DQCheck

Clinical Events and Specialty Agreement: Multi-Site, Anomaly Detection, Cross-Sectional Analysis


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Publisher

PEDSnet

This check computes types of clinical events that occur across clinician or facility specialties and across time. It provides a high level summary of the quality of clinical specialty data in a study sample to examine multiple sites. The intention of the check is to evaluate the concordance of specific events (e.g., diagnosis of type I diabetes) and specialist providers or care sites (e.g., endocrinologist or endocrinology clinic) at the visit level.

Probe

Clinical Assessment

Access Package

# install.packages("devtools") devtools::install_github('ssdqa/https://github.com/ssdqa/clinicalevents.specialties')

Visualization Output

This check outputs a line plot that displays the proportion of visits per specialty across time. Time is mapped on the x-axis and the proportion of site,s visits to the specialty in mapped on the y axis. The graph is stratified by a user-selected specialty. Each site is represented by a color. A dotted line represent the overall proportion across all sites. The user can adjust the top n represented specialties. Each site data can be isolated by double clicking on the legend. Hovering over a data point provides a tooltop containing the site name, proportion of the site’s visits, and the time_start.

Raw Output

The raw data output requires the user to annotate the specialty name output before running the output function.

Column Data Type Definition
specialty_name character user provided annotation
specialty_concept_id` numeric / character the concept indicating the specialty of the provider and/or care site
specialty_concept_name character if a vocabulary table is provided, the name of the specialty associated with the specialty_concept_id

{.dqcheck-table}
The resulting raw data output of this check produces eight columns of data:

Column Data Type Definition
specialty_concept_id` numeric / character the concept indicating the specialty of the provider and/or care site
cluster character the cluster assigned in the user provided codeset; if no cluster is indicated, the name of the concept is used
num_visits numeric the number of visits associated with the specialty_concept_id & cluster
codeset_name character the name of the codeset provided for the analysis
site character the name of the site being targeted OR “combined” if multiple sites were provided for a single site analysis
age_grp character if an age group dataframe is provided, a string indicating the age grouping at the time of the specialty visit
visit_type character if a visit type dataframe is provided, the visit type of the specialty visit

Funder(s)

This research was made possible through the generous support of Patient-Centered Outcomes Research Institute. The statements presented in this work are solely the responsibility of the author(s) and do not necessarily represent the views of PCORI, its Board of Governors, or its Methodology Committee.

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Description

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Clinical Subjects Headings

Related Data Quality Result

Clinical Events & Specialties Study Results III: PRESERVE
Created:2025-04-08
The results of a Clinical Events & Specialties check using the Multi-Site, Anomaly Detection, Cross-Sectional parameters. This check evaluates anomalous specialty utilization in visits at each institution associated with serum creatinine labs or antihypertensive prescriptions.

Related Person

Related Code

Study-Specific Quality, Utility, and Breadth Assessment
Created:2025-11
This suite of R packages allows one to investigate multiple facets of data quality and customize analyses based on your study-specific needs. Each module allows up to 8 different analyses in either the OMOP or PCORnet® CDM, all aimed at taking a different view of the data while still addressing the same data quality probe.

##### [View pkgdown summary here.](https://ssdqa.github.io/squba/)

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Creative Commons license

Except where otherwised noted, this item's license is described as a CC-BY Attribution 4.0 License.