Connected diagnostics: linking digital rapid diagnostic tests and mobile health wallets to diagnose and treat brucellosis in Samburu, Kenya
Bibliographic record
Abstract
BACKGROUND: Despite WHO guidelines for testing all suspected cases of malaria before initiating treatment, presumptive malaria treatment remains common practice among some clinicians and in certain low-resource settings the capacity for microscopic testing is limited. This can lead to misdiagnosis, resulting in increased morbidity due to lack of treatment for undetected conditions, increased healthcare costs, and potential for drug resistance. This is particularly an issue as multiple conditions share the similar etiologies to malaria, including brucellosis, a rare, under-detected zoonosis. Linking rapid diagnostic tests (RDTs) and digital test readers for the detection of febrile illnesses can mitigate this risk and improve case management of febrile illness. METHODS: This technical advance study examines Connected Diagnostics, an approach that combines the use of point-of-care RDTs for malaria and brucellosis, digitally interpreted by a rapid diagnostic test reader (Deki Reader) and connected to mobile payment mechanisms to facilitate the diagnosis and treatment of febrile illness in nomadic populations in Samburu County, Kenya. Consenting febrile patients were tested with RDTs and patient diagnosis and risk information were uploaded to a cloud database via the Deki Reader. Patients with positive diagnoses were provided digital vouchers for transportation to the clinic and treatment via their health wallet on their mobile phones. RESULTS: In total, 288 patients were tested during outreach visits, with 9% testing positive for brucellosis and 0.6% testing positive for malaria. All patients, regardless of diagnosis were provided with a mobile health wallet on their cellular phones to facilitate their transport to the clinic, and for patients testing positive for brucellosis or malaria, the wallet funded their treatment. The use of the Deki Reader in addition to quality diagnostics at point of care also facilitated geographic mapping of patient diagnoses in relation to key risk areas for brucellosis transmission. CONCLUSIONS: This study demonstrates that the Connected Dx approach can be effective even when addressing a remote, nomadic population and a rare disease, indicating that this approach to diagnosing, treatment, and payment for healthcare costs is feasible and can be scaled to address more prevalent diseases and conditions in more populous contexts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".