PDF(Pubmed)
Abstract：
BACKGROUND: Provider burnout due to workload is a significant concern in primary care settings. Workload for primary care providers encompasses both scheduled visit care and non-visit care interactions. These interactions are highly influenced by patients\' health conditions or acuity, which can be measured by the Adjusted Clinical Group (ACG) score. However, new patients typically have minimal health information beyond social determinants of health (SDOH) to determine ACG score.
OBJECTIVE: This study aims to assess new patient workload by first predicting the ACG score using SDOH, age, and gender and then using this information to estimate the number of appointments (scheduled visit care) and non-visit care interactions.
METHODS: Two years of appointment data were collected for patients who had initial appointment requests in the first year and had the ACG score, appointment, and non-visit care counts in the subsequent year. State-of-art machine learning algorithms were employed to predict ACG scores and compared with current baseline estimation. Linear regression models were then used to predict appointments and non-visit care interactions, integrating demographic data, SDOH, and predicted ACG scores.
RESULTS: The machine learning methods showed promising results in predicting ACG scores. Besides the decision tree, all other methods performed at least 9% better in accuracy than the baseline approach which had an accuracy of 78%. Incorporating SDOH and predicted ACG scores also significantly improved the prediction for both appointments and non-visit care interactions. The R 2 values increased by 95.2 and 93.8%, respectively. Furthermore, age, smoking tobacco, family history, gender, usage of injection birth control, and ACG were significant factors for determining appointments. SDOH factors such as tobacco usage, physical exercise, education level, and group activities were strongly correlated with non-visit care interactions.
CONCLUSIONS: The study highlights the importance of SDOH and predicted ACG scores in predicting provider workload in primary care settings.
OBJECTIVE: This study aims to assess new patient workload by first predicting the ACG score using SDOH, age, and gender and then using this information to estimate the number of appointments (scheduled visit care) and non-visit care interactions.
METHODS: Two years of appointment data were collected for patients who had initial appointment requests in the first year and had the ACG score, appointment, and non-visit care counts in the subsequent year. State-of-art machine learning algorithms were employed to predict ACG scores and compared with current baseline estimation. Linear regression models were then used to predict appointments and non-visit care interactions, integrating demographic data, SDOH, and predicted ACG scores.
RESULTS: The machine learning methods showed promising results in predicting ACG scores. Besides the decision tree, all other methods performed at least 9% better in accuracy than the baseline approach which had an accuracy of 78%. Incorporating SDOH and predicted ACG scores also significantly improved the prediction for both appointments and non-visit care interactions. The R 2 values increased by 95.2 and 93.8%, respectively. Furthermore, age, smoking tobacco, family history, gender, usage of injection birth control, and ACG were significant factors for determining appointments. SDOH factors such as tobacco usage, physical exercise, education level, and group activities were strongly correlated with non-visit care interactions.
CONCLUSIONS: The study highlights the importance of SDOH and predicted ACG scores in predicting provider workload in primary care settings.
摘要：
背景：在初级保健机构中，由于工作量而导致的提供者倦怠是一个重要问题。初级保健提供者的工作量包括定期访问护理和非访问护理交互。这些相互作用受到患者健康状况或敏锐度的高度影响，这可以通过调整后的临床组（ACG）评分来衡量。然而,除社会健康决定因素(SDOH)外,新患者通常拥有最少的健康信息来确定ACG评分.
目的：本研究旨在通过首先使用SDOH预测ACG评分来评估新患者的工作量，年龄,和性别，然后使用这些信息来估计预约次数（定期就诊护理）和非就诊护理互动。
方法：收集第一年有初次预约请求并有ACG评分的患者的两年预约数据，预约,以及随后一年的非就诊护理计数。采用最先进的机器学习算法来预测ACG得分并与当前基线估计进行比较。然后使用线性回归模型来预测预约和非就诊护理交互，整合人口统计数据，SDOH,并预测ACG分数。
结果：机器学习方法在预测ACG分数方面显示出有希望的结果。除了决策树,所有其他方法的准确度至少比基线方法高9%,基线方法的准确度为78%.合并SDOH和预测的ACG分数也显着改善了约会和非访问护理交互的预测。R2值增加了95.2和93.8％，分别。此外，年龄,吸烟,家族史,性别,注射节育的使用，和ACG是决定预约的重要因素.SDOH因素，如烟草使用，体育锻炼,教育水平,小组活动与非就诊护理互动密切相关。
结论：该研究强调了SDOH和预测ACG评分在预测初级保健机构提供者工作量方面的重要性。
目的：本研究旨在通过首先使用SDOH预测ACG评分来评估新患者的工作量，年龄,和性别，然后使用这些信息来估计预约次数（定期就诊护理）和非就诊护理互动。
方法：收集第一年有初次预约请求并有ACG评分的患者的两年预约数据，预约,以及随后一年的非就诊护理计数。采用最先进的机器学习算法来预测ACG得分并与当前基线估计进行比较。然后使用线性回归模型来预测预约和非就诊护理交互，整合人口统计数据，SDOH,并预测ACG分数。
结果：机器学习方法在预测ACG分数方面显示出有希望的结果。除了决策树,所有其他方法的准确度至少比基线方法高9%,基线方法的准确度为78%.合并SDOH和预测的ACG分数也显着改善了约会和非访问护理交互的预测。R2值增加了95.2和93.8％，分别。此外，年龄,吸烟,家族史,性别,注射节育的使用，和ACG是决定预约的重要因素.SDOH因素，如烟草使用，体育锻炼,教育水平,小组活动与非就诊护理互动密切相关。
结论：该研究强调了SDOH和预测ACG评分在预测初级保健机构提供者工作量方面的重要性。
