Assessing the harmonization of structured electronic health record data to reference terminologies and data completeness through data provenance

Introduction

(1) Assess the harmonization of structured electronic health record data (laboratory results and medications) to reference terminologies and characterize the severity of issues. (2) Identify issues of data completeness by comparing complementary data domains, stratifying by time, care setting, and provenance.

Methods

Queries were distributed to 3 Data Partners (DP). Using harmonization queries, we examined the top 200 laboratory results and medications by volume, identifying outliers and computing summary statistics. The completeness queries looked at 4 conditions of interest and related clinical concepts. Counts were generated for each condition, stratified by year, encounter type, and provenance. We analyzed trends over time within and across DPs.

Results

We found that the median number of codes associated with a given laboratory/medication name (and vice versa) generally met expectations, though there were DP-specific issues that resulted in outliers. In addition, there were drastic differences in the percentage of patients with a given concept depending on provenance.

Conclusions

The harmonization queries surfaced several mapping errors, as well as issues with overly specific codes and records with “null” codes. The completeness queries demonstrated having access to multiple types of data provenance provides more robust results compared with any single provenance type. Harmonization errors between source data and reference terminologies may not be widespread but do exist within CDMs, affecting tens of thousands or even millions of records. Provenance information can help identify potential completeness issues with EHR data, but only if it is represented in the CDM and then populated by DPs.