Users’ perspectives of key factors to implementing electronic health records in Canada: a Delphi study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Interoperable electronic health record (EHR) solutions are currently being implemented in Canada, as in many other countries. Understanding EHR users' perspectives is key to the success of EHR implementation projects. This Delphi study aimed to assess in the Canadian context the applicability, the importance, and the priority of pre-identified factors from a previous mixed-methods systematic review of international literature. METHODS: A three-round Delphi study was held with representatives of 4 Canadian EHR user groups defined as partners of the implementation process who use or are expected to use EHR in their everyday activity. These groups are: non-physician healthcare professionals, health information professionals, managers, and physicians. Four bilingual online questionnaire versions were developed from factors identified by the systematic review. Participants were asked to rate the applicability and the importance of each factor. The main outcome measures were consensus and priority. Consensus was defined a priori as strong (≥ 75%) or moderate (≥ 60-74%) according to user groups' level of agreement on applicability and importance, partial (≥ 60%) when participants agreed only on applicability or importance, or as no consensus (< 60%). Priority for decision-making was defined as factors with strong consensus with scores of 4 or 5 on a five-point Likert scale for applicability and importance. RESULTS: Three Delphi rounds were completed by 64 participants. Levels of consensus of 100%, 64%, 64%, and 44% were attained on factors submitted to non-physician healthcare professionals, health information professionals, managers, and physicians, respectively. While agreement between and within user groups varied, key factors were prioritized if they were classified as strong (≥ 75% from questionnaire answers of user groups), for decision-making concerning EHR implementation. The 10 factors that were prioritized are perceived usefulness, productivity, motivation, participation of end-users in the implementation strategy, patient and health professional interaction, lack of time and workload, resources availability, management, outcome expectancy, and interoperability. CONCLUSIONS: Amongst all factors influencing EHR implementation identified in a previous systematic review, ten were prioritized through this Delphi study. The varying levels of agreement between and within user groups could mean that users' perspectives of each factor are complex and that each user group has unique professional priorities and roles in the EHR implementation process. As more EHR implementations in Canada are completed it will be possible to corroborate this preliminary result with a larger population of EHR users.
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.
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it