Assessing core, e-learning, clinical and technology readiness to integrate telemedicine at public health facilities in Uganda: a health facility – based survey
Bibliographic record
Abstract
BACKGROUND: In developing countries like Uganda, there are shortages of health workers especially medical specialists. The referral process is frustrating to both patients and health workers (HWs). This is due to delays in accessing laboratory results/tests, costs of travel with resultant delay in consulting specialists. Telemedicine can help reduce these problems. To facilitate successful and sustainable telemedicine implementation the eHealth readiness of different stakeholders should be undertaken. This study was conducted at public health facilities (HFs) in Uganda to assess eHealth readiness across four domains; core, e-learning, clinical and technology, that might hamper adoption and integration of telemedicine. METHODS: A cross-sectional study using mixed methods for data collection was conducted at health center IVs, regional and national referral hospitals. The study was conducted in three parts. Quantitative data on core, e-learning and clinical readiness domains were collected from doctors and other healthcare providers (nurses/midwives, public health officers and allied healthcare workers). Respondents were categorised into 'aware and used telemedicine', 'aware and not used', 'unaware of telemedicine'. Focus Group Discussions were conducted with patients to further assess core readiness. Technology readiness was assessed using a questionnaire with purposively selected respondents; directors, heads of medical sections, and hospital managers/superintendents. Descriptive statistics and correlations were performed using Spearman's rank order test for relationship between technology readiness variables at the HFs. RESULTS: 70% of health professionals surveyed across three levels of HF were aware of telemedicine and 41% had used telemedicine. However, over 40% of HWs at HC-IV and RRH were unaware of telemedicine. All doctors who had used telemedicine were impressed with it. Telemedicine users and non-users who were aware of telemedicine showed core, clinical, and learning readiness. Patients were aware of telemedicine but identified barriers to its use. A weak but positive correlation existed between the different variables in technology readiness. CONCLUSION: Respondents who were aware of and used telemedicine across all HF levels indicated core, learning and clinical readiness for adoption and integration of telemedicine at the public HFs in Uganda, although patients noted potential barriers that might need attention. In terms of technology readiness, gaps still exit at the various HF levels.
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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 | 0.039 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".