Impact of a clinical decision support system for high-alert medications on the prevention of prescription errors |
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| Affiliation: | 1. Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea;2. Department of Biomedical Informatics, Asan Medical Center, Seoul, Republic of Korea;3. Division of General Internal Medicine, Brigham and Women''s Hospital, Boston, MA, USA;4. Department of Pharmacy, Asan Medical Center, Seoul, Republic of Korea;5. Department of Preventive Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea;6. Office for Performance Improvement, Asan Medical Center, Seoul, Republic of Korea;1. Division of Health Systems Management and Policy, School of Public Health, University of Memphis, Memphis, TN 38152, United States;2. Department of Health Services Research and Administration, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, United States;1. Institute of Medical Informatics, University of Münster, Germany;2. Department of Information Systems, University of Münster, Germany;3. Faculty of Health Care, University of Applied Sciences, Krefeld, Germany;4. Competence Center Chronic Pruritus, Department of Dermatology, University of Münster, Germany;1. Rabin Medical Center, Clalit Health Services, Israel;2. Steyer School of Health Professions, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel;1. Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, UNSW Medicine, The University of New South Wales, Kensington, Sydney, NSW 2052, Australia;2. Director of Emergency Medicine, Concord Repatriation and General Hospital, Concord, Sydney, NSW 2139, Australia;3. Rheumatology Department, Liverpool Hospital, Sydney, NSW 1871, Australia |
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| Abstract: | ObjectiveTo evaluate the impact of a high-alert medication clinical decision support system called HARMLESS on point-of-order entry errors in a tertiary hospital.MethodHARMLESS was designed to provide three kinds of interventions for five high-alert medications: clinical knowledge support, pop-ups for erroneous orders that block the order or provide a warning, and order recommendations. The impact of this program on prescription order was evaluated by comparing the orders in 6 month periods before and after implementing the program, by analyzing the intervention log data, and by checking for order pattern changes.ResultDuring the entire evaluation period, there were 357,417 orders and 5233 logs. After HARMLESS deployment, orders that omitted dilution fluids and exceeded the maximum dose dropped from 12,878 and 214 cases to 0 and 9 cases, respectively. The latter nine cases were unexpected, but after the responsible programming error was corrected, there were no further such cases. If all blocking interventions were seen as errors that were prevented, this meant that 4137 errors (3584 of which were ‘dilution fluid omitted’ errors) were prevented over the 6-month post-deployment period. There were some unexpected order pattern changes after deployment and several unexpected errors emerged, including intramuscular or intravenous push orders for potassium chloride (although a case review revealed that the drug was not actually administered via these methods) and an increase in pro re nata (PRN; administer when required) orders for most drugs.ConclusionHARMLESS effectively implemented blocking interventions but was associated with the emergence of unexpected errors. After a program is deployed, it must be monitored and subjected to data analysis to fix bugs and prevent the emergence of new error types. |
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| Keywords: | Medication errors Patient safety Clinical decision support systems Computerized physician order entry system |
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