OneHealth: Combining Patient-Generated Data With Communities and Feedback Loops

Evolution from a research tool to a platform for patient engagement

Bruce Springer of OneHealth will speak about this topic at the Strata Rx conference. This article was written by Patrick Bane of OneHealth in coordination with Bruce Springer.

According to a recent study performed by the Jesse Brown VA Medical Center and University of Illinois at Chicago, patient-centered care has demonstrated positive outcomes on patients’ health, patients’ self-report of health, and reduced healthcare utilization. The study’s results are consistent with previous research that the patient-centered care model improves the quality of care while simultaneously lowering the cost of care.

OneHealth’s behavior change platform extends the patient-centered model by connecting members anytime, anywhere through mobile and web applications. Member generate data in their daily lives, outside of a clinical setting, which creates a much richer dataset of behaviors that are required to understand the patients’ condition(s), and their readiness to change. Members freely choose what to do and their choices actively generate data in five classes of information:

Emotional indexing
The platform longitudinally tracks an individual’s emotional state via a clinically vetted and simple to use “emoticon” scale.
Content generation
Members can easily create content or respond to others’ content that is automatically delivered to them.
Members join multiple condition communities that yield important insight about a patient’s comorbidities.
Members may participate in self-managed or social programs that deliver clinically significant data about behaviors to guide patients to achieve better health.
Members’ interactions with all other aspects of the platform including peers, OneHealth Coaches, Community Hosts, experts, programs and communities are tracked to identify their support networks, what content they are consuming, and how they interact with other members.

These data sources feed into the member dashboard to create a complete picture of the patient’s self management behavior (Figure 1). All of these components work together to drive member engagement, enable health self-management, and improve patient outcomes. Without one piece of the puzzle, these results would not be possible. Without communities, insight into comorbidities would be lost. Without connections, a member reporting as depressed would receive no support. Without programs to lead patients in condition management, the site would be a simple social network connecting people that share similar health conditions. All of these tools work in cohesion to create and improve patient health, so taking one piece away would not only severely limit the patients opportunity to improve or successfully manage their condition but would limit healthcare providers ability to see how a patient interacts outside their primary care physician’s office, how to improve the patient’s understanding of their health, and ways to improve patient healthcare compliance.

Elements of OneHealth platform

Figure 1. Elements of OneHealth platform

The data is processed at multiple levels, and is used to provide feedback to an individual member about themselves, a group they’re in, or their entire community. The data can be used for the following purposes:

Feedback loops

Members receive feedback loops from a variety of sources to drive engagement. Examples include site activity notifications from peer engagements and dashboards.

  • Site activity notifications are received by members through email, smart phones, and/or tablets and are created in real-time by peers’ activity on the site, or can even be triggered by a member’s inactivity. One of the most visual and obvious applications of OneHealth is emotional indexing. Peers are immediately notified if a member reports at an at-risk state such as depressed, stressed, or craving. However, many notifications are social notifications. For instance, if a member does do not log in for two weeks, that member’s peer connections are notified of her absence and are encouraged to reach out to that person to reconnect. For the more active and engaged, members are awarded badges for achieving milestones or reaching achievements for success in managing their health, and their peer connections are notified about the success of these members. In addition, on a basic social network level, members are notified of personal peer interactions such as responses to posts, status updates, and emoticon check-ins or private messages. The site activity notifications of peers managing their health works to drive members back to the site to actively engage regularly. In all site activity notifications, peer connections are key to success. The member will be supported, because more friends being alerted increases the odds of having at least one reaching out to support the at-risk member or support members for success.
  • Dashboards are a key component of member feedback loops. The member dashboard records a member’s interactions and check-ins for the member to self-analyze their health over time. An essential aspect of the dashboard that is critical to its utilization by the member population is including enough information to be insightful and useful without overwhelming the member. Recording members’ progress and enabling them to see patterns in their health enables them to make changes or modify their behavior in order to improve outcomes.

Partner Feedback

Through the member Terms Of Service, OneHealth partners receive verification through data that their members are being engaged and actively recording data about their health. An important part of any business is ensuring customer satisfaction, and OneHealth achieves this through patient-centered data. In compliance with HIPAA regulations ensuring member privacy, all data reported by members is de-identified when delivered to partners. After de-identifying member data, reporting can be packaged in a number of ways:

  • The highest level of reporting is member population data. Typical data in this set includes the percentage of eligible population engaged, total number of members engaged, how often they are engaged, average number of conditions being self-managed per member, and any additional partner-specific data requests for their population. The population level data set enables partners to see the percent of their population that is actively pursuing self-management of a condition and, by reporting the number of communities joined by members, the extent of comorbidities among their member base.
  • Member-level data reporting is available to partners who wish to see a closer level of detail of their member base. Member-level reporting includes the conditions members are self-managing, such as tobacco cessation, heart health, or diabetes; the most populated communities, and overall mental health demonstrated through check-ins. The benefit of member level data sets permits partners to see which conditions most affect their population and if they need to offer or enhance services and interventions to better benefit those sub-populations.

The ideal result of providing partners with feedback on utilization and communities joined by their member base is to diversify or enhance service offerings to that population for them to better self-manage their health and reduce healthcare utilization costs. An example of this process would be an accountable care organization with at-risk and chronic condition patient population that had an original desire to provide peer support and self-management programs for a variety of chronic conditions. However, after 6 months and engaging a portion of their population, through community-specific data, OneHealth was able to determine approximately 35% of the ACO’s population was comorbid with mental health conditions such as depression, stress, and anxiety. After this discovery, the ACO implemented a mental health information program through primary care physician’s offices and received patient reported feedback of a higher quality of care and higher healthcare compliance.

Product Analysis

OneHealth’s product and services teams can improve its service and product features for members based on utilization data. Figure 2 shows the flow of information that makes continuous improvement possible. Two main types of member utilization data can be used:

  • High-utilization data from a large percentage of members is an easy indicator of a frequently used asset. These high-use assets will obviously be the most visible to members and will need to be polished and improved. After locating a high-use asset, member input can be the most valuable in improving these high-use areas and can be gathered through focus groups or member surveys.
  • An even more important but often overlooked portion of data is the low or underutilized asset. Low utilization or underutilization by a member base can be an indicator of any number of problems, such as low site visibility, members’ misinterpretation of instructions, and off-putting complexity in the tools or phrases used to describe an asset. Although managing a condition must incorporate many different parts of the person–such as mental health, physical health, diet, exercise, and medication–displaying tools and information for all of these can be complex and increases the odds of an important component being overlooked. The complexity and odds only increase dramatically when patients are comorbid and managing multiple conditions. Pairing low-utilization and high-utilization data and comparing them against a variable such as location on a webpage, complexity of description, etc. can provide insights into why an asset receives low utilization by members.

Feedback to improve site offerings

Figure 2. Feedback to improve site offerings

Placing the patient at the center of OneHealth’s data architecture gives insight into how members engage in their health on a daily basis. This data informs healthcare providers how best to improve patient communication, understanding and compliance.

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