Ann Christine Catlin1, William X Malloy2, Karen J Arthur1, Cindy Gaston1, James Young1, Sudheera Fernando1, Ruchith Fernando1. 1. Ann Christine Catlin, M.Sc., is Senior Research Scientist, Rosen Center for Advanced Computing, Purdue University, West Lafayette, IN. William X. Malloy, M.S., Pharm.D., BCPS, is Clinical Coordinator, Pharmacy; and Karen J. Arthur, Pharm.D., BCPS, is Medication Safety Program Manager, Roudebush Veterans Affairs Medical Center, Indianapolis, IN. Cindy Gaston, Pharm.D., BCPS, is Medication Use Policy Analyst, University of Wisconsin Hospital & Clinics, Madison. James Young, Pharm.D., CPHQ, is Quality Assurance/Performance Improvement Pharmacist, Eskenazi Health, Indianapolis. Sudheera Fernando, B.S., is Senior Software Engineer, Rosen Center for Advanced Computing; and Ruchith Fernando, Ph.D., is a graduate from the computer sciences department at Purdue University. 2. Ann Christine Catlin, M.Sc., is Senior Research Scientist, Rosen Center for Advanced Computing, Purdue University, West Lafayette, IN. William X. Malloy, M.S., Pharm.D., BCPS, is Clinical Coordinator, Pharmacy; and Karen J. Arthur, Pharm.D., BCPS, is Medication Safety Program Manager, Roudebush Veterans Affairs Medical Center, Indianapolis, IN. Cindy Gaston, Pharm.D., BCPS, is Medication Use Policy Analyst, University of Wisconsin Hospital & Clinics, Madison. James Young, Pharm.D., CPHQ, is Quality Assurance/Performance Improvement Pharmacist, Eskenazi Health, Indianapolis. Sudheera Fernando, B.S., is Senior Software Engineer, Rosen Center for Advanced Computing; and Ruchith Fernando, Ph.D., is a graduate from the computer sciences department at Purdue University. william.malloy2@va.gov.
Abstract
PURPOSE: A Web-based analytics system for conducting inhouse evaluations and cross-facility comparisons of alert data generated by smart infusion pumps is described. SUMMARY: The Infusion Pump Informatics (IPI) project, a collaborative effort led by research scientists at Purdue University, was launched in 2009 to provide advanced analytics and tools for workflow analyses to assist hospitals in determining the significance of smart-pump alerts and reducing nuisance alerts. The IPI system allows facility-specific analyses of alert patterns and trends, as well as cross-facility comparisons of alert data uploaded by more than 55 participating institutions using different types of smart pumps. Tools accessible through the IPI portal include (1) charts displaying aggregated or breakout data on the top drugs associated with alerts, numbers of alerts per device or care area, and override-to-alert ratios, (2) investigative reports that can be used to characterize and analyze pump-programming errors in a variety of ways (e.g., by drug, by infusion type, by time of day), and (3) "drill-down" workflow analytics enabling users to evaluate alert patterns—both internally and in relation to patterns at other hospitals—in a quick and efficient stepwise fashion. CONCLUSION: The formation of the IPI analytics system to support a community of hospitals has been successful in providing sophisticated tools for member facilities to review, investigate, and efficiently analyze smart-pump alert data, not only within a member facility but also across other member facilities, to further enhance smart pump drug library design.
PURPOSE: A Web-based analytics system for conducting inhouse evaluations and cross-facility comparisons of alert data generated by smart infusion pumps is described. SUMMARY: The Infusion Pump Informatics (IPI) project, a collaborative effort led by research scientists at Purdue University, was launched in 2009 to provide advanced analytics and tools for workflow analyses to assist hospitals in determining the significance of smart-pump alerts and reducing nuisance alerts. The IPI system allows facility-specific analyses of alert patterns and trends, as well as cross-facility comparisons of alert data uploaded by more than 55 participating institutions using different types of smart pumps. Tools accessible through the IPI portal include (1) charts displaying aggregated or breakout data on the top drugs associated with alerts, numbers of alerts per device or care area, and override-to-alert ratios, (2) investigative reports that can be used to characterize and analyze pump-programming errors in a variety of ways (e.g., by drug, by infusion type, by time of day), and (3) "drill-down" workflow analytics enabling users to evaluate alert patterns—both internally and in relation to patterns at other hospitals—in a quick and efficient stepwise fashion. CONCLUSION: The formation of the IPI analytics system to support a community of hospitals has been successful in providing sophisticated tools for member facilities to review, investigate, and efficiently analyze smart-pump alert data, not only within a member facility but also across other member facilities, to further enhance smart pump drug library design.
Authors: Kumiko O Schnock; Patricia C Dykes; Jennifer Albert; Deborah Ariosto; Caitlin Cameron; Diane L Carroll; Moreen Donahue; Adrienne G Drucker; Rosemary Duncan; Linda Fang; Marla Husch; Nicole McDonald; Ray R Maddox; Julie McGuire; Sally Rafie; Emilee Robertson; Melinda Sawyer; Elizabeth Wade; Catherine S Yoon; Stuart Lipsitz; David W Bates Journal: Drug Saf Date: 2018-06 Impact factor: 5.606