Development of an automated ERCP Quality Report Card using structured data fields.
Benchmarking
Cholangiopancreatography
Electronic Health Records
Endoscopic Retrograde
Health Care
Quality Indicators
Journal
Techniques and innovations in gastrointestinal endoscopy
ISSN: 2590-0307
Titre abrégé: Tech Innov Gastrointest Endosc
Pays: United States
ID NLM: 101768614
Informations de publication
Date de publication:
2021
2021
Historique:
entrez:
30
4
2021
pubmed:
1
5
2021
medline:
1
5
2021
Statut:
ppublish
Résumé
Measuring adherence to ERCP quality indicators (QIs) is confounded by variability in indications, maneuvers, and documentation styles. We hypothesized that incorporation of mandatory, structured data fields within reporting software would permit accurate measurement of QI adherence rates and facilitate generation of a provider ERCP report card. At two referral centers, endoscopy documentation software was modified to generate provider alerts prior to finalizing the note. The alerts reminded the provider to document the following components in a standardized manner: indication, altered anatomy, prior interventions, and QIs deemed high priority by society consensus, study authors, or both. Adherence rates for each QI were calculated in aggregate and by provider via data extraction directly from the procedure documentation software. Medical records were reviewed manually to measure the accuracy of automated data extraction. Accuracy of automated measurement for each QI was calculated against results derived by manual review. During the 9-month study period, 1,376 ERCP procedures were completed by 8 providers. Manual medical record review confirmed high (98-100%) accuracy of automatic extraction of ERCP QIs from the endoscopy report, including cannulation rate of the native papilla and complete extraction of common bile duct stones. An ERCP report card was generated, allowing for individual comparison of adherence to ERCP QIs with colleagues at their institution and others. In this pilot study, use of mandatory, structured data fields within clinical ERCP reports permit the accurate measurement of high priority ERCP QIs and the subsequent generation of interval report cards.
Sections du résumé
BACKGROUND AND AIMS
OBJECTIVE
Measuring adherence to ERCP quality indicators (QIs) is confounded by variability in indications, maneuvers, and documentation styles. We hypothesized that incorporation of mandatory, structured data fields within reporting software would permit accurate measurement of QI adherence rates and facilitate generation of a provider ERCP report card.
METHODS
METHODS
At two referral centers, endoscopy documentation software was modified to generate provider alerts prior to finalizing the note. The alerts reminded the provider to document the following components in a standardized manner: indication, altered anatomy, prior interventions, and QIs deemed high priority by society consensus, study authors, or both. Adherence rates for each QI were calculated in aggregate and by provider via data extraction directly from the procedure documentation software. Medical records were reviewed manually to measure the accuracy of automated data extraction. Accuracy of automated measurement for each QI was calculated against results derived by manual review.
RESULTS
RESULTS
During the 9-month study period, 1,376 ERCP procedures were completed by 8 providers. Manual medical record review confirmed high (98-100%) accuracy of automatic extraction of ERCP QIs from the endoscopy report, including cannulation rate of the native papilla and complete extraction of common bile duct stones. An ERCP report card was generated, allowing for individual comparison of adherence to ERCP QIs with colleagues at their institution and others.
CONCLUSION
CONCLUSIONS
In this pilot study, use of mandatory, structured data fields within clinical ERCP reports permit the accurate measurement of high priority ERCP QIs and the subsequent generation of interval report cards.
Identifiants
pubmed: 33928265
doi: 10.1016/j.tige.2021.01.005
pmc: PMC8078858
mid: NIHMS1690965
doi:
Types de publication
Journal Article
Langues
eng
Pagination
129-138Subventions
Organisme : NIDDK NIH HHS
ID : P30 DK123704
Pays : United States
Déclaration de conflit d'intérêts
Potential competing interests: Michael McMurtry is an employee of Provation Medical. All other authors (Gregory Cote, B. Joseph Elmunzer, Erin Forster, Robert Moran, John Quiles, Daniel Strand, Dushant Uppal, Andrew Wang, Peter Cotton, and James Scheiman) report no financial relationships with a commercial entity producing health-care related products and/or services relevant to this article.
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