Clinical Events and Specialty Agreement: Multi-Site, Anomaly Detection, Cross-Sectional Analysis
| dc.contributor | Patient-Centered Outcomes Research Institute |
| dc.contributor.author | PEDSnet Data Coordinating Center |
| dc.contributor.other | PEDSnet Data Coordinating Center |
| dc.date.accessioned | 2024-09-09T17:01:55Z |
| dc.date.created | 2024-06-05 |
| dc.description.abstract | 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. |
| dc.identifier.uri | https://hdl.handle.net/20.500.14642/760 |
| dc.identifier.uri | https://doi.org/10.24373/pdsp-458 |
| dc.publisher | PEDSnet |
| dc.rights | a CC-BY Attribution 4.0 License. |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0 |
| dc.subject | Multi-Site Analysis |
| dc.subject | Data Anomaly Method |
| dc.subject | Cross-Sectional Analysis |
| dc.subject | Visit-Level Analysis |
| dc.title | Clinical Events and Specialty Agreement: Multi-Site, Anomaly Detection, Cross-Sectional Analysis |
| dspace.entity.type | DQCheck |
| local.code.github | https://github.com/ssdqa/clinicalevents.specialties |
| local.code.package | # install.packages("devtools") devtools::install_github('ssdqa/https://github.com/ssdqa/clinicalevents.specialties') |
| local.description.raw | The raw data output requires the user to annotate the specialty name output before running the output function. <br> | 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} <br> The resulting raw data output of this check produces eight columns of data: <br> | 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 | {.dqcheck-table} |
| local.description.viz | 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. |
| local.dqcheck.category | Concordance |
| local.dqcheck.clinicalprobe | Targeted Patient Population |
| local.dqcheck.clinicalprobe | Utilization Patterns |
| local.dqcheck.clinicalprobe | Clinical Consistency |
| local.dqcheck.clinicalprobe | Confirmatory Clinical Data |
| local.dqcheck.measurement | Hotspots Outlier Detection |
| local.dqcheck.probe | Missing Expected Data |
| local.dqcheck.probe | External Benchmarking |
| local.dqcheck.probe | Anomalous Values from Internal Distributions |
| local.dqcheck.requirement | cohort |
| local.dqcheck.requirement | multi_or_single_site |
| local.dqcheck.requirement | age_groups |
| local.dqcheck.requirement | codeset_tbl |
| local.dqcheck.requirement | care_site |
| local.dqcheck.requirement | provider |
| local.dqcheck.requirement | visit_type_tbl |
| local.dqcheck.requirement | time |
| local.dqcheck.requirement | time_span |
| local.dqcheck.requirement | time_period |
| local.dqcheck.requirement | vocab_tbl |
| local.dqcheck.type | Dataset Fitness |
| local.dqcheck.viz | Dot and Star Plot |
| relation.isCodeOfDQCheck | 929c8dfc-2c8b-4e62-8e1d-0fa06c542832 |
| relation.isCodeOfDQCheck.latestForDiscovery | 929c8dfc-2c8b-4e62-8e1d-0fa06c542832 |
| relation.isDQResultOfDQCheck | f110bd30-84f7-4150-a26d-a7a7a2974a9b |
| relation.isDQResultOfDQCheck | 069038ca-a69a-48b0-898f-1afbf1a53cba |
| relation.isDQResultOfDQCheck.latestForDiscovery | f110bd30-84f7-4150-a26d-a7a7a2974a9b |
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