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Decision Support Tool to Estimate and Reduce the Probability of Readmission for Congestive Heart Failure Patients

机译：决策支持工具，用于评估和降低充血性心力衰竭患者再次入院的可能性

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摘要

Congestive Heart Failure (CHF) is a condition where blood flow from the heart through the body is inadequate, causing congestion in the lungs and swelling in the body's tissues. An urban university teaching hospital is able to treat and assign post-discharge resources to patients diagnosed with CHF. Despite the current treatment methods and assignment of post-discharge resources, the rate of readmission for patients returning to the hospital within 30 days remains higher than the level expected by the Center for Medicare and Medicaid Services. This project proposes the development of a decision support tool to assist the hospital in reducing the readmission rate for patients diagnosed with CHF. The project initially analyzes medical comorbidities and social factors of patients to identify correlations with a patient's probability of readmission. A discriminant analysis baseline model constructed from an electronic health record database (September 2015 to December 2018) projects the readmission probability for a patient. Subsequently, a correlation study determines which post-discharge resources are associated with reducing the readmission probability in patients with specific combinations of medical comorbidities and social factors. Ultimately, the decision support tool analyzes a patient's unique combination of medical severity and social factors to project the patient's probability of readmission and provides a tailored list of suggested post-discharge resources to reduce the probability of readmission for that patient.
机译：充血性心力衰竭（CHF）是一种状况，血液从心脏流到身体的血液不足，导致肺部充血和身体组织肿胀。城市大学教学医院能够为出院后诊断为CHF的患者提供治疗和分配出院后的资源。尽管采用了当前的治疗方法并分配了出院后资源，但30天内返回医院的患者的再入院率仍高于Medicare和Medicaid Services中心的预期水平。该项目建议开发一种决策支持工具，以帮助医院降低诊断为CHF的患者的再入院率。该项目首先分析患者的医疗合并症和社会因素，以识别与患者再次入院的可能性的相关性。根据电子病历数据库（2015年9月至2018年12月）构建的判别分析基线模型可预测患者的再入院概率。随后，相关性研究确定哪些出院后资源与降低合并症和社会因素特定组合的患者的再入院率相关。最终，决策支持工具将分析患者病情严重程度和社会因素的独特组合，以预测患者的再入院概率，并提供建议的出院后资源量身定制清单，以降低该患者的再入院率。

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