Literature DB >> 21045220

Does computer-aided clinical decision support improve the management of acute abdominal pain? A systematic review.

Jamie G Cooper1, Robert M West, Susan E Clamp, Tajek B Hassan.   

Abstract

Acute abdominal pain is a common reason for emergency presentation to hospital. Despite recent medical advances in diagnostics, overall clinical decision-making in the assessment of patients with undifferentiated acute abdominal pain remains poor, with initial clinical diagnostic accuracy being 45-50%. Computer-aided decision support (CADS) systems were widely tested in this arena during the 1970s and 1980s with results that were generally favourable. Inception into routine clinical practice was hampered largely by the size and speed of the hardware. Computer systems and literacy are now vastly superior and the potential benefit of CADS deserves investigation. An extensive literature search was undertaken to find articles that directly compared the clinical diagnostic accuracy prospectively of medical staff in the diagnosis of acute abdominal pain before and after the institution of a CADS programme. Included articles underwent meta-analysis with a random-effects model. Ten studies underwent meta-analysis that demonstrated an overall mean percentage improvement in clinical diagnostic accuracy of 17.25% with the use of CADS systems. There is a role for CADS in the initial evaluation of acute abdominal pain, which very often takes place in the emergency department setting.

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Year:  2010        PMID: 21045220     DOI: 10.1136/emj.2009.086801

Source DB:  PubMed          Journal:  Emerg Med J        ISSN: 1472-0205            Impact factor:   2.740


  6 in total

1.  The Practice Guidelines for Primary Care of Acute Abdomen 2015.

Authors:  Toshihiko Mayumi; Masahiro Yoshida; Susumu Tazuma; Akira Furukawa; Osamu Nishii; Kunihiro Shigematsu; Takeo Azuhata; Atsuo Itakura; Seiji Kamei; Hiroshi Kondo; Shigenobu Maeda; Hiroshi Mihara; Masafumi Mizooka; Toshihiko Nishidate; Hideaki Obara; Norio Sato; Yuichi Takayama; Tomoyuki Tsujikawa; Tomoyuki Fujii; Tetsuro Miyata; Izumi Maruyama; Hiroshi Honda; Koichi Hirata
Journal:  Jpn J Radiol       Date:  2016-01       Impact factor: 2.374

2.  The feasibility of using natural language processing to extract clinical information from breast pathology reports.

Authors:  Julliette M Buckley; Suzanne B Coopey; John Sharko; Fernanda Polubriaginof; Brian Drohan; Ahmet K Belli; Elizabeth M H Kim; Judy E Garber; Barbara L Smith; Michele A Gadd; Michelle C Specht; Constance A Roche; Thomas M Gudewicz; Kevin S Hughes
Journal:  J Pathol Inform       Date:  2012-06-30

3.  Impact of clinical experience and diagnostic performance in patients with acute abdominal pain.

Authors:  Helena Laurell; Lars-Erik Hansson; Ulf Gunnarsson
Journal:  Gastroenterol Res Pract       Date:  2015-01-22       Impact factor: 2.260

4.  Surgical decision-making in acute appendicitis.

Authors:  Eva Sandell; Maria Berg; Gabriel Sandblom; Joar Sundman; Ulf Fränneby; Lennart Boström; Åke Andrén-Sandberg
Journal:  BMC Surg       Date:  2015-06-02       Impact factor: 2.102

5.  Resource Management through Artificial Intelligence in Screening Programs-Key for the Successful Elimination of Hepatitis C.

Authors:  Anca Elena Butaru; Mădălin Mămuleanu; Costin Teodor Streba; Irina Paula Doica; Mihai Mircea Diculescu; Dan Ionuț Gheonea; Carmen Nicoleta Oancea
Journal:  Diagnostics (Basel)       Date:  2022-01-29

Review 6.  Reducing diagnostic errors in primary care. A systematic meta-review of computerized diagnostic decision support systems by the LINNEAUS collaboration on patient safety in primary care.

Authors:  Martine Nurek; Olga Kostopoulou; Brendan C Delaney; Aneez Esmail
Journal:  Eur J Gen Pract       Date:  2015-09       Impact factor: 1.904

  6 in total

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