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.
OBJECTIVES/GOALS: The purpose of the project was to create a Tableau dashboard to track metrics on requests for research data at Atrium Health Wake Forest Baptist. The objectives included: 1) define and identify request fulfillment metrics, 2) build a dashboard to capture metrics, and 3) integrate the dashboard into metrics tracking and reporting activities. METHODS/STUDY POPULATION: Project managers and team leaders in the Office of Informatics collaborated to determine which measures would be most relevant and impactful to report on. Metrics that were collected included: total count of tickets fulfilled over time, number of tickets currently open, sum of outstanding quoted hours, quoted hours vs. actual hours needed to fulfill ticket, and hours billed. Tableau's direct connection feature was used to extract the Trac ticket data from its Postgres database and the dashboard was published to Tableau Server. After the initial draft was created, several rounds of revisions were made as new data insights were discovered through further investigation of the data. RESULTS/ANTICIPATED RESULTS: Each morning, Tableau Server runs an automatic refresh of the data. On the dashboard homepage, users can see a quick view of all available metrics; to minimize noise, only the current statuses, active tickets, and stats for the most recent monitoring periods are displayed. Many of the charts give the user the option to link out to a page with related supplemental information (historic data, ticket status history, etc.). With the help of the dashboard, project managers and team leaders can now monitor how long tickets are in each status, increase quote accuracy using the hours quoted and hours billed charts, and examine ticket complexity over time. DISCUSSION/SIGNIFICANCE: Prior to dashboard creation, metrics were sparse and difficult to assemble. By providing information on the quantity, size, and complexity of data requests, the dashboard enables the Office of Informatics to monitor how the process is functioning overall, make informed decisions about resource allocation, and provide quick interventions.
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