Computer technology and clinical work: still waiting for Godot.

By Robert L. Wears, M. Berg. Journal of the American Medical Association (JAMA), 2005

495 citations recorded by the source corpus.

Subfields: General Health Professions, Health Information Management, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/9e0fc37e01e7d97a4593e65d614dd0e4a8f366b2

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Citations in the corpus

Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes (2019, background): “In a review of deployed DSTs, healthcare researchers ranked the lack of HCI considerations as the most likely reason for failure [11, 22].”
A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology (2012, background): “Although the possible use of computers in medical decision making was mentioned 50 years ago, CDS systems are still not widely applied nor accepted in clinical practice [3–6].”
Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial stewardship. (2007, background): “Despite these initial promising studies, matching the linear technology of CPOE with complex clinical management that can be subjective, interpretive, and reactive has been a challenge at other institutions [152].”
Health IT success and failure: recommendations from literature and an AMIA workshop. (2009, result): “Similar failure rates have been reported specifically for health IT.”
Clinical information technologies and inpatient outcomes: a multiple hospital study. (2009, background): “This view holds that a successful information technology implementation jointly optimizes the technology and the social aspects of an organization (eg, its policies, values, norms, and culture).(25) To properly account for the impact of clinical information technologies in a way that can be replicat”
"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making (2019, background): “Despite the high-performing nature of these algorithms, prior work has shown that CDSSes can be difficult to successfully integrate into practice, citing a lack of HCI consideration as one of the primary reasons for failure [41, 44, 55, 76, 78].”
Summary of the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America Guidelines for Developing an Institutional Program to Enhance Antimicrobial Stewardship (2007, background): “Despite these initial promising studies, matching the linear technology of CPOE with complex clinical management that can be subjective, interpretive, and reactive has been a challenge at other institutions [152].”
Technology Evaluation: Workarounds to Barcode Medication Administration Systems: Their Occurrences, Causes, and Threats to Patient Safety (2008, background): “These data show that as with all health care technologies,”
The effectiveness of computerized order entry at reducing preventable adverse drug events and medication errors in hospital settings: a systematic review and meta-analysis (2014, background): “It has long been suspected that variability in the effectiveness of a complex sociotechnical intervention such as CPOE may be related not only to intervention design but also to context and implementation factors [16,77,78].”
Adopting electronic medical records in primary care: Lessons learned from health information systems implementation experience in seven countries (2009, background): “Successful implementations are supported by executives [16,40–51].”
Healthcare professionals' organisational barriers to health information technologies - A literature review (2011, background): “Notwithstanding the fact that technical problems definitely explain failures in HIT implementation, the introduction of HIT should not be viewed as a problem in technology exclusively but rather as a problem in organisational change [22].”
From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis (2015, unknown): “Although in certain systems patient cases are used for probabilistic training, these efforts focus on feature correlations and final clinical outcomes [7-10] rather than actionable policy (see [11,12] for a review).”