JaeHo Lee1, Hyewon Han2, Minsu Ock3, Sang-il Lee3, SunGyo Lee4, Min-Woo Jo5. 1. Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; Department of Biomedical Informatics, Asan Medical Center, Seoul, Republic of Korea; Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA. 2. Department of Pharmacy, Asan Medical Center, Seoul, Republic of Korea. 3. Department of Preventive Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea. 4. Office for Performance Improvement, Asan Medical Center, Seoul, Republic of Korea. 5. Department of Preventive Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea. Electronic address: jominwoo@amc.seoul.kr.
Abstract
OBJECTIVE: To evaluate the impact of a high-alert medication clinical decision support system called HARMLESS on point-of-order entry errors in a tertiary hospital. METHOD: HARMLESS 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. RESULT: During 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. CONCLUSION: HARMLESS 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.
OBJECTIVE: To evaluate the impact of a high-alert medication clinical decision support system called HARMLESS on point-of-order entry errors in a tertiary hospital. METHOD: HARMLESS 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. RESULT: During 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. CONCLUSION: HARMLESS 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.
Authors: Angela Mastrianni; Aleksandra Sarcevic; Lauren S Chung; Issa Zakeri; Emily C Alberto; Zachary P Milestone; Randall S Burd; Ivan Marsic Journal: DIS (Des Interact Syst Conf) Date: 2021-06-28
Authors: Happiness Pius Saronga; Els Duysburgh; Siriel Massawe; Maxwell Ayindenaba Dalaba; Peter Wangwe; Felix Sukums; Melkizedeck Leshabari; Antje Blank; Rainer Sauerborn; Svetla Loukanova Journal: BMC Health Serv Res Date: 2017-08-07 Impact factor: 2.655