Our Research
As a public benefit company, Waymark is committed to learning from our research, sharing our findings, and moving community-based care forward.
Integrating healthcare system context to improve risk prediction and assess racial disparities among dual-eligible Medicare–Medicaid beneficiaries: a retrospective cohort study using national fee-for-service claims
Most Medicare–Medicaid duals are labelled “high risk” based on demographics, diagnoses, prescriptions, and other individual-level characteristics. Using 100% of national fee-for-service claims for 3.9 million duals, our study found that adding delivery system context—provider networks, facility characteristics, market structure, and access barriers—raises prospective spending prediction from R² 0.45 to 0.62 and improves acute care prediction models' sensitivity from 25.0% to 33.8% while keeping specificity above 97%.
Clinical decision support for population health management: development and validation of integrated acuity and intervention prediction models
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.
An Artificial Intelligence Oracle for Proactive Population Health Quality Improvement
For patients with complex health needs, the periods between clinical encounters are times of significant vulnerability, during which unobserved risks can escalate into acute events. The Waymark community-based care management program was designed to reduce this vulnerability for the patients it serves, but the organization faced the challenge of processing thousands of unstructured daily encounter notes from its field-based teams. To meet this challenge in a scalable way, the authors developed and implemented an artificial intelligence (AI) oracle, a system that continuously analyzes these notes.
Early detection of high risk pregnancies using clinical and social data to improve health outcomes
Traditional risk models flag patients after diagnosis codes appear in claims. By then, the early intervention window has closed. Our Signal for Maternity tool (driven by finding signals of domestic violence, undiagnosed heart disease, and other key causes of morbidity and mortality) can identify high-risk pregnancies among patients receiving Medicaid 55 days before traditional clinical indicators emerge.
