School of Nursing and Data Science Institute, Columbia University, New York, New York, United States of America.
Visiting Nurse Service of New York, New York, New York, United States of America.
PLoS One. 2022 Aug 4;17(8):e0271884. doi: 10.1371/journal.pone.0271884. eCollection 2022.
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.
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.
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).
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.
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.
哮喘是一种常见的慢性病，影响着美国 1900 万成年人。吸入性皮质类固醇是治疗哮喘的安全有效方法，但患者的药物依从性仍然很差。共同决策，一种患者激活策略，可以提高患者对吸入性皮质类固醇的依从性。本研究旨在探讨音频记录的医患互动是否可以用于：1. 评估患者在互动中感知到的共同决策水平，以及 2. 预测患者吸入皮质类固醇的依从性水平。
使用 SDM 问卷-9 和哮喘用药依从性报告量表（MARS-A）评估共同决策和吸入皮质类固醇的依从性。语音转文本算法用于自动转录 80 名初级保健提供者和哮喘患者之间的音频记录互动。机器学习算法（朴素贝叶斯、支持向量机、决策树）被应用于实现研究的预测目标。
自动语音转文本转录的准确性相对较高（ROUGE F 分数=0.9）。机器学习算法在共同决策（朴素贝叶斯的最高 F 分数=0.88）和吸入皮质类固醇依从性（支持向量机的最高 F 分数=0.87）方面取得了良好的预测性能。
这是第一项在音频记录的医患互动数据集上训练机器学习算法以成功评估 SDM 质量并预测患者吸入皮质类固醇依从性的研究。
机器学习方法可以帮助初级保健提供者识别药物依从性差的患者，并通过测量共同决策水平来评估护理质量。进一步的工作应该在更大的样本和其他健康领域探索我们结果的可复制性。
