Strategic Implementation and Automation of an Administrative Database into Practice while Minimizing Human- Resources JONATHAN GOSS, MS4 YAZAN DUWAYRI, MD
Data Abstraction How does it make you feel?
The Patient Protection and Affordable Care Act Designed to improve the quality and efficiency of U.S. medical care services for everyone, especially for those enrolled in Medicare and Medicaid Linked Medicare payments to quality performance on common, high-cost conditions such as cardiac, surgical and pneumonia care Physician Quality Reporting Initiative, which incentives physicians to report Medicare quality data Hospitals will be held accountable for procedure costs and outcomes Physician reimbursement will shift to reward performance quality
Barriers to Administrative Database Utilization Human Resource Consumption Time involvement Data abstraction, data collection Learning curve Fees
Advantages of VQI over existing databases Monitors outcomes rather than adverse events Track outcomes for 365 days as opposed to 30 days Variables are specialty specific to vascular surgery Aims to translate observations into action plans; regional groups facilitate implementation to impact quality improvement Data collection occurs prospectively at different stages
Variables per Procedure Open AAA repair: 101 Endovascular AAA repair: 108 Carotid Artery Stent: 77 Carotid Endarterectomy: 101 Suprainguinal Bypass: 139 Infrainguinal Bypass: 130
Vascular Quality Initiative Rapid Pace Implementation Designing EMR notes in Emory s EMR system that mirrors VQI s data requirements for the relevant procedures Carotid endarterectomy, endovascular & open AAA repair, carotid artery stent, suprainguinal & infrainguinal bypass Procedure specific EMR notes were created for each module at the pre-operative, intra-procedural, and post-operative stages Translation of data from custom-made EMR notes into the VQI database by the database manager Contracting with Emory Healthcare s IT department and VQI s IT department to set up automated data mining of EMR to generate VQI patient profiles Module-specific EMR notes created a data source that can be mined automatically or manually at a faster pace
Designing EMR notes
Manual Process List of procedures done during the previous week is generated automatically by CPT codes Database manager reviews the above list and translates data from EMR into VQI database utilizing custom EMR notes (H&P, Brief Op Note, D/C summary) Takes an average of 17 minutes to complete one VQI profile w/ custom EMR notes
Graph of Translation Time w/ Custom EMR Notes 25 20 Minutes 15 10 5 0 January February March
ACS NSQIP- Abstraction Method Performed by two nurse abstractors at Emory University Hospital. In 2012, 1254 cases were captured This averages to 24 cases/week 3 cases/day/fte The amount of time spent on each chart is variable, and depends on the length of stay and the complexity of the patient s condition. ~65 variables being tracked per vascular procedure
Automation Process List of procedures done during the week is generated automatically by CPT codes Data mined for each patient Demographics, past medical & surgical history, pre-op medications Schedule of data mining for the procedures done that week Every Saturday morning at 9:00 a.m. Fidelity of imports On the following Monday, profiles are verified by database manager and any potential missing VQI information is added
Conclusions VQI allows for: Analysis of inter-healthcare physician performance Comparison to other vascular surgery groups around the United States that participate in VQI Identification of areas of variance amongst physicians that can lead to improved quality of administered care Generate risk-adjusted data for surgical methods (graft type, intra-op & post-op medications, etc) Cost and time utilized for such a quality project should be taken into consideration Meaningful use of EMR, as demonstrated, allows for significant time and cost savings
Expanded procedures Future Directions IVC filters, dialysis access, amputations, thoracic aneurysms and complex aortic intervention procedures Quantitatively compare the effectiveness of Emory s NSQIP method with our customized VQI method Serve as a model for automated data abstraction for other institutions, as well as EMR companies Partnering with VQI IT department to aide in expanding the functionality of their database to allow mining of intra-operative and post-operative data as well