Factors influencing clinician performance post-electronic health record implementation: an empirical analysis in Moroccan hospitals
Bibliographic record
Abstract
BACKGROUND: In recent years, the Moroccan Ministry of Health and Social Protection has invested considerable resources in implementing electronic health record (EHR) systems to provide citizens with quality healthcare services through efficient structures. However, the rhythm of EHR deployment across the country is very slow, requiring urgent evaluation to remove barriers to successful EHR adoption. OBJECTIVE: This study aims to investigate the critical factors affecting healthcare providers' performance post-EHR implementation in Moroccan public hospitals. METHODS: A cross-sectional study was conducted in three hospitals affiliated with Hassan II University Hospital Center in Fez. Data were collected using a questionnaire survey administered to a sample of 368 healthcare providers from March 2021 to July 2021. Clinician performance was assessed using a proposed research model that integrates the Information System Success Model and the Technology-Organization-Environment framework. The final model was analyzed and tested by using structural equation modeling. Statistical analyses were conducted using SPSS version 25 and Amos version 26. RESULTS: The findings highlighted that the most critical factors influencing clinician performance are clinician satisfaction (β = 0.5, p < 0.001), followed by organization (β = 0.28, p < 0.001), and system quality (β = 0.17, p = 0.01). Additionally, information quality indirectly affects clinician performance (β = 0.19, p < 0.001). However, the environmental factor does not appear to have a significant impact (β = -0.004, p = 0.94). CONCLUSION: This study, performed for the first time in Morocco, identifies key factors for policymakers and healthcare organizations to enhance the successful implementation of EHR systems. Additionally, it serves as a valuable framework for future studies in this area.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".