Introduction:
A potential barrier to nursing home research is the limited availability of research quality data in electronic form. We describe a case study of converting electronic health data from five skilled nursing facilities to a research quality longitudinal dataset by means of open-source tools produced by the Observational Health Data Sciences and Informatics (OHDSI) collaborative.
Methods:
The Long-Term Care Minimum Data Set (MDS), drug dispensing, and fall incident data from five SNFs were extracted, translated, and loaded into version 4 of the OHDSI common data model. Quality assurance involved identifying errors using the Achilles data characterization tool and comparing both quality measures and drug exposures in the new database for concordance with externally available sources.
Findings:
Records for a total 4,519 patients (95.1%) made it into the final database. Achilles identified 10 different types of errors that were addressed in the final dataset. Drug exposures based on dispensing were generally accurate when compared with medication administration data from the pharmacy services provider. Quality measures were generally concordant between the new database and Nursing Home Compare for measures with a prevalence ≥ 10%. Fall data recorded in MDS was found to be more complete than data from fall incident reports.
Conclusions:
The new dataset is ready to support observational research on topics of clinical importance in the nursing home including patient-level prediction of falls. The extraction, translation, and loading process enabled the use of OHDSI data characterization tools that improved the quality of the final dataset.