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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».