Editor’s Note: This article is based on the Population Health Expert Dialogue Series session at the 2022 Healthcare Analytics Summit. The panel consisted of David Ingham, DO, Chief Information Officer, Allina Health; Joseph M. Taylor, CEBS, RHU, HIA, Senior Vice President, Redeemer Health; Manjit Randhawa, MD, MPH, Physician Data Scientist, MemorialCare; and Edward Sheen, MD, MPH, MBA, Chief Population Health Officer and Senior Vice President, Health Catalyst. The session also featured recommendations from Amy Flaster, MD, MBA, Chief Medical Officer, ConcertoCare; Associate Physician, Brigham and Women’s Hospital; Instructor of Medicine, Part-Time, Harvard Medical School.
Setting achievable population health initiatives requires the ability to sift through enormous amounts of data, identify improvement areas, and select the areas where success can be achieved. During the 2022 Healthcare Analytics Summit, population health executives and leaders recommended three key strategies to form and successfully execute population health goals. These recommendations can help leaders and data analysts identify feasible population health initiatives that move organizations toward more equitable care.
Healthcare organizations are never starting with perfect data to achieve population health goals, no matter how robust and accurate the information. Instead, they’re embarking on a journey to gather as much accurate data as possible, examine data inputs for institutionalized biases, and allow continual learning to inform how to use their data to drive health equity across their populations.
To best use available data, population health leaders can start by identifying trends and patterns. Additionally, human faculties—including awareness of biases, knowledge bases, cultural awareness, and personal experiences—can help organizations identify and adjust for inequities, helping make up for incomplete data.
Clinicians have competing priorities and demands on their time, yet their input in data analytics teams is vital. Clinicians should be included in data definitions, visualizations, and exclusions when identifying initiatives, assessing data relevancy, and creating dashboards. When brought in early and consulted often, they can become data champions, increasing the likelihood of a project’s success.
Front-line clinicians also play a critical role in vetting potential opportunities for clinical improvement and cost efficiencies. While some opportunities may look good on paper, clinicians can help data analytics teams weed out initiatives that aren’t feasible. For example, surgeons might vote down a proposal to swap out an expensive surgical tape for a less expensive option, given their knowledge of correlations between the costlier tape and better care outcomes.
As healthcare resources are increasingly strained, patient risk stratification is fundamental to helping the most vulnerable patients get needed care. In addition to focusing on high-risk patients, organizations will also want to consider cost- and time-effective interventions for low-risk rising-risk patients.
Manjit Randhawa, MD, MPH, Physician Data Scientist, MemorialCare, recommends the following: “Be sure to develop initiatives that transcend risk levels. For example, if you identify an initiative for high-risk diabetic patients, you may benefit from identifying interventions for rising-risk patients who may also have diabetes and low-risk patients who are pre-diabetic.” Dr. Randhawa added, “Engaging rising- and low-risk patients in advance will likely require less-intensive interventions and can help prevent a costly escalation in risk status.”
While leveraging data to set and achieve population health initiatives is complex, organizations can take a pragmatic approach. Accessible recommendations include using available resources (i.e., don’t wait for that “perfect” data), starting sooner rather than later, consulting the organization’s clinicians, and maintaining a longitudinal, realistic view. Finally, population health leaders can remember to leverage the uniquely human faculties of self-awareness, cultural awareness, and personal experiences to improve interpretations and understanding of available data during the goal-setting process.
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