Ilona Leviatan1, Bernice Oberman2, Eyal Zimlichman1,3, Gideon Y Stein1,4. 1. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. 2. Gertner Institute for Epidemiology and Health Policy Research, Tel HaShomer, Ramat Gan, Israel. 3. Management Wing, Chaim Sheba Medical Center, Tel HaShomer, Ramat Gan, Israel. 4. Internal Medicine "A," Meir Medical Center, Clalit Health Services, Kfar Saba, Israel.
Abstract
OBJECTIVE: We aimed to assess associations of physician's work overload, successive work shifts, and work experience with physicians' risk to err. MATERIALS AND METHODS: This large-scale study included physicians who prescribed at least 100 systemic medications at Sheba Medical Center during 2012-2017 in all acute care departments, excluding intensive care units. Presumed medication errors were flagged by a high-accuracy computerized decision support system that uses machine-learning algorithms to detect potential medication prescription errors. Physicians' successive work shifts (first or only shift, second, and third shifts), workload (assessed by the number of prescriptions during a shift) and work-experience, as well as a novel measurement of physicians' prescribing experience with a specific drug, were assessed per prescription. The risk to err was determined for various work conditions. RESULTS: 1 652 896 medical orders were prescribed by 1066 physicians; The system flagged 3738 (0.23%) prescriptions as erroneous. Physicians were 8.2 times more likely to err during high than normal-low workload shifts (5.19% vs 0.63%, P < .0001). Physicians on their third or second successive shift (compared to a first or single shift) were more likely to err (2.1%, 1.8%, and 0.88%, respectively, P < .001). Lack of experience in prescribing a specific medication was associated with higher error rate (0.37% for the first 5 prescriptions vs 0.13% after over 40, P < .001). DISCUSSION: Longer hours and less experience in prescribing a specific medication increase risk of erroneous prescribing. CONCLUSION: Restricting successive shifts, reducing workload, increasing training and supervision, and implementing smart clinical decision support systems may help reduce prescription errors.
OBJECTIVE: We aimed to assess associations of physician's work overload, successive work shifts, and work experience with physicians' risk to err. MATERIALS AND METHODS: This large-scale study included physicians who prescribed at least 100 systemic medications at Sheba Medical Center during 2012-2017 in all acute care departments, excluding intensive care units. Presumed medication errors were flagged by a high-accuracy computerized decision support system that uses machine-learning algorithms to detect potential medication prescription errors. Physicians' successive work shifts (first or only shift, second, and third shifts), workload (assessed by the number of prescriptions during a shift) and work-experience, as well as a novel measurement of physicians' prescribing experience with a specific drug, were assessed per prescription. The risk to err was determined for various work conditions. RESULTS: 1 652 896 medical orders were prescribed by 1066 physicians; The system flagged 3738 (0.23%) prescriptions as erroneous. Physicians were 8.2 times more likely to err during high than normal-low workload shifts (5.19% vs 0.63%, P < .0001). Physicians on their third or second successive shift (compared to a first or single shift) were more likely to err (2.1%, 1.8%, and 0.88%, respectively, P < .001). Lack of experience in prescribing a specific medication was associated with higher error rate (0.37% for the first 5 prescriptions vs 0.13% after over 40, P < .001). DISCUSSION: Longer hours and less experience in prescribing a specific medication increase risk of erroneous prescribing. CONCLUSION: Restricting successive shifts, reducing workload, increasing training and supervision, and implementing smart clinical decision support systems may help reduce prescription errors.
Authors: Usha Sethuraman; Nirupama Kannikeswaran; Kyle P Murray; Marwan A Zidan; James M Chamberlain Journal: Acad Emerg Med Date: 2015-05-21 Impact factor: 3.451
Authors: Pierre Elias; Eric Peterson; Bob Wachter; Cary Ward; Eric Poon; Ann Marie Navar Journal: Appl Clin Inform Date: 2019-11-27 Impact factor: 2.342
Authors: Penny J Lewis; Darren M Ashcroft; Tim Dornan; David Taylor; Val Wass; Mary P Tully Journal: Br J Clin Pharmacol Date: 2014-08 Impact factor: 4.335
Authors: Karl Y Bilimoria; Jeanette W Chung; Larry V Hedges; Allison R Dahlke; Remi Love; Mark E Cohen; David B Hoyt; Anthony D Yang; John L Tarpley; John D Mellinger; David M Mahvi; Rachel R Kelz; Clifford Y Ko; David D Odell; Jonah J Stulberg; Frank R Lewis Journal: N Engl J Med Date: 2016-02-02 Impact factor: 91.245
Authors: Richard N Keers; Steven D Williams; Joe J Vattakatuchery; Petra Brown; Joan Miller; Lorraine Prescott; Darren M Ashcroft Journal: BMJ Open Date: 2014-10-01 Impact factor: 2.692
Authors: Eilidh M Duncan; Jill J Francis; Marie Johnston; Peter Davey; Simon Maxwell; Gerard A McKay; James McLay; Sarah Ross; Cristín Ryan; David J Webb; Christine Bond Journal: Implement Sci Date: 2012-09-11 Impact factor: 7.327
Authors: David C Classen; A Jay Holmgren; Zoe Co; Lisa P Newmark; Diane Seger; Melissa Danforth; David W Bates Journal: JAMA Netw Open Date: 2020-05-01