The impact of real-time clinical alerts on the compliance of anesthesia documentation: A retrospective observational study.


Journal

Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513

Informations de publication

Date de publication:
Jul 2020
Historique:
received: 09 09 2019
revised: 06 02 2020
accepted: 14 02 2020
pubmed: 9 3 2020
medline: 13 4 2021
entrez: 9 3 2020
Statut: ppublish

Résumé

Clinical alert systems (CAS) have been used to analyze deviations from hospital standards in the electronic medical record to identify missing documentations and send alerts to the appropriate providers to increase adherence to required elements. To improve compliance, an alert system for documentation of the Immediate Preoperative Assessment (IPOA) was implemented at our institution in August 2018 with the goal of improving documentation compliance rates. We hypothesized that implementation of this alert system would increase the compliance of on-time documentation of the IPOA. An initial data query in our institutional data warehouse was made for all patients who had a completed anesthetic during our study period. This date range corresponded to 6 months before and after August 2nd, 2018, the date when the IPOA alert was implemented and the anesthesiology department. The following analyses were performed: testing the proportion of cases compliant with on-time documentation of the IPOA pre- versus post-implementation for the full cohort and among subsets of interest, testing the time when the IPOA was completed relative to anesthesia end, and testing whether time of day of when surgery occurred had an impact on the time when the IPOA was completed relative to the drapes off/IPOA alert sent time. The proportion of compliance for pre- versus post-implementation was tested by Chi-square test. Through retrospective chart review of electronic patient records, 47,417 cases matched our inclusion criteria of patients that had a completed anesthetic between February 2nd, 2018 to February 2nd, 2019. In total, we excluded 5132 cases. The compliance rate of IPOA completion increased from 76% to 88% (P < 0.001) before and after the alert implementation date. In the initial month following alert implementation, the compliance rate immediately increased to 83% and stayed in the high 80's for the balance of the study period. In summary, we demonstrate that automated Clinical Alert Systems operating via a single page notification can improve the compliance rate for documentation of key anesthesia events and that this observation is sustained six months after the implementation date. Furthermore, improvement in compliance is highest shorter cases and cases that occur early in the day. This study shows promising results in the use of automatic CAS system alerts to help hospitals meet the Center for Medicare and Medicaid Services (CMS) and The Joint Commission (TJC) standards.

Identifiants

pubmed: 32146211
pii: S0169-2607(19)31532-9
doi: 10.1016/j.cmpb.2020.105399
pmc: PMC7283007
mid: NIHMS1575364
pii:
doi:

Types de publication

Journal Article Observational Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

105399

Subventions

Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interests LET is a grant recipient through Merck Investigator Studies Program (MISP) to fund clinical trial at MSKCC (NCT03808077). LET serves a consultancy and advisory role for Merck & Co. Pharmaceutical Company.

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Auteurs

Luis E Tollinche (LE)

Memorial Sloan Kettering Cancer Center Department of Anesthesiology and Critical Care Medicine, United States. Electronic address: tollincl@mskcc.org.

Richard Shi (R)

New York Medical College School of Medicine, United States.

Margaret Hannum (M)

Memorial Sloan Kettering Cancer Center Department of Biostatistics and Epidemiology, United States.

Patrick McCormick (P)

Memorial Sloan Kettering Cancer Center Department of Anesthesiology and Critical Care Medicine, United States.

Alisa Thorne (A)

Memorial Sloan Kettering Cancer Center Department of Anesthesiology and Critical Care Medicine, United States.

Kay See Tan (KS)

Memorial Sloan Kettering Cancer Center Department of Biostatistics and Epidemiology, United States.

Gloria Yang (G)

Memorial Sloan Kettering Cancer Center Department of Anesthesiology and Critical Care Medicine, United States.

Meghana Mehta (M)

Memorial Sloan Kettering Cancer Center Department of Anesthesiology and Critical Care Medicine, United States.

Cindy Yeoh (C)

Memorial Sloan Kettering Cancer Center Department of Anesthesiology and Critical Care Medicine, United States.

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