What Health Systems Really Want: 5 Health Tech Takeaways From DiMe’s New AI Playbook
TL;DR
Health systems are interested in AI but increasingly skeptical of its promises. The Digital Medicine Society (DiMe) has released a new AI Implementation Playbook, helping both vendors and health systems bridge the gap between innovation and adoption.
If you’re a growth-stage health tech company selling AI-powered solutions, this Playbook is your roadmap for success. Here are the five essentials you must master to turn pilots into partnerships!
Checkout the full breakdown Here
1. Solve the Right Problem
Most AI vendors start with the tech.
Health systems start with the problem.
DiMe’s Playbook emphasizes aligning AI solutions with real-world clinical and operational pain points (e.g., clinician burnout, capacity, and patient throughput.)
If your AI can’t demonstrate measurable value in these areas, it won’t survive the procurement review.
Lead your pitch with outcomes, not algorithms.
Tie your use case directly to measurable ROI and reduced burden for end users.
Health Tech Sales Strategist Brendan McAdams talks about this more here.
2. Readiness and Governance Matter
According to DiMe, successful AI deployment requires mutual readiness between the vendor and health system.
Health systems are demanding AI governance frameworks that include model ethics, validation, accountability, and cybersecurity.
Vendors must be prepared to show documentation of how their model is trained, tested, secure, and monitored.
Use governance as your trust signal
A strong cybersecurity and governance framework makes health systems more comfortable partnering with startups.
Book a 15-minute vCISO strategy call to discuss aligning your AI Governance strategy with health system standards 👈
3. Evidence Wins Every Time
Health systems buy on evidence!
The Playbook urges vendors to back every claim with peer-reviewed studies, real-world validation, and bias testing.
Demonstrating safety, efficacy, and fairness is the new baseline.
Trust is earned through proof.
Make validation stats part of your sales narrative, versus an afterthought.
4. Integration Is Non-Negotiable
Even the most sophisticated AI model fails if it doesn’t integrate seamlessly into existing workflows.
DiMe stresses that health systems expect vendors to embed AI into current EHR and workflow structures, not disrupt them.
Complex deployments and interoperability challenges can be deal-breakers.
Integration is your competitive advantage.
Invest in FHIR, data mapping, and user-centric design so your AI enhances, not hinders clinical efficiency.
5. Continuous Oversight and Model Maintenance
AI performance drifts.
Health systems want partners who monitor, retrain, and refine models to prevent bias, degradation, security vulnerabilities, and inaccurate outputs.
DiMe calls for a lifecycle approach to AI oversight, ensuring accountability after go-live.
Reliability is the new success metric.
AI oversight earns renewals and protects your reputation.
Action Steps for Health Tech Companies
If your company is aiming to sell AI solutions to hospitals, clinics, or large health systems, start here:
Map your solution against DiMe’s five pillars.
Identify documentation gaps (validation, explainability, governance).
Run cybersecurity and algorithmic risk assessments before demos.
Develop a transparent buyer packet with governance, cybersecurity, and ROI metrics.
Align your Go To Market (GTM) strategy with health system readiness frameworks.
Don’t sell AI...sell reliability, transparency, and trust!
Download the HIPAA EXP Guide to ensure your cybersecurity strategy supports your sales story 👈
FAQ
What do health systems prioritize when evaluating AI vendors?
They look for evidence of safety, explainability, and operational fit. Solutions that solve specific, measurable problems.
Why does governance matter so much in AI implementations?
Governance ensures accountability, mitigates bias, and gives buyers confidence that your model is ethical and safe.
How can startups prove ROI and safety without full clinical trials?
By publishing validation data, pilot outcomes, or performance benchmarks in controlled deployments.
What is model drift and why does it affect adoption?
Model drift occurs when AI accuracy declines over time due to changing data patterns. Continuous monitoring and retraining are critical.
Final Thoughts
Health systems aren’t opposed to innovation but it has to be reliable.
DiMe’s Playbook is a reality check...and a good one!
AI adoption is about building smart models that are trustworthy, secure, transparent, and aligned with provider goals!
P.S. Which of DiMe’s five AI essentials do you think health tech vendors overlook the most?