The shift from volume-based care to value-based care created countless opportunities and obstacles for healthcare organizations. One of the most important changes has been the impact on member engagement and population health management. More than ever before, care providers must develop a deep understanding of their members to deliver personalized, effective care. Accomplishing this takes the right approach.
Finding that right approach is impossible without the right data and analytics. Unlocking value within data and using it across predictive and prescriptive analytics can help healthcare organizations better understand their members and more precisely identify opportunities for improvement. To achieve this goal, healthcare organizations should consider four best practices for data-driven total population health management.
Best Practice #1: Go Beyond Measuring Clinical Risks
A lack of data typically means many health organizations only focus on clinical performance metrics. This limited view makes it difficult to accurately measure the current and predicted future risks for members or patients. By pairing clinical history with other data, including social and behavioral variables, healthcare organizations can take a more comprehensive and proactive approach towards managing member health and mitigating risk. Care teams can look at the whole member, locate barriers to care, and create effective care programs that address individual member.
One simple example of leveraging an expanded member view is knowing a member’s social barriers to health, and pairing that information with their clinical history, risk factors, level of impactability, and their motivation to make any needed health improvements. Bringing all of this information into a single view allows care teams to identify where they should intervene, what care should be delivered, and how to best engage with those members.
Best Practice #2: Let the Data Guide Effective Engagement Methods
As care teams strive toward effectively managing current risks, there is an increased focus on getting mitigating future costs and negative health outcomes. In order to achieve both goals, healthcare organizations must understand which members have treatable conditions, closable gaps in care, and are willing to modify their behaviors to close those care gaps. Once an organization knows who to target for interventions and the prioritized care gaps, predictive and prescriptive analytics can guide care teams to identifying the specific needs of members. What resources are needed to support the member in accessing care or following treatment plans? What care is needed, and how can it be delivered based on the best engagement strategies? Is there education that can be provided, and what is the best channel for providing it?
For example, capturing and using data on patient social determinants of health can identify that a certain population of very high risk diabetic members all have various open care gaps. Layering this analysis with insight into their level of impactability provides the care team with a clear view into the members and care gaps that should be prioritized in order to gain the greatest impact on both clinical and financial outcomes. Further analysis demonstrates that a certain amount of this population has a low intervenability score, which measures their ability and willingness to change their behaviors or health outcomes. This insight shifts care team focus on the members that are likely to engage. With member-level insight into social determinants and behavior patterns, care plans can meet members where they are in their healthcare journey to address their unique needs.
Best Practice #3: Embed Analytics and Insights into Workflows
Even if a health plan has vast amounts of data and advanced analytics capabilities, any insights it produces will be useless unless they’re turned into action. By tying the insights gained from the data and analytics back into care management workflows, health care organizations can create a continuous feedback look for more proactive and nimble care management.
Best Practice #4: Measure. Measure. Measure.
Knowing the results and progress of interventions is critical to the success of all population health initiatives. Healthcare organizations must ensure care management resources are allocated to the right members and provide the right care in order to show a return on investment and progress on their goals.
Performance Powered by Data
Delivering comprehensive population health management requires comprehensive insights. By incorporating these strategies, healthcare organizations can break down barriers to care and better engage and intervene with their members. For a more complete list of best practices and strategies for generating insights and improving patient results, read EXL’s white paper Total Population Health Management for Total Results: Data Unlocks Decreased Costs and Better Health Outcomes.
Re-published from SCIOKnow, the SCIO Health Analytics (an EXL company) blog.