The Key to Next-Level Population Health? Dynamic, Patient-Level Data
Health plans in the US are facing a unique opportunity to optimize the health of their members and reduce disease burden nationally. That opportunity lives in their patient-level data.
By finding patterns in patients with similar histories or studying the historic gaps in care experienced by their members, payers have a unique opportunity to improve population health outcomes and stem the tide of runaway health spending. This can also lead to better insight to inform underwriting risk models, thus reducing the financial risk they assume on behalf of their members.
But that doesn’t happen with the fragmented, unreliable data that’s commonly provided to payers.
Look behind any failed population health or risk mitigation initiative and you’ll probably find a story of a missed opportunity in data strategy. Healthcare data is an incredibly complex, moving target – for example, LexisNexis research found that 2–2.5% of provider demographic data changes each month, while 30–40% of the provider data that drives critical payer decisions has been found to contain errors. And COVID-19 has thrown new challenges into the mix. Patients’ insured statuses are changing right when many people are avoiding visits to healthcare facilities.
As payers seek to provide value in healthcare, they’ll find increasing benefit in basing their daily decisions on reliable, actionable, patient-level data – from driving better disease management to risk scoring and underwriting. Reliable data needs to be complete, longitudinal, accurate, and timely in order to support the robust payer-provider partnerships that empower healthy value-based care models.
A New Generation of Data Strategy Tools
To move beyond population health efforts based on incomplete data and false signals and transition into thorough, timely, and longitudinal insights, payers need powerful software that can:
- Consume and transform large amounts of data from disparate systems
- Enable seamless access to robust and privileged datasets
- Provide flexibility to integrate external data assets that enrich strategic insights
- Deliver the tools and data assets that support a population health data strategy founded on complete patient journey data
Reliable population health data can clarify priorities to mitigate risk and optimize health.
Enhancing Patient Risk Profiling and Keeping Healthy Patients Healthy
A patient-centered approach rests on the patient risk profile. Population health data analysis tools need to support care improvement tracking metrics like admissions and readmissions, length of stay, and number of healthcare provider (HCP) visits, along with clinical history and comorbidity indices.
But insights need to go deeper than simple key performance indicators. Underwriting tools should provide insight on the patient journey, enabling payers to compare and contrast population cohorts against a comprehensive view of patient journeys as the basis of risk profiles compared to relevant cohorts. This approach goes beyond serving the members of a health plan – it’s the basis of identifying healthy patients and keeping them healthy.
Building Precision Into Population Health ModelsNot every population health data model is created equal. Models built on incomplete data can lead to incorrect and unnecessary rejections. For example, a 2016 study of underwriting models found that 30% of individuals whose wealth met minimum industry standards for long-term care insurance would have their applications rejected for medical reasons. Even the study itself was hampered by data gaps in the Health and Retirement Study (HRS) – information that is crucial to underwriting models.
Built on Komodo’s Healthcare Map, tools like Sentinel and Prism allow payers to create more complete risk models, assessing how membership is priced and building awareness of how risk models compare to the broader insured population.
Supporting Patient Outcomes
From the Quadruple Aim to improving outcomes, every year the healthcare industry gains a deeper understanding of the critical role of health plans to motivate positive health behaviors, from drug adherence to prevention and wellness. Consider the concept of financial toxicity and how distress around out-of-pocket costs impacts the journey of cancer patients. The opportunity to apply advanced data analytics to support patient experience initiatives can be significant.
As payers continue to lead the way toward a value-based system, these goals represent the next level of underwriting data analysis. At Komodo, we use these insights along with our expertise in healthcare and data science to blend together disparate data for amazingly robust predictive algorithms – the algorithms that can advance superior underwriting models.
Click here to learn how Komodo’s software can create a sandbox of innovation, combining your algorithms with Komodo’s predictive capabilities to improve patient outcomes.