# Data-driven shared decision-making to reduce symptom burden in atrial fibrillation

> **NIH NIH K99** · WEILL MEDICAL COLL OF CORNELL UNIV · 2021 · $87,632

## Abstract

PROJECT SUMMARY
Atrial fibrillation (AF) is the most common cardiac arrhythmia with symptoms that directly impair health-related
quality of life (HRQoL). While catheter ablation is routinely performed to reduce AF symptoms and improve
HRQoL, we lack evidence about which symptoms are likely to improve and for which patients. Ablations
themselves may cause complications that lead to lower HRQoL. Shared decision-making (SDM) is a widely
encouraged practice to navigate such complex choices by aligning treatment benefits and risks with the
patient's stated values. However, no SDM interventions have focused explicitly on AF symptoms due to a lack
of rigorous evidence about post-ablation symptom patterns and the decision aids necessary to communicate
those findings. In this K99/R00 application, we propose to use data from electronic health records (EHRs) to
characterize post-ablation symptom patterns, and display them in decision-aid visualizations to support
personalized SDM about the best treatment modalities for an individual's patient's AF symptoms. In the K99
phase, we will use natural language processing (NLP) and machine learning (ML) to extract and analyze
symptom data from narrative notes in EHRs. We will also employ a rigorous, user-centered design protocol
created during my postdoctoral work to develop decision-aid visualizations. In the R00 phase, we will conduct
a feasibility study in which the interactive decision-aid visualizations are introduced during consultations about
ablation in clinical electrophysiology practices. Our specific aims are: (1) identify common symptom patterns in
patients with paroxysmal AF post-catheter ablation (n>32,014); (2) develop and evaluate decision-aid
visualizations of common AF symptom patterns (n=50); and (3) evaluate the feasibility of implementing the
decision-aid visualizations in clinical practice (n=75). The training objectives of this project include mastering
competencies in NLP, ML, human-computer interaction, symptom science, and implementation science. The
long-term training goal is to assist Dr. Reading Turchioe to become a faculty member with an independent
program of research. She seeks to lead an interdisciplinary team of scientists and clinicians committed to
improving symptom management and HRQoL for individuals living with AF and other chronic cardiovascular
conditions, with an eye towards health equity. To ensure success for the planned research and training
activities, a multidisciplinary team of mentors with complementary expertise, established, well-funded programs
of research, and a record of mentoring high-quality trainees will advise her. Moreover, this research will be
conducted in a world-class academic medical center with exceptional resources for building and implementing
technology and data science methods using EHR data. The proposed research is both significant and
innovative: NLP and ML methods to extract EHR data for decision-aid visualizations are a novel approach to
SDM ...

## Key facts

- **NIH application ID:** 10145798
- **Project number:** 5K99NR019124-02
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Meghan Reading Turchioe
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $87,632
- **Award type:** 5
- **Project period:** 2020-04-15 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10145798

## Citation

> US National Institutes of Health, RePORTER application 10145798, Data-driven shared decision-making to reduce symptom burden in atrial fibrillation (5K99NR019124-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10145798. Licensed CC0.

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