Fu Katherine A, Kerbel Russell, Obrien Rylan J, Li Joshua S, Jackson Nicholas J, Keselman Inna, Reider-Demer Melissa
Department of Neurology, University of California, Los Angeles, CA, USA.
Department of Medicine, University of California, Los Angeles, CA, USA.
Neurohospitalist. 2024 Jan;14(1):5-12. doi: 10.1177/19418744231194680. Epub 2023 Aug 4.
Clinical documentation of patient acuity is a major determinant of payer reimbursement. This project aimed to improve case mix index (CMI) by incorporating a novel electronic health record (EHR) discharge documentation tool into the inpatient general neurology service at the University of California, Los Angeles (UCLA) Medical Center.
We used data from Vizient AMC Hospital: Risk Model Summary for Clinical Data Base (CBD) 2017 to create a discharge diagnosis documentation tool consisting of dropdown menus to better capture relevant secondary diagnoses and comorbidities. After implementation of this tool, we compared pre- (July 2017-June 2019) and post-intervention (July 2019-June 2021) time periods on mean expected length of stay (LOS) and mean CMI with two sample T-tests and the percentage of encounters classified as having Major Complications/Comorbidities (MCC), with Complication/Comorbidity (CC), and without CC/MCC with tests of proportions.
Mean CMI increased significantly from 1.2 pre-intervention to 1.4 post-intervention implementation ( < .01). There was a pattern of increased MCC percentages for "Bacterial infections," "Other Disorders of Nervous System", "Multiple Sclerosis," and "Nervous System Neoplasms" diagnosis related groups post-intervention.
This pilot study describes the creation of an innovative EHR discharge diagnosis documentation tool in collaboration with neurology healthcare providers, the clinical documentation improvement team, and neuro-informaticists. This novel discharge diagnosis documentation tool demonstrates promise in increasing CMI, shifting diagnosis related groups to a greater proportion of those with MCC, and improving the quality of clinical documentation.
患者病情严重程度的临床记录是支付方报销的主要决定因素。本项目旨在通过将一种新型电子健康记录（EHR）出院文档工具纳入加利福尼亚大学洛杉矶分校（UCLA）医学中心的住院普通神经科服务，以提高病例组合指数（CMI）。
我们使用了Vizient AMC医院2017年临床数据库（CBD）风险模型总结中的数据，创建了一个由下拉菜单组成的出院诊断文档工具，以更好地捕捉相关的次要诊断和合并症。在实施该工具后，我们使用双样本t检验比较了干预前（2017年7月至2019年6月）和干预后（2019年7月至2021年6月）两个时间段的平均预期住院时间（LOS）和平均CMI，以及将就诊分类为有严重并发症/合并症（MCC）、有并发症/合并症（CC）和无CC/MCC的比例检验。
平均CMI从干预前的1.2显著增加到干预后实施时的1.4（<0.01）。干预后，“细菌感染”、“其他神经系统疾病”、“多发性硬化症”和“神经系统肿瘤”诊断相关组的MCC百分比呈上升趋势。
这项试点研究描述了与神经科医疗服务提供者、临床文档改进团队和神经信息学家合作创建创新的EHR出院诊断文档工具的过程。这种新型出院诊断文档工具在提高CMI、将诊断相关组转向更大比例的有MCC患者以及改善临床文档质量方面显示出前景。
