Our Research

As a public benefit company, Waymark is committed to learning from our research, sharing our findings, and moving community-based care forward.

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How AI can bring better care to Medicaid patients

STAT+, December 2024
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Sadiq Patel

Sadiq Patel, Waymark's VP of Data Science and AI, examines barriers to AI/ML adoption in Medicaid and lays out potential solutions to address these challenges — including actively seeking input from patients and the people who serve them: PCPs, community health workers, pharmacists, therapists, care coordinators, and community organizations.

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Simulating A/B testing versus SMART designs for LLM-driven patient engagement to close preventive care gaps

Nature Digital Medicine, November 2024
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Sanjay Basu, Dean Schillinger, Sadiq Patel and Joseph Rigdon

Using microsimulations, we compared both the statistical power and false positive rates of A/B testing and Sequential Multiple Assignment Randomized Trials (SMART) for developing personalized communications across multiple effect sizes and sample sizes. SMART showed better cost-effectiveness and net benefit across all scenarios, but superior power for detecting heterogeneous treatment effects (HTEs) only in later randomization stages, when populations were more homogeneous and subtle differences drove engagement differences.

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Geographic Variations and Facility Determinants of Acute Care Utilization and Spending for ACSCs

The American Journal of Managed Care, November 2024
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Sadiq Patel and Aaron Baum

We analyzed data for 48.4 million patients receiving Medicaid across 34 states and Washington DC, and found that nearly 40% are for conditions that coule be prevented or managed through timely access to primary care. As many Medicaid programs struggle to manage rising costs, these findings demonstrate that early interventions can meaningfully improve outcomes and reduce costs for their Medicaid populations.

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Supporting Rising-Risk Medicaid Patients Through Early Intervention

NEJM Catalyst, October 2024
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Aaron Baum, Rajaie Batniji, Hannah Ratcliffe, Margalit DeGosztonyi and Sanjay Basu

We assessed outcomes from our early interventions for 64,278 patients covered by two Medicaid health plans and assigned to 2,298 primary care providers (PCPs) spanning multiple practices in the states of Washington and Virginia. We found that Waymark achieved a 22.9% reduction in all-cause ED and hospital visits, including a 20.4% reduction in avoidable ED visits and 48.3% reduction in avoidable hospitalizations for rising-risk patients receiving Waymark services (compared to a matched control group of rising-risk patients over a 6-month follow-up period).

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Population Health Implications of Medicaid Prerelease and Transition Services for Incarcerated Populations

The Milbank Quarterly, October 2024
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Elizabeth T. Chin, Yiran E. Liu, C. Brandon Ogbunu and Sanjay Basu

A large population of incarcerated people may be eligible for prerelease and transition services under the new Medicaid Reentry Section 1115 Demonstration Opportunity. We found that several disease prevalence rates were sufficiently high among incarcerated populations to likely skew overall Medicaid population prevalence of these diseases when prerelease and transition services are expanded, implying the need for planning of additional data exchange and service delivery infrastructure by state Medicaid plans.

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The Risk Of Perpetuating Health Disparities Through Cost-Effectiveness Analyses

Health Affairs, August 2024
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Sanjay Basu, Atheendar S. Venkataramani and Dean Schillinger

We examined how competing risks, baseline health care costs, and indirect costs can differentially affect cost-effectiveness analyses for racial and ethnic minority populations. We illustrate that these structural factors can reduce estimated quality-adjusted life-years and cost savings for disadvantaged groups, making interventions focused on disadvantaged populations appear less cost-effective.

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From Veneers To Value: Data Science Can Enable High-Value Care In Medicaid

Health Affairs, July 2024
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Rajaie Batniji, Sanjay Basu and Sadiq Patel

We argue that persistent data quality issues and insufficient integration of clinical and social risk factor data have contributed to the growth of value veneers by limiting the ability of Medicaid programs to proactively identify and deliver targeted interventions to at-risk patients. We also outline how recent advances in data science can enable Medicaid programs to deliver higher-value care to beneficiaries.

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Medicaid Expansion and Racial-Ethnic and Sex Disparities in Cardiovascular Diseases Over 6 Years: A Generalized Synthetic Control Approach

Epidemiology, March 2024
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Roch A Nianogo, Fan Zhao, Stephen Li, Akihiro Nishi and Sanjay Basu

Findings report that Medicaid expansion was associated with a reduction in cardiovascular disease (CVD) mortality overall and in particular among minority and female subpopulations.

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Financing Thresholds for Sustainability of Community Health Worker Programs for Patients Receiving Medicaid Across the United States

Journal of Community Health, February 2024
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Sanjay Basu, Sadiq Patel, Kiiera Robinson and Aaron Baum

We sought to estimate minimum threshold Medicaid payment rates to enable community health worker (CHW) program sustainability, and found that higher Medicaid fee-for-service and capitated rates than currently used may be needed to support financial viability of CHW programs. We also present a revised payment estimation approach that may help state officials, health systems and plans discussing CHW program sustainability.

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Prediction of non emergent acute care utilization and cost among patients receiving Medicaid

Nature Scientific Reports, January 2024
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Sadiq Patel, Aaron Baum and Sanjay Basu

Patients receiving Medicaid often experience limited access to primary care, leading to high utilization of emergency departments for non-emergent conditions. We tested alternative widely-debated strategies to improve Medicaid risk models, the results of which demonstrate a modeling approach to substantially improve risk prediction performance and patient equity.

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