Point‐of‐care Cognitive Support Technology in Emergency Departments: A Scoping Review of Technology Acceptance by Clinicians
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".