Facilitating remote access to specialist medical expertise through the scaled up adoption of a smartphone application: A South African case
Notice bibliographique
Résumé
BACKGROUND: In the context of a shortage of medical specialists, a medical referral app, designed for use on smartphones, was launched in 2014 for use by doctors in the public health service in South Africa. OBJECTIVES: As this is a novel intervention, with potential to have an impact on the use of scarce resources, and because not much was known about the use of the app, a descriptive study was undertaken to assess its adoption in Western Cape Government Health (WCGH) facilities. METHODS: Usage data of the app in WCGH facilities, in terms of referral and user numbers, were obtained from the date of its introduction in 2014. In addition, all the referrals to WCGH facilities for July 2019, stripped of any identifying data of patients or doctors, were analysed for origin, destination, outcome and response times. Descriptive statistics were used to analyse the data. RESULTS: Use of the app grew rapidly from 40 referrals per quarter to 16 437 per quarter after 5 years in use, with a cumulative total of 95 381 referrals. In July 2019, active users of the system included 913 sending doctors and 298 receiving doctors, representing 20 medical specialties. The senders and receivers were representative of every level in the healthcare system, from clinic to tertiary hospital. In July 2019, a total of 5 941 referrals were sent by means of the app to public facilities in Western Cape Province. Of the referrals, 80% were classified as acute and 20% as non-urgent. The referral outcomes included 51% accepted for transfer, 19% accepted for a specialist appointment, and 13% concluded with advice alone without the need for a specialist appointment or patient transfer - this category accounted for 28% of non-urgent referrals and 9% of acute referrals. In 50% of referrals, advice was given to the referring doctor, either as an additional or the only outcome. The median response times were 9 minutes for acute referrals and 19 minutes for non-urgent referrals. CONCLUSIONS: This study documents the scale-up of a mobile phone consultation and referral app from pilot phase to significant growth in use across a resource-constrained healthcare system. In a large proportion of cases, advice was given to the referring doctor by means of the app, frequently obviating the need for a specialist appointment or patient transfer. This finding demonstrates that a mobile app has the potential to reduce the need for face-to-face specialist visits, thereby improving the use of scarce medical resources.
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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 | 0,007 | 0,006 |
| 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 | 0,005 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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.
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