PCCI CEO featured in the New England Journal of Medicine
08/19/2026
PCCI CEO featured in the New England Journal of Medicine: "Data Innovation Investment Is Booming, but Insights Lag"
PCCI CEO, Steve Miff, PhD, is featured in the September edition of the New England Journal of Medicine Catalyst. In this NEJM Catalyst report, Dr. Miff shares his perspectives on healthcare AI, where the disparity between investment and successful implementation is the key takeaway.
Read the report here: 📊 Insights Report: Data Innovation Investment Is Booming, but Insights Lag https://nej.md/4zm6ZZ3
Steve Miff overall conclusion:
"These results highlight an emerging trend that we are seeing across health systems: the investment is real, the intent is serious, but the gap between piloting and enterprise-scale impact remains the defining challenge of this era. When two-thirds of leaders have increased their data innovation investment yet fewer than one in ten report measurable value at scale, it tells us the problem is not ambition, it is execution. Health systems must move beyond the proof-of-concept mindset and build the governance, talent, and interoperability infrastructure that turns promising pilots into durable operational advantage. The organizations that crack that code in the next three years will set the standard for the decade."
Top 5 Key Takeaways
1. Investment Is Accelerating, But Value Realization Lags Dangerously Behind 64% of organizations globally (70% in the U.S.) have significantly or moderately increased their data innovation investment over two years. Yet only 9.8% have achieved significant, measurable value at enterprise scale. The vast majority remain at the stage of promising pilots or limited-domain gains.
Strategic Insight: The pattern of high investment paired with low enterprise-scale ROI signals that most health systems are spending on technology without adequately building the organizational infrastructure: governance, talent, change management, that are required to operationalize it. This mirrors early EHR adoption cycles.
2. Generative AI Is the Breakout Technology: Adoption Is Outpacing Readiness Gen AI for documentation support leads all technologies in active adoption: 51% globally, 63% in the U.S. Gen AI for administrative tasks is at 44%. This has happened remarkably fast. By contrast, AI-assisted clinical decision support (39%) and social determinants of health data integration (28%) lag significantly and presents a differentiation opportunity.
Strategic Insight: Adoption is concentrating in the lowest-friction, highest-visibility use cases. Organizations risk building AI capabilities on a fragile data foundation if they don't simultaneously invest in data quality, governance, and interoperability.
3. Operational Efficiency and Quality Are the Twin Imperatives Top goals driving data innovation: operational efficiency and workflow redesign (54.5%), quality/safety/reliability (50.3%), and clinical decision-making and outcomes (47.9%). U.S. organizations prioritize efficiency even more strongly (61%) than non-U.S. (46%), reflecting sharper margin compression.
Strategic Insight: Financial pressures, not necessarily a strategic innovation vision, is the primary motivator for most systems. While staff optimization and cost reduction are critical short-term needs, exclusive focus on efficiency risks underinvesting in the clinical intelligence and population health capabilities that create long-term differentiation.
4. Budget Constraints and Talent Gaps Are the Twin Throttles Budget constraints are the #1 barrier (51.7%), followed by talent/skills gaps (36.1%), interoperability challenges (32.8%), workflow integration (31.9%), and governance complexity (31.3%). The top accelerators mirror the barriers exactly: additional funding (46.1%) and more skilled staff (36.6%).
Strategic Insight: The system is caught in a resource paradox. Data innovation is still competing on annual budget cycles rather than being treated as strategic capital investment. Analytics staffing and data literacy among frontline staff are among the weakest organizational capabilities, yet targeted workforce programs remain underfunded.
5. Organizational Maturity Is Still Early: Scaling Pilots Is the Critical Unsolved Problem Fewer than 21% of organizations rate themselves "Advanced or Leading" in any single capability. Privacy/security is the strongest area (37% advanced or leading); ability to scale pilots into operations is the weakest (only 17%). Data literacy at the frontline is critically underdeveloped (14% advanced or leading).
Strategic Insight: Most health systems can spin up proofs of concept but lack the enterprise data strategy, governance, and cross-functional integration needed to operationalize them. The scaling gap, not the ideation gap, is the defining challenge of this era.
Implications for Health Systems: 2026–2029
- The "Scale-or-Stall" Inflection Point Will Arrive by 2027: The field will bifurcate between systems that scale and those trapped in endless pilot cycles. Scaling must be treated as a distinct discipline, not an afterthought.
- Talent Will Become the Scarcest Competitive Resource: Systems that invest in internal data literacy programs and embed analytics support into clinical teams will build durable advantages that compensation alone cannot buy.
- Interoperability Will Become a Market Differentiator: Organizations that build robust data-sharing ecosystems proactively will unlock care coordination and population health capabilities that siloed competitors cannot replicate.
- Data and AI Governance Will Shift from Compliance Overhead to Strategic Asset: As generative AI embeds more deeply in clinical workflows, governance failures will carry increasing reputational and clinical risk. AI ethics and outcome surveillance frameworks will become patient trust differentiators.
- Financial Pressure Will Force a Reckoning With ROI Measurement : CFOs and boards will demand accountability for data innovation spend. Systems that cannot demonstrate impact/ROI will face reallocation away from innovation, accelerating market consolidation toward platforms over point solutions.
- The Vendor List will Become Smaller and More Focused: Organizations will not have the luxury to contract and manage point solutions, particularly for localization and post AI deployment support. Internal capabilities to evaluate and asses solution maturity (fact vs fiction); true product readiness by EHR vendors; localization and retraining of solutions to local patient populations; and the deep integration required to monitor and support solutions post deployment will create barriers of entry for new vendors.