OBJECTIVES/GOALS: The Informatics Program in the Wake Forest CTSI is experiencing rapid growth. To accommodate an influx of both staff and clinical investigators this program Invests resources in self-service tools to increase researcher capabilities Automates resource intensive activities Creates transparency of operational processes for researchers. METHODS/STUDY POPULATION: Self-service tools (immediate/automated) The i2b2 tool queries clinical data for feasibility numbers and cohort identification; and provides demographic breakdowns of patient sets The Data Puller tool pulls identified patient data (with IRB approval) The SKAN NLP tool pulls aggregate numbers from over 3 million clinical notes Automation A custom-built tracking system automates parts of tracking requests for data and checking IRB protocols Operational transparency The Data Request Dashboard shows requesters information about their request and where it is in the process of being fulfilled The Data Quote tool was constructed leveraging the integrated CTSA informatics network and uses details of the request to estimate how long it will take to complete. RESULTS/ANTICIPATED RESULTS: i2b2 has had over 300 unique users each year; 80% are faculty or research staff, 20% are clinicians or students. From 2017-2021 there have been an average of 300 i2b2 queries and 45 Data Puller pulls each month. SKAN has had 58 unique users since its implementation in late 2020, averaging 5 new users per month. The automated data request tracking system took approximately 30 staff hours to create and saves an average of 4 hours of staff time per week. It also decreases human error by pulling/pushing information directly between systems. The Informatics program has received positive feedback from researchers who use the Data Request Dashboard. The Data Quote Tool is being used to give standardized quotes to researchers. DISCUSSION/SIGNIFICANCE: Investing resources in developing and implementing self-service tools and operational transparency ultimately reduces overall resource consumption, saving staff and investigator time and effort. This enables the Informatics program to maintain a high standard of service while experiencing rapid growth.
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.
Introduction: Electronic health records (EHR) offer the potential to facilitate research examining the real-world effectiveness and safety of medical interventions. However, few studies have used EHR data to evaluate medical devices. This study sought to devise a method using current procedural terminology (CPT) and international classification of disease (ICD) codes captured in EHR data to identify patients with implantable cardiac rhythm device lead failures. Hypothesis: CPT and ICD diagnosis codes within an EHR can be used to correctly identify implantable cardiac rhythm device lead failures. Methods: Study data were extracted from the EHR of a large North Carolina health system. Patients who had implantable cardiac rhythm devices implanted during January 2013 to December 2018 were identified using predetermined CPT codes. Patients were followed longitudinally to identify those undergoing subsequent lead insertions or removals through December 2019. Medical records were reviewed to determine reasons for the re-insertion or removal and identify true lead failures. Random forest modeling was used to develop an algorithm for predicting true lead failure using CPT and ICD codes for mechanical breakdown or cardiac device complication. Patients with potential lead failure were split into two cohorts: 60% for model training and 40% for testing. Results: A total of 4,148 encounters with billed CPT codes for implantable cardiac rhythm devices were initially identified. After applying study exclusion criteria, 2,390 patients met study inclusion criteria. Of those, 175 patients had a subsequent insertion indicating a potential lead failure. A total of 31 patients were found to have true lead failures. A random forest algorithm predicted true lead failures with good discrimination, achieving an AUROC (area under receiver operating curve) of 0.908. The model accurately detected 66% of true lead failure cases in the testing dataset. Conclusions: Applying a specific combination of CPT and diagnosis codes to a cohort of patients undergoing subsequent implantable cardiac rhythm device procedures following an index insertion can correctly identify patients with lead failure with strong accuracy and moderate sensitivity.
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