Literature DB >> 28960689

Point-of-care Cognitive Support Technology in Emergency Departments: A Scoping Review of Technology Acceptance by Clinicians.

Shelly Jun1, Amy C Plint2, Sandy M Campbell3, Sarah Curtis1, Kyrellos Sabir4, Amanda S Newton1.   

Abstract

OBJECTIVE: Cognitive support technologies that support clinical decisions and practices in the emergency department (ED) have the potential to optimize patient care. However, limited uptake by clinicians can prevent successful implementation. A better understanding of acceptance of these technologies from the clinician perspective is needed. We conducted a scoping review to synthesize diverse, emerging evidence on clinicians' acceptance of point-of-care (POC) cognitive support technology in the ED.
METHOD: We systematically searched 10 electronic databases and gray literature published from January 2006 to December 2016. Studies of any design assessing an ED-based POC cognitive support technology were considered eligible for inclusion. Studies were required to report outcome data for technology acceptance. Two reviewers independently screened studies for relevance and quality. Study quality was assessed using the Mixed-Methods Appraisal Tool. A descriptive analysis of the features of POC cognitive support technology for each study is presented, illustrating trends in technology development and evaluation. A thematic analysis of clinician, technical, patient, and organizational factors associated with technology acceptance is also presented.
RESULTS: Of the 1,563 references screened for eligibility, 24 met the inclusion criteria and were included in the review. Most studies were published from 2011 onward (88%), scored high for methodologic quality (79%), and examined POC technologies that were novel and newly introduced into the study setting (63%). Physician use of POC technology was the most commonly studied (67%). Technology acceptance was frequently conceptualized and measured by factors related to clinician attitudes and beliefs. Experience with the technology, intention to use, and actual use were also more common outcome measures of technology acceptance. Across studies, perceived usefulness was the most noteworthy factor impacting technology acceptance, and clinicians generally had positive perceptions of the use of POC cognitive support technology in the ED. However, the actual use of POC cognitive support technology reported by clinicians was low-use, by proportion of patient cases, ranged from 30% to 59%. Of the 24 studies, only two studies investigated acceptance of POC cognitive support technology currently implemented in the ED, offering "real-world" clinical practice data. All other studies focused on acceptance of novel technologies. Technical aspects such as an unfriendly user interface, presentation of redundant or ambiguous information, and required user effort had a negative impact on acceptance. Patient expectations were also found to have a negative impact, while patient safety implications had a positive impact. Institutional support was also reported to impact technology acceptance.
CONCLUSIONS: Findings from this scoping review suggest that while ED clinicians acknowledge the utility and value of using POC cognitive support technology, actual use of such technology can be low. Further, few studies have evaluated the acceptance and use of POC technologies in routine care. Prospective studies that evaluate how ED clinicians appraise and consider POC technology use in clinical practice are now needed with diverse clinician samples. While this review identified multiple factors contributing to technology acceptance, determining how clinician, technical, patient, and organizational factors mediate or moderate acceptance should also be a priority.
© 2017 by the Society for Academic Emergency Medicine.

Entities:  

Mesh:

Year:  2017        PMID: 28960689     DOI: 10.1111/acem.13325

Source DB:  PubMed          Journal:  Acad Emerg Med        ISSN: 1069-6563            Impact factor:   3.451


  11 in total

1.  "A catalyst for action": Factors for implementing clinical risk prediction models of infection in home care settings.

Authors:  Dawn Dowding; David Russell; Margaret V McDonald; Marygrace Trifilio; Jiyoun Song; Carlin Brickner; Jingjing Shang
Journal:  J Am Med Inform Assoc       Date:  2021-02-15       Impact factor: 4.497

2.  Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study.

Authors:  Pascale Carayon; Peter Hoonakker; Ann Schoofs Hundt; Megan Salwei; Douglas Wiegmann; Roger L Brown; Peter Kleinschmidt; Clair Novak; Michael Pulia; Yudi Wang; Emily Wirkus; Brian Patterson
Journal:  BMJ Qual Saf       Date:  2019-11-27       Impact factor: 7.035

3.  Scope and Influence of Electronic Health Record-Integrated Clinical Decision Support in the Emergency Department: A Systematic Review.

Authors:  Brian W Patterson; Michael S Pulia; Shashank Ravi; Peter L T Hoonakker; Ann Schoofs Hundt; Douglas Wiegmann; Emily J Wirkus; Stephen Johnson; Pascale Carayon
Journal:  Ann Emerg Med       Date:  2019-01-03       Impact factor: 5.721

4.  Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments.

Authors:  Gwen Costa Jacobsohn; Margaret Leaf; Frank Liao; Apoorva P Maru; Collin J Engstrom; Megan E Salwei; Gerald T Pankratz; Alexis Eastman; Pascale Carayon; Douglas A Wiegmann; Joel S Galang; Maureen A Smith; Manish N Shah; Brian W Patterson
Journal:  Healthc (Amst)       Date:  2021-12-16

5.  Usability barriers and facilitators of a human factors engineering-based clinical decision support technology for diagnosing pulmonary embolism.

Authors:  Megan E Salwei; Pascale Carayon; Douglas Wiegmann; Michael S Pulia; Brian W Patterson; Peter L T Hoonakker
Journal:  Int J Med Inform       Date:  2021-12-09       Impact factor: 4.730

6.  Usability of a Human Factors-based Clinical Decision Support in the Emergency Department: Lessons Learned for Design and Implementation.

Authors:  Megan E Salwei; Peter Hoonakker; Pascale Carayon; Douglas Wiegmann; Michael Pulia; Brian W Patterson
Journal:  Hum Factors       Date:  2022-04-14       Impact factor: 3.598

7.  Theories Predicting End-User Acceptance of Telemedicine Use: Systematic Review.

Authors:  Lorenz Harst; Hendrikje Lantzsch; Madlen Scheibe
Journal:  J Med Internet Res       Date:  2019-05-21       Impact factor: 5.428

8.  Clinician Perspectives on mRehab Interventions and Technologies for People with Disabilities in the United States: A National Survey.

Authors:  John Morris; Mike Jones; Nicole Thompson; Tracey Wallace; Frank DeRuyter
Journal:  Int J Environ Res Public Health       Date:  2019-10-31       Impact factor: 3.390

9.  Using a Clinical Workflow Analysis to Enhance eHealth Implementation Planning: Tutorial and Case Study.

Authors:  Stephanie Staras; Justin S Tauscher; Natalie Rich; Esaa Samarah; Lindsay A Thompson; Michelle M Vinson; Michael J Muszynski; Elizabeth A Shenkman
Journal:  JMIR Mhealth Uhealth       Date:  2021-03-31       Impact factor: 4.773

Review 10.  Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence-Enabled Clinical Decision Support Systems: Literature Review.

Authors:  Michael Knop; Sebastian Weber; Marius Mueller; Bjoern Niehaves
Journal:  JMIR Hum Factors       Date:  2022-03-24
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