Department of Orthopaedic Surgery, Orthopaedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Department of Orthopaedic Surgery, Leiden University Medical Center, Leiden University, Leiden, The Netherlands.
J Eval Clin Pract. 2025 Feb;31(1):e14248. doi: 10.1111/jep.14248.
Limited health literacy (HL) leads to poor health outcomes, psychological stress, and misutilization of medical resources. Although interventions aimed at improving HL may be effective, identifying patients at risk of limited HL in the clinical workflow is challenging. With machine learning (ML) algorithms based on readily available data, healthcare professionals would be enabled to incorporate HL screening without the need for administering in-person HL screening tools.
Develop ML algorithms to identify patients at risk for limited HL in spine patients.
Between December 2021 and February 2023, consecutive English-speaking patients over the age of 18 and new to an urban academic outpatient spine clinic were approached for participation in a cross-sectional survey study. HL was assessed using the Newest Vital Sign and the scores were divided into limited (0-3) and adequate (4-6) HL. Additional patient characteristics were extracted through a sociodemographic survey and electronic health records. Subsequently, feature selection was performed by random forest algorithms with recursive feature selection and five ML models (stochastic gradient boosting, random forest, Bayes point machine, elastic-net penalized logistic regression, support vector machine) were developed to predict limited HL.
Seven hundred and fifty-three patients were included for model development, of whom 259 (34.4%) had limited HL. Variables identified for predicting limited HL were age, Area Deprivation Index-national, Social Vulnerability Index, insurance category, Body Mass Index, race, college education, and employment status. The Elastic-Net Penalized Logistic Regression algorithm achieved the best performance with a c-statistic of 0.766, calibration slope/intercept of 1.044/-0.037, and Brier score of 0.179.
Elastic-Net Penalized Logistic Regression had the best performance when compared with other ML algorithms with a c-statistic of 0.766, calibration slope/intercept of 1.044/-0.037, and a Brier score of 0.179. Over one-third of patients presenting to an outpatient spine center were found to have limited HL. While this algorithm is far from being used in clinical practice, ML algorithms offer a potential opportunity for identifying patients at risk for limited HL without administering in-person HL assessments. This could possibly enable screening and early intervention to mitigate the potential negative consequences of limited HL without taxing the existing clinical workflow.
有限的健康素养（HL）会导致不良的健康结果、心理压力和医疗资源的误用。尽管旨在提高 HL 的干预措施可能是有效的，但在临床工作流程中识别有 HL 受限风险的患者具有挑战性。通过基于现成数据的机器学习（ML）算法，医疗保健专业人员可以在无需进行面对面 HL 评估工具的情况下，将 HL 筛查纳入其中。
开发用于识别脊柱患者中 HL 受限风险的 ML 算法。
在 2021 年 12 月至 2023 年 2 月期间，对新进入城市学术门诊脊柱诊所的年龄在 18 岁及以上的连续讲英语的患者进行了横断面调查研究。使用最新生命体征（Newest Vital Sign）评估 HL，评分分为有限（0-3）和充足（4-6）HL。通过社会人口统计学调查和电子健康记录提取其他患者特征。随后，通过随机森林算法进行特征选择，并开发了五种 ML 模型（随机梯度提升、随机森林、贝叶斯点机、弹性网惩罚逻辑回归、支持向量机）来预测有限 HL。
共纳入 753 名患者进行模型开发，其中 259 名（34.4%）患者 HL 受限。预测有限 HL 的变量包括年龄、国家区域贫困指数、社会脆弱性指数、保险类别、体重指数、种族、大学教育程度和就业状况。弹性网惩罚逻辑回归算法的表现最佳，c 统计量为 0.766，校准斜率/截距为 1.044/-0.037，Brier 评分 0.179。
与其他 ML 算法相比，弹性网惩罚逻辑回归算法的表现最佳，c 统计量为 0.766，校准斜率/截距为 1.044/-0.037，Brier 评分 0.179。在一个门诊脊柱中心就诊的患者中，超过三分之一的患者被发现 HL 受限。虽然该算法远未在临床实践中使用，但 ML 算法为识别 HL 受限风险的患者提供了潜在机会，而无需进行面对面的 HL 评估。这可能使筛查和早期干预成为可能，以减轻 HL 受限的潜在负面影响，而不会给现有的临床工作流程带来负担。
