Abstract
Background: First-generation algorithms resulted in high-cost features as a representation of need but unintentionally introduced systemic bias based on prior ability to access care. Improved precision health approaches are needed to reduce bias and improve health equity. Purpose: To integrate nursing expertise into a clinical definition of high-need cases and develop a clinical classification algorithm for implementing nursing interventions. Methods: Two-phase retrospective, descriptive cohort study using 2019 data to build the algorithm (n = 19,20,848) and 2021 data to test it in adults ≥18 years old (n = 15,99,176). Discussion: The COMPLEXedex-SDH algorithm identified the following populations: cross-cohort needs (10.9%); high-need persons (cross-cohort needs and other social determinants) (17.7%); suboptimal health care utilization for persons with medical complexity (13.8%); high need persons with suboptimal health care utilization (6.2%). Conclusion: The COMPLEXedex-SDH enables the identification of high-need cases and value-based utilization into actionable cohorts to prioritize outreach calls to improve health equity and outcomes.
| Original language | English |
|---|---|
| Article number | 102044 |
| Journal | Nursing Outlook |
| Volume | 71 |
| Issue number | 5 |
| DOIs | |
| State | Published - Sep 1 2023 |
Keywords
- Care coordination
- Provider–provider communication
- Social determinants of health
- Social need
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