overload pose a challenge to the adoption of wearables by providers.

Objective: This study aimed to review the landscape of wearable health technology and data integration to provider EHRs, specifically Epic, because of its prevalence among health systems. The objectives of the study were to (1) identify the current innovations and new directions in the field across start-ups, health systems, and insurance companies and (2) understand the associated challenges to inform future wearable health technology projects at other health organizations.

Methods: We used a scoping process to survey existing efforts through Epic's Web-based hub and discussion forum, UserWeb, and on the general Web, PubMed, and Google Scholar. We contacted Epic, because of their position as the largest commercial EHR system, for information on published client work in the integration of patient-collected data. Results from our searches had to meet criteria such as publication date and matching relevant search terms.

Results: Numerous health institutions have started to integrate device data into patient portals. We identified the following 10 start-up organizations that have developed, or are in the process of developing, technology to enhance wearable health technology and enable EHR integration for health systems: Overlap, Royal Philips, Vivify Health, Validic, Doximity Dialer, Xealth, Redox, Conversa, Human API, and Glooko. We reported sample start-up partnerships with a total of 16 health systems in addressing challenges of the meaningful use of device data and streamlining provider workflows. We also found 4 insurance companies that encourage the growth and uptake of wearables through health tracking and incentive programs: Oscar Health, United Healthcare, Humana, and John Hancock.

Conclusions: The future design and development of digital technology in this space will rely on continued analysis of best practices, pain points, and potential solutions to mitigate existing challenges. Although this study does not provide a full comprehensive catalog of all wearable health technology initiatives, it is representative of trends and implications for the integration of patient data into the EHR. Our work serves as an initial foundation to provide resources on implementation and workflows around wearable health technology for organizations across the health care industry.

JMIR Mhealth Uhealth 2019;7(9):e12861

doi:10.2196/12861

Keywords

Electronic Health Record Adoption and Expanded Access to Patient-Collected Data

Although electronic health records (EHRs) date back to the 1960s, widespread adoption was stagnant until the more recent passage of the Health Information Technology for Economic and Clinical Health Act in 2009 [-]. Between 2001 and 2011, the number of physicians using EHR systems increased from 18% to 57% []. Policies such as Meaningful Use (which prioritized quality, care coordination, and security of personal health information) incentivized the continued adoption of EHRs. By 2015, nearly 9 in 10 (87%) of office-based physicians adopted an EHR system [-]. Of all EHR vendors, Epic, Cerner, and Meditech are the most prevalent among health care systems [].

In addition to driving EHR adoption among providers and health systems, legislation supporting meaningful use also paved the way for continued development of EHR capabilities to enhance the patient experience. Health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data []. Features include secure messaging with patients and features to view, download, and transmit their EHR. Such capabilities are becoming more prevalent to facilitate streamlined patient data exchanges with their provider [].

Availability and Adoption of Wearable Devices

Wearable devices include wristbands, smartwatches, wearable mobile sensors, and other mobile hub medical devices that collect a large range of data from blood sugar and exercise routines to sleep and mood. Patient data are collected either through consumer reporting or passively through sensors in apps that communicate with devices through application programming interfaces (APIs); these data are then shared through data aggregators such as Apple's HealthKit that pools data from multiple health apps [].

According to a recent consumer survey on digital health by Accenture, a significant percentage of US adults were willing to wear technology that tracks their health statistics (see ) []. Due to mobile integration platforms such as Google Fit and Apple HealthKit, we can expect to see an increase in the number of health-wearable users over the next few years [,]. The upward trend in device usage to monitor health-related data additionally suggests there will be a correlated rise in patient data available for health management []. Large health systems are likely to trend toward larger rollouts of wearable technology in the next few years, potentially incorporating wearables as part of their preventative care strategy by monitoring heart rate, blood pressure, and other information [,]. There are currently more than 400 EHR-compatible devices on the market, a number that is expected to rise exponentially in the coming years [].

‎
Figure 1. Percentage of US adults who were willing to wear technology that tracks select health statistics as of 2018. Screenshot from www.statista.com [].
View this figure

Clinical Impact of Wearable Devices

The purpose of this paper was to conduct a scoping review of the wearable health technology field to provide an overview of current wearable innovations in the EHR. Similar to a number of existing scoping reviews, we used internet search engines in addition to our database searches to capture the rapid updates in the area of health system integration of remotely collected patient data []. We used these sources to generate a targeted list of organizations that are leaders in the overall field of wearable health technology, along with their partnerships.

This paper provides an overview of (1) our process in determining the current landscape of wearable health technology and (2) descriptions of some leading innovations and partnerships by start-ups, providers, and insurance companies. By sharing our results, we hope to create a process to identify relevant organizations in this field and provide resources for organizations that are interested in joining or learning more about implementation and workflows around wearable health technology and patient data integration to EHRs. This study is specific to integration into the Epic portal and is not a comprehensive search; however, results are representative of the field because of Epic's prominence in the US acute care hospital market (25.8%) [].

Search Process

To better understand the scope of wearables and other health tracking devices and the resulting impact on EHRs, we used a scoping process to survey existing efforts on the Web. Although not directly relevant to a scoping review, we reviewed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to enhance the quality of our search. In our search, to identify the leaders in the field, we contacted the largest commercial EHR system (Epic) for information on client work in the integration of patient-collected data. We used this information to further inform our search terms in Epic's UserWeb portal, the primary platform for Epic users to share and discuss topics such as innovative idea generation and event postings, and the general Web, PubMed, and Google Scholar database searches from July 2018 to January 2019.

We recognize the risk of bias in this study, as our search process was limited to Epic clients and the information publicly available on the World Wide Web.

Inclusion Criteria

We used a set of inclusion criteria in UserWeb to ensure that postings were accurate and up to date. Results had to meet the following standards: (1) be posted after June 2017 and (2) have responses to topic threads. Key search terms included Apple HealthKit, Patient remote data integration, Fitbit integration, Withings integration, and Wearables.

Similarly, our findings on wearable technology companies and initiatives from the general Web, PubMed, and Google Scholar database searches had to meet the following criteria: (1) be posted after June 2017 and (2) match search terms including but not limited to Device integration, EHR data integration, Epic MyChart integration, Patient MyChart integration, Patient remote data integration, Patient data access, Wearables, Provider wearables, Hospital wearables, Hospitals AND Apple HealthKit device integration, Apple HealthKit device integration AND Epic, Start-ups AND EHR integration, Insurance companies AND device integration, APIs AND device integration.

Challenges of Wearable Device Integration

Although wearable health technology has the potential to transform patient care, issues such as concerns with patient privacy, system interoperability, and the immense amount of patient data pose a challenge to the adoption of wearables by providers [,]. Such challenges are critical to consider for future wearable use to deliver safe and quality care for patients. Although there are potential solutions for these implementation issues, more innovative work is required for wide-scale adoption of wearable health technology.

Protecting the Confidentiality and Privacy of Patients

The HIPAA is a US legislation that protects the privacy of individuals' medical records and applies to health providers and plans []. With the continuous stream of data from personal devices, data privacy and security for health information must be addressed as to meet HIPAA standards and not impede patients' willingness to share their data []. demonstrates that patients have some concerns about the electronic exchange of data between providers; the percentage of individuals expressing these concerns has remained relatively the same since 2011 []. To protect against potential cybersecurity attacks and missing or stolen patient records through the implementation of wearable health technologies, hospitals must ensure that devices are connected to a secure network and monitor the hospital data network continuously []. To prioritize data privacy, health systems are likely to be required to set up another secure network for wearable devices, separate from the main network [].

‎
Figure 2. Individuals' perceptions of the privacy and security of medical records and health information exchange in 2017. Screenshot from https://dashboard.healthit.gov/quickstats/quickstats.php [].
View this figure

Lack of System Interoperability and Connectivity

As a result, this barrier has created subsets of data collected from patients that become secondary in value because they cannot be easily integrated into patient historical data [,]. Researchers have recently looked to achieve plug-and-play interoperability to standardize platforms and integrate these information islands, a standard that already exists in the world of consumer electronics as consumers demand simple and seamless functionality []. Plug-and-play standards require ease of use, device compatibility, and streamlined scalability and reconfigurability between different vendors; systems must be able to detect new devices, negotiate communication, and allow devices to synchronize and work with each other [].

As the need for system interoperability grows, third-party applications aimed to address interoperability issues have become more prominent []. Increased partnerships and opportunities between makers of these applications and health systems are necessary to reach high interoperability and streamlined communication between EHR platforms, patient devices, and providers. Improving these relationships can improve health care efficiency, provider safer transitions of care, and help lower health care costs [].

Patient Information and Data Overload

In addition, a strong user interface (UI) for providers is important for provider buy-in and engagement during implementation. As a result, there has been an increasing trend within health care organizations to incorporate user experience and UI designers into a cross-functional information technology (IT) team to address this need []. The multidisciplinary skills of such teams can offer improved UIs combined with IT expertise and enhance the ability to comprehend wearable patient data. These improvements in provider engagement and workflow could improve overall time efficiency for providers and quality of care for patients.

Innovations in Wearable Health Technology

However, EHRs still cannot connect to many other devices and require the development of new solutions to address challenges such as interoperability and visualization for the information they are currently collecting []. The wearable health technology space features numerous start-up partnerships with health care providers and insurance company innovations that are working to address these key challenges and promote growth in wearable usage and EHR integration capabilities.

The overall themes that we used to describe the different focus areas of each partnership included personalized patient experience, rewards program, data analytics, remote monitoring, access to patient records, and AI technology. A summary of key organizations working in wearable health technology compiled from the general Web search and Epic's UserWeb portal (as of May 2018) is presented in and , respectively.

Start-Up Partnerships

As listed in below, we identified the following 10 start-up organizations that have developed or are in the process of developing technology to improve wearable health technology and/or patient data integration to EHRs: Overlap, Royal Philips, Vivify Health, Validic, Doximity Dialer, Xealth, Redox, Conversa, Human API, and Glooko. We reported sample start-up partnerships with a total of 16 health systems in addressing challenges of meaningful use of device data and streamlining provider workflows. The partnerships between these start-ups and health systems serve to improve the data collection process, synthesize actionable information for providers to review, and create a more personalized experience between patients and providers. Due to the rapidly moving field of wearables, our research represents a snapshot in time of wearable health technologies and is not meant to be a fully exhaustive list.

Table 1. Wearable health technology start-up partnerships.

Start-up organizations

Select hospital partnership(s)

Theme(s)

Technology overview

Overlap 2019 []

Columbia University Medical Center and UC Davis Health

Data analytics and remote monitoring

Collects patient data through a customizable Overlap app that integrates with EHRs a and various wearable devices

Doximity Dialer 2018 []

Johns Hopkins Hospital

Access to patient records and personalized patient experience

Allows providers to access their patients' records and make patient calls on the go from their personal cell phones, using the office as the caller ID b while on personal phones

a EHR: electronic health data.

b ID: identification.

Table 2. Insurance companies.

Organization

Theme(s)

Technology overview

Insurance Companies

In addition, we compiled a number of insurance companies that encourage the growth and uptake of wearable health technology through incentive programs: Oscar Health, United Healthcare, Humana, and John Hancock (see ). These companies all offer health tracking through devices and promote the use of remote patient data to improve patient engagement and health. Key focus areas included patient data tracking and rewards programs for customers who use devices to track their health and achieve milestones. These rewards programs gamify health goals into point systems and offer incentives for customers, including gift cards, electronic devices, and travel. These initiatives by insurance companies support the uptake of wearable health technologies and expand the use of patient-collected data to improve patient health.

Data Analysis

On the basis of the information collected from our survey sample of 10 start-up organizations and 4 insurance companies, the most common themes included a personalized patient experience based on health goals and past medical history, gamification through a rewards program, and data analytics capabilities (see ).

We also recorded several key observations based on analyzed data:

1. Current rewards programs are strongly linked with wearable devices. Of the identified organizations, all rewards programs relied on the use of wearable devices to track data that could be used for patient incentives. The most common data point was step tracking; a patient could earn money or points to be traded in for prizes when they walked a certain number of steps each day.

2. AI capabilities are still limited. AI has yet to become fully established in the field of wearable health technology. A limited number of organizations are leveraging these digital capabilities to collect, analyze, and integrate patient data and monitoring and creating ongoing dialog about patient health activities.

3. There are varied approaches for personalization of patient information. Personalization of a patient's experience was a prevalent theme across several of the surveyed organizations. The personalized experience was created through various approaches, including recommending health apps, facilitating ongoing conversations with a doctor or AI bot, or providing assessments so that a patient could better understand their health.

4. There are challenges and risks to all aspects of wearable health technology. Addressing system interoperability, patient privacy, and data overload risks will be critical to the use of wearable health technology. We mapped out the previously discussed challenges for each of the 6 themes in .

Table 3. Prevalence of wearable health technology themes across surveyed start-ups and insurance companies.

Theme

Number of surveyed organizations addressing themes

Personalized patient experience

4

Rewards program

4

Data analytics

3

Remote monitoring

2

Access to patient records

1

AI a technology

1

a AI: artificial intelligence.

Table 4. Challenges and risks associated with wearable health technology.

Theme

Challenges

System interoperability

Patient privacy

Data overload

Personalized patient experience

- a

X b

-

Rewards program

-

X

-

Data analytics

-

-

X

Remote monitoring

X

X

X

Access to patient records

X

X

X

AI c technology

-

X

-

a No expected challenge or risk associated with wearable technology theme.

b X: challenge or risk associated with wearable technology theme.

c AI: artificial intelligence.

Principal Findings

This scoping study reviewed current innovations of wearable health technology and EHRs across health care systems, start-ups, and insurance companies and documented key innovation trends, partnerships, and incentives, along with challenges of wearables. Our findings reflect the movement toward the adoption of mobile health devices through the availability of digital tools and gamification of health data collection. However, numerous barriers to the efficient implementation of wearable health technology exist and are likely to hinder widespread adoption across health systems. Our report presents several current approaches to addressing wearable health technology and EHR integration barriers; these findings highlight the direction of wearable health innovation and serve to identify potential partnerships for future wearable adoption.

The development of technologies by start-ups outside of EHR systems highlights the interest in solving challenges in wearable health technology, such as information overload and system interoperability. Companies such as Redox are addressing interoperability issues by creating the technology to link hospitals' EHR systems to outside applications regardless of software vendor. Others, such as Validic and Human API, are working to improve the workload for providers by simplifying the data collection from devices and outputting processed and easily understandable results.

Across the field of wearable health technology, maintaining patient privacy with the expanding use of wearables, rewards programs, remote monitoring, and AI continues to pose the greatest challenge to the growth of wearable health technology. Obtaining informed patient consent will be critical to provide clarity regarding what data are collected and which third parties can access patient data; this will continue to be a key discussion topic, as organizations seek to create a personalized patient experience based on patient-collected data. For example, companies such as Conversa allow for automated and personalized virtual care using conversational AI technology and patient remote data.

The implementation of health tracking rewards programs by insurance companies additionally signifies the interest and direction in which wearable health technology is moving to improve consumer health. These health insurance companies' decisions to engage in wearables through rewards programs can offer increased opportunities in data collection and need for the above start-up technologies to provide a seamless experience for both providers and consumers. As wearable health technology becomes linked to gamification and rewards program initiatives for insurance companies, patient data integration across other platforms is also likely to become more commonplace.

This report serves as a starting point for those interested in wearable innovations rather than a comprehensive summary because of the rapidly changing nature of wearable health technology. As more institutions share their work in this area, address challenges, and create more efficient workflows/processes, the ability to transform patient care and streamline the integration of mobile health devices will improve the health outcomes and quality of care for patients.

Limitations

Although we developed a detailed process to search and document the current state of wearables through our study, several challenges exist to create a comprehensive list. The nature of this report is Epic centric, as we were not able to access other internal EHR portals. There were a limited number of health systems actively publicizing or publishing their work on new integration methods through the general Web. Those that did also used different names (ie, remote data integration and device integration) that may not have been included in our search terms. Furthermore, based on the growing adoption of wearable health technology in health systems over the past few years, we anticipate that new names would have been added to this list since our search.

Conclusions

Wearable health technology will play a critical role in greater transparency between patients and providers and chronic condition management. Devices and technologies that enable the streamlined movement of data from patients to providers are key to improving a patient's care journey and empowering them to manage their own health. The future design and development of digital technology in this space will rely on continued analysis of best practices, pain points, and potential solutions to mitigate existing challenges.

By sharing our results, we have presented key challenges and emerging solutions to this rapidly evolving field. Our work serves as an initial foundation to the creation of a streamlined process to identify relevant entities in this field and provide resources on the implementation of and workflows around wearable health technology and EHR integration for organizations across the health care industry. As much of this work is still ongoing, we anticipate that these findings will serve as the foundation for future studies on wearable health technology.

# data[].attributes.attachment_link.title
Wearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions

# data[].attributes.attachment_link.description
Background: Due to the adoption of electronic health records (EHRs) and legislation on meaningful use in recent decades, health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data. A novel capability offered by health systems encompasses the integration between EHRs and wearable health technology.

# data[].attributes.headline
Week 7: Health Information Exchange Barriers

# data[].attributes.subject
Week 7: Health Information Exchange Barriers

# data[].attributes.body
This article addresses the laws and jurisdictions within states negatively impacting the electronic health information exchange (HIE). As research indicates, HIE allows more efficient sharing of patient data through the different healthcare entities and systems. With the barriers discussed in this article, the benefits of an interoperable system will not exist. Extensive research has been done on the laws preventing these advances however, many states have not taken proper action to change the existing regulations.

# data[].attributes.attachment_link.readable_text
Abstract

BACKGROUND AND SIGNIFICANCE

Health information technology (HIT) infrastructure is critical to sustaining long-term health care innovation. To support HIT infrastructure, the Health Information Technology for Economic and Clinical Health (HITECH) Act's Meaningful Use regulations promote adoption of interoperable electronic health record systems and incentivize the exchange of health information among health care providers. Further, the Office of the National Coordinator for Health Information Technology supports the creation of health information exchanges (HIEs) to facilitate the sharing of patient health data among providers and across health care settings (note that we use the abbreviation "HIE" to refer exclusively to organizations that facilitate information exchange among health care providers, as opposed to the act of exchanging data).

Traditionally, unaffiliated entities stored health information in disparate systems, or "data siloes." HIEs are electronic platforms that break down data silos by sharing and receiving patient data across multiple organizations. Critically, HIEs have the potential to advance instances of coordinated and calculated episodes of patient care and improve the quality of health care, while reducing duplication of care and waste. By supporting HIT infrastructure, HIEs can enable future HIT applications like precision medicine and learning health systems that demand data portability, while maintaining appropriate privacy and security protections.,

Despite the potential benefits, HIEs are underutilized, full integration remains elusive, and several barriers prevent their implementation and operation, including issues relating to governance, resource deficiencies, stakeholder involvement, patient privacy and consent, and data protection.,, Researchers have identified and advocated for select HIE attributes to promote implementation and operation. HIE laws relate to many of these issues.

Law is a barrier to and enabler of HIEs

HIT is heavily regulated. A myriad of federal laws, including the Health Insurance Portability and Accountability Act, HITECH, the Privacy Act, and the Common Rule research protections, regulate uses of health information. Still, the federal framework is relatively simple in comparison to the state framework of >2300 state statutes and regulations relating to electronic health information., In addition to the persistence of antiquated laws, overlapping, conflicting, and inconsistent state and federal laws add complexity and threaten efforts to promote information exchange nationally., In 2007, few states had enacted or considered legislation designed to eliminate information exchange barriers, but the number of states considering legislative changes increased in the years leading up to, and following, HITECH. This highly complex legal framework threatens to stall innovation.,

Barriers and enablers identified in the literature

In this section, we will address several factors cited in literature as barriers to or enablers of HIE implementation. Then, we describe how states address these factors in their laws.

Governance

Oversight, policies, procedures, and operational mechanisms are key components of successful HIEs. A formalized HIE governance body can clarify resource priorities and facilitate operations. Alternatively, an insufficient governance structure can impede statewide HIE implementation and is considered a threat to HIE sustainability.,

Privacy, confidentiality, and security

Participation incentives and mandates

The Agency for Healthcare Research and Quality (AHRQ) identifies a "lack of critical mass electronically exchanging data" as a top barrier to widespread HIE implementation. Laws and policies that incentivize or even mandate HIE participation aim to address critical mass deficiencies. Financial incentives are frequently cited as necessary to overcome initial cost barriers, promote widespread implementation, and increase provider participation rates.,,, Beyond financial incentives, some studies identify HIE-related liability immunity laws as incentives for participation by eliminating the concern of future legal costs.

HIE participation beyond providers: public health, payers, clinical research, and patients

HIEs must demonstrate and maintain their value to ensure long-term sustainability. An HIE's value increases as it becomes more useful to additional stakeholders.,, Consequently, researchers recommend policies that permit expanded HIE use by public health agencies, providers, pharmacies, and laboratories. Other research suggests that laws and policies supporting health care payer engagement can enhance HIE operation and value by promoting insurance coverage, better case management, reduction of unnecessary medical services, efficient Medicaid prior authorization, quality improvement, and cost reductions.,

Funding and financial sustainability

Funding and long-term sustainability are critical HIE issues. Legislative authorizations constrain state-controlled or -operated HIEs, so they need a specific legal authority to receive grants and private donations, or require participant fees. Without these laws, state HIEs are limited in their financial strategic planning.

OBJECTIVE

This research identifies states that are addressing these factors in their laws. It addresses a substantial research gap in how comprehensive state law frameworks support HIEs.

METHODS

We followed accepted policy surveillance research guidelines., We identified statutes and regulations relating to HIEs in effect on June 7, 2016, using the Westlaw legal database. We limited jurisdictions to US states, territories, and the District of Columbia with statutes and regulations in Westlaw. We included laws if they related to HIEs, ie, organizations that facilitate access to identifiable health information among different entities for the purpose of patient treatment.

We used general and targeted search terms to identify HIE laws. Table 1 includes a list of all search terms used to identify relevant laws in all included jurisdictions. We identified the terms in Table 1 through a literature review and a preliminary review of HIE laws. To supplement the searches in Table 1, we searched Westlaw using approximately 150 US HIE trade names, using Google and Wikipedia articles for the Health Information Exchange and Regional Health Information Organization (accessed June 6, 2016).

adv: SD("health information exchange")

adv: SD("health information organization")

adv: SD("HIE")

adv: SD("HIO")

adv: SD("RHIO")

adv: SD("record locator service")

adv: SD("master patient index")

adv: SD("clinical data repository")

adv: SD("continuity of care document")

adv: SD("clinical decision support")

adv: SD("health information network")

adv: SD("electronic health record network")

adv: SD("health records exchange")

adv: SD("health data exchange")

adv: SD("health information partnership")

adv: SD("learning health system")

adv: SD("clinical data exchange")

adv: SD("health information highway")

adv: SD("health information system")

adv: SD("e-health partnership")

adv: SD("health information initiative")

adv: SD("e-health network")

adv: SD("medical information network")

adv: SD("clinical information exchange")

adv: SD("health data organization")

We developed our coding scheme through a 4-stage iterative process: (1) question development, (2) question testing using a sample of legal provisions, (3) question adequacy analysis, and (4) question revision. We developed initial draft questions based on significant HIE features and functions identified by the literature and an initial review of laws in 10 jurisdictions. We repeated the question revision process 4 times. We coded each jurisdiction on the finalized coding criteria, consisting of 80 questions.

Prior to the final revision, we tested the questions on a trained legal researcher who had not interacted with the researchers during the coding process. This served as a check on potential convergence bias between blinded coders developed from research process interactions. We then revised the questions to reduce the likelihood that other researchers would interpret coding questions differently when evaluating the same law.

Two independent blinded researchers coded legal provisions within each jurisdiction. Researchers coded data in a custom Microsoft Access database. After coding sessions, researchers analyzed and corrected coding discrepancies in peer-review meetings. We recorded consensus codes in the master database. Blinded coders had initial agreement on 87.6% of the questions.

We performed 2 additional validation steps to lessen the likelihood that laws outside our scoping criteria would invalidate our findings. First, we evaluated laws that more generally relate to the act of exchanging health information (as opposed to an HIE entity). Second, we examined laws that primarily relate to electronic health records but implicate an HIE as identified by a study of electronic health information laws by Schmit et al.

The literature identifies at least 9 broad legal themes believed to be important to the function and future of HIEs. Since our coding questions relate to types of provisions observed in the laws (in addition to legal issues discussed in the literature), not all of the 80 coding questions relate to legal issues discussed in the literature. Therefore, we selected and matched 20 coding questions that most closely relate to the 9 broad legal themes reflected in the literature (See Table 2).

Governance

Does the law authorize implementation of a statewide HIE?

Who retains control over the statewide HIE's operations?

Participation Incentives and Mandates

Does the law incentivize HIE participation (excluding funds to be spent solely on HIE creation or implementation)?

Does the law provide immunity from liability for the HIE/health information organization or its participants?

Does the law mandate that any providers must access or contribute to HIE data?

Funding and Sustainability

Does the law address HIE funding?

Does the law specify funding sources to cover initial HIE implementation costs?

Does the law specify funding sources for long-term operability?

HIE Participation and Use

Does the law authorize state and local health authorities to access the HIE?

Does the law authorize any other state agency or entity (other than public health) to access the HIE?

Does the law authorize medical labs to convey medical test results through HIE?

Does the law authorize pharmacists and pharmacies to access the HIE (eg, for the purposes of filling prescription drug orders and documenting other pharmaceutical care)?

Does the law authorize educational or other research institutions to access HIE data through an HIE portal or user interface (excluding research requiring express consent for the use of an individual's information)?

Does the law authorize payers to access HIE data?

Patient Engagement

Are patients authorized to access their HIE data electronically through a portal or user interface (ie, excluding access to e-mailed, faxed, or mailed copies of their records)?

Does the law include provisions that address public transparency of HIE activities, including but not limited to public participation in HIE policy and public disclosures of policies, operations, and activities?

Privacy

Does the law require privacy protections for HIE (excluding direct reference to state or federal privacy laws)?

Is patient consent required for disclosure of patient data in the HIE?

Confidentiality

Are there express duties for providers or other HIE users to protect patient data from unauthorized access/disclosure?

Security

Does the law address administrative, technical, or physical safeguards to secure HIE data (excluding direct references to state or federal laws)?

RESULTS

We identified HIE laws in 42 states, the District of Columbia, and 2 territories (Puerto Rico and the US Virgin Islands). Eight states do not have HIE laws (Alabama, Georgia, Hawaii, Indiana, Michigan, Montana, South Dakota, and Tennessee). On average, jurisdictions address 8.32 of the 20 criteria selected in statutes and regulations. Twenty jurisdictions unambiguously address ≤5 criteria in statutes and regulations ( Table 3).

Jurisdiction

Governance

HIE Participation and Use

Patient Engagement

Jurisdiction

Participation Incentives and Mandates

Funding and Sustainability

Privacy

Confidentiality

Security

Thirty-one states address the creation of a statewide HIE in law. Nine of those states leave control of the statewide HIE to a state entity, 17 have designated a non-state entity (SDE) to control the statewide HIE, and 2 states have reserved the option to control the HIE. The remaining states have laws with ambiguous language relating to governance structure.

Thirty-one states address privacy with specific legal provisions. Five states use the term "privacy" but do not specifically detail the extent of privacy rights. Twenty-five states give patients a choice regarding participation in the HIE, with 16 states describing an opt-out consent procedure, 8 states describing an opt-in procedure, and 2 states ambiguously describing a voluntary HIE. Only 23 states impose specific confidentiality duties on HIE users, and 5 states mention confidentiality without providing additional specific requirements. Twenty-three states address security with specific language. Eight states use the term "security" but do not specifically describe required security measures.

Only 19 jurisdictions address HIE participation incentives, with 9 addressing financial incentives, 3 addressing nonfinancial incentives, and 7 addressing both financial and nonfinancial incentives. Twenty-one states provide some form of HIE liability immunity: 8 provide immunity to providers, 1 provides immunity to the HIE, and 12 provide immunity to both providers and the HIE. Eleven states have established HIE participation mandates for health care providers.

Thirty-two states have authorized public health officials to use the HIE, and 18 states have authorized other government agencies to access the HIE. Sixteen states have authorized medical laboratories and 20 have authorized pharmacies to use the HIE. Only 5 states have unambiguously authorized the use of the HIE for research. Twenty-eight jurisdictions have authorized payer use of the HIE.

Fifteen states have laws that authorize patients to use the HIE to access their records or communicate with their providers, and 18 states have laws that increase public transparency or otherwise promote public engagement in HIE activities.

Just over half the jurisdictions (28) address HIE funding in the law. Twenty-two have identified funding sources for implementation and 24 have identified long-term funding sources.

DISCUSSION

Governance: designate an entity to operate HIE

Privacy, confidentiality, and security: establish HIE-supporting protections

Privacy, confidentiality, and security are important issues concerning both providers and patients.,, Moreover, evidence suggests that laws that protect health information might promote HIE implementation and operation., About 60% of states have laws addressing privacy, while fewer than half of all states address confidentiality or security with specific language. Interestingly, several states have laws that only reference privacy (5), confidentiality (5), or security (8) without describing any specific legal requirements. At best, unspecific laws encourage patient support by creating an illusion of legal protections. At worst, ambiguous laws create confusion among providers and HIEs about what the law actually requires, creating imagined barriers to data exchange.

Participation incentives and mandates: implement laws to encourage critical mass use

Participation mandates provide a strong policy lever to increase the number of users, improve HIE value, and prevent information-blocking behaviors. For example, hospitals in competitive markets might be less willing to share patient data with outside entities. Some studies suggest that states might need laws to mandate HIE use and prevent deliberate efforts to block information exchange.,,,,, However, only 11 states have legal mandates for providers to use HIEs.

HIE uses and participants: data use restrictions stall HIT's social benefits

Numerous studies suggest that an HIE's value increases when a variety of participants (eg, public health authorities, laboratories, and pharmacies) add new functionalities and services.,, Only 32 states address public health uses of HIE data via law, and 18 states address HIE use by other government agencies through law. The numbers for laboratories and pharmacies are even lower. Only 16 states address HIE use by laboratories and 20 states address pharmacy use. However, ambiguously worded laws can authorize laboratory and pharmacy use in 12 and 13 states, respectively.

Research uses of HIEs hold significant promise for the future of HIT.,, However, state laws might pose a significant barrier to such usage. Only 5 states address HIE use for research. Moreover, stringent protections for identifiable health information are notoriously vexatious to health data analytics.

Patients are also a critical stakeholder group. Increasing patient involvement in HIE activities has the potential to improve an HIE's value to both providers and patients. While research suggests that patient-centered HIE tools require legal and policy developments, fewer than 1 in 3 states have laws that authorize patients to use an HIE to access their records or communicate with their providers. In some cases, HIE sustainability is linked to patient marketing efforts., However, only 18 states have laws that increase public transparency or otherwise promote public engagement in HIE activities.

Funding and financial sustainability: empower HIEs to achieve sustainability

Insufficient HIE funding is a well-recognized barrier to HIE adoption and long-term sustainability. Researchers recommend models where multiple stakeholders, including payers, contribute to HIE financing. Others recommend subscription fee models that take into account hospitals' and providers' capacity to support HIE sustainability without being overly burdensome. The AHRQ report emphasizes that HIE "requires considerable investment by sponsors, which have included governments as well as health care organizations."

States without HIE laws

We found 8 jurisdictions without HIE laws. Implementing new laws is not easy. Political obstacles, competing legislative priorities, and the absence of compelling evidence are all barriers to implementing new HIE laws. Moreover, legislation is one of many strategies to support HIEs.

While existing literature suggests that some laws enable HIEs, there are other relevant factors. For example, a viable business plan is critical for an HIE's success. While laws can shape the boundaries of permissible economic activities, daily business decisions can have more immediate impact on an HIE's sustainability.

There is also a growing body of literature suggesting that complex legal frameworks can impede emerging technologies like HIT., While some empirical studies have demonstrated positive associations between laws and HIE outcomes, these studies are narrow and do not examine the comprehensive legal framework. Consequently, there is a plausible alternative hypothesis that HIEs are more likely to succeed in states with less complex legal frameworks. Until now, no study has comprehensively measured HIE legal frameworks to test this hypothesis against the existing recommendations in the literature.

Limitations

Many factors are relevant to the impact of a law. Differences in interpretation, implementation, organizational capacity, funding, enforcement, legal awareness, cultural acceptance, and political priorities can all affect the impact of identically worded laws across different jurisdictions. Moreover, changes to laws will likely affect well-established HIEs differently than newly emerging HIEs. Future research using these data to evaluate the impact of laws on HIEs should carefully consider these factors.

We used recommendations reported in the literature as the basis for our comparisons, but the existing evidence base has "very significant limitations." Few of the literature's recommendations have been systematically and rigorously tested as called for by the AHRQ evidence report. Consequently, it is possible that some recommendations overestimate the power of law to fix the difficult issues associated with HIE. For example, Indiana is one of the 8 jurisdictions without HIE laws, yet the Indiana Health Information Exchange is one of the largest and has operated successfully since 2004. This underscores the need to evaluate the impact of these laws rigorously using scientific legal research methods.

CONCLUSION

This new Centers for Disease Control and Prevention (CDC) dataset of HIE laws addresses the current evidence gap. It contains data on 80 legal attributes related to HIEs; we report only 20 attributes in this paper. We believe that all legal attributes contained in this dataset will have value to future empirical research on HIE effectiveness and implementation. However, the existing literature lacks specificity to explore the implications of legal variations between jurisdictions meaningfully.

ACKNOWLEDGMENTS

This research was supported by the CDC Office for State, Tribal, Local, and Territorial Support. We have no conflicts of interest, including relevant financial interests, activities, relationships, and affiliations. We would like to thank Elizabeth Booker Houston, MPH, JD candidate 2017, who assisted with protocol validation, and Matthew Penn, JD, MLIS, and Tara Ramanathan, JD, MPH, attorneys with the CDC Public Health Law Program, who assisted with assessment design. No additional compensation was provided to these acknowledged individuals for their contributions. The Public Health Law Program cannot provide legal advice on any issue and cannot represent any individual or entity in any matter. The findings and conclusions in this summary are those of the authors and do not necessarily represent the official views of the CDC.

FUNDING

This work was supported by the US Centers for Disease Control and Prevention, Office for State, Tribal, Local, and Territorial Support, Public Health Law Program.

COMPETING INTERESTS

The authors have no competing interests to declare.

CONTRIBUTORSHIP

CDS and SAW contributed to the conception and design of the work; the acquisition, analysis, and interpretation of data for the work; and drafting the work and revising it critically for important intellectual content. BAK contributed to the analysis and interpretation of data for the work and revising the work critically for important intellectual content. All authors provided final approval of the manuscript version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

REFERENCES

# data[].attributes.attachment_link.title
Falling short: how state laws can address health information exchange barriers and enablers

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Health information technology (HIT) infrastructure is critical to sustaining long-term health care innovation. To support HIT infrastructure, the Health Information Technology for Economic and Clinical Health (HITECH) Act's Meaningful Use regulations promote adoption of interoperable electronic health record systems and incentivize the exchange of health information among health care providers.

# data[].attributes.headline
Week 8: Telehealth Innovations Since 2020

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Week 8: Telehealth Innovations Since 2020

# data[].attributes.body
I found this Ted Talk helpful in discussing the advances in telehealth and the improvements made during the COVID-19 pandemic. Discussed in this video are the changes in coverage made by Medicare and Medicaid since 2020. Prior to the pandemic, Medicare and Medicaid were not reimbursing providers for telehealth visits, limiting this convenience to a small percentage of patients and providers. This is an important change in insurance and one that is so important to the future of medicine.

# data[].attributes.attachment_link.title
Why Innovative Uses of Telehealth Should Be Championed

# data[].attributes.attachment_link.description
This interview with Christine Zack was recorded on June 8, 2020, in Reno, Nevada. Christine discusses how telehealth services expanded during the COVID-19 pandemic. She makes the case for why we need to continue to increase broadband connectivity and innovative uses of telehealth to improve health e…

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Week 8: Telehealth Usage & The COVID-19 Pandemic 

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Week 8: Telehealth Usage & The COVID-19 Pandemic 

# data[].attributes.body
As we know the COVID-19 pandemic increased the usage of telehealth between patients and providers. We saw the importance of how telehealth was not only convenient but safe while still providing effective care. This research article discusses the increase in the use of telehealth technology since the COVID-19 pandemic and some of the challenges faced because of the quick increase. What I found most useful in this article was the recommendations that were provide to the specific challenges that the telehealth industry currently faces on how to rectify them for the best quality of care.

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Highlights

*

State-of-the-art Literature Categorization for Telehealth utilization during COVID-19.

*

Challenges, motivations and recommended solutions are identified for Telehealth during COVID-19.

*

Different Applications of Telehealth during the COVID-19 pandemic.

Abstract

During the coronavirus disease (COVID-19) pandemic, different technologies, including telehealth, are maximised to mitigate the risks and consequences of the disease. Telehealth has been widely utilised because of its usability and safety in providing healthcare services during the COVID-19 pandemic. However, a systematic literature review which provides extensive evidence on the impact of COVID-19 through telehealth and which covers multiple directions in a large-scale research remains lacking. This study aims to review telehealth literature comprehensively since the pandemic started. It also aims to map the research landscape into a coherent taxonomy and characterise this emerging field in terms of motivations, open challenges and recommendations. Articles related to telehealth during the COVID-19 pandemic were systematically searched in the WOS, IEEE, Science Direct, Springer and Scopus databases. The final set included (n = 86) articles discussing telehealth applications with respect to (i) control (n = 25), (ii) technology (n = 14) and (iii) medical procedure (n = 47). Since the beginning of the pandemic, telehealth has been presented in diverse cases. However, it still warrants further attention. Regardless of category, the articles focused on the challenges which hinder the maximisation of telehealth in such times and how to address them. With the rapid increase in the utilization of telehealth in different specialised hospitals and clinics, a potential framework which reflects the authors' implications of the future application and opportunities of telehealth has been established. This article improves our understanding and reveals the full potential of telehealth during these difficult times and beyond.

View Abstract

# data[].attributes.attachment_link.title
Telehealth utilization during the Covid-19 pandemic: A systematic review

# data[].attributes.attachment_link.description
State-of-the-art Literature Categorization for Telehealth utilization during COVID-19. * Challenges, motivations and recommended solutions are identified for Telehealth during COVID-19. * Different Applications of Telehealth during the COVID-19 pandemic. During the coronavirus disease (COVID-19) pandemic, different technologies, including telehealth, are maximised to mitigate the risks and consequences of the disease.

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Week 9: Gamification in Undergraduate Nursing Education

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Week 9: Gamification in Undergraduate Nursing Education

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This article discusses the use of gamification to amplify the nursing education teaching models for undergraduate students. In the qualitative study, the students represented a positive view on using this technology to enhance their learning. As a visual, hands-on leaner I found this research applicable and relevant to my own views on this learning style.

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Abstract

Background

With the development of evidence-based nursing practice, nursing research courses are important in undergraduate programs. However, nursing research courses are highly theoretical, which is difficult for students to understand. Improving the teaching quality of nursing research courses is a challenge for educators.

Objectives

To understand the learning experience and the effect of gamification teaching on nursing undergraduates, and to provide new ideas for nurse educators.

Design

A qualitative research design using individual semi-structured interviews.

Settings

Gamification teaching was applied in an undergraduate nursing research course over one semester at a university in Central China.

Participants

Purposive sampling was adopted to select nine undergraduate nursing students who had recently completed gamification teaching in a nursing research course.

Methods

Data were collected through semi-structured interviews and analyzed using Colaizzi's phenomenological method.

Results

Three major themes and seven subthemes emerged from the interviewees' experience: (1) positive attitude (e.g., acceptance with pleasure and flow experience); (2) self-perceived competence improvement (e.g., creative thinking, collaboration, and knowledge internalization and application); and (3) challenges of gamification teaching (e.g., learning pressure and rationality of game design).

Conclusions

The gamification teaching mode based on the flow theory had a positive effect on the students' learning experience in the nursing research course. It is recommended to improve the game design model and to enhance its effectiveness for classrooms in the future.

View full text

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Exploring the experience of nursing undergraduates in using gamification teaching mode based on the flow theory in nursing research: A qualitative study

# data[].attributes.attachment_link.description
With the development of evidence-based nursing practice, nursing research courses are important in undergraduate programs. However, nursing research courses are highly theoretical, which is difficult for students to understand. Improving the teaching quality of nursing research courses is a challenge for educators.

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Week 9: Gamification and Patient Engagement

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Week 9: Gamification and Patient Engagement

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This article discusses the use of gamification and patient engagement through an online health community. Patient engagement is a topic that I view as an important part of a patient's health outcome. With gamification included in this important aspect of the treatment plan, physicians are seeing more success with patient's under their care. The article also discusses the importance of provider feedback on the use of gamification and how positive it has been for all.

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To read the full version of this content please select one of the options below:

Peng Ouyang (School of Economics and Management , Harbin Institute of Technology , Harbin, China)

Jian-Jun Wang (School of Economics and Management , Dalian University of Technology , Dalian, China)

Usman Ali (School of Economics and Management , Dalian University of Technology , Dalian, China)

Abstract

Purpose

Gamification has been widely implemented to improve user engagement in the online health community (OHC). While its effect on the physicians' engagement has recently been documented, whether and how gamification influences the patients' engagement in the OHC remains an untapped research area. The purpose of this study is to fill this dearth by encompassing the gamification strategy of Haodf.com, which awards the "Annual Physician" badges to the physicians, to analyze how this gamification approach motivates patients' engagement in the OHC.

Design/methodology/approach

Real-world data are leveraged from the OHC. The Tobit model is employed for modeling the gamification-patient's engagement nexus in an OHC. Robust findings are obtained by incorporating different measures of a dependent variable, a set of control variables about the physician's characteristics and hospital's features, and alternative estimation techniques.

Findings

The results reveal that a patient's engagement in the OHC in the form of appointments and review-posting behavior is enhanced by the gamification strategy. Besides, the positive influence of gamification on the patient's engagement is further strengthened by the physician's professional capital. It is basically obtained that the gamification is an efficacious tool to accelerate not only the physicians' engagement but also of the patients in the OHC platform.

Originality/value

The study provides both theoretical and empirical discussion to enrich the understanding on how OHCs enhance patients' engagement by developing gamification techniques. The findings guide the practitioners of OHC to better understand the implications of their gamification design to optimize user engagement.

Keywords

Acknowledgements

The authors thank the editor, Professor Dan Wu, for her precious time, the associate editor, and two anonymous reviewers for their valuable comments and suggestions.

Funding: This research was supported by the National Natural Science Foundation of China (71903020), The Ministry of education of Humanities and Social Science project (19YJC790174), the Fundamental Research Funds for the Central Universities (DUT19RW212).

Declaration of interests: None.

Citation

Ouyang, P., Wang, J.-J. and Ali, U. (2022), "The impact of gamification on the patient's engagement in the online health community", Aslib Journal of Information Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/AJIM-10-2021-0314

Publisher
:
Emerald Publishing Limited

Copyright

© 2022 Emerald Publishing Limited

# data[].attributes.attachment_link.title
The impact of gamification on the patient's engagement in the online health community

# data[].attributes.attachment_link.description
To read the full version of this content please select one of the options below: Peng Ouyang (School of Economics and Management , Harbin Institute of Technology , Harbin, China) Jian-Jun Wang (School of Economics and Management , Dalian University of Technology , Dalian, China) Usman Ali (School of Economics and Management , Dalian University of Technology , Dalian, China) Gamification has been widely implemented to improve user engagement in the online health community (OHC).

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This article discusses alert fatigue and its role in clinical decision support software for prescribing. I found this article interesting because it discusses the reasons for the safety alerts in medication prescribing given the high percentage of prescribing error among providers. Outlined in the article is how these safety alerts can also limit prescribing in certain circumstances, leading to alert fatigue. Further discussed in this research is how to reduce these alert fatigues through interactive design and role tailoring among healthcare workers.

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Abstract

introduction

According to the most recent U.S. government reports, 1 in every 20 deaths in the United States has been attributable to an adverse drug event (ADE)., Many ADEs result from erroneous prescriptions. By the most conservative estimates, 1 in every 50 prescriptions is inappropriate.

CDS can also fail to improve patient safety due to alert fatigue. Alert fatigue occurs when a high number of irrelevant alerts leads users to habitually override them. It is a term derived from alarm fatigue, which psychologists and human factors researchers have used when studying high false alarm rates in fields such as aviation and nuclear power plant operation.

Some researchers have focused on increasing alert sensitivity and specificity by modifying CDS rulesets., The results have been mixed. It is often difficult to justify disabling alerts due to safety concerns or pressure from patient safety groups (eg, Leapfrog).,

Psychologists and human factors researchers have developed strategies to reduce alarm fatigue via interaction design-the design of the way the "dialogue" unfolds between the human user and the computer. Some have applied these strategies in CDS, with promising results. For example, tiered alarms indicate the likelihood or severity of an adverse event, and they seem to have been well received in CDS. As another example, "patient" alarms-those that avoid distracting airplane pilots when they are busy-may be accepted more often than "impatient" alarms. Similarly, in CDS, researchers have implemented alerts that avoided requiring attention at a particular time-again, with some success.

To address whether the interactive design of CDS affects clinical alert fatigue, in the aggregate, we conducted a systematic Preferred Reporting Items for Systematic Reviews and Meta-Analyses review. Existing systematic reviews have tended to focus on prescriber performance and patient outcomes rather than alert acceptance. We only found 3 published reviews that addressed interactive design.,, In 2006, van der Sijs et al conducted a conceptual analysis, noting that they had identified only 9 studies that reported override rates. Subsequent reviews by Horsky et al and Miller et al deferred to prior authors' assessments of effectiveness. These assessments were based on a variety of factors, ranging from provider usability and satisfaction to patient morbidity and mortality. In this review, we continue this line of inquiry by centralizing alert fatigue and specifically examining the relationship between interactive designs and prescriber acceptance rates.

Defining acceptance

We defined acceptance-our main outcome-as a change to a prescription based on computerized advice. This definition excluded "intention to monitor" and "acknowledgment"-explanations of these concepts follow.

Some CDS alerts have allowed prescribers to select "intention to monitor" as an override justification, and some researchers have counted this justification-selection as evidence of "acceptance." However, Slight et al found evidence of monitoring in only 36% of instances in which the prescriber indicated an intention to monitor.

Many CDS alerts have been presented as modal dialogs (also known as pop-ups), and some of these have provided a button that indicates "acknowledgment," but which takes no action. Some researchers have considered a click of this button to count as "acceptance"-but this, too, may rely on an incorrect assumption. Under alert fatigue, modal dialogs become obstacles, and "acknowledgment" buttons become the work-around.

Additionally, in this review, we paid attention to the clinical role of the recipient of the automated guidance, eg, a prescriber or a pharmacist. Other authors, have identified that delivering the right guidance to the right recipient is crucial to the acceptance of the alert.

MATERIALS AND METHODS

We conducted a systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses model. We started by searching the PubMed, Embase, CINAHL, and Cochrane literature databases. The search terms we used are shown in Table 1. We identified articles published between 2007 and 2017. Two of the authors (T.L.R., M.I.H.) screened the search results, extracting relevant details (interactive features, clinical roles, acceptance rates, and methods) from included studies for analysis. T.L.R. and M.I.H. met often to ensure consistency.

Eligibility criteria

We included peer-reviewed, English-language articles reporting empirical studies about CDS for medication safety. We included articles that documented acceptance rates, as defined in the Introduction, or enough information to calculate an acceptance rate. When we found more than 1 article documenting the same CDS and setting, we retained the more thorough version.

While screening, we used the following additional criteria. First, as our goal was to understand how prescribers acting of their own free will responded to different interventions, we excluded "hard stops," which impose heavy time penalties to override, and which therefore materially restrict the prescriber's range of action. Readers interested in an analysis of hard stops should refer to a 2018 systematic review by Powers et al. Second, we excluded articles that did not describe the interactive design in enough detail to produce a description. Third, we excluded articles in which researchers made global changes to an alerting system, but only reported acceptance rates for those alerts intended to convey the most urgency, for certain drug categories, or for a selected subset of users exposed to the alert; some of these authors may have chosen to report only the most palatable results. If, on the other hand, the researchers set out to improve acceptance of a certain type of alert, like antibiotic stewardship or renal dosing, then reporting the acceptance rate for only those alerts was considered appropriate for our analysis.

Data extraction process

The same 2 authors (T.L.R., M.I.H.) split this data extraction workload evenly and checked one another's work. Doubts on inclusion were handled by interpreting the inclusion criteria in-person to achieve consensus.

Data analysis and synthesis of results

We coded features and measurement methods as short descriptions (eg, "tiered modal dialog presented to the prescriber," "counted dialog button-clicks"). We sorted these descriptions into categories as commonalities emerged.

We also paid attention to the methods used to construct acceptance rates. In this article, we refer to the 2 main methods as in-dialog action analysis and event analysis.

In-dialog action analysis is only applicable when the CDS intervention takes the form of a dialog that features a button that the prescriber can click to modify or discard their order (eg, "Discard Warfarin Order"). Researchers count the number of times the "acceptance" button was clicked, and divide that count by the total number of dialogs that appeared.

Event analysis may be applied to any form of CDS, including dialogs. When conducting an event analysis, researchers search the patient chart for evidence that the prescriber accepted advice, in addition to any changes that prescribers may have made by clicking buttons inside CDS dialogs. For example, a prescriber might dismiss a modal dialog warning against a warfarin order, and then reduce the dose later. Or, a pharmacist might receive an alert from a CDS system, and counsel the prescriber by phone-in which case the researchers must check to see if the prescriber made a change to the chart.

We plotted the frequencies of measurement methods by publication year to examine their popularity over time. For those studies that used more than 1 measurement method, we compared the results of those measurement methods. We also plotted the frequencies of interactive and role-tailoring features reported over time, to identify trends.

Next, we used a t test to compare acceptance rates between CDS systems by interactive design and clinical role-tailoring. In addition, we constructed a plot to holistically examine prescribers' acceptance rates by feature.

RESULTS

Study selection

As shown in Figure 1, we initially identified 2699 records by querying the literature databases. After removing duplicates, screening titles, and abstracts, and examining full-texts to determine eligibility, we determined that 39 articles met our inclusion criteria. Extracting results from these articles yielded 42 different interventions, since there were 3 articles that reported 2 interventions each.

Study characteristics

The study characteristics are shown in Figure 2. Twenty-four (61%) of the 39 included articles reported studies conducted in the United States, and 3 (8%) reported studies from Taiwan. There were 2 studies from Switzerland, 2 from the Netherlands, and 1 from each of the following: the United Kingdom, China, Canada, and Belgium. Nine of the 24 (38%) studies conducted in the United States were conducted in Harvard-affiliated institutions.

Seventeen (44%) studies were conducted in inpatient settings only, 12 (31%) were conducted in outpatient settings only, 6 (15%) studied both inpatient and outpatient settings, and the remaining 4 (10%) studies were conducted in the emergency department, in the emergency department and outpatient settings, or in an unspecified setting. Twenty-five of the 39 (64%) included articles studied academic healthcare settings, 12 (31%) studied nonacademic settings, and the remaining 2 (5%) studied both settings.

Twenty-five of the 39 (64%) included articles documented an electronic health record (EHR)-integrated CDS, 9 (23%) documented a standalone CDS, and 5 (13%) did not specify whether the CDS was integrated into an EHR. Three of the 39 included articles (8%) reported more than 1 intervention,,; each intervention was treated as a separate study.

Twenty-four (60%) of the articles solely studied physician behavior. Ten (26%) studied both physician and nurse practitioner behavior and 5 (13%) did not specify the clinical roles that were studied.

Trends in measuring acceptance

Trends in CDS interventions

Features present in 4 or more included studies are plotted cumulatively, over time, in Figure 3. Three of the most common interactive features-tiering alerts, providing shortcuts for common corrective actions, and requiring a reason to override-are described and illustrated in Table 2.

The most commonly reported type of CDS-which comprised 83% of results-interrupted prescribers with modal dialogs. The most common variants were tiered to convey levels of risk, provided shortcuts for common corrections, or required a reason to override.

CDS acceptance by feature

For the analysis of feature acceptance, we included the 22 studies that used event analysis. Of those studies, 15 (68%) were based on CDS systems that interrupted prescribers with modal dialogs. Among the 7 alternatives, 4 (18%) presented alerts pertaining to areas such as antimicrobial stewardship or renal dosing to pharmacists, 2 (9%) delivered fax or mail alerts to prescribers,, and 1 (4.5%) depended on the prescriber to manually trigger a review process.

We compared those interventions that interrupted prescribers with modal dialogs with all other interventions. The group of alternative interventions included any alerts that were sent to the pharmacist instead of the prescriber, as well as any alerts that were sent to the prescriber but were not modal dialogs. Using a t test, we found that prescriber-interrupting modals were accepted significantly less often, as predicted (38.67% v. 61.57%; P =  .026). The acceptance rate distributions are shown in Figure 4.

Our plot of acceptance rates by CDS feature is shown in Figure 5. In that figure, CDSs with multiple features appear on multiple lines. For example, a CDS that interrupted prescribers with tiered modal dialogs will appear twice in the figure, once on the "Modals Interrupted Prescribers" line, and once on the "Alerts Tiered to Convey Risk" line.

Visual inspection suggested that prescribers accepted advice from CDS-guided pharmacists more frequently and with less variability than they accepted advice when interrupted by modal dialogs.

DISCUSSION

In this systematic review, we found that interrupting prescribers with modal dialogs have become the least accepted-yet the most prevalent-design. In this section, we analyze possible reasons to account for this observation. Afterward, we discuss some methodological dilemmas faced in CDS research. Some of these have been a matter of methodological inconsistency-and they presented a practical barrier to meta-analysis. Finally, we conclude this section with our recommendations to improve the quality of CDS design and research.

Reasons why prescriber-interruptive modals seem to elicit alert fatigue

When we compared prescriber-interruptive modal dialogs with alternatives, we found evidence favoring the alternatives-in particular, those that tailored CDS to the roles of pharmacists and physicians. We believe there are 3 explanations for this finding. The first concerns etiquette-"proper" etiquette often makes advice easier to receive. The second concerns the division of expert labor between prescribers and pharmacists. The third concerns relevance, which comes naturally to humans, but which remains difficult to compute.

Etiquette. In the Introduction, we mentioned that psychologists and human factors researchers tend to endorse presenting guidance "politely"-even in emergencies. Prescribers might have accepted pharmacists' advice so readily because those pharmacists produced behavioral patterns culturally recognized as "polite." In some cases, it is appropriate to carefully design and program computers to produce similar "behavior" to solicit the user's reciprocity. Some of the modal dialogs that we saw, which featured large, capitalized red text, and which required several clicks and keystrokes to dismiss, might have been seen as patronizing, rather than polite. In our review, attempts to imitate "politeness" in CDS were rare to find.

Division of expert labor. It has been common practice for prescribers to consult pharmacists about the appropriateness of particular medications for patient cases. Presenting certain medication-related CDS to the pharmacist-such as those concerning antibiotic targeting and renal dosing-therefore may support (rather than disrupt) an established clinical practice. This division of expert labor might have a hidden advantage: Sheltering prescribers from most of the details of pharmacy review might allow prescribers to focus more of their attention on the key details of clinical cases, so that they may think more clearly. We understand this may not be feasible in certain cases until regulatory barriers are changed.

Relevance. Prescribers may have found pharmacist-mediated CDS alerts highly acceptable because pharmacists filtered out irrelevant advice. Whether computers might, someday, handle relevance and context as capably as humans has been a matter of debate. Indeed, CDS does not seem to perform at the same level of precision and relevance as the humans they advise.

CDS homogeneity presented 1 of several barriers to meta-analysis. The other barriers were primarily due to methodological inconsistencies in the literature. Next, we discuss methodological issues.

Mediation analysis may address methodological dilemmas

As mentioned in the Results, we found that researchers had been using 2 main ways to measure how often a prescriber accepted computer-generated advice: in-dialog action analysis and event analysis. Some studies explicitly conducted comparative analyses of the validity of the 2 methods.,,

As previously mentioned, when using in-dialog action analysis, the researchers dichotomize the actions taken inside a modal dialog: The prescriber either accepts the alert (eg, by clicking "Discard Order") or overrides it (eg, by clicking "Proceed Anyway"). We note 3 problems with this method's validity. First, those clicks provide a rather partial story of the order-for example, they do not account for possible corrections that the prescriber may take after responding to the alert. This is related to the second problem: Applying in-dialog action analysis to modals that feature action shortcuts may artificially inflate acceptance rates with respect to other modal dialogs, because more actions that would otherwise take place outside the dialog would instead take place inside the dialog. Third, in-dialog action analysis cannot be used with CDS interventions that do not offer decision-buttons to prescribers-these interventions must be studied with event analysis.

When using event analysis, the researcher additionally searches for corrective actions that the prescriber made after dismissing any alert, including a modal dialog. For example, the prescriber may change a dose, or switch to a narrow-spectrum antibiotic, after dismissing a modal dialog. These adjustments are taken as evidence of acceptance. This main problem with this method's validity is that there is no way to know whether the prescriber would have taken the same action if the intervention had not been delivered.

One might expect that in-dialog action analysis errs on the side of specificity (it systematically fails to recognize corrective actions), while event analysis errs on the side of sensitivity (it may misattribute some corrections to interventions). The evidence we gathered from 3 studies that compared these 2 methods,, suggest that this intuition is correct. These methods are biased, in a traditional sense: they produce results that predictably depart from the results that one would expect from the most accurate instrument imaginable.

Despite their limitations, we believe these methods to be valuable, as they seem to be the most cost-effective ways to capture data for CDS acceptance. However, we must caution that these methods produce results that are noncomparable. We suggest using event analysis, to enable rigorous comparisons between modal and modeless forms of CDS.

Appropriateness panel reviews,, were rare to see. We imagine these reviews to be particularly costly. Indeed, half of the included articles that reported an appropriateness review were from well-resourced academic institutions.

In fact, the scarcity of information that was useful in our review was surprising given the quantity of available CDS literature. Nearly 9 in 10 of the articles we excluded did not report prescriber acceptance, an important mediating variable between the technological intervention and patient outcomes that seems to have been assumed. Earlier, we described a homogeneity of CDS interventions in the reported literature-specifically, prescriber-interruptive modal dialogs comprised 5 in 6 included results. This also constrained the analyses that could be conducted with adequate statistical power. Finally, a roughly 50-50 split between 2 incomparable measurement methods precluded meta-analysis.

This review revealed several issues in the literature. Future work is needed to develop standardized, low-cost, informative measures for determining acceptance for CDS, and for relating CDS acceptance to patient outcomes. Next, we present some feasible recommendations for improving the quality of the CDS literature.

Recommendations for future work

Given the preceding discussion, we propose the following 3 recommendations for future CDS research:

First, we recommend that researchers consider alternatives to prescriber-interruptive modal dialogs, since there is evidence that the latter suffers from relatively lower acceptance. Role-based tailoring appeared to improve acceptance rates, and further work is needed in this area. Ideally, those who will receive the alerts should be involved in role-tailoring decisions. Alternatives to modal dialogs should also be explored.

Second, recommend measuring acceptance rates using event analysis, rather than in-dialog action analysis. Because event analysis is more widely applicable, using it will enable meta-analyses that accommodate varied CDS interventions.

Last, we recommend reporting both acceptance rates and patient outcomes. Much of the literature that we saw in our review reported one or the other; few reported both. This has made it difficult to analyze patient outcomes as a function of CDS design and role-tailoring, mediated by acceptance.

CONCLUSION

Alert fatigue remains a persistent challenge in CDS. Among prescriber-interruptive modal dialogs, acceptance rates have been highly variable. In our analysis, prescribers accepted alternative interventions more often-especially those which tailored CDS to the areas of expertise associated with clinical roles. Although there are plausible reasons why some alternative CDS interventions would improve acceptance, contemporary literature has not supported detailed analyses. We recommend that future studies pay more attention to alternative designs, measure acceptance using event analysis, and report patient outcomes as well as acceptance rates.

FUNDING

The project described was supported in part by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant (UL1 TR001414). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The project was also supported in part by Academic Senate Council on Research, Computing and Libraries (CORCL), and by the U.S. Department of Education (DoE), Graduate Assistance in Areas of National Need (GAANN).

Author CONTRIBUTIONS

Mustafa I. Hussain provided substantial contributions to conception, design, data acquisition, analysis, interpretation, and drafting. Tera L. Reynolds provided substantial contributions to the acquisition and analysis of data for the work, as well as critical revisions for important intellectual content. Kai Zheng provided substantial contributions to the conception of the work, and the interpretation of data for the work, as well as critical revisions for important intellectual content. All authors provided final approval of the version to be published, and have agreed to be accountable for all aspects of the work.

SUPPLEMENTARY MATERIAL

Supplementary material is available at Journal of the American Medical Informatics Association online.

ACKNOWLEDGMENTS

The authors would like to thank the Editors and Reviewers for their careful consideration of this work.

CONFLICT OF INTEREST STATEMENT

None declared.

REFERENCES

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Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review

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According to the most recent U.S. government reports, 1 in every 20 deaths in the United States has been attributable to an adverse drug event (ADE)., Many ADEs result from erroneous prescriptions. By the most conservative estimates, 1 in every 50 prescriptions is inappropriate. CDS can also fail to improve patient safety due to alert fatigue.

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This video describes first hand experience with alert fatigue among healthcare workers and the dangers it can cause for patient safety. Bob Wachter, MD goes on to discuss how many alerts providers and pharmacists get in a single day and how alert fatigue can lead to error. To reduce alert fatigue, it is discussed that software improvement should evaluate the alerts that are frequently overlooked and eliminate them to reduce alert fatigue. Often, these type of alerts do more harm than good which is a valid point.

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What Are the Dangers of Alert Fatigue? | IHI - Institute for Healthcare Improvement

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In a new IHI Open School short, patient safety expert Dr. Bob Wachter talks about the dangers of alert fatigue in health care.

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Week 11: Interoperability Improvements between EHR and Lab Information Management Systems

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Week 11: Interoperability Improvements between EHR and Lab Information Management Systems

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Shadowbox is a HIPPA compliant, innovative platform connecting electronic health records (EHR) to other systems used by providers. This automated platform is now being used by the largest PCR laboratory, HealthTrack Rx, allowing more quick and efficient, lab results. I found this article interesting because it is a software that can be used to create interoperability among many EHRs, while allowing, a safe and private exchange of patient information. Additionally, it also reduces the time providers previously required to exchange this patient data safely, reduces costs and allows faster results because of the efficiency and accuracy of the data provided.

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, July 13, 2022 /PRNewswire/ -- Shadowbox, a leading innovator in healthcare automation solutions, is excited to announce that HealthTrackRx, the nation's leading PCR laboratory, has selected Shadowbox to automate Electronic Health Records ("EHR") and Lab Information Management Systems ("LIMS") interoperability with their clinician customers, expanding their approach beyond traditional integration options.

"This is especially important as it relieves pressure on our IT team," said Guhan Raghu, HealthTrackRx COO

"Shadowbox tech makes sense," said Guhan Raghu, HealthTrackRx Chief Operating Officer. "We can set up a practice in a few minutes and the clinician can start ordering immediately. Orders are not only processed faster, they are also accurate and complete because the patient information is securely sourced from the patient chart in the clinic EHR. The increased velocity, higher volume of clean claims, and reduced cost significantly improves our bottom line while aiding in client retention."

Shadowbox is a patented, HIPAA compliant application that connects web-based EHRs with other healthcare applications to automate workflow and streamline the exchange of sensitive patient information. Shadowbox simplifies the lab ordering process by providing a lab-branded application that automates the real-time transfer of complete patient data as part of an electronic order.

"There's no silver bullet when it comes to interoperability, but this is a massive step in getting information exchanges set up quickly," said Mr. Raghu. "This is especially important as it relieves pressure on our IT team, allowing them to focus on other priorities instead of repeatedly burning time on sequential 6-month clinic-by-clinic integration projects."

"We are especially proud of our partnership with HealthTrackRx," said Greg Stein, Shadowbox CEO. "We have expanded our product offering to meet the needs of HealthtrackRx and their customers, rigorously testing and enhancing the functionality of our platform to improve its utility."

Shadowbox is a ground-breaking, patented integration and automation platform built for healthcare. By powering a browser with security, AI, and user-driven cross-application connections, Shadowbox offers instant integration and automation across the healthcare ecosystem. For more information see www.shadowbox.com.

About HealthTrackRx

HealthTrackRx is the nation's leading PCR-based infectious disease laboratory, delivering industry-leading testing turnaround times to clinicians nationwide. For more than 20 years, the company has mobilized accurate clinical decisions through advances in pathogen testing, antimicrobial stewardship, and value-based care programs. HealthTrackRx is among a limited number of labs in the U.S. granted FDA Emergency Use Authorization for Molecular Laboratory Developed (LDT) COVID-19 Authorized Test. For more information, visit HealthTrackRx at healthtrackrx.com and LinkedIn.

View original content to download multimedia: https://www.prnewswire.com/news-releases/healthtrackrx-selects-shadowbox-to-enhance-ehr-interoperability-301585329.html

SOURCE Shadowbox, Inc.

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HealthTrackRx selects Shadowbox to Enhance EHR Interoperability

# data[].attributes.attachment_link.description
Shadowbox, a leading innovator in healthcare automation solutions, is excited to announce that HealthTrackRx, the nation's leading PCR laboratory, has selected Shadowbox to automate Electronic Health Records ("EHR") and Lab Information Management Systems ("LIMS") interoperability with their clinician customers, expanding their approach beyond traditional integration options.

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Week 12: Data Mining with Machine Learning

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Week 12: Data Mining with Machine Learning

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This article includes details on 5 current machine learning applications that allow data mining of medical records. Among the article are also useful videos with further description on the different applications discussed. One of the application examples I found most interesting was the CareSkore platform which allows for improved patient engagement by analysis patient data based on personaized health data in the EMR, insurance claims and patient demographics. This data then allows providers to make more personalized care plans for better patient management and outcomes.

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Data Mining Medical Records With Machine Learning - 5 Current Applications

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Our research of healthcare industry applications explores how artificial intelligence and data mining could draw useful information form massive data sets.

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Week 12: Artificial Intelligence & Machine Learning

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Week 12: Artificial Intelligence & Machine Learning

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I found this news update important because it discusses the innovations for a major company, Parexel, which utilizes artificial intelligence and machine learning for drug development and life sciences. Recently they acquired a natural language processing technology through Roam Analytics software company which will further strengthen their capabilities as a company. In acquiring these new capabilities, Parexel will be able to provide safer, more efficient and cost effective treatments options for patients.

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Parexel has completed the purchase of the Natural Language Processing (NLP) technology assets and transfer of key personnel of Roam Analytics, Inc., a healthcare software company based in San Francisco, CA. This strategic acquisition strengthen Parexel's Artificial Intelligence (AI) and Machine Learning (ML) capabilities to drive new innovations across drug development and life sciences. Financial terms were not disclosed.

It also enhances Parexel's Pharmacovigilance and other Real-World Data capabilities through the application of NLP technology across the company's various data sets to further streamline and extract insights from unstructured data.

Roam Analytics' technology platform enables organizations to leverage any source of healthcare language data, from clinical notes to patient surveys, regulatory documents, marketing materials, telemedicine interactions, social media and more, to improve operational workflows.

"Over the course of our partnership with Roam Analytics, its technology has shown exceptional strength and reliability in not only reading a wide range of clinical data sources, but also in annotating and categorizing data for leading life sciences companies that we serve," said Michelle Hoiseth, Chief Data Officer, Parexel. "By integrating the Roam Analytics' technology into Parexel's suite of services, we'll be able to provide customers with an added level of safety and security and pursue our aim to reduce the time and cost of developing new treatments and get them in the hands of patients sooner."

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Parexel Acquires Roam Analytics' NLP Capability

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Parexel has completed the purchase of the Natural Language Processing (NLP) technology assets and transfer of key personnel of Roam Analytics, Inc., a healthcare software company based in San Francisco, CA. This strategic acquisition strengthen Parexel's Artificial Intelligence (AI) and Machine Learning (ML) capabilities to drive new innovations across drug development and life sciences.

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Week 14: Ethics & Artificial Intelligence 

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Week 14: Ethics & Artificial Intelligence 

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This article discusses the ethical issues with artificial intelligence in healthcare, specifically with medical imaging. Artificial intelligence has allowed for many medical advances but unfortunately, bias can be seen through these computer analysis of certain sub populations. Specifically, with many vulnerable populations, the bias can be seen with certain artificial intelligence algorithms. The article discusses some mitigation strategies used to allow for a more just system analysis but the issues continues to require awareness.

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A plethora of work has shown that AI systems can systematically and unfairly be biased against certain populations in multiple scenarios. The field of medical imaging, where AI systems are beginning to be increasingly adopted, is no exception. Here we discuss the meaning of fairness in this area and comment on the potential sources of biases, as well as the strategies available to mitigate them. Finally, we analyze the current state of the field, identifying strengths and highlighting areas of vacancy, challenges and opportunities that lie ahead.

With the exponential growth in the development of artificial intelligence (AI) systems for the analysis of medical images, hospitals and medical centers have started to deploy such tools in clinical practice1. These systems are typically powered by a particular type of machine learning (ML) technique known as deep learning (DL). DL methods learn complex data representations by employing multiple layers of processing with different levels of abstraction, which are useful to solve a wide spectrum of tasks. In the context of medical image computing (MIC), examples of such tasks include pathology classification, anatomical segmentation, lesion delineation, image reconstruction, synthesis, registration and super-resolution, among many others2. While the number of scientific publications related to DL methods applied to different MIC problems in laboratory conditions has grown exponentially, clinical trials aimed at evaluating medical AI systems have only recently started to gain momentum. In fact, according to the American College of Radiology, to date less than 200 AI medical products related to radiology and other imaging domains have been cleared by the United States Food and Drug Administration3.

Recently, the research community of fairness in ML has highlighted that ML systems can be biased against certain sub-populations, in the sense that they present disparate performance for different sub-groups defined by protected attributes such as age, race/ethnicity, sex or gender, socioeconomic status, among others4,5.

In the field of healthcare, the potential unequal behavior of algorithms towards different population sub-groups could even be considered to go against the principles of bioethics: justice, autonomy, beneficence and non-maleficence6. In this context, fostering fairness in MIC becomes essential. However, this is far from being a simple task: ensuring equity in ML deployments requires tackling different and multiple aspects along the whole design, development and implementation pathway. While the implications of fairness in ML for the broad field of healthcare have recently been surveyed and discussed7, in this comment we focus on the sub-field of medical imaging. Indeed, when it comes to biases in ML systems that can benefit certain sub-populations in detriment of others, the field of medical imaging is not the exception8,9. In what follows we will comment on recent work in the field and highlight valuable unexplored areas of research, discussing potential challenges and available strategies.

What does it mean for an algorithm to be fair?

Let us start by considering this question in the context of patient sub-groups defined by skin tone or race/ethnicity, where a number of recent articles have compared the performance of MIC systems for suspected ophthalmologic, thoracic and/or cardiac pathologies. For example, when it comes to diagnosing diabetic retinopathy, a severe imbalance in the data used to train a model may result in a strong gap in the diagnostic accuracy (73% vs. 60.5%) for light-skinned vs. dark-skinned subjects10. In the same vein, it has been detected that models fed with chest radiography for pathology classification have a higher rate of underdiagnosis for under-served sub-populations, including Black patients9, so that the use of these tools could increase the probability of those patients being sent home without receiving the care they need. Lower performance of AI models designed for cardiac MRI segmentation (in terms of Dice coefficient) in this group has also been found11, which may result in compound biases if any further diagnostic analysis were required to be done on the automatically delineated silhouette.

After reading these examples, we immediately and automatically recognize these situations as unfair. However, establishing a criterion to determine whether an algorithm can be called fair is actually a thorny issue. In the previous paragraph we have purposely mentioned examples where different metrics were employed in each case. Indeed, the first issue one encounters is that a large number of candidate measures exist. One can for instance evaluate fairness by comparing standard ML performance metrics across different sub-groups, such as accuracy10,12,13,14,15,16, or AUC ROC (the area under the receiver operating characteristic curve)8,9,10,14,15,16,17,18,19,20,21,22, among others. Alternatively, one can choose to employ one of the (no less than ten) different fairness-specific criteria formulated by the community23 in order to audit the presence of bias in a given model16,18. To complicate matters further, even if one carries out a multi-dimensional study by simultaneously employing multiple metrics9,10,14,15,16,20,21,24, which model to select at the end in a given setting might be no trivial matter and additional information will in general be required. Along these lines, on those occasions when the prevalence of the target condition is different between sub-groups (Fig. 1, top row), special care must be taken in the selection of the fairness definition to be used25. For example, the demographic parity criterion (Fig. 1, bottom row, right side) which requires equal chances of positive predictions in each group, would here suggest the algorithm is unfair for presenting a higher probability of a positive result for the sub-group with a greater target condition prevalence. This criterion assumes that the prediction of an algorithm is independent of the protected attribute that defines each sub-group, so it may be suitable in settings such as loan eligibility prediction or hiring for job vacancies, but not for disease prediction cases where the prevalence depends on the aforementioned attribute. In these cases, it would be more appropriate to resort to definitions such as the equal opportunity criterion (Fig. 1, bottom row, right side), which will compare the equality of true positive rates between sub-groups whose computation is independent of the pre-test probability. Overall, it becomes clear that a one-size-fits-all definition of fairness in MIC will not exist.

Here we include a toy-example in the context of disease classification, where two sub-populations characterized by different protected attributes (in red and blue) present different disease prevalence (40% and 20% for blue and red subjects respectively, top row, x marks positive cases). A model optimized for discriminative performance was assessed on a test set achieving 100% accuracy (bottom row left side, + marks positive predictions). Algorithm fairness was audited according to two common metric choices (bottom row, right side). In this case, as a consequence of the difference in disease frequency, the model would not fulfill the demographic parity criterion (bottom row, right side) since the positive prediction rates vary between sub-groups : 40% (8 positive predictions over 20 cases) for the blue sub-group vs. 20% (4 positive predictions over 20 cases) for the red sub-group. On the other hand, the model would fulfill the equal opportunity criterion, as true positive rates match for both sub-groups reaching the value of 100%: 8 true positives out of 8 positive ground truth cases for the blue sub-group and 4 true positives out of 4 positive ground truth cases for the red sub-group . FN false negatives, FP false positives, TN true negatives, TP true positives. See legend-box with symbols on the top right corner.

Three reasons behind biased systems: data, models and people

Providing effective solutions to disparities in the outcomes of AI systems starts by identifying which may be their underlying causes (Fig. 2). The lack of diversity and proper representation of the target population in the training databases has been identified as one of the main reasons behind this phenomenon4 (Fig. 2①). In the context of MIC, ML systems are trained using big databases of images, usually accompanied by annotations or labels indicating the desired output that we expect from the system (e.g., X-ray images with labels associated with the radiological finding of interest like pneumonia or cardiomegaly). When the demographics of such databases do not match that of the target population, the trained model may be biased, presenting lower performance in the underrepresented groups11. Indeed, in chest X-ray pathology classification, only few of the major available datasets in that domain include information about race/ethnicity and, in cases where this information is included, databases tend to be skewed in terms of those attributes26.

The data being fed to the system during training (1), design choices for the model (2), and the people who develop those systems (3), may all contribute to biases in AI systems for MIC.

One point to keep in mind is that a ML system violating one particular definition of fairness should not necessarily be considered biased. In this sense, the selection of appropriate metrics to assess and ensure fairness according to the specific use case is a delicate task that requires careful human intervention. Moreover, such a choice will also be conditioned by the fact that some of these metrics are mutually exclusive27, implying that, for example, building a classifier to be simultaneously maximally fair in terms of outcomes, opportunities and calibration will not be feasible most of the time. In addition, other choices related to model design, such as the architecture, loss function, optimizer or even hyper-parameters, may also play a fundamental role in bias amplification or mitigation28 (Fig. 2②). The same happens with sampling criteria for database construction. For the above reasons, if decisions are made exclusively by developers, engineers, medical specialists, or data scientists in isolation, or by groups of people who share the same ethnic or social background, there is a risk that their own biases may be unintentionally incorporated into the system based on what they choose to prioritize (Fig. 2 ③).

Taking a step back, complex structural reasons for bias need also be taken into account. We highlight some of these here (see ref. 7 for an in depth analysis). Unequal treatment of patients, as well as disparate access to the healthcare system due to economic inequalities conspires against investigating certain pathologies in underrepresented populations. Anatomical differences and even variability in the manifestation of diseases across sub-groups can moreover act as confounders. Likewise, many health problems of particular relevance to low income countries are often understudied due to lack of research funding in those countries. Finally, while auditing systems for potential biases, people may unintentionally only search within the possibilities and the reality with which they are familiar.

Bias mitigation strategies

Several studies in recent years have proposed solutions to mitigate bias and develop fairer algorithms10,11,14,15,16,17,19,20,24. There are three main stages at which bias mitigation strategies can be adopted11: before, during and after training. Before training, one would ideally seek to rebalance datasets by collecting more representative data (Fig. 2①). However, in the medical context this is far from trivial as this process requires patients giving consent to their data being used for research purposes as well as the involvement of specialists analyzing each case and providing ground truth labels. Moreover, the low prevalence of certain conditions might hinder finding sufficient examples. In this sense, a compromise solution involves removing features linked to sensitive information, or the use of data resampling strategies. During training, several alternatives exist to mitigate model biases (Fig. 2②), such as the use of data augmentation10,14,19 and adversarial training17,20,24, with the combination of both having even been employed15. The use of generative methods as a way to augment the dataset, for instance, has proven effective in reducing the disparity in the diagnostic accuracy of diabetic retinopathy between light-skinned and dark-skinned individuals10. On the other hand, adversarial schemes have been shown to reduce biases in skin lesion classification24. In this case, adversarial methods intend to increase the performance of a primary model on the target variable while minimizing the ability of a second (adversarial) model to predict the protected attribute from the features learned by the primary model23. Finally, after training, model outcomes can be post-processed so as to calibrate the predictions across the different sub-groups. These methods focus on the second reason behind biased systems we mentioned before, namely models.

It must be noted, however, that methods designed to improve algorithmic fairness may lead in practice to different outcomes. In the best-case scenario, applying bias mitigation strategies increases the performance of the algorithm for all sub-groups14, posing no additional constraints. At the other end of the spectrum, a reduction in the performance for all sub-groups may result from trying to achieve algorithmic fairness17. Indeed, interventions to achieve group fairness may result in tensions with the primary goal of the algorithms, requiring a compromise solution. This outcome poses a dilemma in healthcare settings, since it could be interpreted to violate the principles of bioethics, specifically that of non-maleficence. These two extremes are however rare, and a frequent outcome observed in the existing MIC fairness studies analyzed in this article, is performance improvement for the disadvantaged group at the expense of a reduction for another group or groups11. This trade-off is also not free of controversies, and once again we find ourselves in a situation where the decision of what is acceptable in a given setting requires careful human consideration. That is why, as discussed in the previous section, diversity is key not only in terms of databases, but also in team composition (Fig. 2③). Hence, considering participatory design practices that explicitly incorporate perspectives from a diverse set of stakeholders29 is a fundamental aspect to consider when dealing with algorithmic bias.

Challenges and outlook for fairness studies in MIC

Even though the field has been steadily growing over the past few years, there are still challenges and open research questions that we believe need to be addressed.

Areas of vacancy

While this growing trend is highly encouraging, the efforts have been far from even across the landscape of medical specialties and problems being tackled, leaving several areas of vacancy. Firstly, so far algorithmic justice analysis has mostly been carried out in four medical imaging specialties: radiology8,9,16,18,19,20,21,22, dermatology12,13,17,19,24, ophthalmology10,14,15 and cardiology11. We believe that this uneven coverage is partly due to the limited availability of MI databases with demographic information on the population (Table 1), something which has been highlighted in several previous studies8,17. The absence of this information may be related to the trade-off between data utility and privacy when releasing public databases, in the sense that including sensitive attributes useful for bias audit may go against the privacy of the individuals. To overcome these limitations, the implementation of technical solutions to simultaneously address the demands for data protection and utilization becomes extremely important30. Moreover, it must be noted that the subset of sensitive attributes either directly reported or estimated varies from dataset to dataset. The currently most widely reported characteristics are age and sex or gender16,20,31,32,33,34,35,36,37,38, followed by skin tone or race/ethnicity13,16,20,33,34,37,38, and to a lesser extent socioeconomic characteristics33,38. In some cases, where protected attributes are not available, estimates can be computed using image processing methods12,13,15,19,24, and eventually manual labeling by professionals can be used10,13. These strategies bring with them however an additional level of complexity and subtlety in their implementation which can limit reproducibility and comparison of results across sub-groups.

Table 1 Databases commonly used in fairness in MIC studies

Secondly, important vacancies exist regarding the MIC task to be tackled. The vast majority of studies conducted to date deal with pathology classification tasks8,9,10,12,13,14,15,16,17,18,19,20,21,22,24. The study of fairness in the context of segmentation is however rare11, and those of regression, registration, synthesis and super-resolution are rarer still, leaving entire areas to be explored.

Incorporating fairness audits as common practice in MIC studies

As highlighted by a recent article17 which analyzed the common practices when reporting results for diagnostic algorithms in one of the major conferences on MIC, demographics are rarely mentioned, and disaggregated results are infrequently discussed by scientific publications in this domain. This matter is also addressed by the FUTURE-AI Guidelines39, which include principles and consensus recommendations for trustworthy AI in medical imaging, and not only focus on fairness but also cover other fundamental dimensions like universality, traceability, usability, robustness and explainability. In that sense, we believe the FUTURE-AI guidelines may constitute a practical tool to improve the publication practices of our community.

Increasing diversity in database construction

As researchers working in Latin America, we want to stress the importance of widening geographic representation in the building of publicly available MI datasets. It has been acknowledged by several studies that the vast majority of MI databases employed for AI developments originate from high income countries, mostly in Europe and North America40,41,42. This introduces a clear selection bias since the demographics of these countries do not match that of other areas like Africa, Asia or Latin America. This fact, combined with experimental studies suggesting that race/ethnicity imbalance in MI databases may be one of the reasons behind unequal performance11, calls for action towards building truly international databases which include patients from low income countries. This issue becomes even more relevant in the light of recent findings which confirm that AI can trivially predict protected attributes from medical images, even in a setting where clinical experts cannot like race/ethnicity in chest X-ray26 and ancestry in histologic images43. While this fact by itself does not immediately mean that systems will be biased, in combination with a greedy optimization scheme in a setting with strong data imbalance, it may provide a direct vector for the reproduction of pre-existing racial disparities.

In this regard, initiatives such as the All of Us Research Program, which invite participants from different sub-groups in the United States to create a more diverse health database, hope to promote and improve biomedical research, as well as medical care44. Efforts such as this one, currently focused on an individual country, could be replicated and lay the groundwork for a collaborative enterprise that transcends geographic barriers.

Rethinking fairness in the context of medical image analysis

For some time now, research on fairness in ML has been carried out in decision-making scenarios such as loan applications, hiring systems, criminal behavior reexamination, among others23. However, the field of healthcare in general, and medical imaging in particular, exhibit unique characteristics that require adapting the notion of fairness to this context. Take chest X-ray images for example: particular diagnostic tasks could be easier in one sub-population than the other due to anatomical differences45. How to ensure fairness across sub-populations in this case is far from obvious.

Another example is that of existing bias mitigation strategies which may result in reducing model performance for the majority, or even all sub-populations, in exchange for reducing the variance across them. This might be admissible in other contexts, but in the case of healthcare this implies purposely deteriorating the quality of the predictions for a given sub-group, causing ethical and legal problems related to the provision of alternative standards of care for different sub-groups21. Moreover, how to define such sub-groups is already an open question: the group-fairness framework, usually applied in problems like loan granting or intended to deal with legal notions of anti-discrimination, reinforces the idea that groups based on pre-specified demographic attributes are well-defined constructs that correspond to a set of homogeneous populations29. However, certain attributes like gender identity46, are fluid constructs difficult to categorize which require rethinking this framework. Similar issues may arise when using race or ethnicity47 as protected attributes to define groups of analysis and evaluate fairness metrics.

While some factors influencing fairness and model performance metrics such as target class imbalance are common to several ML domains, others such as differences in disease prevalence across sub-populations have to be carefully taken into consideration when it comes to MIC. The same holds for the cognitive biases that may be introduced by medical specialists when interpreting and annotating imaging studies48. While AI has been postulated as a potential tool to help out in reducing such biases, if not properly addressed, it could also become a mean to amplify and perpetuate them.

Overall there is no denying that the nascent field of fairness in ML studies for MIC still presents important vacancies both in terms of medical specialties and in terms of the types problems being tackled, which will require increased efforts from the community. However, the rapid growth of the field, the development of new guidelines, and the gain of attention reported here, are highly positive and encourage the MIC community to increase its effort to contribute towards delivering a more equitable standard of care.

References

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Acknowledgments

We thank the Fundar foundation for supporting M.A.R.L. with a FunDatos Scholarship and the Program for Artificial Intelligence in Health at Hospital Italiano de Buenos Aires for providing the space to discuss and work on these issues. This work was supported by Argentina's National Scientific and Technical Research Council (CONICET), who covered the salaries of R.E. and E.F. The work of E.F. was partially supported by the ARPH.AI project funded by a grant (Number 109584) from the International Development Research Center (IDRC) and the Swedish International Development Cooperation Agency (SIDA). We also acknowledge the support of Universidad Nacional del Litoral (Grants CAID-PIC-50220140100084LI, 50620190100145LI), Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación (Grants PICT 2018-3907, PRH 2017-0003) and Santa Fe Agency for Science, Technology and Innovation (Award ID: IO-138-19).

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Author notes

These authors contributed equally: Rodrigo Echeveste, Enzo Ferrante.

Authors and Affiliations

1. Health Informatics Department, Hospital Italiano de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina

María Agustina Ricci Lara

2. Universidad Tecnológica Nacional, Ciudad Autónoma de Buenos Aires, Argentina

María Agustina Ricci Lara

3. Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Santa Fe, Argentina

Rodrigo Echeveste & Enzo Ferrante

Contributions

E.F. provided the initial concept for this article, which was further developed by all authors. M.A.R.L. conducted the literature search and performed the systematic analysis across areas of application, methods as well as strengths and vacancies. M.A.R.L. and R.E. produced the figures. R.E. and E.F. supervised the analysis. All authors wrote the paper.

Corresponding authors

Correspondence to María Agustina Ricci Lara, Rodrigo Echeveste or Enzo Ferrante.

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Ricci Lara, M.A., Echeveste, R. & Ferrante, E. Addressing fairness in artificial intelligence for medical imaging. Nat Commun 13, 4581 (2022). https://doi.org/10.1038/s41467-022-32186-3
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Addressing fairness in artificial intelligence for medical imaging - Nature Communications

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A plethora of work has shown that AI systems can systematically and unfairly be biased against certain populations in multiple scenarios. The field of medical imaging, where AI systems are beginning to be increasingly adopted, is no exception.

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Week 14: Ethical Considerations in Healthcare Informatics

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Week 14: Ethical Considerations in Healthcare Informatics

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This article discusses the importance of ethics as it applies to informatics in healthcare. I found this article to be important because it presented the specific issues surrounding the advances in medical technologies through the field of informatics. Specifically, the article goes into detail on the ethical dilemma on informatics creating more convenience for healthcare workers and maintaining quality care. We know that the advances in technology can assist with better care but it is important to understand the pitfalls in these conveniences as well.

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Informatics Ethics - StatPearls - NCBI Bookshelf

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Ethics are inherent in the practice of medicine. In the world of modern healthcare, the area of informatics plays a pivotal role in the maintenance and delivery of care.[1] As such, over recent years informatics ethics has further come into the spotlight. Similar to other codes of ethics, informatic…

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Week 13: Visualization Using Deep Learning & Fisher Information Networks

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Week 13: Visualization Using Deep Learning & Fisher Information Networks

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This article discuss the importance of visualizing patient health data using deep learning as a subset of machine learning algorithms to further identify predictability of disease in certain populations. The issue with this is that the clinical data is given with no explanation on how the machine arrived at this result, specifically with diagnosing breast cancer. Through using the Fisher information metrics, visualization of the data has been an additional bonus to reviewing the information sourced from these algorithms.

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Abstract

Breast cancer is the most commonly diagnosed female malignancy globally, with better survival rates if diagnosed early. Mammography is the gold standard in screening programmes for breast cancer, but despite technological advances, high error rates are still reported. Machine learning techniques, and in particular deep learning (DL), have been successfully used for breast cancer detection and classification. However, the added complexity that makes DL models so successful reduces their ability to explain which features are relevant to the model, or whether the model is biased. The main aim of this study is to propose a novel visualisation to help characterise breast cancer patients using Fisher Information Networks on features extracted from mammograms using a DL model. In the proposed visualisation, patients are mapped out according to their similarities and can be used to study new patients as a 'patient-like-me' approach. When applied to the CBIS-DDSM dataset, it was shown that it is a competitive methodology that can (i) facilitate the analysis and decision-making process in breast cancer diagnosis with the assistance of the FIN visualisations and 'patient-like-me' analysis, and (ii) help improve diagnostic accuracy and reduce overdiagnosis by identifying the most likely diagnosis based on clinical similarities with neighbouring patients.

Introduction

Breast cancer is the most commonly diagnosed female malignancy globally with over two million women diagnosed annually with 685,000 deaths1. Outcomes from breast cancer are linked to the stage at diagnosis, with early diagnosis associated with better survival rates2. The principle underpinning screening for breast cancer is that detecting the disease at an early stage and the institution of effective management, treatment and follow-up earlier in the disease process will improve the patients' outcomes. The NHS breast cancer screening programme commenced in 1988 following the Forrest report3 and is based on three-yearly mammography to detect changes in the breast tissue that may indicate the presence of cancer. The potential benefits and the harms of mammography, particularly overdiagnosis, have been extensively discussed, most recently in the Marmot report4.

Magnetic resonance imaging (MRI) can also be used to detect breast cancer and it is a more comprehensive test used for screening, recommended especially for high-risk patients. However, a breast MRI has a much higher false-positive rate than mammography as they highlight both cancerous and benign tumours with little difference in the characteristics of both, triggering more tests and biopsies. Breast MRIs are also costlier than mammograms and are less available. The UK's National Institute for Health and Care Excellence does have a stratified surveillance programme for patients in different risk groups (including breast density) on who should be offered a mammogram, an MRI or both5. Having a mechanism helping to elucidate further insights from mammograms to reduce overdiagnosis as well as missing cancers would be highly beneficial, especially before the need to request a biopsy.

The application of Machine learning (ML) in healthcare, and particularly breast cancer classification, has proven to be successful. Various studies in the problem area6,7,8,9,10,11 have shown the suitability of ML to discriminate between tumours and classify effectively in a manner of approaches. More recently, models developed12,13,14 using deep learning (DL), a subset of ML algorithms, have shown the capability to attain strong predictive outcome measures, although at the expense of sacrificing the ability to explain how the model arrived at its conclusions or was making its decisions.

Convolutional Neural Networks (CNN) are one of the most representative DL algorithms, extensively used in many aspects of medical image analysis, and allowing for great progress in this area in recent years15. CNNs consist of multiple layers of artificial neurons, each of them extracting features that are passed to the next layer for more complex representations or features, which eventually will enable the recognition of objects, etc. This work studies features that are extracted from the deepest layer (before the dense layer) of a CNN model, called convolutional features. This is done to take advantage of the CNN model's effective representation of the information, which has led to highly accurate prediction results reported in this area14,16,17,18,19,20.

As ML models become more complex, such as CNN models, the level of explainability reduces, i.e. it becomes more difficult to understand what variables/features the model is taking into account to make a prediction, or whether the model is biased. Common approaches such as Saliency Maps and GradCAM have been used to help understand which parts of an image a CNN model found of interest to make a prediction. However, recent studies21,22 have reported that the evaluation of these techniques in carefully-designed user studies has lagged behind technique development, in some cases providing explanations that users cannot understand or find too complex.

Understanding how a model makes predictions is especially relevant in healthcare applications, and it is a major driver for clinical take-up, enabling clinicians to accept and trust these technologies so that they become part of routine clinical practice23. Hence our focus in this work is to propose a novel method that provides that explanation, not in the form of saliency features, but instead as a smart representation of the complete dataset in a low-dimensional space, that can provide intelligence about a specific data point based on its neighbouring data points. This is what we call a 'patient-like-me' approach.

The proposed patient-like-me approach could help not only to identify the most likely patient's diagnosis but even potentially the patient's prognosis based on the clinical similarities with some other patients. It could also inform the possible course of treatment, as it would allow checking the kind of treatments or therapies that were (and were not) successful for those nearby cases, playing a role in the decision-making process of a clinician. Lastly, it could help reduce overdiagnosis as well as missing cancers.

To achieve this, our method involves the use of the extracted convolutional features to form new input data to train a Multi-Layer Perceptron (MLP). This is then followed by the creation of a Fisher Information (FI)24,25,26 network using probability density estimates over the classes involved in the study, used to inform a learning metric (FI metric) in a latent variable space. The FI metric measures dissimilarity for small changes between the data points according to their degree of relevance with respect to class membership. The resulting visualisation of the latent space can be used to project the current and future observations, enabling the analysis of individual cases in the context of all the other cases. Used in a medical application, this can be used to provide a representation for all patients and to enable the study of individual ones, which enhances the clinical understanding, confidence, and take-up.

This global view of the data derived by the FI metric displays a meaningful structure that implicitly informs about underlying class probabilities. The FI network framework, which has been successfully used in previous studies27,28,29,30, can be used not only to create a meaningful neighbourhood mapping to visualise data but also to create an interpretable retrieval-based classifier, as connection weights contain accurate information about data points' similarity. For the proposed methodology, an FI network is constructed using probability density estimates, which are calculated across five classes from a model created by Shen et al.16, to produce a visualisation of the latent space of breast cancer patients using convolutional features extracted from mammography images. Shen's mammogram patch model discriminates between five classes-four lesions and one image background-and is used to inform a transfer learning model on full images over two classes detecting the presence of cancer or not in a full mammogram.

We expect that a visualisation of the latent space obtained with the FI network will help elucidate the underlying data structure, with the ultimate aim of assisting the diagnosis of new patients. The latter can be achieved by projecting new unseen instances/observations into this latent space, given that a huge amount of information can be learnt from the closest neighbours, which would be potentially relevant to those new patients. The proposed approach will be compared against a new state-of-the-art method called uniform manifold approximation and projection (UMAP)31, which has been reported32 to be more efficient than t-distributed stochastic neighbourhood embedding (t-SNE)33 in terms of computation time and data representation.

For the analysis and evaluation of the proposed method, we used an existing, publicly available dataset of mammograms, namely the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM)34. This dataset has been used previously for classification tasks14,16,17,18,35,36,37,38, for which the primary focus is to attain strong predictive performance, achieved using DL.

The aims of this study are, from the methodological point of view, to:

1. 1.
Propose a novel visualisation of a meaningful neighbourhood mapping containing accurate information about data points' similarities that can provide intelligence about neighbouring data points.

2. 2.
Ensure that the resulting visualisation can be used for training and independent test cases, enabling the analysis of future observations in the context of all the other cases.

And from the clinical point of view, to:

1. 1.
Apply this methodology to breast cancer data and assess whether the visualisation of all patients can assist the study of individual ones ('patient-like-me' approach).

2. 2.
Facilitate the analysis and decision-making process in breast cancer diagnosis with the assistance of this novel visualisation and help improve diagnostic accuracy and reduce overdiagnosis.

Methods

Description of the dataset

In this study, we used a publicly available dataset, the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM)34, which is a standardised version of the Digital Database for Screening Mammography (DDSM)39. The DDSM is a rich mammography database, containing over 2000 scanned film mammography studies with verified pathology information from 1246 women. The CBIS-DDSM includes a subset of the DDSM data selected and curated by trained mammographers. Within the dataset, updated regions of interest (ROI) from the images have been provided and the pathologic diagnosis is also included.

For this dataset, both craniocaudal (CC) and mediolateral oblique (MLO) views are available for most of the exams, which are the views usually used for routine screening mammography. It also contains associated metadata for each lesion, including the BI-RADS assessment (BI-RADS stands for Breast Imaging Reporting and Data System). The latter is traditionally used to assess lesions found in different mammography images into categories, depending on their severity. BI-RADS score ranges from 0 to 6, however in this dataset, only scores from 0 to 5 are available. This dataset contains both breast masses and calcifications and holds a pathology label for each case, which is either benign or malignant with verified pathology information. We used the same split separation of training and test cases followed by Shen et al.16, which used an 85-15 split at the patient level, stratified to maintain the same proportion of cases per class in the training and test sets.

Visualising the latent space of breast cancer patients-proposed methodology

The main objective of this study is to create a visualisation of the latent space of breast cancer patients that represents the variability that can be found in the dataset, from which we gain insights on how different patients can be related, leading to the development of a 'patient-like-me' approach. To achieve this, we propose a methodology illustrated in Fig. 1.

Proposed methodology. Utilising the FIN to create a visualisation of the latent space of cancer patients, exploiting the predictive capabilities of a deep learning model.

Extracting CNN features and developing MLP model

To study a CNN classifier as proposed, this work required a strongly performing deep learning model. The Resnet50-based model40 is a 5-class patch classifier, trained on patches of mammograms around the lesion. Using the CBIS-DDSM dataset, the five classes are: calcification malignant, calcification benign, mass malignant, mass benign and (image) background. It is used later in Shen's work to develop a new classifier through transfer learning to inform a 2-class full image model, discriminating between the presence or not of cancer. A description of any data pre-processing, how the patches were attained and the curation of the five classes is described in Shen et al.16.

This method can be computationally expensive and therefore to reduce the extracted 2,048 extracted CNN features, PCA has been used for dimensionality reduction. Out of the 2048 CNN features, 1,363 principal components are kept after applying PCA, representing nearly 67% of the original number of features, and capturing 90% of the variance. This CNN is used as a feature extractor in this work-the outputs of the penultimate layer are extracted and used as the dataset for this work.

Using the CNN features as the input dataset, an MLP was developed to discriminate between the five classes. The MLP was initialised with random weights, one hidden layer of 30 nodes and an output layer, a learning rate of 0.01 and a momentum of 0.9. Weight decay regularisation was implemented at a rate of 0.2. As a five-class classifier, the activation function was soft-max.

Creating the latent space of breast cancer patients

Firstly, the FI metric is derived using the probability densities of the classes estimated with the MLP and is then used to calculate pairwise distances. The FI is usually defined in terms of the joint distribution of data values, x, and a parameter set, \(\theta\), by integrating over the data to obtain a function of a given parameter. In Ruiz et al.26 this is reversed and the posterior probability of class membership \(p\left(c|x\right)\), estimated with an MLP, forms the basis to calculate the FI by summing over both classes, so that it is a function of a given data point, x, as follows (see Eq. 1):

$$FI\left( x \right) = \mathop \sum \limits_{c} \left( {\nabla_{{\text{x}}} {\text{log}} p\left( {c|x} \right)} \right)\left( {\nabla_{{\text{x}}} {\text{log}} p\left( {c|x} \right)} \right)^{T} p\left( {c|x} \right) = - \mathop \sum \limits_{c} \nabla_{{\text{x}}}^{2} {\text{log}} p\left( {c|x} \right)p\left( {c|x} \right)$$

where \({\nabla }_{x}\) is the gradient with respect to \(x\). The theoretical justification for this equation is explained in Appendix A of Ruiz et al.26. It has the standard form of the FI, expressed in two equivalent ways.

It is well known that the FI matrix is a metric which therefore generates a Riemannian space over the range of the input data. This forms the basis for using the posterior probability to map the data into a structure based on similarity, which is measured locally by the quadratic differential form in Eq. 2:

$$d\left( {x,x + \Delta x} \right)^{2} = \Delta x^{T} FI\left( x \right)\Delta x$$

The distance between a pair of data points is ideally measured along a geodesic, but this is computationally very expensive. It is shown in Ruiz et al.26 that a good approximation is obtained in a computationally efficient manner with the Floyd-Warshall algorithm41,42. This involves calculating the shortest path between distant points by hopping from each data point to a nearest neighbour, estimating the geodesic distance between neighbouring points by integrating ( 2) over a straight-line path, for which an analytical expression is given in Ruiz et al.26 (this is Eq. 11 in the referred study26).

Using the distance matrix, the similarity matrix is then calculated through the use of a Gaussian radial kernel, which produces an adjacency matrix that defines the network structure (see Eq. 3).

$$A = {\text{exp}}\left( { - \frac{{\Delta x^{2} }}{{\sigma_{G}^{2} }}} \right)$$

where the Gaussian kernel width σ G is a natural length which was taken to be the average pairwise geodesic distance between points belonging to the same predicted label.

Multidimensional scaling is utilised to represent the data in a low-dimensional Euclidean space, by matching as closely as possible the distance between respective cases in higher dimensional Riemannian space. Since this is now a projective space, the possibility of using signal-processing techniques is enabled, still retaining the structural representation identified by the FI matrix. The obtained visualisation of the patients' latent space facilitates the 'patient-like-me' approach.

Application to new, unseen patients

To satisfy the 'patient-like-me' objective of this paper, test cases are projected onto the latent space of trained observations. It is expected that, without informing the model of the pathology of the cases in the test set, some characteristics of these cases will be revealed as they would fall in the proximity of similar cases. To achieve this, the test data go through similar steps in the methodology as the training data, which is also illustrated in Fig. 1. Firstly, CNN features were extracted from the test set and PCA was applied, performing the same data transformations followed for the training set. Then, the trained MLP model was applied to the test data to estimate the class probabilities of each observation, which were then used as part of the calculations of the FI distances from each test case to each of the training cases. Based on this, and mapping the test cases back to the Euclidean space, each of them is then projected on the pre-constructed latent space (created using the training data), where it can then be visualised and analysed against its neighbouring cases.

Results

Model results

Although the classification results are not the focus of this work, they have been included to structure the discussion of utilising the visualisation to assess the capabilities of the patch model in Shen et al.16. Table 1 shows the confusion matrix for the independent test set. The overall accuracy in the test set is 72%.

Table 1 Confusion matrix of the CNN classifier for the test set.

Exploration using Principal Component Analysis (PCA)

PCA analysis on the extracted features has been used to visualise the separation of the classes without applying the FIN process, as well as for dimensionality reduction before the process. Figure 2 shows the first two principal components (PC) when applying PCA on the convolutional features, where the data points represent the patient's mammograms colour-coded by the four true (originally assigned) labels and the background area. Figure 2A visualises the training data and Fig. 2 B visualises both the training and test data, both before applying the FIN methodology. Although some grouping can be seen across both training and test, the mixing of the classes is apparent. All classes overlap in the centre of the visualisation, and both malignant classes are deeply mixed. There is some improvement with the benign classes however mixing still occurs.

Visualisation using Principal Component Analysis. Convolutional features extracted are projected on the first two principal components (PC). Data points represent patient mammograms, which are colour-coded by their assigned label. ( A) shows only the training data; ( B) only the test data.

Visualisation using Uniform Manifold Approximation and Projection (UMAP)

UMAP31 is a non-linear dimension reduction algorithm that can be used to learn an embedding of the data for an alternative 'patient-like-me' approach. It is a versatile method that can be used in unsupervised and supervised ways. In this study, both versions (unsupervised and supervised) of UMAP were applied to the set of extracted convolutional features. As expected, the supervised UMAP performed better than the unsupervised, and several tests were performed varying different parameters, e.g. the number of neighbours. Figure 3 shows the results obtained with supervised UMAP for training and test sets when using 15 neighbours and 42 random states. The groupings in Fig. 3 seem much more clear than those obtained with PCA (Fig. 2), although still there is mixing, especially notorious in the test set. It is also interesting to see how a good proportion of the class that represents image background was well separated from the rest of the lesions.

Visualisation using supervised UMAP. Supervised UMAP is applied to the set of extracted convolutional features. As in Fig. 2, data points represent patient mammograms, which are colour-coded by their assigned label. ( A) shows only the training data; ( B) only the test data.

FIN visualisations of the training cases

Figure 4A shows the visualisation of the latent space obtained with our proposed approach using the FIN methodology on the convolutional features derived from the DL model of the five-class classifier. Similar to Figs. 2 and 3, the data points also represent the patient's mammograms colour-coded by the four true (originally assigned) labels and the background area. However, as opposed to Figs. 2 and 3, only very limited mixing can be seen.

Visualisation of the training cases in the latent space of the FIN embedding. Cases/patient mammograms are represented by points: ( A) colours representing the true, original labels; ( B) colours representing the MLP prediction labels.

Figure 4B shows the visualisation but of how the model predicted the training cases. This is an expected result and gives a view of how the classification process of the CNN model works. In Fig. 4 B the classes are very well separated with most of the mixing occurring between the background and the calcification malignant classes. This figure can be used to assess how well the FIN representation reflects the MLP classifier. Through comparison of the visualisation with the true labels applied and the MLP predictions applied, the FIN representation looks to be suitable. The MLP structure is well reflected in this study and can be seen as a reliable mapping of the training cases, for which test cases will be projected onto at a later stage.

Projecting the test cases onto the trained embedding

Figure 5 shows the correctly classified test cases of each class highlighted as a black marker on top of the more transparent training cases. These are the cases that have been classified correctly. For the test subset of the data, the background class contains the most correctly classified cases, with an accuracy of 89.6%. It is possible to see some of these correctly classified cases slightly spread out although these appear to be concentrated around the benign classes. The next best performing is the mass malignant class, with 66.3% of those test cases correctly classified.

FIN map: all training cases and correctly classified test cases according to the MLP. Correctly classified test cases (black stars) projected into the trained FIN embedding, where every case is represented by a point-transparency was added mainly to allow for overlapping cases to be seen, and as a result, areas with the majority of cases in one class will display a more solid colour.

The visualisation in Fig. 5 shows that the correctly classified cases are projected within the well-defined class groups. However, not all cases are correctly classified. A more detailed analysis of some of these incorrectly classified cases has been conducted, to assess the model and better understand the reasons behind the misclassifications.

Detailed analysis of individual cases

Similar to the previous section, it is proposed that these trained embeddings can be utilised as a part of a decision support system for clinicians and radiologists in the diagnostic process of a patient's treatment care plan, which could help reduce the need for a pathological label in several cases. Through the extraction of the convolutional features, using the same pre-trained model can lead to the new case being projected onto the embedding. This can provide new insight into the case as it can be studied in the context of the neighbouring cases in the embedding, i.e. a 'patient like me' analysis.

Figure 6 shows a 'patient like me' analysis of four correctly classified cases. Additional metadata, unseen by the classifier and also unseen by the proposed FIN approach, has been included to assess these individual cases and understand the outcome of the classification to validate the proposed approach. Figure 6 highlights the location of where these four selected test cases were projected in the trained embedding, along with a selection of neighbouring cases, and their corresponding ROI patches. All malignant cases shown here have high BI-RADS scores of 4 and 5, suggesting concern and high suspicion of malignancy, respectively. For the calcification malignant cases in Fig. 6, they are all of the type amorphous or pleomorphic. This follows clinical literature as these are more likely to be malignant lesions43. Similarly, for the malignant mass cases, these are all of irregular or lobulated shapes which are more likely to be malignant.

'Patient like me' analysis of four selected cases that were correctly classified. Top left: patient 1678 (calcification benign); top right: patient 534 (calcification malignant); bottom left: patient 1332 (mass benign); and bottom right: patient 146 (mass malignant).. Cases are represented by points with added transparency as in Fig. 5. Black stars are the correctly classified test cases in each of the four groups, and the selected cases are represented with solid red circles. Neighbouring cases were also selected and visualised (empty black circles) for each of the four previous cases.

Figure 7 presents a similar 'patient like me' analysis for four cases that were misclassified. Importantly for these cases, the metadata can provide insight into why the cases were misclassified. For example, for both case 1569-a calcification benign case incorrectly classified as calcification malignant-and case 420-a mass malignant case incorrectly classified as mass benign-the associated metadata shows that the BI-RADS score assigned is 4 (BI-RADS was not a feature in the classification, and is denoted from studying a mammogram alone, pre-pathology). This score of 4 is defined as 'suspicious of malignancy', with a score of 3 or 5 leaning towards benign or malignant, respectively. This could support the fact that the cases were misclassified.

'Patient like me' analysis for four misclassified cases. Patient 1569 is a misclassified benign calcification (classed as malignant calcification), patient 1545 is a misclassified malignant calcification (classed as malignant mass), patient 630 is a misclassified benign mass (classed as benign calcification), and patient 420 is a misclassified malignant mass (classed as benign mass). The selected cases are represented with black solid circles.

Discussion

The focus of this work is to propose an ML methodology able to (1) facilitate the analysis and decision-making process in breast cancer diagnosis, which is key for the reduction in mortality from breast cancer; (2) help improve the diagnostic accuracy, to avoid both, false negatives (missed cancers) and false positives (which involves a psychological impact and recall for further tests)44; and (3) help reduce overdiagnosis, one of the major harms reported of the well-established breast cancer screening programmes4. To achieve this, the proposed methodology was applied in this study to mammography data, to visualise all patients in a single latent space from where comparisons and associations between patients can be made, aiding diagnosis and further clinical investigations.

To illustrate how it works, the proposed methodology started from a pre-trained model available in the literature16, which provides a 72% classification accuracy on test data. This pre-trained model is a 5-class patch classifier implemented and used in Shen et al.16 as an intermediate step to determine whether there is cancer or not in a mammogram. However, this methodology is not restricted to the specific pre-trained model referenced-it can also be applied to any other available or newly trained model.

The trained embeddings shown in Fig. 4 provide clear indication of the quality and usefulness of the separation using the FIN framework, showing its ability to greatly improve the five class separation as compared with the ones obtained on the same data with PCA (Fig. 2) and the supervised UMAP (Fig. 3). Figure 4 also shows that this methodology can accurately represent the model trained using the CNN features derived from the mammograms, and produce a visualisation of a latent space where a large set of patients are mapped out according to their similarities. The major benefit of the latter is that not only further knowledge and understanding can be generated, but also that new patients can be added or projected onto this embedding, which would provide intelligence about them based on their neighbouring patients in the visualisation.

As per the patient-like-me analysis, we have shown that it is possible to study a new case (that has been unseen to the classifier) in the context of those around it, to gain extra insight into it. For example, the correctly classified cases (as shown in Fig. 6) can be used as examples of correctly labelled and classified patients. Studying the associated training cases within the embedding can strengthen the confidence in the findings arising during the investigation of new patients.

The proposed method has not been correct in the classification of 28% of the test cases since the proposed approach relies on the quality of the initial CNN model to produce the embedding. However, it is worth noting that several of those misclassified cases (as shown in Fig. 7) had BI-RADS scores that would indicate suspicion of malignancy, the borderline score in this classification, which may justify the decision made by the classifier based on the features extracted automatically from the mammograms. The selection of the model is justified through its strong predictive capabilities originally, and its use is for the features of the publicly available dataset and model.

It is intended that the FIN methodology proposed can be used to provide useful and insightful visualisations to clinicians and radiologists, for the use of this extra information to assist in a 'patient like me' approach to attain further insights into the workings of the classifier and visualisations. To improve this further, future work could consider the use of active learning45 to continuously improve the embedding. Before any pathology is taken, a case can be projected onto the FIN embedding and studied in the context of their neighbouring cases. Then, where pathology is taken, this information can be included and the methodology updated to improve the model, leading to a better 'patient like me' approach for future patients.

One of the endeavours of this study was to exploit the predictive capabilities of a deep learning model. The FIN methodology has helped to further the understanding of how the CNN classification process works in this study. The Fisher Information metric has provided a robust mechanism to maximise the separation between the different classes, whilst retaining the cohesion of the group of cases with similar characteristics and has allowed for a visual representation of a pre-trained model which exploits the predictive capabilities of an image classification task using CNN. Associated information using clinical literature is able to append reasoning to the classification, such as information on the shape or type of lesion and the BI-RADS scores. This assists with the link between the human clinician and the machine learning classifier.

The use of the FIN methodology shows that this process can be applied to represent clinical data, classified through a CNN model which is very powerful but traditionally difficult to interpret. A Fisher-informed approach can add insight into the predictive capabilities and as described, can assist in the breast cancer diagnostic process.

In summary, in this study we propose a novel visualisation using FIN containing accurate information about data points' similarities that can provide intelligence about neighbouring data points. This was successfully applied to training and independent test cases and enabled the analysis of breast cancer data using a 'patient-like-me' approach. We showed how this visualisation could be used in a clinical setting to better understand the characteristics of a patient and facilitate the analysis and decision-making process in breast cancer diagnosis, which may help improve diagnostic accuracy and reduce overdiagnosis. A limitation of the proposed method is that the calculation of the FI distances when creating the embedding may be slow depending on the number of data points and the sizes of the images. However, existing implementations can be used in a high-performance computing cluster which can reduce the time considerably.

Data availability

The dataset analysed in the current study is publicly available in the Cancer Imaging Archive repository, https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM.

Abbreviations

BI-RADS:

Breast Imaging Reporting and Data System

CLAHE:

Contrast Limited Adaptive Histogram Equalisation

CNN:

Convolutional Neural Network

FI:

Fisher Information

FIN:

Fisher Information Network

MDS:

Multidimensional Scaling

ML:

Machine Learning

MLP:

Multi-Layer Perceptron

ROI:

Region of Interest

Acknowledgements

This work was partially supported by a Vice-Chancellor LJMU PhD Scholarship.

Author information

Authors and Affiliations

1. School of Computer Science and Mathematics, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool, L3 3AF, UK

Sandra Ortega-Martorell, Patrick Riley, Ivan Olier & Paulo J. G. Lisboa

2. Institute of Visual Computing & Human-Centred Technology, TU Wien, Vienna, Austria

Renata G. Raidou

3. Department of Electronic Engineering, University of Valencia, Valencia, Spain

Raul Casana-Eslava

4. The Clatterbridge Cancer Centre, NHS Foundation Trust, Liverpool, UK

Marc Rea

5. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA

Li Shen

6. Institute of Systems, Molecular and Integrative Biology, Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK

Carlo Palmieri

Contributions

S.O.M. conceptualised and supervised the study. S.O.M., I.O. and P.J.G.L. designed the methodology. S.O.M., P.R. and R.C.E. implemented the code and ran the experiments. L.S. provided the data and initial deep learning model. R.G.R. and S.O.M. produced the visualisations. M.R. and C.P. evaluated the results. C.P. provided the clinical expertise. S.O.M., P.R., I.O. and P.J.G.L. drafted the early versions of the manuscript. All authors contributed to the writing, reviewing and editing, and approved the final manuscript.

Corresponding author

Correspondence to Sandra Ortega-Martorell.

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The authors declare no competing interests.

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Ortega-Martorell, S., Riley, P., Olier, I. et al. Breast cancer patient characterisation and visualisation using deep learning and fisher information networks. Sci Rep 12, 14004 (2022). https://doi.org/10.1038/s41598-022-17894-6
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Breast cancer patient characterisation and visualisation using deep learning and fisher information networks - Scientific Reports

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Breast cancer is the most commonly diagnosed female malignancy globally, with better survival rates if diagnosed early. Mammography is the gold standard in screening programmes for breast cancer, but despite technological advances, high error rates are still reported. Machine learning techniques, and in particular deep learning (DL), have been successfully used for breast cancer detection and classification.

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