Perspective
Who Owns the Pipeline? Toward a Practical Data Governance Model for Learning Health Systems
Daniel Hofstetter, MS, Dr. Erik Johansson (Halcyon Health Plans)
Volume 5, Number 3 · June 2026 · pp. 168–180 · doi:10.59821/jhds.2026.0305
Received January 9, 2026 · Accepted April 22, 2026 · Published June 15, 2026
Abstract
Data governance is usually written as policy and then ignored in practice because no one owns the day-to-day decisions. We propose a lighter-weight governance model that assigns clear ownership to the people who actually build and maintain data pipelines.
Introduction
Health systems increasingly depend on predictive models embedded in electronic health records. Yet most published evaluations describe performance at a single moment in time, leaving practitioners with little evidence about how these tools behave months or years after go-live.
Methods
We conducted a retrospective cohort study across participating sites between 2023 and 2025. Performance was assessed monthly using discrimination (AUROC), calibration slope, and subgroup-specific false positive rates. The study was approved by each site's institutional review board with a waiver of consent.
Results
Of the models studied, a substantial share showed statistically significant degradation, most commonly in calibration rather than discrimination. Degradation was concentrated in periods following documentation template changes and shifts in patient mix.
Discussion
Our findings suggest that routine, low-cost monitoring can detect meaningful performance changes well before they surface through clinician complaints. We recommend health systems assign a named owner to every deployed model.
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