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How to Use Analytics to Guide the Day-to-Day Operations of Your Population Health Intervention

In a previous blog post, we emphasized the three pronged analytic strategy that risk-bearing organizations should employ when implementing chronic care management and other population health programs.  In this post, I will review the final component of the strategy we have outlined: Quality Assurance – the best practice.

Teams in charge of implementing a population health intervention may be logically divided into two responsibility sets – one group responsible for implementing the program and another group responsible for evaluating the program. There are good reasons to do this, such as avoiding bias in the evaluation and both groups usually require different skill sets. We advocate however that analytics should play a large role in your ongoing quality assurance process – that role should be to ensure that leading indicators of change are occurring, and tracked as part of the day-to-day operations of the intervention.

You may have heard this referred to as a “Logical Model” or a “Logical Framework”. It simply means that in order to believe the outcomes from your evaluation, there will be a set of leading indicators that will give you confidence in this result. Analytics should be involved in each step of your intervention to ensure that these leading indicators are present, and at the appropriate level to effect change in the ultimate evaluation. These indicators generally fall into three categories: 1) process measures, 2) goal-setting and 3) goal attainment.

Process measures:

  • Members that received outreach: Individuals who have received the program’s initial outreach (Web/Email/Telephonic/Mail/In-Person).
  • Members that accepted outreach: Individuals who have accepted (opt-in) the program’s outreach.
  • Members that actively engaged in outreach: Individuals who further engaged in program after the initial outreach and working towards achieving a better health outcome.

Goal-setting:

  • Acknowledgement of behavior change needed: Members who recognize the gap in their current health status and acknowledge the need for the behavior change to bridge the gap.
  • Understanding of steps toward accomplishing behavior change: Members who understand the specific steps required achieve a better health outcome (example: physical activity and better nutrition requirements to achieve weight loss).
  • Motivations tracked for accomplishing behavior change: Recording of specific motivations behind a member’s willingness to engage in order to achieve a better health outcome.

Goal-attainment

  • Self-reporting of behavior change: Members self-reporting the behavior change and tracking those changes by each goal (example: Weigh Loss, Quit Smoking, etc.)
  • Clinical and biometric data indicating behavior change: Capturing the clinical and biometric data which indicate the goals that are set and achieved by the members.

As we referenced last month, tracking these types of leading indicators will allow you to tailor your population health intervention in “real-time” to ensure successful outcomes.  In a more traditional model, programing change wouldn’t have been effected until after the evaluation of the program has been completed – sometimes a year or much later. An added bonus to this approach is the ability to report intermediate outcomes (such as engagement, goal-setting, and behavioral impacts), demonstrating that change is happening in the at-risk populations.

Learn how Health Dialog’s innovative analytics and Care Pathways methodology uncover actionable population health management opportunities. For more insight on Care Pathways, read our latest white paper: Delaying Disease Progression Across a Population.

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