Blood delivery by drone: a faltering step in a promising direction
Bibliographic record
Abstract
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
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".