November 16 2017 | 0 Comments | 177 reads Average Rating: 4

5 Steps on the Path of Population Health Success

by Dr. Kevin Keck in Population Health

Spurred on by factors such as the growing geriatric population, the increase in the prevalence of chronic disease and advancing big data capabilities, population health is growing a decidedly quick clip. In fact, the population health market is expected to expand from just $13.85 billion in 2016 to $42.54 billion in 2021, according to a report from MarketsandMarkets.

As population health becomes increasingly common, healthcare organizations are struggling to realize its coveted cost savings. The biggest opportunity is with patients with chronic conditions, which represents 86% of the $3.0 trillion spent on healthcare overall in the United States, according to the Centers for Disease Control and Prevention.

But that is a big ocean to boil – and typical population health programs often wind up producing some luke-warm results. The problem: Most population health programs take a broad approach and try to treat patients with chronic conditions such as diabetes, hypertension or high cholesterol with one-size-fits-all-interventions that ultimately fail to work on these large populations. The end result: Organizations wind up spinning their wheels, spending significant money and realizing very little return on their investment.

But there is a better way. In fact, you can get your population health program on the path to success by following these five steps:

#1: Create a granular and actionable population analysis. To do so, you need to move beyond solely relying on clinical and claims data and leverage outside information such as zip code, demographic and psychographic data. These additional data points make it possible to construct a 360-degree view of patients.

#2: Leverage new insights. When using zip code data, for example, your healthcare organization can determine where patients live, down to a four-block radius. As such, you can arrive at a better idea of how they live. Therefore, you will know when diabetic patients live in a food desert – and, therefore, realize that educational efforts designed to prompt healthy eating are likely to fail.

#3: Assess impactability. These measures determine how likely patients are to benefit from particular interventions and also evaluate the financial impact of the interventions. As such, you can arrive at a better idea of expected or potential results.

#4: Assess intervenability. This is a measure that describes how receptive a patient or a group of patients will be to making the changes necessary to improve their health and to listening to the messaging around how they can move in the right direction to do so. For example, if a diabetic patient is from a high-income socioeconomic group and also lives near several organic markets and farm-to-table restaurants, then the patient is apt to be receptive to educational programs that stress healthy eating.

#5: Identify high target patients. If a patient is high risk due to comorbidities, age, smoking, lack of exercise, etc. and has a high impactability and intervenability score, then this is a high priority, target patient. Similarly, if a patient has medium risk but has a high impactability and intervenability score, then this is also a high priority, target patient.

By implementing population health programs that focus on these high target patients, your healthcare organization can more successfully keep patients away from the emergency department and out of the hospital. As such, you can finally realize the cost savings that is supposed to emanate from population health programs.

Want to learn even more about making your population health programs successful by using data the right way? Download the survey results brief and my Q&A column entitled “Containing Costs: Using Analytics to Target Interventions for Greater ROI.”

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Dr. Kevin Keck
Chief Medical Officer

Dr. Keck is SCIO’s Chief Medical Officer and leads the Clinical Analytics development team. His responsibilities include assessing the leakage and the impact of quality and cost in Provider Analytics.

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