Literature DB >> 31972381

Do computerized clinical decision support systems improve the prescribing of oral anticoagulants? A systematic review.

Anne-Laure Sennesael1, Bruno Krug2, Barbara Sneyers3, Anne Spinewine4.   

Abstract

BACKGROUND: Serious adverse drug reactions have been associated with the underuse or the misuse of oral anticoagulant therapy. We systematically reviewed the impact of computerized clinical decision support systems (CDSS) on the prescribing of oral anticoagulants and we described CDSS features associated with success or failure.
METHODS: We searched Medline, Embase, CENTRAL, CINHAL, and PsycINFO for studies that compared CDSS for the initiation or monitoring of oral anticoagulants with routine care. Two reviewers performed study selection, data collection, and risk-of-bias assessment. Disagreements were resolved with a third reviewer. Potentially important CDSS features, identified from previous literature, were evaluated.
RESULTS: Sixteen studies were included in our qualitative synthesis. Most trials were performed in primary care (n = 7) or hospitals (n = 6) and included atrial fibrillation (AF) patients (n = 9). Recommendations mainly focused on anticoagulation underuse (n = 11) and warfarin-drug interactions (n = 5). Most CDSS were integrated in electronic records or prescribing and provided support automatically at the time and location of decision-making. Significant improvements in practitioner performance were found in 9 out of 16 studies, while clinical outcomes were poorly reported. CDSS features seemed slightly more common in studies that demonstrated improvement.
CONCLUSIONS: CDSS might positively impact the use of oral anticoagulants in AF patients at high risk of stroke. The scope of CDSS should now evolve to assist prescribers in selecting the most appropriate and tailored medication. Efforts should nevertheless be made to improve the relevance of notifications and to address implementation outcomes.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Clinical decision support systems; Drug prescribing; Oral anticoagulants; Quality improvement

Mesh:

Substances:

Year:  2020        PMID: 31972381     DOI: 10.1016/j.thromres.2019.12.023

Source DB:  PubMed          Journal:  Thromb Res        ISSN: 0049-3848            Impact factor:   3.944


  4 in total

1.  Impact of a Machine Learning-Based Decision Support System for Urinary Tract Infections: Prospective Observational Study in 36 Primary Care Practices.

Authors:  Willem Ernst Herter; Janine Khuc; Giovanni Cinà; Bart J Knottnerus; Mattijs E Numans; Maryse A Wiewel; Tobias N Bonten; Daan P de Bruin; Thamar van Esch; Niels H Chavannes; Robert A Verheij
Journal:  JMIR Med Inform       Date:  2022-05-04

2.  Cochrane's risk of bias tool for non-randomized studies (ROBINS-I) is frequently misapplied: A methodological systematic review.

Authors:  Erik Igelström; Mhairi Campbell; Peter Craig; Srinivasa Vittal Katikireddi
Journal:  J Clin Epidemiol       Date:  2021-08-23       Impact factor: 6.437

3.  An e-Delphi study to obtain expert consensus on the level of risk associated with preventable e-prescribing events.

Authors:  Jude Heed; Stephanie Klein; Ann Slee; Neil Watson; Andy Husband; Sarah Patricia Slight
Journal:  Br J Clin Pharmacol       Date:  2022-03-08       Impact factor: 3.716

4.  Inappropriate Use of Oral Antithrombotic Combinations in an Outpatient Setting and Associated Risks: A French Nationwide Cohort Study.

Authors:  Lorène Zerah; Dominique Bonnet-Zamponi; Aya Ajrouche; Jean-Philippe Collet; Yann De Rycke; Florence Tubach
Journal:  J Clin Med       Date:  2021-05-27       Impact factor: 4.241

  4 in total

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