Electronic Health Record (EHR) systems help healthcare providers treat and care for patients by storing crucial clinical data. However, there are no uniform standards nor a common language they all share. This leads to inconsistent data collection, documentation, and reporting of clinical information that is important for healthcare organizations related to quality care, metrics, etc., and ultimately for patients.
Data curation is a process that improves data that doesn’t meet a quality standard due to missing or incorrect values, thereby reducing the amount of unusable data. This process includes activities like data selection, classification, validation, and remediation of disparate data that comes from multiple sources.
EHRs and EMRs alone can present data quality problems.
For example, there are upwards of 1,000 different EHRs in existence, and the average health system uses 18 different EMR vendors across affiliated providers.
Data curation makes patient data more usable and more powerful while:
- Reducing information management burden with frictionless deployment
- Helping integrate data across HIT vendors
- Saving manual hours spent obtaining clean clinical data
- Decreasing administrative burden to translate data into a usable format for data analysis, population health, care management, etc.
Data curation improves the quality, completeness, and usability of healthcare data from extraction to data cleansing to enrichment.