ArticleThe Journal of surgical research2020
Automating the Classification of Complexity of Medical Decision-Making in Patient-Provider Messaging in a Patient Portal.
Article in The Journal of surgical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational Use of Patient-Provider Secure Messaging Data to Achieve Better Clinical Efficiency and Quality of Communication: A Systematic Review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Pooled it
- A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions.npj health systems · 2026Article
- Automating Responses to Patient Portal Messages Using Generative AI.Applied clinical informatics · 2025Article
- Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Identifying Medication-Related Intents From a Bidirectional Text Messaging Platform for Hypertension Management Using an Unsupervised Learning Approach: Retrospective Observational Pilot Study.Journal of medical Internet research · 2022Observational
- Patient Portal Messaging for Asynchronous Virtual Care During the COVID-19 Pandemic: Retrospective Analysis.JMIR human factors · 2022Article
- Characterizing Patient-Clinician Communication in Secure Medical Messages: Retrospective Study.Journal of medical Internet research · 2022Article
- Forecasting Erroneous Neural Machine Translation of Disease Symptoms: Development of Bayesian Probabilistic Classifiers for Cross-Lingual Health Translation.International journal of environmental research and public health · 2021Article
- Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers.International journal of environmental research and public health · 2021Article
- Analyzing Patient Secure Messages Using a Fast Health Care Interoperability Resources (FIHR)-Based Data Model: Development and Topic Modeling Study.Journal of medical Internet research · 2021Article
- Classification and analysis of asynchronous communication content between care team members involved in breast cancer treatment.JAMIA open · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatient portals are consumer health applications that allow patients to view their health information. Portals facilitate the interactions between patients and their caregivers by offering secure messaging. Patients communicate different needs through portal messages. Medical needs contain requests for delivery of care (e.g. reporting new symptoms). Automating the classification of medical decision complexity in portal messages has not been investigated. MATERIALS AND
methodsWe trained two multiclass classifiers, multinomial Naïve Bayes and random forest on 500 message threads, to quantify and label the complexity of decision-making into four classes: no decision, straightforward, low, and moderate. We compared the performance of the models to using only the number of medical terms without training a machine learning model.
resultsOur analysis demonstrated that machine learning models have better performance than the model that did not use machine learning. Moreover, machine learning models could quantify the complexity of decision-making that the messages contained with 0.59, 0.45, and 0.58 for macro, micro, and weighted precision and 0.63,0.41, and 0.63 for macro, micro, and weighted recall.
conclusionsThis study is one of the first to attempt to classify patient portal messages by whether they involve medical decision-making and the complexity of that decision-making. Machine learning classifiers trained on message content resulted in better message thread classification than classifiers that employed medical terms in the messages alone.
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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.