# 71% of Hospitals Now Use EHR-Integrated Predictive AI in 2024 as Vendor Models Lead > Hospital use of EHR-integrated predictive AI hit 71% in 2024, up from 66% in 2023. Growth centers on billing and risk scoring, with vendor models leading and oversight improving. - Type: AI news - Page: https://completeaitraining.com/news/71-of-hospitals-now-use-ehr-integrated-predictive-ai-in/ - Published: 2025-09-27 - Updated: 2025-09-27 - Author: Jeroen Erne - Jobs: Healthcare - Reading time: 3 min ### Hospital Adoption of EHR-Integrated Predictive AI Jumps to 71% in 2024 Hospitals are moving from pilots to production. In 2024, 71% of hospitals used predictive AI integrated into their EHR, up from 66% in 2023, according to new federal data. Most are leaning on models from their EHR vendor, with billing and patient risk identification leading the use cases. #### What the data covers The report from ASTP/ONC tracks how hospitals use, evaluate and govern predictive AI-defined as statistical and machine learning models that classify or produce risk scores (readmissions, early disease detection, no-shows, treatment recommendations). Findings draw on the AHA IT Supplement surveys from 2023 and 2024. The 2024 survey ran April-September 2024 across 2,253 non-federal acute care hospitals (51% response). The 2023 survey ran March-August 2023 across 2,547 hospitals (58% response). #### Who is adopting fastest - Medium and large hospitals outpaced small hospitals. - Non-critical access hospitals led critical access hospitals. - System-affiliated and urban hospitals led independent and rural hospitals. - EHR vendor effect: among hospitals on the market-leading EHR, 90% used predictive AI in 2024 vs 50% for other EHRs. #### Where hospitals are using predictive AI - Revenue cycle: simplifying or automating billing was a top growth area. - Operations: scheduling and throughput optimization. - Care management: identifying high-risk outpatients to guide follow-up. Source matters. Use of predictive AI for billing was higher with third-party or self-developed models (73%) than with EHR vendor models (58%). Identification of high-risk outpatients also grew faster among hospitals using third-party or self-developed AI than those using EHR vendor tools. #### How hospitals source models - 80% used models from their EHR developer in 2024. - 52% used third-party models. - 50% built models internally. Most hospitals mix sources. EHR vendor models offer speed and integration. Third-party and in-house models offer flexibility and niche capabilities. #### Evaluation and governance are catching up - 82% evaluated models for accuracy. - 74% assessed for bias. - 79% conducted post-implementation evaluation or monitoring. - Governance typically spans multiple entities: 66% reported a dedicated committee or task force; 60% cited division/department leaders. IT staff were least cited as the primary evaluation body. #### Why this matters Predictive AI is now a standard EHR feature set, not a side project. The next advantage comes from choosing the right use cases, validating models against local data and building guardrails that clinicians trust. #### Practical steps for hospital leaders - Prioritize 2-3 high-yield workflows: billing edits, readmission risk, no-show prediction, ED triage, care gap closure. - Benchmark model performance locally. Validate AUROC/calibration on your population before go-live. - Design human-in-the-loop workflows. Define who acts on a score, within what time window, and with what documentation. - Track benefit and harm. Monitor precision/recall by subgroup, alert burden, time to action, downstream cost and outcomes. - Use a tiered sourcing strategy. Start with EHR-native models for speed; add third-party or in-house where you need specialty depth or better fit. - Stand up a cross-functional AI committee. Include clinical leaders, quality/safety, equity, compliance, privacy, security, revenue cycle and IT. - Publish a model registry. List purpose, data inputs, training data provenance, validation metrics, monitoring plan and retirement criteria. - Align with external frameworks (e.g., risk management, bias testing, documentation) and bake them into procurement and change control. - Invest in training for clinicians, data teams and operations staff so they understand what a score means-and what it doesn't. #### Key questions to ask your EHR and AI vendors - What training data was used? How similar is it to our patient mix and care setting? - Do you provide site-level calibration and drift monitoring out of the box? - What are the model's top features/inputs and known failure modes? - What subgroup performance gaps exist, and how are bias and fairness monitored? - What is the expected clinical or financial ROI, and how will we measure it jointly? - What safeguards exist for explainability, override, audit logging and rollback? #### What good governance looks like - Policy: clear criteria for model approval, re-approval and retirement. - Process: pre-implementation testing, staged rollout, post-implementation surveillance. - People: accountable owners for each model; escalation paths for issues; regular reporting to quality and executive committees. - Equity: routine subgroup analysis; mitigation plans where gaps are detected. - Security and privacy: data minimization, PHI handling standards, vendor risk assessments and BAAs. #### Helpful resources - [ONC: Artificial Intelligence in Health IT](https://www.healthit.gov/topic/artificial-intelligence?ref=completeaitraining.com) - [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework?ref=completeaitraining.com) #### Build team capability If you are formalizing AI education for clinicians, quality, or data teams, consider curated training to accelerate adoption and governance maturity. - [Complete AI Training: Courses by Job](https://completeaitraining.com/courses-by-job/) - [AI Certification for Data Analysis](https://completeaitraining.com/certification/12b-ai-certification-for-data-analysis/) --- This is the Markdown form of https://completeaitraining.com/news/71-of-hospitals-now-use-ehr-integrated-predictive-ai-in/. 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