Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting.
<h4>Objective</h4>Asthma 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 ad...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0271884&type=printable |
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| author | Maxim Topaz Maryam Zolnoori Allison A Norful Alexis Perrier Zoran Kostic Maureen George |
| author_facet | Maxim Topaz Maryam Zolnoori Allison A Norful Alexis Perrier Zoran Kostic Maureen George |
| author_sort | Maxim Topaz |
| collection | DOAJ |
| description | <h4>Objective</h4>Asthma 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.<h4>Materials and methods</h4>Shared 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.<h4>Results</h4>The 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).<h4>Discussion</h4>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.<h4>Conclusion</h4>Machine 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. |
| format | Article |
| id | doaj-art-132ad0303aaa4ffa8e5be73ccc25220f |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-132ad0303aaa4ffa8e5be73ccc25220f2025-08-20T03:25:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01178e027188410.1371/journal.pone.0271884Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting.Maxim TopazMaryam ZolnooriAllison A NorfulAlexis PerrierZoran KosticMaureen George<h4>Objective</h4>Asthma 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.<h4>Materials and methods</h4>Shared 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.<h4>Results</h4>The 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).<h4>Discussion</h4>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.<h4>Conclusion</h4>Machine 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0271884&type=printable |
| spellingShingle | Maxim Topaz Maryam Zolnoori Allison A Norful Alexis Perrier Zoran Kostic Maureen George Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting. PLoS ONE |
| title | Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting. |
| title_full | Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting. |
| title_fullStr | Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting. |
| title_full_unstemmed | Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting. |
| title_short | Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting. |
| title_sort | speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0271884&type=printable |
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