School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, United States.
Department of Quality and Patient Safety, University of Alabama at Birmingham Medicine, Birmingham, AL, United States.
JMIR Res Protoc. 2024 Sep 25;13:e56049. doi: 10.2196/56049.
The use of both clinical factors and social determinants of health (SDoH) in referral decision-making for case management may improve optimal use of resources and reduce outcome disparities among patients with diabetes.
This study proposes the development of a data-driven decision-support system incorporating interactions between clinical factors and SDoH into an algorithm for prioritizing who receives case management services. The paper presents a design for prediction validation and preimplementation assessment that uses a mixed methods approach to guide the implementation of the system.
Our study setting is a large, tertiary care academic medical center in the Deep South of the United States, where SDoH contribute to disparities in diabetes-specific hospitalizations and emergency department (ED) visits. This project will develop an interpretable artificial intelligence model for a population with diabetes using SDoH and clinical data to identify which posthospitalization cases have a higher likelihood of subsequent ED use. The electronic health record data collected for the study include demographics, SDoH, comorbidities, hospitalization-related factors, laboratory test results, and medication use to predict posthospitalization ED visits. Subsequently, a mixed methods approach will be used to validate prediction outcomes and develop an implementation strategy from insights into patient outcomes from case managers, clinicians, and quality and patient safety experts.
As of December 2023, we had abstracted data on 174,871 inpatient encounters between January 2018 and September 2023, involving 89,355 unique inpatients meeting inclusion criteria. Both clinical and SDoH data items were included for these patient encounters. In total, 85% of the inpatient visits (N=148,640) will be used for training (learning from the data) and the remaining 26,231 inpatient visits will be used for mixed-methods validation (testing).
By integrating a critical suite of SDoH with clinical data related to diabetes, the proposed data-driven risk stratification model can enable individualized risk estimation and inform health professionals (eg, case managers) about the risk of patients' upcoming ED use. The prediction outcome could potentially automate case management referrals, helping to better prioritize services. By taking a mixed methods approach, we aim to align the model with the hospital's specific quality and patient safety considerations for the quality of patient care and the optimization of case management resource allocation.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56049.
在为病例管理制定转介决策时，同时使用临床因素和健康的社会决定因素（SDoH），可能会提高资源的最佳利用，并减少糖尿病患者之间的结果差异。
本研究旨在开发一种数据驱动的决策支持系统，将临床因素和 SDoH 之间的相互作用纳入算法中，以便对接受病例管理服务的人员进行优先级排序。本文提出了一种预测验证和实施前评估的设计，该设计采用混合方法来指导系统的实施。
我们的研究地点是美国南部的一家大型三级保健学术医疗中心，那里的 SDoH 导致了糖尿病患者的特定住院和急诊就诊方面的差异。该项目将使用 SDoH 和临床数据为糖尿病患者开发一个可解释的人工智能模型，以识别哪些出院后病例更有可能随后使用急诊。为研究收集的电子健康记录数据包括人口统计学数据、SDoH、合并症、住院相关因素、实验室检测结果和药物使用情况，以预测出院后的急诊就诊情况。随后，将采用混合方法从病例管理员、临床医生和质量与患者安全专家对患者结局的见解中验证预测结果并制定实施策略。
截至 2023 年 12 月，我们已经从 2018 年 1 月至 2023 年 9 月期间的 174871 次住院患者就诊中提取了数据，涉及符合纳入标准的 89355 名住院患者。这些患者就诊均包括临床和 SDoH 数据项。总共，85%的住院就诊（N=148640）将用于培训（从数据中学习），其余 26231 次住院就诊将用于混合方法验证（测试）。
通过将一套关键的 SDoH 与与糖尿病相关的临床数据相结合，所提出的数据驱动的风险分层模型可以实现个体化风险估计，并使卫生专业人员（例如，病例管理员）了解患者即将使用急诊的风险。预测结果可能会使病例管理转介自动化，有助于更好地确定服务优先级。通过采用混合方法，我们旨在使模型与医院特定的质量和患者安全考虑因素保持一致，以确保患者护理质量和病例管理资源分配的最优化。
国际注册报告标识符（IRRID）：DERR1-10.2196/56049。
