Literature DB >> 12501817

Development and implementation of computerized clinical guidelines: barriers and solutions.

M H Trivedi1, J K Kern, A Marcee, B Grannemann, B Kleiber, T Bettinger, K Z Altshuler, A McClelland.   

Abstract

Research indicates that computerized decision support systems (CDSSs) can improve clinical performance and patient outcomes, and yet CDSSs are not in widespread use. Physician guidelines, in general, face barriers in implementation. Guidelines in a computerized format can overcome some of the barriers to conventional text-form guidelines; however, computerized programs have novel aspects that have to be considered, aspects such as technical problems/support and user interface issues that can act as barriers. Though the literature points out that human, organizational, and technical issues can act as barriers in the implementation of CDSSs, studies clearly indicate that there are methods that can overcome these barriers and improve CDSS acceptance and use. These methods come from lessons learned from a variety of CDSS implementation ventures. Notably, most of the methods that improve acceptance and use of a CDSS require feedback and involvement of end-users. Measuring and addressing physician or user attitudes toward the computerized support system has been shown to be important in the successful implementation of a CDSS. This article discusses: 1) the barriers of implementation of guidelines in general and of CDSSs; 2) the importance of the physician's role in development, implementation, and adherence; 3) methods that can improve CDSS acceptance and use; and 4) the types of tools needed to obtain end-user feedback.

Entities:  

Mesh:

Year:  2002        PMID: 12501817

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  25 in total

1.  A framework for classifying decision support systems.

Authors:  Ida Sim; Amy Berlin
Journal:  AMIA Annu Symp Proc       Date:  2003

Review 2.  Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support.

Authors:  Kristen Miller; Danielle Mosby; Muge Capan; Rebecca Kowalski; Raj Ratwani; Yaman Noaiseh; Rachel Kraft; Sanford Schwartz; William S Weintraub; Ryan Arnold
Journal:  J Am Med Inform Assoc       Date:  2018-05-01       Impact factor: 4.497

Review 3.  Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.

Authors:  Kensaku Kawamoto; Caitlin A Houlihan; E Andrew Balas; David F Lobach
Journal:  BMJ       Date:  2005-03-14

4.  Use of a computerized guideline for glucose regulation in the intensive care unit improved both guideline adherence and glucose regulation.

Authors:  Emmy Rood; Robert Jan Bosman; Johan Ids van der Spoel; Paul Taylor; Durk Freark Zandstra
Journal:  J Am Med Inform Assoc       Date:  2004-11-23       Impact factor: 4.497

5.  Measurement-based care for refractory depression: a clinical decision support model for clinical research and practice.

Authors:  Madhukar H Trivedi; Ella J Daly
Journal:  Drug Alcohol Depend       Date:  2007-02-22       Impact factor: 4.492

Review 6.  The design of decisions: Matching clinical decision support recommendations to Nielsen's design heuristics.

Authors:  Kristen Miller; Muge Capan; Danielle Weldon; Yaman Noaiseh; Rebecca Kowalski; Rachel Kraft; Sanford Schwartz; William S Weintraub; Ryan Arnold
Journal:  Int J Med Inform       Date:  2018-05-21       Impact factor: 4.046

7.  A computational framework for converting textual clinical diagnostic criteria into the quality data model.

Authors:  Na Hong; Dingcheng Li; Yue Yu; Qiongying Xiu; Hongfang Liu; Guoqian Jiang
Journal:  J Biomed Inform       Date:  2016-07-19       Impact factor: 6.317

8.  A computerized decision support system for depression in primary care.

Authors:  Benji T Kurian; Madhukar H Trivedi; Bruce D Grannemann; Cynthia A Claassen; Ella J Daly; Prabha Sunderajan
Journal:  Prim Care Companion J Clin Psychiatry       Date:  2009

9.  Information resources used in antimicrobial prescribing.

Authors:  Jonathan S Sellman; Douglas Decarolis; Anne Schullo-Feulner; David B Nelson; Gregory A Filice
Journal:  J Am Med Inform Assoc       Date:  2004-04-02       Impact factor: 4.497

10.  Feasibility of using algorithm-based clinical decision support for symptom assessment and management in lung cancer.

Authors:  Mary E Cooley; Traci M Blonquist; Paul J Catalano; David F Lobach; Barbara Halpenny; Ruth McCorkle; Ellis B Johns; Ilana M Braun; Michael S Rabin; Fatma Zohra Mataoui; Kathleen Finn; Donna L Berry; Janet L Abrahm
Journal:  J Pain Symptom Manage       Date:  2014-05-29       Impact factor: 3.612

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