Implementation of a Maternal Child Knowledgebase.

To advance the application of clinical data to address maternal health we developed and implemented a Maternal Child Knowledgebase (MCK). The MCK integrates data from every pregnancy that received care at the University of Iowa Hospitals & Clinics (UIHC) and links information from the pregnancy episode to the delivery episode and between the mother and child. This knowledgebase contains integrated information regarding diagnoses, medications, mother and child vitals, hospital admissions, depression screenings, laboratory value results, and procedure information. It also collates information from the electronic health record (EPIC), the Social Security Death Index, and the Medication Administration Record into one knowledgebase. To enhance usability, we designed a custom viewer with several pre-designed queries and reports that eliminates the need for users to be proficient in SQL coding. The recent implementation of the MCK has supported multiple projects and reduced the number of Obstetrics-related data queries to the Biomedical Informatics group.

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.

OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.