Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 31390471

Abstract

BACKGROUND: Drug prescription errors are made, worldwide, on a daily basis, resulting in a high burden of morbidity and mortality. Existing rule-based systems for prevention of such errors are unsuccessful and associated with substantial burden of false alerts.

Authors

  • G Segal
    Internal Medicine "T," Chaim Sheba Medical Center, Tel-Hashomer, Ramat Gan, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • A Segev
    Internal Medicine "T," Chaim Sheba Medical Center, Tel-Hashomer, Ramat Gan, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • A Brom
    Internal Medicine "T," Chaim Sheba Medical Center, Tel-Hashomer, Ramat Gan, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Y Lifshitz
    Internal Medicine "T," Chaim Sheba Medical Center, Tel-Hashomer, Ramat Gan, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Y Wasserstrum
    Internal Medicine "T," Chaim Sheba Medical Center, Tel-Hashomer, Ramat Gan, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • E Zimlichman
    Management Wing, Chaim Sheba Medical Center, Tel-Hashomer, Ramat Gan, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

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