Optimizing Care-Gap Identification with Member Segmentation
When targeting members to address care gaps, Medicare Advantage and Medicaid health plans often struggle with reducing false positives, which can lead to wasted outreach efforts and decreased member engagement. One key metric worth tracking to identify areas for improvement is the proportion of members who have been incorrectly identified as needing care gap closure interventions.
This metric, when applied through machine learning analysis, can be used to evaluate the performance of algorithms and model updates over time. By monitoring this metric, plans can assess whether their care-gap identification processes are increasingly accurate and effective.
This metric matters for Stars because the effectiveness of care gap closure initiatives directly affects a plan's quality ratings. Inaccurate identification of care gaps can lead to misguided outreach efforts, which can result in poor member experiences and ultimately, lower Star ratings.
To act on this metric, plans should prioritize member segmentation techniques that utilize longitudinal data to better understand individual member needs. This can involve incorporating additional data sources, such as pharmacy claims, laboratory results, and patient-reported outcomes, to refine care-gap identification models. By leveraging advanced analytics and machine learning, plans can enhance the accuracy of their care-gap identification processes, ultimately driving more effective outreach and improved member engagement. This focus on accuracy can also help plans concentrate their efforts on the highest-impact members, reducing waste and optimizing resource allocation.
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