Metrics on this tab
- Lab Component Inclusion Rate
- Measurement Integration Rate
Motivation
The purpose of the Measurement Integration dashboard is to assess the frequency of labs that fall into a large set of wide net labs and a smaller subset of recommended labs. By pushing labs into these sets, they can be aggregated more easily during downstream research queries.
Definitions
*All of the lists of labs below can be found in this google sheet.
Downloadable version here.
Tracked Components
Currently 42 lab components, derived from the LOINC ontology hierarchy, are tracked by this dashboard. They represent high level substances that are reported in common lab panels.
A list of the OMOP lab components is found below:
Wide-net Labs
From the OMOP hierarchy, a wide-net list of labs is automatically derived for each component in the list of tracked components. Any lab that has a "has_component" relationship with a component or one of its descendants will fall into the component's "wide net".
The full list of OMOP labs considered to be wide-net is found below:
Recommended Labs
Within the set of wide-net labs are sets of recommended labs that have manually been deemed to be most clinically relevant by a staff clinician. These are considered to be the most relevant labs for observational research and it is suggested that ideally all wide net labs be migrated to these groups.
The full list of OMOP labs considered to be recommended is found below:
Metrics
Lab Component Inclusion Rate
Lab Component Inclusion Rate = [# of tracked lab components with >= 1 wide-net labs] / [total # of tracked lab components]
This rate measures the portion of the 42 tracked lab components that appear at least once in the wide net labs for a site. High rates indicate that a large selection of common labs have been submitted.
Recommended Measurement Rate
Recommended Measurement Rate = [# of recommended labs] / [# of wide-net labs]
This rate measures the proportion of submitted lab concepts in the wide net of labs for a site that are considered to be recommended. While wide-net labs are acceptable, a high rate indicates high specificity for the ideal labs.
Full queries for this metric can be found in the EHR Ops Github Repo here.