Evidence map›Paper›PMID 38235019›Full record

ArticleThe Neurohospitalist2024

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

Katherine A Fu, Russell Kerbel, Rylan J Obrien, Joshua S Li, Nicholas J Jackson, Inna Keselman, Melissa Reider-Demer

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Neurohospitalist, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Katherine A FuDepartment of Neurology, University of California, Los Angeles, CA, USA.ORCID https://orcid.org/0000-0003-2043-2997
Russell KerbelDepartment of Medicine, University of California, Los Angeles, CA, USA.
Rylan J ObrienQuality Informatics & Analytics, University of California, Los Angeles, CA, USA.
Joshua S LiDepartment of Medicine Statistics Core, University of California, Los Angeles, CA, USA.
Nicholas J JacksonDepartment of Medicine Statistics Core, University of California, Los Angeles, CA, USA.
Inna KeselmanDepartment of Neurology, University of California, Los Angeles, CA, USA.
Melissa Reider-DemerDepartment of Neurology, University of California, Los Angeles, CA, USA.

Funding

UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
NCATS NIH HHS UL1 TR001881
6 · The paper itself

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 ( 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.

Indexed as

documentationelectronic health recordgeneral neurologyquality improvementquality outcomes

Identifiers

PMID38235019
PMCPMC10790608

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.