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.
Preventing Tomorrow’s High-Cost Claims: The Rising-Risk Patient Opportunity in Medicaid
This commentary notes the superiority of targeting rising-risk patients rather than high-cost claimants for Medicaid cost containment based on analysis of 13.1 million beneficiaries across 15 states. Early identification of and intervention for rising-risk patients is a more effective way to prevent the progression of chronic conditions and manage associated costs than attempting to reduce extreme utilization, which tends to decrease naturally over time.
Reinforcement Learning to Prevent Acute Care Events Among Medicaid Populations: Mixed Methods Study
Multidisciplinary care management teams must rapidly prioritize interventions for patients with complex medical and social needs. Current approaches rely on individual training, judgment, and experience, missing opportunities to learn from longitudinal trajectories and prevent adverse outcomes through recommender systems. This study aims to evaluate whether a reinforcement learning approach could outperform standard care management practices in recommending optimal interventions for patients with complex needs.
Predicting quality measure completion among 14 million low-income patients enrolled in Medicaid
Analyzing 14.2 million Medicaid recipients—including those excluded from electronic health records and without prior utilization—we developed models to predict gaps in nine nationally adopted quality measures,including preventive care and chronic disease management. Using clinical data to prioritize outreach, the clinical-only model improved accuracy by 32.5 percentage points over non-predictive methods such as alphabetical calling or birthday reminders.
Projected Health System and Economic Impacts of 2025 Medicaid Policy Proposals
Our new peer-reviewed research examines what health plans, providers, and state policymakers can expect as recent Medicaid policy changes in H.R. 1 take effect. The findings paint a stark picture: excess mortality and preventable hospitalizations are projected to surge, community health centers could lose up to 26% of their revenue, rural hospitals face heightened risk of closure, unemployment will rise, and local economic output will fall.
Optimizing AI solutions for population health in primary care
Artificial intelligence (AI) has primarily enhanced individual primary care visits, yet its potential for population health management remains untapped. Effective AI should integrate longitudinal patient data, automate proactive outreach, and mitigate disparities by addressing barriers such as transportation and language. Properly deployed, AI can significantly reduce administrative burden, facilitate early intervention, and improve equity in primary care.
Impact of Community Health Center Losses on County-Level Mortality: A Natural Experiment in the United States, 2011–2019
We conducted a natural experiment study using difference-in-differences analysis of propensity score–matched US counties from 2011 through 2019. Loss of CHC sites was associated with an increase in age-adjusted all-cause mortality of 3.54 deaths per 100 000 population (95% CI: 1.19, 5.90; p = 0.003) in the year following the loss. The largest increase was observed for cancer mortality (2.61 per 100 000; 95% CI: 0.59, 4.62; p = 0.011). Primary care physician density and patient volume loss both mediated the relationship.
