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Enregistrement W4285082546 · doi:10.1016/s2214-109x(22)00272-8

Blood delivery by drone: a faltering step in a promising direction

2022· letter· en· W4285082546 sur OpenAlexaboutno aff
Qiang Li, Jing Xia, Fangmin Ge, Qin Lu, Mao Zhang

Notice bibliographique

RevueThe Lancet Global Health · 2022
Typeletter
Langueen
DomaineBusiness, Management and Accounting
ThématiqueBlood donation and transfusion practices
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDronePopulationChinaMedicineMedical emergencyEnvironmental healthGeographyBiology

Résumé

récupéré en direct d'OpenAlex

We read with great interest the Article in The Lancet Global Health by Marie Paul Nisingizwe and colleagues, which showed the value of drone blood delivery in terms of reducing delivery time and blood component wastage in hospitals in Rwanda, where 83% of residents live in rural areas.1Nisingizwe MP Ndishimye P Swaibu K et al.Effect of unmanned aerial vehicle (drone) delivery on blood product delivery time and wastage in Rwanda: a retrospective, cross-sectional study and time series analysis.Lancet Glob Health. 2022; 10: e564-e569Summary Full Text Full Text PDF PubMed Scopus (7) Google Scholar The authors found that 14 of 20 hospitals studied delivered blood faster by drone than by car, but in three hospitals the difference between the two delivery systems was less than 5 min, and in three other hospitals, drone deliveries were slower, with the largest time difference being 34 min. These results were not discussed. This phenomenon was also observed in urban areas. In March, 2021, we implemented a rotor-wing drone blood delivery system in Hangzhou, a city in eastern China with a population of 12 million people. After collecting 9 months of operational data, we found that blood products delivered by drones reduced delivery time by 50%, but some drone deliveries took 10% longer than those made by car. There might be multiple reasons for this finding. First, commercially available rotor-wing drones have a maximum range of less than 20 km, requiring battery replacement at relay points. The time advantage weakens as the number of relays increases for a longer flight distance. If the drone is to fly over a river, additional battery replacement before or after the crossing is to be expected to anticipate slower speed and increased power consumption in stronger headwinds. Second, various factors, such as avoiding airports and railway stations, densely populated areas, nature heritage reserves, and high-rise buildings, prevent the drone from flying in a straight line, sometimes making the flight distance greater than the ground distance. For economic reasons, different air routes must share the same relay points, which extends the distance of some flights. Finally, in urban areas, the comparison of delivery times must consider the traffic situation, and the time savings from drones could be greater in cities with more congested traffic. Blood delivery by drone has the advantage of being fast and safe, and unaffected by uneven roads and traffic jams.2Homier V Brouard D Nolan M et al.Drone versus ground delivery of simulated blood products to an urban trauma center: the Montreal Medi-Drone pilot study.J Trauma Acute Care Surg. 2021; 90: 515-521Crossref PubMed Scopus (13) Google Scholar, 3Amukele T Ness PM Tobian AA Boyd J Street J Drone transportation of blood products.Transfusion. 2017; 57: 582-588Crossref PubMed Scopus (82) Google Scholar However, to achieve the desired goals, certain conditions must be met. Pending the advancement of drone technology, the exploration and gradual integration of different scenarios, such as the geographical distribution of hospitals in a city, air and ground itineraries and road conditions, cost-effectiveness,4Zailani MA Azma RZ Aniza I et al.Drone versus ambulance for blood products transportation: an economic evaluation study.BMC Health Serv Res. 2021; 211308Crossref PubMed Scopus (7) Google Scholar and so on, would enable the implementation of a citywide drone medical logistics system in the near future. We declare no competing interests. Effect of unmanned aerial vehicle (drone) delivery on blood product delivery time and wastage in Rwanda: a retrospective, cross-sectional study and time series analysisWe found that drone delivery led to faster delivery times and less blood component wastage in health facilities. Future studies should investigate if these improvements are cost-effective, and whether drone delivery might be effective for other pharmaceutical and health supplies that cannot be easily stored at remote facilities. Full-Text PDF Open Access

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,992

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,026
Tête enseignante GPT0,285
Écart entre enseignants0,259 · 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.

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

Citations9
Publié2022
Routes d'admission1
Résumé présentoui

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