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Enregistrement W2410073954 · doi:10.1097/qad.0000000000000907

Use of mobile phone technology to improve the quality of point-of-care testing in a low-resource setting

2015· letter· en· W2410073954 sur OpenAlexaboutno aff
Ilesh Jani, Jorge I. Quevedo, Ocean Tobaiwa, Timothy Bollinger, Nádia Sitoe, Patrina Chongo, Lara Vojnov, Jonathan Lehe, Trevor Peter

Notice bibliographique

RevueAIDS · 2015
Typeletter
Langueen
DomaineHealth Professions
ThématiqueMobile Health and mHealth Applications
Établissements canadiensnon disponible
Organismes subventionnairesAlere
Mots-clésMedicinePoint of carePoint-of-care testingPsychological interventionQuality (philosophy)Test (biology)Health carePhoneResource (disambiguation)Mobile phoneRisk analysis (engineering)Medical emergencyOperations managementComputer scienceNursingTelecommunicationsEngineering

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesIntégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,258
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,081
Tête enseignante GPT0,439
Écart entre enseignants0,358 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations15
Publié2015
Routes d'admission1
Résumé présentoui

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