社区卫生中心预约未到诊的数据分析与建模
Data Analytics and Modeling for Appointment No-show in Community Health Centers.
作者信息
Mohammadi Iman, Wu Huanmei, Turkcan Ayten, Toscos Tammy, Doebbeling Bradley N
机构信息
1 Department of BioHealth Informatics, School of Informatics and Computing, Indianapolis, IN, USA.
2 Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA.
出版信息
J Prim Care Community Health. 2018 Jan-Dec;9:2150132718811692. doi: 10.1177/2150132718811692.
OBJECTIVES
Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations.
METHODS AND MATERIALS
We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models' ability to identify patients missing their appointments.
RESULTS
Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier).
DISCUSSION
Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues.
CONCLUSION
EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions.
目的
运用预测建模技术，我们开发并比较了预约未就诊预测模型，以更好地了解服务不足人群的预约依从情况。
方法和材料
我们收集了三年期间的电子健康记录（EHR）数据和预约数据，包括患者、提供者和临床就诊特征。所有患者数据均来自一个拥有10个设施的城市社区健康中心系统。我们试图通过逻辑回归、人工神经网络和朴素贝叶斯分类器模型识别关键变量，以预测未就诊情况。我们使用10折交叉验证来评估模型识别未就诊患者的能力。
结果
经过数据预处理和清理后，最终数据集包含73811次独特预约，其中12392次为未就诊预约。未就诊预约与就诊预约的预测因素包括提前期（预约时间与就诊时间之间的间隔）、患者既往未就诊情况、是否拥有手机、吸烟情况以及上次预约后的天数。这三种模型的曲线下面积相对较高（例如，朴素贝叶斯分类器的曲线下面积为0.86）。
讨论
医疗系统内各诊所的患者预约依从情况各不相同。数据分析结果证明了现有临床和运营数据对于解决重要运营和管理问题的价值。
结论
包括患者和预约信息的电子健康记录数据能够预测城市社区健康中心服务不足人群的未就诊情况。我们对预测建模技术的应用有助于优先设计和实施可能提高社区健康中心效率的干预措施，以便更及时地获得医疗服务。社区健康中心将受益于投资所需的技术资源，以便随时获取这些数据，作为为重要运营和政策问题提供信息的一种手段。
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