Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: A randomized clinical trial |
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| Institution: | 1. Division of Emergency Medicine, Cincinnati Children''s, United States;2. Division of Biomedical Informatics, Cincinnati Children''s, United States;3. Department of Pediatrics, University of Arkansas for Medical Sciences, United States;4. Department of Emergency Medicine, Vanderbilt University, United States;5. Center for Asthma Research and Environmental Health, Vanderbilt University, United States;6. Department of Biomedical Informatics, Vanderbilt University, United States;7. Department of Biostatistics, Vanderbilt University, United States;1. Department of Pediatrics, Children''s Hospital Los Angeles, Los Angeles, CA;2. Division of Emergency and Transport Medicine, Children''s Hospital Los Angeles, Los Angeles, CA;3. Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA;1. University of Texas School of Biomedical Informatics and the University of Texas – Memorial Hermann Center for Healthcare Quality & Safety, Houston, TX, USA;2. Department of Clinical Effectiveness and Performance Measurement, St. Luke''s Episcopal Health System, Houston, TX, USA;3. Houston VA HSR&D Center of Innovation at the Michael E. DeBakey Veterans Affairs Medical Center and the Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA;1. Department of Emergency Medicine, Vanderbilt University, Nashville, TN;2. Department of Emergency Medicine, Harvard Medical School, Brigham and Women''s Hospital;3. Emergency Medicine Division, Schumacher Group;4. Departments of Emergency Medicine and Health Policy, George Washington University Medical Center;1. Department of Emergency Medicine, Vanderbilt University, Nashville, TN;2. Carl H. Lindner College of Business, Department of Operations, Business Analytics and Information Systems, University of Cincinnati, Cincinnati, OH;3. Department of Emergency Medicine, University of Cincinnati, Cincinnati, OH;4. James M. Anderson Center for Health Systems Excellence, Cincinnati Children''s Hospital Medical Center, Cincinnati, OH;1. Department of Clinical Epidemiology and Biostatistics, McMaster University, Canada;2. Department of Family Medicine, McMaster University, Canada;3. Kingston Community Health Centres, Canada;4. Department of Pharmacy, St. Joseph''s Health Care Hamilton, Hamilton, Canada;5. Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States;6. Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States;7. Geriatric Research Education and Clinical Center (GRECC), Veterans Affairs Pittsburgh Healthcare System (VAPHS), Pittsburgh, PA, United States;8. Center for Health Equity Research and Promotion (CHERP), VAPHS, Pittsburgh, PA, United States;9. Division of Clinical Pharmacology & Therapeutics, Department of Medicine, McMaster University, Canada;10. Centre for Evaluation of Medicines, St. Joseph''s Healthcare Hamilton, Canada;11. Department of Family Medicine, Queen''s University, Canada;1. National Institute for Health Innovation (NIHI), The University of Auckland, Auckland, New Zealand;2. Ocean Informatics Pty. Ltd., Brisbane, Australia;3. Department of Computer Science, The University of Auckland, Auckland, New Zealand |
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| Abstract: | ObjectiveThe use of evidence-based guidelines can improve the care for asthma patients. We implemented a computerized asthma management system in a pediatric emergency department (ED) to integrate national guidelines. Our objective was to determine whether patient eligibility identification by a probabilistic disease detection system (Bayesian network) combined with an asthma management system embedded in the workflow decreases time to disposition decision.MethodsWe performed a prospective, randomized controlled trial in an urban, tertiary care pediatric ED. All patients 2–18 years of age presenting to the ED between October 2010 and February 2011 were screened for inclusion by the disease detection system. Patients identified to have an asthma exacerbation were randomized to intervention or control. For intervention patients, asthma management was computer-driven and workflow-integrated including computer-based asthma scoring in triage, and time-driven display of asthma-related reminders for re-scoring on the electronic patient status board combined with guideline-compliant order sets. Control patients received standard asthma management. The primary outcome measure was the time from triage to disposition decision.ResultsThe Bayesian network identified 1339 patients with asthma exacerbations, of which 788 had an asthma diagnosis determined by an ED physician-established reference standard (positive predictive value 69.9%). The median time to disposition decision did not differ among the intervention (228 min; IQR = (141, 326)) and control group (223 min; IQR = (129, 316)); (p = 0.362). The hospital admission rate was unchanged between intervention (25%) and control groups (26%); (p = 0.867). ED length of stay did not differ among intervention (262 min; IQR = (165, 410)) and control group (247 min; IQR = (163, 379)); (p = 0.818).ConclusionsThe control and intervention groups were similar in regards to time to disposition; the computerized management system did not add additional wait time. The time to disposition decision did not change; however the management system integrated several different information systems to support clinicians’ communication. |
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| Keywords: | Asthma Emergency medicine Medical informatics Pediatrics Clinical decision support |
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