Clinical decision support for population health management: development and validation of integrated acuity and...

Population health management programs coordinate care for over 80 million Medicaid beneficiaries but lack systematic clinical decision support for determining when to intervene and which interventions to select for patients with complex conditions. Our objective was to develop and validate a clinical decision support system integrating acuity prediction and intervention selection models for population health management programs.

We conducted a retrospective cohort study of 155 631 Medicaid patients enrolled in population health programs across Washington, Virginia, and Ohio (January 2023-July 2025). We developed integrated informatics workflows combining time-to-event prediction models for acute care events with heterogeneous treatment effect estimators for intervention selection. Models used structured electronic health record data, claims, and care management records. Performance was evaluated through clinical validation with 3 blinded physicians reviewing 200 cases.

The integrated decision support system achieved 81.3% sensitivity (95% CI, 79.8%-82.8%) and 82.1% specificity (95% CI, 80.6%-83.6%) for 30-day acute care prediction. The intervention selection component demonstrated 1.59 percentage points absolute risk reduction compared with standard care (95% CI, 0.21-3.04), translating to preventing one acute event for every 63 patients receiving model-guided rather than standard care. Clinical validation revealed systematic differences: physicians relied on recent utilization patterns (explaining 75.8% of decision variance) while models integrated broader clinical signals, identifying intervention opportunities earlier in disease trajectories. Both approaches recommended similar intervention types, suggesting complementary rather than replacement roles.

An integrated clinical decision support system can enhance population health management by providing actionable guidance on intervention timing and selection.

An integrated decision support system's ability to identify opportunities before high utilization manifests offers potential for shifting from reactive to preventive care delivery for vulnerable populations.