Multi-level analysis of electronic health record adoption by health care professionals: A study protocol
Bibliographic record
Abstract
BACKGROUND: The electronic health record (EHR) is an important application of information and communication technologies to the healthcare sector. EHR implementation is expected to produce benefits for patients, professionals, organisations, and the population as a whole. These benefits cannot be achieved without the adoption of EHR by healthcare professionals. Nevertheless, the influence of individual and organisational factors in determining EHR adoption is still unclear. This study aims to assess the unique contribution of individual and organisational factors on EHR adoption in healthcare settings, as well as possible interrelations between these factors. METHODS: A prospective study will be conducted. A stratified random sampling method will be used to select 50 healthcare organisations in the Quebec City Health Region (Canada). At the individual level, a sample of 15 to 30 health professionals will be chosen within each organisation depending on its size. A semi-structured questionnaire will be administered to two key informants in each organisation to collect organisational data. A composite adoption score of EHR adoption will be developed based on a Delphi process and will be used as the outcome variable. Twelve to eighteen months after the first contact, depending on the pace of EHR implementation, key informants and clinicians will be contacted once again to monitor the evolution of EHR adoption. A multilevel regression model will be applied to identify the organisational and individual determinants of EHR adoption in clinical settings. Alternative analytical models would be applied if necessary. RESULTS: The study will assess the contribution of organisational and individual factors, as well as their interactions, to the implementation of EHR in clinical settings. CONCLUSIONS: These results will be very relevant for decision makers and managers who are facing the challenge of implementing EHR in the healthcare system. In addition, this research constitutes a major contribution to the field of knowledge transfer and implementation science.
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 | 0.082 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".