Facilitating remote access to specialist medical expertise through the scaled up adoption of a smartphone application: A South African case
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".