Findings and Guidelines on Provider Technology, Fatigue, and Well-being: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Video and other technologies are reshaping the delivery of health care, yet barriers related to workflow and possible provider fatigue suggest that a thorough evaluation is needed for quality and process improvement. OBJECTIVE: This scoping review explored the relationship among technology, fatigue, and health care to improve the conditions for providers. METHODS: A 6-stage scoping review of literature (from 10 databases) published from 2000 to 2020 that focused on technology, health care, and fatigue was conducted. Technologies included synchronous video, telephone, informatics systems, asynchronous wearable sensors, and mobile health devices for health care in 4 concept areas related to provider experience: behavioral, cognitive, emotional, and physical impact; workplace at the individual, clinic, hospital, and system or organizational levels; well-being, burnout, and stress; and perceptions regarding technology. Qualitative content, discourse, and framework analyses were used to thematically analyze data for developing a spectrum of health to risk of fatigue to manifestations of burnout. RESULTS: Of the 4221 potential literature references, 202 (4.79%) were duplicates, and our review of the titles and abstracts of 4019 (95.21%) found that 3837 (90.9%) were irrelevant. A full-text review of 182 studies revealed that 12 (6.6%) studies met all the criteria related to technology, health care, and fatigue, and these studied the behavioral, emotional, cognitive, and physical impact of workflow at the individual, hospital, and system or organizational levels. Video and electronic health record use has been associated with physical eye fatigue; neck pain; stress; tiredness; and behavioral impacts related to additional effort owing to barriers, trouble with engagement, emotional wear and tear and exhaustion, cognitive inattention, effort, expecting problems, multitasking and workload, and emotional experiences (eg, anger, irritability, stress, and concern about well-being). An additional 14 studies that evaluated behavioral, emotional, and cognitive impacts without focusing on fatigue found high user ratings on data quality, accuracy, and processing but low satisfaction with clerical tasks, the effort required in work, and interruptions costing time, resulting in more errors, stress, and frustration. Our qualitative analysis suggests a spectrum from health to risk and provides an outline of organizational approaches to human factors and technology in health care. Business, occupational health, human factors, and well-being literature have not studied technology fatigue and burnout; however, their findings help contextualize technology-based fatigue to suggest guidelines. Few studies were found to contextually evaluate differences according to health professions and practice contexts. CONCLUSIONS: Health care systems need to evaluate the impact of technology in accordance with the Quadruple Aim to support providers' well-being and prevent workload burden, fatigue, and burnout. Implementation and effectiveness approaches and a multilevel approach with objective measures for clinical, human factors, training, professional development, and administrative workflow are suggested. This requires institutional strategies and competencies to integrate health care quality, technology and well-being outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".