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Temporal Relationship of Computed and Structured Diagnoses in Electronic Health Record Data

By Wade L. Schulz, H. Patrick Young, Andreas Coppi, Bobak J Mortazavi, Zhenqiu Lin, Raymond A. Jean, Harlan Krumholz

Posted 02 Jan 2020
medRxiv DOI: 10.1101/2019.12.28.19015628

The electronic health record (EHR) holds the prospect of providing more complete and timely access to clinical information for studies, quality assessments, and quality improvement compared to other data sources, such as administrative claims. Our goal was to assess the completeness and timeliness of structured diagnoses in the EHR compared to computed diagnoses for hypertension (HTN), hyperlipidemia (HLD), and diabetes mellitus (DM). We determined the amount of time for a structured diagnosis to be recorded in the EHR from when an equivalent diagnosis could be computed from other structured data elements, such as vital signs and laboratory results. Using our local instance of EHR data in the PCORnet common data model (CDM) with encounters from January 1, 2012 through February 10, 2019, we identified patients with at least two observations above threshold separated by at least 30 days. The thresholds were outpatient blood pressure of [≥] 140/90 mmHg, any low-density lipoprotein [≥] 130 mg/dl, or any hemoglobin A1c [≥] 7%, respectively. The primary measure was the length of time between the computed diagnosis and the time at which a structured diagnosis could be identified within the EHR history or problem list. We found that 39.8% of those with HTN, 21.6% with HLD, and 1.0% with DM did not receive a corresponding structured diagnosis recorded in the EHR. For those who received a structured diagnosis, a mean of 389, 198, and 106 days elapsed before the patient had the corresponding diagnosis of HTN, HLD, or DM, respectively, recorded in the EHR. We identified a marked temporal delay between when a diagnosis can be computed or inferred and when an equivalent structured diagnosis is recorded within the EHR. These findings demonstrate the continued need for additional study of the EHR to avoid bias when using observational data and reinforce the need for computational approaches to identify clinical phenotypes.

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