Improving Hospital Metrics Through the Implementation of a Comorbidity Capture Tool and Other Quality Initiatives

Sosa
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Abstract

Background: Case mix index (CMI) and expected mortality are determined based on comorbidities. Improving documentation and coding can impact performance indicators. During and prior to 2018, our patient acuity was under-represented, with low expected mortality and CMI. Those metrics motivated our quality team to develop the quality initiatives reported here. Objectives: We sought to assess the impact of quality initiatives on number of comorbidities, diagnoses, CMI, and expected mortality at the University of Miami Health System. Design: We conducted an observational study of a series of quality initiatives: (1) education of clinical documentation specialists (CDS) to capture comorbidities (10/2019); (2) facilitating the process for physician query response (2/2020); (3) implementation of computer logic to capture electrolyte disturbances and renal dysfunction (8/2020); (4) development of a tool to capture Elixhauser comorbidities (11/2020); and (5) provider education and electronic health record reviews by the quality team. Setting and participants: All admissions during 2019 and 2020 at University of Miami Health System. The health system includes 2 academic inpatient facilities, a 560-bed tertiary hospital, and a 40-bed cancer facility. Our hospital is 1 of the 11 PPS-Exempt Cancer Hospitals and is the South Florida’s only NCI-Designated Cancer Center. Measures: Number of coded diagnoses and Elixhauser comorbidities; CMI and expected mortality were compared between the pre-intervention and the intervention periods using t -tests and Chi-square test. Results: There were 33 066 admissions during the study period—13 689 before the intervention and 19 377 during the intervention period. From pre-intervention to intervention, the mean (SD) number of comorbidities increased from 2.5 (1.7) to 3.1 (2.0) ( P < .0001), diagnoses increased from 11.3 (7.3) to 18.5 (10.4) ( P < .0001), CMI increased from 2.1 (1.9) to 2.4 (2.2) ( P < .0001), and expected mortality increased from 1.8% (6.1) to 3.1% (9.2) ( P < .0001). Conclusion: The number of comorbidities, diagnoses, and CMI all improved, and expected mortality increased in the year of implementation of the quality initiatives.
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通过实施合并症捕获工具和其他质量举措改善医院指标
背景:病例混合指数(CMI)和预期死亡率是根据合并症确定的。改进文档和编码可以影响性能指标。在2018年期间及之前,我们的患者视力不足,预期死亡率和CMI较低。这些指标促使我们的质量团队制定此处报告的质量计划。目的:我们试图在迈阿密大学卫生系统评估质量举措对合并症数量、诊断、CMI和预期死亡率的影响。设计:我们对一系列质量举措进行了观察性研究:(1)对临床文献专家(CDS)进行教育,以了解合并症(10/2019);(2) 促进医生查询响应过程(2/2020);(3) 实现计算机逻辑以捕捉电解质紊乱和肾功能障碍(8/2020);(4) 开发一种捕捉Elixhauser合并症的工具(2020年11月);以及(5)质量小组对提供者教育和电子健康记录的审查。设置和参与者:2019年和2020年迈阿密大学卫生系统的所有招生。卫生系统包括2个学术住院设施、一个拥有560个床位的三级医院和一个拥有40个床位的癌症设施。我们的医院是11家PPS豁免癌症医院之一,也是南佛罗里达州唯一一家NCI指定的癌症中心。测量:编码诊断和Elixhauser合并症的数量;使用t检验和卡方检验比较干预前和干预期的CMI和预期死亡率。结果:研究期间共有33066人入院,干预前为13689人,干预期间为19377人。从干预前到干预,合并症的平均(SD)数从2.5(1.7)增加到3.1(2.0)(P<.0001),诊断从11.3(7.3)增加到18.5(10.4)(P<0.0001),CMI从2.1(1.9)增加到2.4(2.2),预期死亡率在实施高质量举措的那一年有所上升。
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