Optimizing Neurology Inpatient Documentation: A Pilot Study of a Novel Discharge Documentation EHR Tool

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 (P < .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.