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

Abstract

Background and purpose: 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.

Methods: 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.

Results: 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.

Conclusions: 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.