Are We There Yet? Human Factors Knowledge and Health Information Technology – the Challenges of Implementation and Impact
Bibliographic record
Abstract
Summary Objective: To review the developments in human factors (HF) research on the challenges of health information technology (HIT) implementation and impact given the continuing incidence of usability problems and unintended consequences from HIT development and use. Methods: A search of PubMed/Medline and Web of Science® identified HF research published in 2015 and 2016. Electronic health records (EHRs) and patient-centred HIT emerged as significant foci of recent HF research. The authors selected prominent papers highlighting ongoing HF and usability challenges in these areas. This selective rather than systematic review of recent HF research highlights these key challenges and reflects on their implications on the future impact of HF research on HIT. Results: Research provides evidence of continued poor design, implementation, and usability of HIT, as well as technology-induced errors and unintended consequences. The paper highlights support for: (i) strengthening the evidence base on the benefits of HF approaches; (ii) improving knowledge translation in the implementation of HF approaches during HIT design, implementation, and evaluation; (iii) increasing transparency, governance, and enforcement of HF best practices at all stages of the HIT system development life cycle. Discussion and Conclusion: HF and usability approaches are yet to become embedded as integral components of HIT development, implementation, and impact assessment. As HIT becomes ever-more pervasive including with patients as end-users, there is a need to expand our conceptualisation of the problems to be addressed and the suite of tactics and strategies to be used to calibrate our pro-active involvement in its improvement.
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 | 0.137 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".