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Pharmacovigilance

Pharmacovigilance

Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...

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Updated: May 5, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

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Published on: July 27, 2018

Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study.

Richard M Yoo¹, Ben T Viggiano¹, Krishna N Pundi²

¹Department of Medicine, School of Medicine, Stanford University, Stanford, CA, United States.

JMIR Medical Informatics

|April 10, 2024

Summary

This summary is machine-generated.

Consumer wearables can provide atrial fibrillation (AF) prediagnoses. This study developed an effective electronic health record (EHR) surveillance system to track these prediagnoses and found they correlate with increased anticoagulant use and AF diagnosis.

Keywords:

EHR NLP arrhythmia artificial intelligence atrial fibrillation cardiology classifier consumer consumer wearable devices consumers device devices diagnose diagnosis electronic health record electronic health records evaluation heart labeler labeling machine learning monitoring natural language processing postmarket surveillance surveillance wearable wearables

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Area of Science:

Medical Informatics
Cardiology
Digital Health

Background:

Consumer wearables offer prediagnoses, potentially impacting healthcare.
Electronic health record (EHR) systems lack standardized terms for wearable data, hindering postmarket surveillance.
Atrial fibrillation (AF) prediagnoses from wearables are a key area of interest.

Purpose of the Study:

To develop and validate an EHR-based surveillance system for consumer wearable AF prediagnoses.
To demonstrate the feasibility and efficacy of using weak supervision for EHR data labeling.
To compare patient characteristics and care patterns following wearable-detected AF prediagnoses.

Main Methods:

Applied data programming and the Snorkel framework to create labeling functions for AF prediagnoses.
Developed a labeler model to probabilistically label clinical notes for AF prediagnoses.
Fine-tuned a Clinical-Longformer classifier on labeled notes to identify patients with AF prediagnoses.
Conducted a retrospective cohort study comparing patients with and without AF prediagnoses.

Main Results:

The labeler model achieved high accuracy (0.92) and F1-score (0.77).
The Clinical-Longformer classifier accurately identified AF prediagnoses (0.95 accuracy, 0.83 F1-score).
Patients with AF prediagnoses were older, male, White, and had higher CHA2DS2-VASc scores.
Prediagnoses correlated with increased anticoagulant use (e.g., apixaban, rivaroxaban) and eventual AF diagnosis.

Conclusions:

An EHR-based surveillance system for wearable AF prediagnoses is feasible and effective.
The system can identify patient cohorts for further study and verify findings from large trials.
Further research is needed to generalize findings across diverse patient populations and healthcare settings.