Adoption of e-health technology by physicians: a scoping review
Bibliographic record
Abstract
Chloe de Grood,1 Aida Raissi,2 Yoojin Kwon,3 Maria Jose Santana1 1Department of Community Health Sciences, W21C Research and Innovation Centre, University of Calgary, Calgary, 2University of Alberta, Edmonton, AB, 3Toronto Public Library, Toronto, ON, Canada Objective: The goal of this scoping review was to summarize the current literature identifying barriers and opportunities that facilitate adoption of e-health technology by physicians.Design: Scoping review.Setting: MEDLINE, EMBASE, and PsycINFO databases as provided by Ovid were searched from their inception to July 2015. Studies captured by the search strategy were screened by two reviewers and included if the focus was on barriers and facilitators of e-health technology adoption by physicians.Results: Full-text screening yielded 74 studies to be included in the scoping review. Within those studies, eleven themes were identified, including cost and liability issues, unwillingness to use e-health technology, and training and support.Conclusion: Cost and liability issues, unwillingness to use e-health technology, and training and support were the most frequently mentioned barriers and facilitators to the adoption of e-health technology. Government-level payment incentives and privacy laws to protect health information may be the key to overcome cost and liability issues. The adoption of e-health technology may be facilitated by tailoring to the individual physician’s knowledge of the e-health technology and the use of follow-up sessions for physicians and on-site experts to support their use of the e-health technology. To ensure the effective uptake of e-health technologies, physician perspectives need to be considered in creating an environment that enables the adoption of e-health strategies. Keywords: medical informatics, electronic medical records, diffusion of innovation, attitude of health personnel, information seeking behavior
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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 | 0.029 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 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".