Equipping States for the Rural Health Transformation Program: Evolving Federal Health Evaluation Priorities and AI Techniques Evaluators Can Leverage

The Centers for Medicare and Medicaid Services’ (CMS’s) Rural Health Transformation Program (RHTP) represents a transformative opportunity for rural health, providing $50 billion in funding and engaging all 50 states. As states prepare to design their RHTP evaluations, they must navigate CMS’s guidance, which calls for time-bound metrics to demonstrate impact across domains such as access, quality, outcomes, rural hospital finance, workforce, technology use, and initiative programming, per the RHTP Notice of Funding Opportunity. CMS also requires rigorous financial tracking to ensure that, by the end of the five-year program, funded activities are financially self-sustaining. Advances in data collection and analytics—including the use of artificial intelligence (AI) techniques—have not only increased the accessibility and speed of information processing but also raised the bar in terms of evaluation design and implementation. State analysts and policymakers will be able to leverage these sorts of tools to generate more timely, precise, and actionable insights. 

Advancements in data collection have intensified demands for robust, multi-dimensional impact analyses and heighted program accountability (Stanford Social Innovation Review). For the RHTP in particular, states must develop evaluation strategies that meet federal requirements and drive meaningful, sustainable improvements in rural health. Further, states and their evaluation partners should heed new findings as observed in recent federal evaluation policy shifts, including emerging requirements and best practices that directly impact RHTP-funded initiatives. In particular, federal evaluation requirements have evolved around: (1) cost and cost savings projections to support return on investment analyses; (2) engagement of a broader range of stakeholders in evaluation activities; (3) expectations that evaluators leverage public use data sets such as the Transformed Medicaid Statistical Information System (T-MSIS) and Behavioral Risk Factor Surveillance System (BRFSS); and (4) when to employ best practices such as randomized controlled trials and comparative interrupted-time series for causal inference to pinpoint policy-specific impact. 

Below we highlight key aspects of an evaluation approach and potential ways to accelerate, de-risk, and optimize by leveraging AI techniques. 

Developing Actionable Frameworks: Selecting and integrating the right mix of evaluation approaches—rigorous research-quality impact to make causal inferences; Plan-Do-Study-Act (PDSA)-style assessment cycles for continuous quality improvement; and compliance-focused monitoring and data collection to ensure resources and expenditures are used as intended—to satisfy CMS guidance and local priorities. 

AI Techniques: 

• AI can recommend optimal evaluation designs (randomized controlled trials (RCTs), comparative interrupted-time series, PDSA cycles, monitoring frameworks) based on program type, data availability, sample sizes, and the specific RHTP domain (access, quality, workforce, etc.). 

• AI tools can auto‑generate difference‑in‑differences models, synthetic control analyses, or propensity models to evaluate policy impact. 

• AI tools can also be used to examine data and identify issues with quality, structure, sample size, or other features that could violate model assumptions and then recommend alternative approaches to explore. 

Integration and Use of Public Dataset: Selecting and using public datasets can add power and credibility to analyses, yet these take enormous effort to clean and align with state-level program data. Leveraging these strategies can accelerate one of the slowest and most expensive parts of evaluation work, high-quality multisource data preparation. 

AI Techniques: 

• Automate data ingestion and normalization tools for T‑MSIS, BRFSS, and Census datasets. 

• Develop resolution algorithms to match state program participants across disparate datasets. 

• Use pre‑trained models to quickly surface CMS‑aligned indicators (quality, equity, access, utilization, chronic conditions, and social needs). 

Stakeholder Engagement: Strategies for engaging diverse rural stakeholders in evaluation activities, ensuring that local voices shape program design and outcomes. 

AI Techniques: 

• Use common Natural Language Processing (NLP) to analyze community listening sessions, open‑ended survey responses, and public comments. 

• Create sentiment and theme analysis, surfacing concerns by county, demographic group, or stakeholder type. 

• Deploy multilingual tools to engage populations with limited English proficiency and chatbots or SMS‑based data collection for rural areas with limited broadband. 

Sustainability and Impact: One of the hardest RHTP requirements is proving that funded activities will become financially self-sustaining by year five. Evaluation that can support compliance while helping states demonstrate progress in meeting RHTP goals to avoid recoupment of funds and support long-term sustainability to maximize the value of RHTP funding will be vital. 

AI Techniques: 

• Dynamic financial forecasting models using machine learning to project: cost trajectories, break‑even timelines, downstream savings, financial health of rural hospitals under different scenarios. 

• Automated cost allocation validation to ensure spending aligns with RHTP guidance and permitted uses. 

• Predictive modeling for return on investment that incorporates utilization trends, workforce availability, reimbursement changes, and adoption of telehealth and digital tools that can scale efforts and impact. 

As the RHTP enters its implementation phase, states face both heightened accountability and unprecedented opportunity. Evaluation approaches that integrate rigorous methodological design, effective use of public data, meaningful stakeholder engagement, and advanced analytic techniques—including AI—will be essential to meeting CMS requirements while producing timely, policy relevant insights. By adopting evaluation strategies that are both compliant and adaptive, states can not only demonstrate relevant insights.