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Tytuł pozycji:

Evaluating a mobile application for improving clinical laboratory test ordering and diagnosis.

Tytuł:
Evaluating a mobile application for improving clinical laboratory test ordering and diagnosis.
Autorzy:
Meyer AND; Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
Thompson PJ; Centers for Disease Control and Prevention, Division of Laboratory Systems, Atlanta, GA, USA.
Khanna A; Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
Desai S; Michael E. DeBakey Veterans Affairs Medical Center and Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
Mathews BK; Department of Hospital Medicine, HealthPartners and University of Minnesota Medical School, Saint Paul, MN, USA.
Yousef E; Department of Hospital Medicine, Cleveland Clinic, Cleveland, OH, USA.
Kusnoor AV; Michael E. DeBakey Veterans Affairs Medical Center and Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
Singh H; Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
Źródło:
Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2018 Jul 01; Vol. 25 (7), pp. 841-847.
Typ publikacji:
Evaluation Study; Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.
Język:
English
Imprint Name(s):
Publication: 2015- : Oxford : Oxford University Press
Original Publication: Philadelphia, PA : Hanley & Belfus, c1993-
MeSH Terms:
Attitude of Health Personnel*
Clinical Decision-Making*
Clinical Laboratory Techniques*
Mobile Applications*
Partial Thromboplastin Time*
Attitude to Computers ; Female ; Humans ; Internal Medicine ; Male ; Physicians ; Prothrombin Time ; United States
References:
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Grant Information:
CC999999 United States ImCDC Intramural CDC HHS; R21 HS023602 United States HS AHRQ HHS
Entry Date(s):
Date Created: 20180425 Date Completed: 20191129 Latest Revision: 20210914
Update Code:
20240105
PubMed Central ID:
PMC6947660
DOI:
10.1093/jamia/ocy026
PMID:
29688391
Czasopismo naukowe
Objective: Mobile applications for improving diagnostic decision making often lack clinical evaluation. We evaluated if a mobile application improves generalist physicians' appropriate laboratory test ordering and diagnosis decisions and assessed if physicians perceive it as useful for learning.
Methods: In an experimental, vignette study, physicians diagnosed 8 patient vignettes with normal prothrombin times (PT) and abnormal partial thromboplastin times (PTT). Physicians made test ordering and diagnosis decisions for 4 vignettes using each resource: a mobile app, PTT Advisor, developed by the Centers for Disease Control and Prevention (CDC)'s Clinical Laboratory Integration into Healthcare Collaborative (CLIHC); and usual clinical decision support. Then, physicians answered questions regarding their perceptions of the app's usefulness for diagnostic decision making and learning using a modified Kirkpatrick Training Evaluation Framework.
Results: Data from 368 vignettes solved by 46 physicians at 7 US health care institutions show advantages for using PTT Advisor over usual clinical decision support on test ordering and diagnostic decision accuracy (82.6 vs 70.2% correct; P < .001), confidence in decisions (7.5 vs 6.3 out of 10; P < .001), and vignette completion time (3:02 vs 3:53 min.; P = .06). Physicians reported positive perceptions of the app's potential for improved clinical decision making, and recommended it be used to address broader diagnostic challenges.
Conclusions: A mobile app, PTT Advisor, may contribute to better test ordering and diagnosis, serve as a learning tool for diagnostic evaluation of certain clinical disorders, and improve patient outcomes. Similar methods could be useful for evaluating apps aimed at improving testing and diagnosis for other conditions.

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