Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data.

Many administrative health data-based studies define patient cohorts using procedure and diagnosis codes. The impact these criteria have on a study's final cohort is not always transparent to co-investigators or other audiences if access to the research data is restricted. We developed a SAS an...

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Main Authors: Kelsey Chalmers, Valérie Gopinath, Adam G Elshaug
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2023-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0266154
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author Kelsey Chalmers
Valérie Gopinath
Adam G Elshaug
author_facet Kelsey Chalmers
Valérie Gopinath
Adam G Elshaug
author_sort Kelsey Chalmers
collection DOAJ
description Many administrative health data-based studies define patient cohorts using procedure and diagnosis codes. The impact these criteria have on a study's final cohort is not always transparent to co-investigators or other audiences if access to the research data is restricted. We developed a SAS and R Shiny interactive research support tool which generates and displays the diagnosis code summaries associated with a selected medical service or procedure. This allows non-analyst users to interrogate claims data and groupings of reported diagnosis codes. The SAS program uses a tree classifier to find associated diagnosis codes with the service claims compared against a matched, random sample of claims without the service. Claims are grouped based on the overlap of these associated diagnosis codes. The Health Services Research (HSR) Definition Builder Shiny application uses this input to create interactive table and graphics, which updates estimated claim counts of the selected service as users select inclusion and exclusion criteria. This tool can help researchers develop preliminary and shareable definitions for cohorts for administrative health data research. It allows an additional validation step of examining frequency of all diagnosis codes associated with a service, reducing the risk of incorrect included or omitted codes from the final definition. In our results, we explore use of the application on three example services in 2016 US Medicare claims for patients aged over 65: knee arthroscopy, spinal fusion procedures and urinalysis. Readers can access the application at https://kelsey209.shinyapps.io/hsrdefbuilder/ and the code at https://github.com/kelsey209/hsrdefbuilder.
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spelling doaj.art-7ff13a3f25b44ada841d369c54771f702023-03-16T05:32:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01181e026615410.1371/journal.pone.0266154Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data.Kelsey ChalmersValérie GopinathAdam G ElshaugMany administrative health data-based studies define patient cohorts using procedure and diagnosis codes. The impact these criteria have on a study's final cohort is not always transparent to co-investigators or other audiences if access to the research data is restricted. We developed a SAS and R Shiny interactive research support tool which generates and displays the diagnosis code summaries associated with a selected medical service or procedure. This allows non-analyst users to interrogate claims data and groupings of reported diagnosis codes. The SAS program uses a tree classifier to find associated diagnosis codes with the service claims compared against a matched, random sample of claims without the service. Claims are grouped based on the overlap of these associated diagnosis codes. The Health Services Research (HSR) Definition Builder Shiny application uses this input to create interactive table and graphics, which updates estimated claim counts of the selected service as users select inclusion and exclusion criteria. This tool can help researchers develop preliminary and shareable definitions for cohorts for administrative health data research. It allows an additional validation step of examining frequency of all diagnosis codes associated with a service, reducing the risk of incorrect included or omitted codes from the final definition. In our results, we explore use of the application on three example services in 2016 US Medicare claims for patients aged over 65: knee arthroscopy, spinal fusion procedures and urinalysis. Readers can access the application at https://kelsey209.shinyapps.io/hsrdefbuilder/ and the code at https://github.com/kelsey209/hsrdefbuilder.https://doi.org/10.1371/journal.pone.0266154
spellingShingle Kelsey Chalmers
Valérie Gopinath
Adam G Elshaug
Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data.
PLoS ONE
title Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data.
title_full Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data.
title_fullStr Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data.
title_full_unstemmed Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data.
title_short Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data.
title_sort health service research definition builder an r shiny application for exploring diagnosis codes associated with services reported in routinely collected health data
url https://doi.org/10.1371/journal.pone.0266154
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