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Enregistrement W4313390731 · doi:10.4103/lungindia.lungindia_345_22

Innovative approach to delivery of TB Medicines

2022· article· en· W4313390731 sur OpenAlexaboutno aff
Yatin Dholakia

Notice bibliographique

RevueLung India · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueTuberculosis Research and Epidemiology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicinePandemicPharmacyAgency (philosophy)Work (physics)Public healthEconomic growthCoronavirus disease 2019 (COVID-19)Family medicineNursingInfectious disease (medical specialty)Disease

Résumé

récupéré en direct d'OpenAlex

Sixty-seventh World Health Assembly approved the End TB Strategy[1] to achieve United Nations SDG 3 targets. The strategy proposes three pillars: integrated patient-centred care and prevention; bold policies, supportive systems and intensified research and innovation.[2] India’s National Strategic Plan 2020–2025[3] envisages ending TB by 2025 ahead of the global strategy. National TB Elimination Program has pressed in all efforts to bring this to fruition by enlisting multisectorial support. A key imperative to eliminate TB is reducing the pool of infectious cases. Fundamental to this is the early identification of cases and treating them promptly and completely. TB treatment is arduous and rigorous adherence is essential to achieve a cure. This presupposes equitable access to medicines. Challenges to access and adherence to TB treatment are: (a) inconvenient timings of the public health institutes may overlap work hours or involve long travel distances to collect medicines which often lead to drop out or interruptions. (b) Migration, frequent travel either on a job or seasonal for activities related to agriculture, festivals, family functions, or due to unforeseen circumstances. Natural calamities and the ongoing Covid19 pandemic are major reasons for mass migrations. Migration affects the continuity of treatment and may lead to the development of drug resistance and increased transmission of TB. (c) The large private sector in India is being engaged through a phased implementation of Public Private Support Agency program where medicines are provided through approved pharmacies, which are few and far and access to these are also not universal. These challenges are indeed real but surmountable. It is of great importance that TB patients can avail treatment once diagnosed anywhere and at any time. We need to learn to improve access to medicines from other sectors. Banks manage Any Time Money (ATM) effectively across the length and breadth of the country in compliance with the national statutes and within the available resources of the client. Similarly, vending machines are popular for a variety of products. These require little space, reduce labour cost, provide excellent information connectivity and monitoring remotely. India’s TB program is way ahead of other countries in care delivery in more ways than one. The digital information ecosystem with its real-time data management through the NIKSHAY portal is commendable. Through this portal, cases can be assessed for diagnosis, treatment and direct benefit transfer for Nikshay Poshan Yojna, notification by private providers is being undertaken and the new innovative nutritional support through the Nikshay Mitra will also be synchronised. This portal has the potential to be the backbone of any future development in tuberculosis care and support. India is a leader in information technology (IT). Artificial intelligence (AI)-based solutions can address the issue of scarce personnel, laboratory facilities and also help overcome barriers to access.[4] The use of AI in radiological diagnosis has been a boon for rural areas where experts are few and far. There is already a lot of interest in public health interventions from the industry.[5] This can be tapped through initiatives under the Atma Nirbhar Bharat schemes and support to start-ups, specially to move towards digital India initiatives. THE CONCEPT: ANY WHERE MEDICINES (AWM) Imbibing from the banking institutions and combining with the expertise in both the TB program and IT industry, TB medicines can be made accessible to the remotest of villages in the country through medicine vending machines which are designed and tailored to incorporate all components of the program, thus achieving the concept of AWM. Briefly, diagnosed individuals can be given a QR-coded prescription which can be read by the machine. This will identify the drugs and the doses to be dispensed. Being internet enabled, the event can be directly recorded on Nikshay and allow the program to keep track of patients ensuring that they do not drop out of treatment. Follow-up symptom assessment and adverse drug reaction management can be done 24 × 7 via telemedicine through specific system programming. The concept of medicine vending machines is not new. Prescription drug dispensing machines have been in use in Canada for over 5 years.[6] In South Africa, ATM pharmacies provide access to HIV medicines.[7] Currently, the medicine vending market is growing at 7% annually.[8] Various players are engaged in developing advanced solutions by integrating modern technologies. The machines can be installed in any covered location like gram panchayat offices, post offices, bus depots, banks and other public places. The key considerations are the availability of security and support partnership, built in UPS/power supply, internet connectivity, climate control to preserve drug potency, among a few. This could be piloted in some key towns and scaled up based on results. This innovative approach to improve access and delivery of TB medicines to the last case in remote areas will bring us closer to our goal of ending TB. Financial support and sponsorship Nil. 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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,078

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0030,003
Science ouverte0,0020,003
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0230,007

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 Opus0,027
Tête enseignante GPT0,330
Écart entre enseignants0,303 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

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