Partners involved: Pfizer, Regenstrief Institute, and Eskenazi Health

The Question

Atrial fibrillation (AF) is a major risk factor for ischemic stroke, yet it frequently remains undiagnosed until patients present with downstream events. In the United States, AF affects an estimated 12 million adults, with approximately 11% remaining undiagnosed and up to 23% undiagnosed over a two-year period, despite the availability of effective preventive therapies.

While predictive models for AF risk exist, most have been evaluated retrospectively or outside of routine clinical workflows.

The question driving this work was not simply whether an AF risk model could predict disease—but whether Regenstrief could build that model from real-world data, validate it rigorously, and, carry it all the way into live clinical workflows in a way that generates implementation-informed real-world evidence relevant to prevention, management, and downstream outcomes.

The Value of Research Collaborations that Start at the Build Stage

Prior validation studies established that AF risk models could perform well statistically, but they left several practical uncertainties unresolved: Would clinicians engage with risk information presented during routine visits? Could screening be integrated without disrupting workflow or increasing burden? Would model-enabled identification translate into diagnostic evaluation or treatment decisions in real care settings?

Answering these questions required a partner who understood the model’s architecture and data provenance from the start – not one brought in after the fact to test whether someone else’s tool would survive contact with a real clinical environment.  The Regenstrief team’s continuity across the full project made it possible to design the clinical pilot in a way that was directly informed by what the earlier development and validation work had revealed

The Clinical Pilot

The clinical evaluation embedded UNAFIED, a validated EHR-based model estimating patients’ two-year risk of developing atrial fibrillation, into real-world cardiology workflows at Eskenazi Health. Rather than testing the model in isolation, the collaboration focused on the surrounding process:

• Embedding non-interruptive clinical decision support within Epic electronic health record workflows to surface elevated AF risk
• Pairing model outputs with clinician-led screening using FDA-approved single-lead ECG devices
• Allowing diagnosis and management decisions to follow usual clinical practice

The study examined multiple dimensions of real-world use:

• Identification of patients flagged as elevated AF risk by the model
• Uptake of screening during routine cardiology visits
• New AF or atrial flutter diagnoses documented during the study period
• Clinician perceptions of workflow fit, usability, and clinical value

The collaboration demonstrated that an EHR-embedded AF risk model can be integrated into cardiology workflows without disrupting care delivery. New AF or atrial flutter diagnoses were documented among patients identified through the workflow, and some patients initiated anticoagulant therapy consistent with guideline-based management.

A follow-up outcomes study examining the cohort identified during proof-of-concept implementation is currently under review.

Importantly, the analysis was designed to distinguish model-enabled identification from downstream clinical decisions—recognizing that diagnoses and treatment reflect multiple inputs beyond any single tool.

The Relevance of Such Models for Life Science Partners

For life-science organizations evaluating where to invest in real-world evidence generation, this collaboration illustrates what becomes possible when a research partner can operate across the full development lifecycle:

• Model development grounded in a longitudinal, multi-institutional data asset that reflects the heterogeneity of real patient populations
• Validation designed to travel — tested in INPC data and subsequently validated in national EHR datasets
• Implementation science expertise to move from a working model to a live clinical workflow, with the study design to evaluate what actually changes in practice
• A continuous chain of evidence from initial development through outcomes follow-up, with the same institutional team

Regenstrief’s role in this project was not to stress-test a finished product in a real-world setting. It was to build the product, prove it worked, carry it into practice, and measure what happened — with Pfizer as a partner throughout.

Read the Research

Grout RW et al. Screening for undiagnosed atrial fibrillation using an electronic health record–based clinical prediction model: clinical pilot implementation initiative. BMC Medical Informatics and Decision Making (2024)

Grout RW et al. Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED). BMC Medical Informatics and Decision Making (2021). DOI: 10.1186/s12911-021-01482-1