ArticlePloS one2022
Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Machine learning approaches to predicting medication nonadherence: a scoping review.International journal of medical informatics · 2025Article
- Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Association of shared decision-making cessation model and adult smoking cessation rate: A prospective cohort study.Tobacco induced diseases · 2025Article
- Leveraging artificial intelligence for collaborative care planning: Innovations and impacts in shared decision-making - A systematic review.Open medicine (Warsaw, Poland) · 2025Review
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Authors and funding
6 authors.
Funding
Abstract
objectiveAsthma is a common chronic illness affecting 19 million US adults. Inhaled corticosteroids are a safe and effective treatment for asthma, yet, medication adherence among patients remains poor. Shared decision-making, a patient activation strategy, can improve patient adherence to inhaled corticosteroids. This study aimed to explore whether audio-recorded patient-primary care provider encounters can be used to: 1. Evaluate the level of patient-perceived shared decision-making during the encounter, and 2. Predict levels of patient's inhaled corticosteroid adherence. MATERIALS AND
methodsShared decision-making and inhaled corticosteroid adherence were assessed using the SDM Questionnaire-9 and the Medication Adherence Report Scale for Asthma (MARS-A). Speech-to-text algorithms were used to automatically transcribe 80 audio-recorded encounters between primary care providers and asthmatic patients. Machine learning algorithms (Naive Bayes, Support Vector Machines, Decision Tree) were applied to achieve the study's predictive goals.
resultsThe accuracy of automated speech-to-text transcription was relatively high (ROUGE F-score = .9). Machine learning algorithms achieved good predictive performance for shared decision-making (the highest F-score = .88 for the Naive Bayes) and inhaled corticosteroid adherence (the highest F-score = .87 for the Support Vector Machines). DISCUSSION: This was the first study that trained machine learning algorithms on a dataset of audio-recorded patient-primary care provider encounters to successfully evaluate the quality of SDM and predict patient inhaled corticosteroid adherence.
conclusionMachine learning approaches can help primary care providers identify patients at risk for poor medication adherence and evaluate the quality of care by measuring levels of shared decision-making. Further work should explore the replicability of our results in larger samples and additional health domains.
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