Healthcare providers' perception and knowledge of the use of artificial intelligence in healthcare service delivery in the Limbe and Buea Health Districts: a cross-sectional study
Bibliographic record
Abstract
Background Artificial Intelligence (AI) in healthcare is rapidly growing in recent years, and has substantially improved the quality of care and health outcomes of patients. Understanding healthcare providers' perception and knowledge of AI in healthcare is crucial for its effective adoption, and its use. This study aimed to determine healthcare providers' awareness, assess their knowledge of healthcare AI, assess their perceived benefits, readiness to adopt AI in healthcare in Limbe and Buea Health Districts. Methods A hospital-based cross-sectional study was conducted using a multi-staged sampling technique that recruited participants from seven hospitals in Limbe and Buea Health Districts. A questionnaire designed on koboCollect was used for data collection through face-to-face interviews from 494 participants recruited through a multi-stage sampling technique. The data was analyzed using SPSS version 26 where descriptive statistics and logistic regressions were done to determine the factors associated with readiness to adopt AI in healthcare. A P -value of <0.05 at 95% CI was considered statistically significant. Results A total of 494 participants were recruited into the study with a mean age of 32.6 ± 7.5 years, the majority 355 (71.9%) were females, 448 (90.7%) had attained tertiary education and the highest proportion 295 (59.7%) were Nurses. The study revealed that 373 (75.5%) were aware of the use of AI in healthcare, 261 (52.8%) had used AI tools, 213 (43.1%) had good knowledge of healthcare AI, 283 (57.3%) had good perception of its benefits and 230 (46.6%) were ready to adopt its use. Those who had access to AI tools were about 5 times more ready to adopt AI use (AOR: 4.5, CI: 3.05–6.72, p : <0.001). The main challenges reported were job displacement, lack of understanding of AI, and limited access to quality health data. A majority of 465 (94.1%) believed training is important to effectively use AI in healthcare. Conclusion Healthcare providers' awareness and perceived benefits of AI use in healthcare were good, the knowledge was below average, and an average of the population were ready to adopt AI. Despite the benefits of AI, most of them fear AI will replace their jobs and believe training is important for the effective adoption of AI in healthcare.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".