Jan Horsky1, Jos Aarts2, Leonie Verheul3, Diane L Seger4, Heleen van der Sijs5, David W Bates6. 1. Division of General Internal Medicine and Primary Care, Brigham &Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States. Electronic address: jhorsky@partners.org. 2. Department of Biomedical Informatics, SUNY Buffalo, NY, United States; Institute of Health Policy and Management, Erasmus University Rotterdam, The Netherlands. 3. Institute of Health Policy and Management, Erasmus University Rotterdam, The Netherlands. 4. Division of General Internal Medicine and Primary Care, Brigham &Women's Hospital, Boston, MA, United States. 5. Department of Hospital Pharmacy, Erasmus Medical Center, Rotterdam, The Netherlands. 6. Division of General Internal Medicine and Primary Care, Brigham &Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
Abstract
OBJECTIVE: Describe and analyze reasoning patterns of clinicians responding to drug-drug interaction alerts in order to understand the role of patient-specific information in the decision-making process about the risks and benefits of medication therapy. Insights could be used to inform the design of decision-support interventions. METHODS: Thirty-two clinicians working with five EHRs in two countries completed sets of six medication orders each and responded to high- and low-severity drug-drug interaction alerts while verbalizing their thoughts in a standard think-aloud protocol. Tasks were recorded and analyzed to describe reasoning patterns about patient-risk assessment and strategies to avoid or mitigate it. RESULTS: We observed a total of 171 prescribing decisions. Clinicians actively sought to reduce risk when responding to high-severity alerts, mostly by monitoring patients and making dose adjustments (52 alerts, 40%). In contrast, they routinely left prescriptions unchanged after low-severity alerts when they felt confident that patients would tolerate the drug combination and that treatment benefits outweighed the risks (30 alerts, 71%). Clinicians used similar reasoning patterns regardless of the EHR used and differences in alert design. DISCUSSION: Clinicians conceptualized risk as a complex set of interdependent tradeoffs specific to individual patients and had a tendency not to follow advice they considered of low clinical value. Omission of patient-specific data, which was not shown in alerts or included in trigger logic, may have contributed to the constancy of reasoning and to similarities in risk-control strategies we observed despite significant differences in interface design and system function. CONCLUSION: Declining an alert suggestion was preceded by sometimes brief but often complex reasoning, prioritizing different aspects of care quality and safety, especially when the perceived risk was higher. Clinicians believed that the risk indicated in drug-drug interaction alerts needs to be interpreted as one factor in the broader context of care, specific to a patient. Copyright Â
OBJECTIVE: Describe and analyze reasoning patterns of clinicians responding to drug-drug interaction alerts in order to understand the role of patient-specific information in the decision-making process about the risks and benefits of medication therapy. Insights could be used to inform the design of decision-support interventions. METHODS: Thirty-two clinicians working with five EHRs in two countries completed sets of six medication orders each and responded to high- and low-severity drug-drug interaction alerts while verbalizing their thoughts in a standard think-aloud protocol. Tasks were recorded and analyzed to describe reasoning patterns about patient-risk assessment and strategies to avoid or mitigate it. RESULTS: We observed a total of 171 prescribing decisions. Clinicians actively sought to reduce risk when responding to high-severity alerts, mostly by monitoring patients and making dose adjustments (52 alerts, 40%). In contrast, they routinely left prescriptions unchanged after low-severity alerts when they felt confident that patients would tolerate the drug combination and that treatment benefits outweighed the risks (30 alerts, 71%). Clinicians used similar reasoning patterns regardless of the EHR used and differences in alert design. DISCUSSION: Clinicians conceptualized risk as a complex set of interdependent tradeoffs specific to individual patients and had a tendency not to follow advice they considered of low clinical value. Omission of patient-specific data, which was not shown in alerts or included in trigger logic, may have contributed to the constancy of reasoning and to similarities in risk-control strategies we observed despite significant differences in interface design and system function. CONCLUSION: Declining an alert suggestion was preceded by sometimes brief but often complex reasoning, prioritizing different aspects of care quality and safety, especially when the perceived risk was higher. Clinicians believed that the risk indicated in drug-drug interaction alerts needs to be interpreted as one factor in the broader context of care, specific to a patient. Copyright Â
Authors: Mustafa Ozkaynak; Noel Metcalf; Daniel M Cohen; Larissa S May; Peter S Dayan; Rakesh D Mistry Journal: Appl Clin Inform Date: 2020-09-09 Impact factor: 2.342