Tyler Anstett1, Chris Smith2, Kaitlyn Hess3, Luke Patten4, Sharon Pincus5, Chen-Tan Lin6, P Michael Ho7. 1. Division of Hospital Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States. 2. Division of Hospital Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico, United States. 3. UCHealth, Aurora, Colorado, United States. 4. Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States. 5. University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States. 6. Department of General Internal Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States. 7. Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States.
Abstract
BACKGROUND: Venipunctures and the testing they facilitate are clinically necessary, particularly for hospitalized patients. However, excess venipunctures lead to patient harm, decreased patient satisfaction, and waste. OBJECTIVES: We sought to identify contributors to excess venipunctures at our institution, focusing on electronic health record (EHR)-related factors. We then implemented and evaluated the impact of an intervention targeting one of the contributing factors. METHODS: We employed the quality improvement (QI) methodology to find sources of excess venipunctures, specifically targeting add-on failures. Once an error was identified, we deployed an EHR-based intervention which was evaluated with retrospective pre- and postintervention analysis. RESULTS: We identified an error in how the EHR evaluated the ability of laboratories across a health system to perform add-on tests to existing blood specimens. A review of 195,263 add-on orders placed prior to the intervention showed that 165,118 were successful and 30,145 failed, a failure rate of 15.4% (95% confidence interval [CI]: 15.1-15.6). We implemented an EHR-based modification that changed the criteria for add-on testing from a health-system-wide query of laboratory capabilities to one that incorporated only the capabilities of laboratories with feasible access to existing patient samples. In the 6 months following the intervention, a review of 87,333 add-on orders showed that 77,310 were successful, and 10,023 add-on orders failed resulting in a postintervention failure rate of 11.4% (95% CI: 11.1, 11.8) (p < 0.001). CONCLUSION: EHR features such as the ability to identify possible add-on tests are designed to reduce venipunctures but may produce unforeseen negative effects on downstream processes, particularly as hospitals merge into health systems using a single EHR. This case report describes the successful identification and correction of one cause of add-on laboratory failures. QI methodology can yield important insights that reveal simple interventions for improvement. Thieme. All rights reserved.
BACKGROUND: Venipunctures and the testing they facilitate are clinically necessary, particularly for hospitalized patients. However, excess venipunctures lead to patient harm, decreased patient satisfaction, and waste. OBJECTIVES: We sought to identify contributors to excess venipunctures at our institution, focusing on electronic health record (EHR)-related factors. We then implemented and evaluated the impact of an intervention targeting one of the contributing factors. METHODS: We employed the quality improvement (QI) methodology to find sources of excess venipunctures, specifically targeting add-on failures. Once an error was identified, we deployed an EHR-based intervention which was evaluated with retrospective pre- and postintervention analysis. RESULTS: We identified an error in how the EHR evaluated the ability of laboratories across a health system to perform add-on tests to existing blood specimens. A review of 195,263 add-on orders placed prior to the intervention showed that 165,118 were successful and 30,145 failed, a failure rate of 15.4% (95% confidence interval [CI]: 15.1-15.6). We implemented an EHR-based modification that changed the criteria for add-on testing from a health-system-wide query of laboratory capabilities to one that incorporated only the capabilities of laboratories with feasible access to existing patient samples. In the 6 months following the intervention, a review of 87,333 add-on orders showed that 77,310 were successful, and 10,023 add-on orders failed resulting in a postintervention failure rate of 11.4% (95% CI: 11.1, 11.8) (p < 0.001). CONCLUSION: EHR features such as the ability to identify possible add-on tests are designed to reduce venipunctures but may produce unforeseen negative effects on downstream processes, particularly as hospitals merge into health systems using a single EHR. This case report describes the successful identification and correction of one cause of add-on laboratory failures. QI methodology can yield important insights that reveal simple interventions for improvement. Thieme. All rights reserved.
Authors: Adam C Salisbury; Kimberly J Reid; Karen P Alexander; Frederick A Masoudi; Sue-Min Lai; Paul S Chan; Richard G Bach; Tracy Y Wang; John A Spertus; Mikhail Kosiborod Journal: Arch Intern Med Date: 2011-08-08
Authors: Paaladinesh Thavendiranathan; Akshay Bagai; Albert Ebidia; Allan S Detsky; Niteesh K Choudhry Journal: J Gen Intern Med Date: 2005-06 Impact factor: 5.128