Hospitals Get a Practical AI Playbook: DiMe, Google Health and 30+ Partners Set a New Standard
The Digital Medicine Society (DiMe), with support from Google Health and more than 30 organizations, released a new playbook to help hospitals and health systems build AI strategies grounded in real clinical needs and organizational capacity.
The core message: pause the hype. Ask, "What problem are we trying to solve?" Then build from there.
Why this matters
Rushing into fragmented pilots and weak change management burns time, money and trust. It can also increase patient risk.
DiMe notes that leaders must weigh risks with limited evidence, clinical teams must fit disparate tools into workflows, and technical teams must scale without compromising accessibility or patient safety.
What's inside the AI implementation playbook
- Assessment and readiness: Checklists and tools to identify operational and clinical pain points, and to measure AI readiness.
- Tool selection: Resources to evaluate and choose AI that fits care delivery needs.
- Deployment framework: Guidance for rollout, governance, training and ongoing monitoring.
The playbook helps IT strategy leaders (including CMIOs) balance clinical priorities, financial sustainability and enterprise goals, while giving clinical leaders a clear view of workflow, training and safety impact. "This playbook offers the guidance health leaders need to move from experimentation to responsible adoption at scale," said Pete Clardy, senior staff clinical specialist at Google for Health.
Support for technical leaders
Technical teams are often told to "add AI" without clear priorities, guardrails or resources. The framework lays out technical requirements, data pipeline foundations, production monitoring of model performance, and other core development practices.
Who contributed
Development included input from a broad coalition of healthcare, clinical, academic and industry groups:
- American Academy of Pediatrics, American College of Radiology, American Nurses Association
- Cedars-Sinai, Kaiser Permanente, Beth Israel Deaconess Medical Center
- Coalition for Health AI, Consumer Technology Association, Intel
- Stanford University, UPMC Enterprises, Vanderbilt University Medical Center
- U.S. Department of Veterans Affairs
As DiMe CEO Jennifer Goldsack put it at launch: "If we don't get implementation right … I don't know what the sustainable future of the healthcare industry is and why any investor would continue to invest in healthcare AI if we can't take it to scale."
The bigger picture
DiMe's mission is to advance digital health for systems, clinicians, patients and the public. Last year, it introduced a platform to evaluate privacy, security and equity standards, plus a DiMe Seal recognizing products that meet its quality and trust standards. At the time, DiMe cited 400,000 consumer health apps and 30,000 apps for providers and enterprises.
AI activity has grown since, but data and integration challenges remain. A report from Bessemer Venture Partners, Amazon Web Services and Bain & Company noted many organizations are in "test and learn" mode under pressure from boards and CEOs, with mid-to-large providers more likely to bring AI into production.
What leaders should do now
- Define the clinical and operational problems worth solving. Prioritize by impact and feasibility.
- Run an honest readiness assessment across data, workflows, governance and change management.
- Select tools that fit existing care delivery, not the other way around. Require evidence and clear outcome measures.
- Stand up governance early: clinical safety, bias review, privacy, security, and incident response.
- Invest in workforce training and role clarity. Pair clinicians with technical owners for each deployment.
- Build the plumbing: data pipelines, monitoring, drift detection and feedback loops into clinical teams.
- Measure results, decommission what doesn't work, scale what does.
"Seventy percent of health AI pilots fail, not because of technology, but because of people and process challenges," said Goldsack. The opportunity is clear: implementation quality will determine outcomes at scale.
Explore the work and tools from the Digital Medicine Society and the Coalition for Health AI for governance, evaluation and best practices.
If your teams need structured upskilling to support safe AI rollout, see curated programs by role at Complete AI Training.
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