Leveraging Data Analytics to Advance Personal, Population, and System Health

Thompson, Michael; Dulin, Michael

doi:10.18043/ncm.80.4.214

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…Secondary analysis of electronic health record (EHR) data is essential to the development of learning health care systems, [1][2][3] oncologic comparative effectiveness research, [4][5][6][7] and precision oncology decision support. [8][9][10][11] This often relies on patient diagnosis (DX) data for cohort selection 12,13 in spite of its quality limitations.…”

Section: Introductionmentioning

confidence: 99%

Workflow Differences Affect Data Accuracy in Oncologic EHRs: A First Step Toward Detangling the Diagnosis Data Babel

Diaz-Garelli

Strowd

Lawson

et al. 2020

JCO Clinical Cancer Informatics

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PURPOSE Diagnosis (DX) information is key to clinical data reuse, yet accessible structured DX data often lack accuracy. Previous research hints at workflow differences in cancer DX entry, but their link to clinical data quality is unclear. We hypothesized that there is a statistically significant relationship between workflow-describing variables and DX data quality. METHODS We extracted DX data from encounter and order tables within our electronic health records (EHRs) for a cohort of patients with confirmed brain neoplasms. We built and optimized logistic regressions to predict the odds of fully accurate (ie, correct neoplasm type and anatomic site), inaccurate, and suboptimal (ie, vague) DX entry across clinical workflows. We selected our variables based on correlation strength of each outcome variable. RESULTS Both workflow and personnel variables were predictive of DX data quality. For example, a DX entered in departments other than oncology had up to 2.89 times higher odds of being accurate ( P < .0001) compared with an oncology department; an outpatient care location had up to 98% fewer odds of being inaccurate ( P < .0001), but had 458 times higher odds of being suboptimal ( P < .0001) compared with main campus, including the cancer center; and a DX recoded by a physician assistant had 85% fewer odds of being suboptimal ( P = .005) compared with those entered by physicians. CONCLUSION These results suggest that differences across clinical workflows and the clinical personnel producing EHR data affect clinical data quality. They also suggest that the need for specific structured DX data recording varies across clinical workflows and may be dependent on clinical information needs. Clinicians and researchers reusing oncologic data should consider such heterogeneity when conducting secondary analyses of EHR data.

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