Readiness of healthcare providers for eHealth: the case from primary healthcare centers in Lebanon
Bibliographic record
Abstract
BACKGROUND: eHealth can positively impact the efficiency and quality of healthcare services. Its potential benefits extend to the patient, healthcare provider, and organization. Primary healthcare (PHC) settings may particularly benefit from eHealth. In these settings, healthcare provider readiness is key to successful eHealth implementation. Accordingly, it is necessary to explore the potential readiness of providers to use eHealth tools. Therefore, the purpose of this study was to assess the readiness of healthcare providers working in PHC centers in Lebanon to use eHealth tools. METHODS: A self-administered questionnaire was used to assess participants' socio-demographics, computer use, literacy, and access, and participants' readiness for eHealth implementation (appropriateness, management support, change efficacy, personal beneficence). The study included primary healthcare providers (physicians, nurses, other providers) working in 22 PHC centers distributed across Lebanon. Descriptive and bivariate analyses (ANOVA, independent t-test, Kruskal Wallis, Tamhane's T2) were used to compare participant characteristics to the level of readiness for the implementation of eHealth. RESULTS: Of the 541 questionnaires, 213 were completed (response rate: 39.4 %). The majority of participants were physicians (46.9 %), and nurses (26.8 %). Most physicians (54.0 %), nurses (61.4 %), and other providers (50.9 %) felt comfortable using computers, and had access to computers at their PHC center (physicians: 77.0 %, nurses: 87.7 %, others: 92.5 %). Frequency of computer use varied. The study found a significant difference for personal beneficence, management support, and change efficacy among different healthcare providers, and relative to participants' level of comfort using computers. There was a significant difference by level of comfort using computers and appropriateness. A significant difference was also found between those with access to computers in relation to personal beneficence and change efficacy; and between frequency of computer use and change efficacy. CONCLUSION: The implementation of eHealth cannot be achieved without the readiness of healthcare providers. This study demonstrates that the majority of healthcare providers at PHC centers across Lebanon are ready for eHealth implementation. The findings of this study can be considered by decision makers to enhance and scale-up the use of eHealth in PHC centers nationally. Efforts should be directed towards capacity building for healthcare providers.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".