Pallavi Ranade-Kharkar, PhD, Enterprise Director of Research Informatics and Genomics, Intermountain Health, laid out a disciplined approach to AI adoption that balances rising organizational pressure with patient-centered guardrails, emphasizing governance, security, and measurable value for clinicians and consumers.
Health systems are under strain to automate, improve access, and lower costs. National spending projections point upward and boards are pushing digital programs. Against that backdrop, Ranade-Kharkar described a portfolio view of AI—predictive, generative, and hybrid tools—where pilots scale only when they deliver reliable outcomes. “There is tremendous potential for AI to do more, and it’s constantly evolving,” she said, noting that many models still require tight guardrails to prevent errors and protect trust.
Technology budgets already represent a meaningful share of revenue across hospitals, with increases likely as organizations pursue digital health gains. She urged leaders to fund projects that produce tangible clinical and operational results. Overconfidence, she warned, remains a risk in this fast-moving market: “Over reliance on AI can be detrimental.” In her view, executive sponsors should embrace rapid learning cycles while insisting on independent validation and post-go-live monitoring.
Privacy and Security as Table Stakes
The obligation to protect patients and their data sets the tone for every AI conversation. She emphasized that privacy, consent, and security must be integrated into solution design, procurement, and operations. CISOs, compliance, and data leaders should be at the table early and remain involved when tools retrain on fresh data or expand to new use cases. “Data security and patient privacy is everybody’s problem,” adding that governance must clearly assign responsibility for controls, audits, and incident response.

Beyond baseline controls, she called for transparency about model training and validation. Leaders should request documentation that details the datasets used, testing methods, and known limitations, and should insist on bias assessments when deploying to new populations. Policies should require continuous quality monitoring, especially for models that write to the electronic record or influence clinical decisions. In parallel, procurement language should define access, retention, and deletion requirements for any vendor handling patient data or prompts.
Vendor Transparency and Trust
As AI features arrive embedded in routine upgrades, leaders face a new dynamic with long-time vendors. Ranade-Kharkar urged explicit notifications when AI functions are introduced, along with clear instructions for risk review, activation, and controls. She recommended a two-layer test strategy: validate vendor claims on sample datasets and then confirm performance on the health system’s own data before broad release. Trust accrues when partners show their work—how models were trained, how outputs are evaluated, how drift is detected, and how issues are remediated.
She also advocated for human oversight calibrated to the task’s risk. Ambient documentation and meeting-note summarization differ from tools that generate orders or discharge instructions. For high-impact workflows, committees should define required reviewer roles, escalation paths, and thresholds for intervention. For lower-risk automation, product owners can tune prompts, track exceptions, and iterate quickly—while still reporting outcomes and issues to governance.
Measuring Experience,
Turning on technology is not the goal; rather sustained, satisfied use is. She argued for a patient-experience lens that gives as much weight to convenience and clarity as it does to accuracy. Abandonment rates for kiosks, chatbots, and virtual check-in are a leading indicator: if patients quit, the design or flow is failing. Leaders should test these experiences personally an...