A mobile health technology platform for quality assurance and quality improvement of malaria diagnosis by community health workers
Bibliographic record
Abstract
BACKGROUND: Community health workers (CHWs) play an important role in improving access to services in areas with limited health infrastructure or workforce. Supervision of CHWs by qualified health professionals is the main link between this lay workforce and the formal health system. The quality of services provided by lay health workers is dependent on adequate supportive supervision. It is however one of the weakest links in CHW programs due to logistical and resource constraints, especially in large scale programs. Interventions such as point of care testing using malaria rapid diagnostic tests (RDTs) require real time monitoring to ensure diagnostic accuracy. In this study, we evaluated the utility of a mobile health technology platform to remotely monitor malaria RDT (mRDT) testing by CHWs for quality improvement. METHODS: As part of a large implementation trial involving mRDT testing by CHWs, we introduced the Fionet system composed of a mobile device (Deki Reader, DR) to assist in processing and automated interpretation of mRDTs, which connects to a cloud-based database which captures reports from the field in real time, displaying results in a custom dashboard of key performance indicators. A random sample of 100 CHWs were trained and provided with the Deki Readers and instructed to use it on 10 successive patients. The CHWs interpretation was compared with the Deki Reader's automatic interpretation, with the errors in processing and interpreting the RDTs recorded. After the CHW entered their interpretation on the DR, the DR provided immediate, automated feedback and interpretation based on its reading of the same cassette. The study team monitored the CHW performance remotely and provided additional support. RESULTS: A total of 1251 primary and 113 repeat tests were performed by the 97 CHWs who used the DR. 91.6% of the tests had agreement between the DR and the CHWs. There were 61 (4.9%) processing and 52 (4.2%) interpretation errors among the primary tests. There was a tendency towards lower odds of errors with increasing number and frequency of tests, though not statistically significant. Of the 62 tests that were repeated due to errors, 79% achieved concordance between the CHW and the DR. Satisfaction with the use of the DR by the CHWs was high. CONCLUSIONS: Use of innovative mHealth strategies for monitoring and quality control can ensure quality within a large scale implementation of community level testing by lay health workers.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".