Wide variation and patterns of physicians' responses to drug-drug interaction alerts

被引:11
|
作者
Cho, Insook [1 ,2 ,3 ]
Lee, Yura [4 ]
Lee, Jae-Ho [4 ,5 ]
Bates, David W. [2 ,3 ,6 ]
机构
[1] Inha Univ, Dept Nursing, Incheon 22212, South Korea
[2] Brigham & Womens Hosp, Div Gen Internal Med, Ctr Patient Safety Res & Practice, 75 Francis St, Boston, MA 02115 USA
[3] Harvard Med Sch, Boston, MA 02115 USA
[4] Asan Med Ctr, Dept Biomed Informat, Seoul, South Korea
[5] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Emergency Med, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea
[6] Partners Healthcare Syst, Wellesley, MA 02481 USA
基金
美国医疗保健研究与质量局;
关键词
computerized physician order entry; drug-drug interaction; alert override; behavior pattern; variation analysis; BEHAVIOR; OVERRIDE;
D O I
10.1093/intqhc/mzy102
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Objectives Providing physicians with alerts about potentially harmful drug-drug interactions (DDIs) is only moderately effective due to high alert override rates. To understand high override behavior on DDI alerts, we investigated how physicians respond to DDIs and their behavior patterns and variations. Design Retrospective system log data analysis and records review (sampling 2% of total overrides). Setting A large tertiary academic hospital. Participants About 560 physicians and their override responses to DDI alerts generated from 1 September to 31 December 2014. Interventions Not applicable. Main Outcome Measure(s) DDI alert frequency and override rate. Results We found significant variation in both the number of alerts and override rates at the levels of physicians, departments and drug-class pairs. Physician-level variations were wider for residents than for faculty staff (number of alerts: t = 254.17, P = 0.011; override rates: t = -4.77, P < 0.0001). Using the number of alerts and their override rate, we classified physicians into four groups: inexperienced incautious users, inexperienced cautious users, experienced cautious users and experienced incautious users. Medical department influenced both alert numbers and override rates. Nearly 90% of the overrides involved only five drug-class combinations, which had a wide range of appropriateness in the chart review. Conclusion The variations at drug-class levels suggest issues with system design and the DDI rules. Department-level variation may be best addressed at the department level, and the rest of the variation appears related to individual physician responses, suggesting the need for interventions at an individual level.
引用
收藏
页码:89 / 95
页数:7
相关论文
共 50 条
  • [31] Recommendations to improve the usability of drug-drug interaction clinical decision support alerts
    Payne, Thomas H.
    Hines, Lisa E.
    Chan, Raymond C.
    Hartman, Seth
    Kapusnik-Uner, Joan
    Russ, Alissa L.
    Chaffee, Bruce W.
    Hartman, Christian
    Tamis, Victoria
    Galbreth, Brian
    Glassman, Peter A.
    Phansalkar, Shobha
    van der Sijs, Heleen
    Gephart, Sheila M.
    Mann, Gordon
    Strasberg, Howard R.
    Grizzle, Amy J.
    Brown, Mary
    Kuperman, Gilad J.
    Steiner, Chris
    Sullins, Amanda
    Ryan, Hugh
    Wittie, Michael A.
    Malone, Daniel C.
    JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2015, 22 (06) : 1243 - 1250
  • [32] Drug-drug Interaction Prediction with Common Structural Patterns
    Zhang, Jiongmin
    Yang, Xing
    Qian, Ying
    2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,
  • [33] THE EFFECT OF REDESIGNED COMPUTERIZED DRUG-DRUG INTERACTION ALERTS ON MEDICATION ERRORS AND PRESCRIBING EFFICIENCY
    Chen, S.
    Zillich, A. J.
    Melton, B. L.
    Saleem, J. J.
    Johnson, E.
    Weiner, M.
    Russell, S. A.
    McManus, M. S.
    Doebbeling, B. N.
    Russ, A. L.
    VALUE IN HEALTH, 2013, 16 (03) : A13 - A13
  • [34] Drug-Drug Interaction Alerts in Hospital Setting: Distribution, the Prevalence of Overrides, and Prescriber Determinants
    Eguale, Tewodros
    Seger, Diane L.
    Slight, Sarah P.
    Amato, Mary G.
    Nanji, Karen C.
    Maniam, Nivethietha
    Dykes, Patricia C.
    Fiskio, Julie M.
    Bates, David W.
    PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2014, 23 : 241 - 241
  • [35] Physicians' responses to computerized drug interaction alerts with password overrides (vol 15, 74, 2015)
    Nasuhara, Yasuyuki
    Sakushima, Ken
    Endoh, Akira
    Umeki, Reona
    Oki, Hiromitsu
    Yamada, Takehiro
    Iseki, Ken
    Ishikawa, Makoto
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2016, 16
  • [36] The Effect of Patient-Specific Drug-Drug Interaction Alerting on the Frequency of Alerts: A Pilot Study
    Horn, John
    Ueng, Stephen
    ANNALS OF PHARMACOTHERAPY, 2019, 53 (11) : 1087 - 1092
  • [37] An Evaluation of Drug-Drug Interaction Alerts Produced by Clinical Decision Support Systems in a Tertiary Hospital
    Alanazi, Abdullah
    Alalawi, Wejdan
    Aldosari, Bakheet
    CUREUS JOURNAL OF MEDICAL SCIENCE, 2023, 15 (08)
  • [38] Development and validation of a survey instrument for assessing prescribers' perception of computerized drug-drug interaction alerts
    Zheng, Kai
    Fear, Kathleen
    Chaffee, Bruce W.
    Zimmerman, Christopher R.
    Karls, Edward M.
    Gatwood, Justin D.
    Stevenson, James G.
    Pearlman, Mark D.
    JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2011, 18 : I51 - I61
  • [39] Do user preferences align with human factors assessment scores of drug-drug interaction alerts?
    Lowenstein, David
    Zheng, Wu Yi
    Burke, Rosemary
    Kenny, Eliza
    Sandhu, Anmol
    Makeham, Meredith
    Westbrook, Johanna
    Day, Richard O.
    Baysari, Melissa T.
    HEALTH INFORMATICS JOURNAL, 2020, 26 (01) : 563 - 575
  • [40] Facilitators and Barriers to Uptake of Drug-Drug Interaction Alerts: Perspectives of Australian End Users and Managers
    Stanceski, Kristian
    Van Dort, Bethany A.
    Lee, Teresa
    McLachlan, Andrew J.
    Day, Richard O.
    Hilmer, Sarah N.
    Li, Ling
    Westbrook, Johanna
    Zheng, Wu Yi
    Barras, Michael
    Mekhail, Karma Z. S.
    Baysari, Melissa T.
    APPLIED CLINICAL INFORMATICS, 2025, 16 (02): : 295 - 304