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

Abstract

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.

The Design of the Electronic Health Record in Type 1 Diabetes Centers: Implications for Metrics and Data Availability for a Quality Collaborative

“…We did not evaluate data quality, but other studies have demonstrated that both the clinical workflows and the clinical stakeholders involved in generating EHR data impact clinical data availability and quality. 11 Optimally designed…”

“…We did not evaluate data quality, but other studies have demonstrated that both the clinical workflows and the clinical stakeholders involved in generating EHR data impact clinical data availability and quality. 11 Optimally designed workflows in the EHR are central for improving HCP efficiency and reducing HCP burnout, but unfortunately in the transition from paper records to the EHR, most health systems simply replicated their tools and workflows from paper, constructing the “note” as the artifact of focus for the HCP. A redesign of local workflows with change management is needed to pivot HCPs from the old “note-centric” approach to the EHR, to a more data-oriented approach.…”

Aligning EHR Data for Pediatric Leukemia With Standard Protocol Therapy

“…26 For example, variations in the quality of diagnosis coding for brain neoplasms have been demonstrated to correlate with workflow, care setting, and personnel. 27 Resolution of these challenges requires the use of emerging systems to provide monitoring of EHR data for data quality issues 28 , 29 and the inclusion of data quality considerations during EHR system implementations.…”