Hospitals Boost Predictive AI, but Rural and Independent Facilities Lag

Predictive AI Use Climbs in Hospitals, But a Digital Divide Persists

Hospitals are scaling up predictive AI, yet the gains are uneven. New data from the Assistant Secretary for Technology Policy and the Office of the National Coordinator for Health IT points to clear gaps by size, location, and ownership.

Adoption is rising across the board, but independent, rural, and critical access hospitals trail system-affiliated and urban peers. The result: a persistent digital divide that could widen disparities in care and operational performance.

Key adoption numbers

• 71% of non-federal acute care hospitals used predictive AI integrated into EHRs in 2024, up from 66% in 2023.
• 86% of system-affiliated hospitals vs. 37% of independent facilities reported using predictive AI.
• 80% of non-critical access hospitals vs. 50% of critical access hospitals reported use.
• 81% of urban hospitals vs. 56% of rural hospitals reported use.

Office of the National Coordinator for Health IT and American Hospital Association data underpin these trends.

Where predictive AI is gaining traction

• Simplifying or automating billing procedures.
• Appointment scheduling and throughput management.
• Identifying high-risk outpatients for targeted follow-up.

Monitoring health and recommending treatments remains less common, largely due to higher clinical risk and the need for strong validation and oversight.

Why smaller and rural hospitals lag

• Limited capital for data infrastructure, integration, and vendor contracts.
• Fewer in-house analytics and clinical informatics resources.
• Higher perceived risk without mature governance or monitoring processes.
• Vendor offerings that favor large, integrated systems for deployment and support.

Evaluation and oversight are becoming standard

• 82% of hospitals evaluate predictive AI for accuracy.
• 74% check for bias.
• 79% conduct post-implementation evaluation or monitoring.
• Nearly three-quarters report multiple accountable entities; about one-quarter involve four or more. Committees and division/department leaders are most often responsible.

This level of oversight is essential as models can drift, EHR workflows change, and local populations shift.

Practical steps for hospital leaders

• Start with low-risk, high-ROI use cases (billing edits, scheduling optimization, readmission risk stratification) before moving to treatment recommendations.
• Stand up AI governance that includes clinical, quality, informatics, compliance, and IT. Define approval, auditing, model updates, and incident response.
• Mandate pre- and post-implementation evaluation: calibration, accuracy, bias checks across subpopulations, and ongoing monitoring with clear thresholds for action.
• Budget for integration and maintenance, not just licenses. Consider consortium purchasing or shared services to reduce cost for smaller facilities.
• Require vendors to provide transparency: data sources, validation cohorts, performance by subgroup, update cadence, and model change logs.
• Train staff on appropriate use, documentation, and escalation paths when outputs conflict with clinical judgment.
• Track clinical and operational outcomes: readmissions, throughput, no-show rates, coding accuracy, staff time saved, and patient experience.

Predictive AI vs. Generative AI

Predictive AI is embedded and growing in core hospital workflows. By contrast, many Generative AI pilots remain early-stage with limited full implementations. The operational and safety demands in healthcare require stronger validation before widespread use of content-generating tools.

The bottom line

Predictive AI is moving from experiment to infrastructure, but access and capability are uneven. Focus on governance, measurement, and pragmatic use cases to deliver value while minimizing risk-especially for smaller and rural settings.

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