Preparing Nursing Home Data from Multiple Sites for Clinical Research - A Case Study Using Observational Health Data...

Area of Science:

• Health Informatics
• Observational Data Science
• Long-Term Care Research

Background:

• Limited availability of research-quality electronic health data hinders nursing home research.
• Electronic health records (EHRs) often lack standardization for research purposes.

Purpose of the Study:

• To describe a case study of converting electronic health data from skilled nursing facilities (SNFs) into a research-quality longitudinal dataset.
• To demonstrate the utility of open-source tools from the Observational Health Data Sciences and Informatics (OHDSI) collaborative for data standardization.

Main Methods:

• Extracted Long-Term Care Minimum Data Set (MDS), drug dispensing, and fall incident data from five SNFs.
• Translated and loaded data into version 4 of the OHDSI common data model.
• Utilized the Achilles data characterization tool for quality assurance and compared data with external sources.

Main Results:

• Successfully created a longitudinal dataset containing records for 4,519 patients (95.1%).
• Identified and addressed 10 types of data errors using Achilles.
• Validated drug dispensing data against pharmacy records and quality measures against Nursing Home Compare.
• Found MDS fall data to be more complete than incident reports.

Conclusions:

• The developed dataset is suitable for observational research in nursing homes, including patient-level fall prediction.
• The ETL process and OHDSI tools enhanced the quality and usability of the nursing home data for research.
• This methodology provides a framework for creating research-ready datasets from diverse long-term care data sources.