ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Improving Emergency Department Visit Risk Prediction: Exploring the Operational Utility of Applied Patient Portal Messages.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Using Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Predict Inpatient Admitting Specialty for Emergency Department Patients: Single Centre Deep Learning Analysis.Emergency medicine Australasia : EMA · 2026Article
- A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions.npj health systems · 2026Article
- Social determinants of health and hospitalization risk in heart failure: A Retrospective analysis.American heart journal plus : cardiology research and practice · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Patient portal messages represent a unique source of clinical data due to how they represent the voice of the patient, provide a glimpse into care delivery between episodic synchronous appointments, and capture variations in patient behavior and health literacy. There is little understanding of how to best apply modern natural language processing (NLP) approaches, such as large, pre-trained language models (LLMs), to patient messages. In this study, we aim to explore different approaches in incorporating patient messages into an existing Emergency Departments (ED) visit risk prediction model currently deployed at Stanford Health Care. With the addition of patient message frequencies to the baseline we were able to achieve an improved AUC of .77 and a jump in the F1 score. In future work, we aim to build upon these findings and further test combination models to incorporate features around patient message content, in addition to message frequencies.
Indexed as
Identifiers
40417468PMC12099376What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.