New Wave Population Health Management: The Move From Clinical To Behavioral Engagement Analytics

By Dogu Celebi, CMO, Decision Point Healthcare Solutions

As population health management matures, both health providers and payers are moving beyond traditional clinical, risk-based analytics to focus on patient behavior. They are mining both traditional and emerging sources of data to gain visibility into patient behavior with the goal of influencing behavior to improve patient engagement and drive better patient clinical outcomes, efficiency, and satisfaction.

Traditional population health management programs focus on understanding and assessing clinical risk, such as disease prevalence, severity, and progression. This enables organizations to pinpoint opportunities for clinical improvement such as gaps in care, clinical quality, and healthcare outcomes.

Designed to empower clinicians to improve care and to promote evidence-based medicine, these traditional population health management analytics are created to facilitate clinical actions. They provide little to no insight into the challenges patients experience with positive clinical outcomes and satisfaction. This is unfortunate, since most problems in healthcare have significant patient engagement components.

Engagement analytics is a new behavior-focused approach to population health management that integrates conventional clinical risk-based analytics with a new breed of analytics and data sources that focus on behavioral risk, behavior change and patient engagement.

Why Are Engagement Analytics So Important?
Healthcare engagement is the emotional, financial, physical, and social involvement or commitment with the health care ecosystem. Consequently, engagement lies at the heart of many healthcare challenges: satisfaction, loyalty, clinical compliance, and clinical outcomes. Clearly, benefits exist to getting engagement right.

The effects of healthcare engagement are experienced throughout the healthcare system:

• Finance and Marketing– feel the pain in voluntary disenrollment from the health plan, satisfaction, appeals, grievances, and complaints
• Individuals – manifest the effects in their health and wellbeing and in the lifestyle choices and preventive care they adopt
• Healthcare delivery systems – experience the impact in provider selection, satisfaction, and out-of-network use
• Clinical care – sees an effect on compliance – adherence to care plans, recommendations, and prescription, as well as secondary and tertiary prevention

How Do Healthcare Engagement Analytics Work?
With analytics focused on engagement and behavior change, organizations can target both individual patients as well as entire populations. They can assess and predict engagement for health and wellness, preventive care, chronic care, system/network behavior, satisfaction, and retention. They also can identify barriers to engagement, such as health literacy, system literacy, provider relationships, socioeconomic conditions, or physical ability.

Analytics help segment populations according to these risk factors. Accordingly, the organization can identify the appropriate success factors – technology adoption, mode of outreach, personal connections and timing, language – on which to base the engagement program. At the same time, analytics focused on clinical risk can help assess disease prevalence and severity, predict disease progression and identify gaps in care.

What Are The Criteria For Successful Engagement Analytics?
Mostly a non-clinical challenge, healthcare engagement analytics delivers visibility into how members/patients will behave and why. In fact, meaningful engagement analytics must consider the fact that behavior is non-linear and multi-faceted. As a result, engagement analytics require creative big data analytical techniques that can access any and all data sources.

A successful engagement analytics platform needs to combine predictive models, micro-segmentation, and patient outreach. Specific behavioral analytical models for such business and clinical challenges as retention, satisfaction, clinical compliance or adherence will help pinpoint patients that are at risk for lack of engagement. In addition, these models can identify the underlying causes for lack of engagement and barriers to success. These, in turn, can be leveraged to micro-segment populations and create meaningful and actionable patient cohorts. Appropriate, focused and personalized outreach methods and interventions are then recommended to improve overall patient engagement.

For example, the outreach recommendation for a patient with a high readmission risk because of low social support, access and mobility might be a phone call followed by home visit, with language crafted to address the patient’s engagement barriers.

Benefits Of Engagement Analytics
Ultimately, healthcare engagement analytics can improve retention and satisfaction, improve quality of care, and lower re-admissions. It can also help prevent unnecessary utilization and improve medication adherence. It does this by identifying engagement issues and promoting behavioral change. Doing so requires identifying risk, prioritizing stakeholders, understanding behavioral drivers and channel preferences, micro segmenting populations, and predicting behaviors.

With healthcare engagement analytics, organizations have a new, proactive tool that provides a multi-dimensional, actionable view of both individuals and populations. Once deployed, It can improve patient outcomes and bottom line value for healthcare providers and payers. We are just beginning to see how effective this practice can be.

About The Author
Dogu Celebi is Chief Medical Officer at Boston-based Decision Point Healthcare Solutions.