Use of mobile phone technology to improve the quality of point-of-care testing in a low-resource setting
Bibliographic record
Abstract
Many patients in resource-limited, high disease burden settings do not have access to essential diagnostic tests for effective HIV care and treatment. Point-of-care (POC) diagnostic technologies may help alleviate critical testing needs, especially in decentralized settings with inadequate laboratories [1]. POC tests are easy-to-use by nonlaboratory staff, do not require significant infrastructure, and can increase access to diagnostics by allowing testing closer to patients [2–4][2–4][2–4]. POC technologies can deliver same-day test results leading to faster clinical decisions and reduced patient loss to follow-up [5,6][5,6]. The introduction of POC technologies has decentralized HIV testing to an expanded number of healthcare facilities. Doing so, however, may affect test quality because the end-users lack laboratory skills. Although the introduction of rapid HIV tests transformed patient care by decentralizing diagnosis and allowing dramatic increases in the number of patients initiated on antiretroviral treatment, several studies have identified testing quality concerns [7–9][7–9][7–9]. The WHO has recently released recommendations for improving the quality of POC testing in resource-limited settings [10]. Current quality interventions rely on training and supervision to ensure appropriate on-site test operation. However, many of the facilities where POC testing is most needed are in difficult-to-reach areas with limited infrastructure, making it inherently more difficult to ensure test quality using traditional methods alone. In 2010, Mozambique's Ministry of Health began implementing POC CD4+ T-cell testing for HIV disease staging and treatment monitoring. The use of CD4+ monitoring will decrease in Mozambique with expansion of viral load testing, but CD4+ testing remains useful for the management of opportunistic diseases and, presently, to stage patients for antiretroviral treatment eligibility [11]. To date, over 140 POC CD4 devices (Alere Pima, Waltham, Massachusetts, USA) have been deployed at decentralized health facilities in all 11 provinces across the country. From November 2012, POC CD4 devices were enabled with a wireless USB data modem for remote data collection. Using this technology, each POC CD4 device relayed data daily to a central database, including the number of tests performed, the error codes encountered, and internal quality control results. Only critical test failure errors requiring device troubleshooting and repeat testing were reported. A web-based management platform aggregated the data into an accessible format to monitor devices, analyze data, build reports, and enable follow-up when necessary. As scale-up of the POC CD4 program progressed, these remote performance monitoring practices were implemented together with follow-up phone calls to healthcare facilities with high error rates, failed daily quality controls, or unexpectedly low test volumes, which may indicate reagent stock-outs or device breakdowns. Occasional site visits were conducted if retraining was required. In 2013, over 125 000 POC CD4+ tests were performed across the country. Despite significant increases in monthly test volumes, nationwide test error rates gradually declined from 13% at the beginning of the remote monitoring intervention to below 5% from June to December 2013 (P < 0.001) and have remained below 5% (Fig. 1). The reduction of error rates resulted in fewer repeat tests and higher testing quality. Remote monitoring allowed for rapid resolution of the issues causing test and instrument errors and retraining as necessary. These responses rarely required visits to healthcare facilities, allowing operational cost savings because of fewer yet targeted facility visits as well as reduced cartridge wastage.Fig. 1: Point-of-care CD4+ T-cell testing volumes and error rates in Mozambique as monitored using mobile technology.Black bars indicate the testing volumes nationwide by month (left y-axis); grey triangles and line indicate the nationwide error rates by month (right y-axis); black squares and line indicate the percentage of devices with monthly error rates above 10% (right y-axis).The POC CD4 testing sites also participated in an external quality assurance (EQA) program (Quality Assurance Systems International, QASI, Canada). Over the 13-month study period, five rounds of EQA were conducted for participating field-based POC devices and laboratory-based CD4 instruments. POC devices and laboratory CD4 instruments had similar average EQA failure rates, 9% and 12%, respectively (P > 0.05). The frequency of EQA failure by both POC and laboratory instruments remained consistent over the study period. Although EQA did not identify all of the errors detected by connectivity, it provided a periodic reference to an external standard. The use of EQA and wireless connectivity to remotely monitor POC test performance may have complimentary utility. Wireless connectivity-based quality assurance for POC CD4 technologies has helped to establish reliable, quality, on-site CD4+ testing in public health facilities in Mozambique. The reduced error rates in Mozambique (<5%) were consistently below those observed in other countries; a recent 2-year study across nine sub-Saharan countries observed a median error rate of 12.2% [12–15][12–15][12–15][12–15]. As other innovative POC diagnostic technologies become available, remote monitoring of POC testing may help improve device management, quality assurance, operator performance, and supply chain in decentralized settings. As such, wireless connectivity may provide an innovative solution for health system strengthening in challenging environments. Acknowledgements Conflicts of interest There are no conflicts of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".