The Factors Contributing to Physicians’ Current Use of and Satisfaction With Electronic Health Records in Kuwait’s Public Health Care: Cross-sectional Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Electronic health record (EHR) has emerged as a backbone health care organization that aims to integrate health care records and automate clinical workflow. With the adoption of the eHealth care system, health information communication technologies and EHRs are offering significant health care advantages in the form of error reduction, improved communication, and patient satisfaction. OBJECTIVE: This study aimed to (1) investigate factors associated with physicians' EHR adoption status and prevalence of EHRs in Kuwait and (2) identify factors predicting physician satisfaction with EHRs in public hospitals in Kuwait. METHODS: This study was conducted at Kuwait's public Al-Jahra hospital from May to September 2019, using quantitative research methods. Primary data were gathered via questionnaires distributed among 295 physicians recruited using convenience sampling. Data were analyzed in SPSS using descriptive, bivariate, and multivariate linear regression, adjusted for demographics. RESULTS: Results of the study revealed that the controlled variable of gender (β=-.197; P=.02) along with explanatory variables, such as training quality (β=.068; P=.005), perception of barriers (β=-.107; P=.04), and effect on physician (β=.521; P<.001) have a significant statistical relationship with physicians' EHR adoption status. Furthermore, findings also suggested that controlled variables of gender (β=-.193; P=.02), education (β=-.164; P=.03), effect on physician (β=.417; P<.001), and level of ease of use (β=.254; P<.001) are significant predictors of the degree of physician satisfaction with the EHR system. CONCLUSIONS: The findings of this study had significant managerial and practical implications for creating an inductive environment for the acceptance of EHR systems across a broad spectrum of health care system in Kuwait.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".