Challenges for the Routine Application of Drones in Healthcare: A Scoping Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Search Methods and Article Selection
2.2. Data Extraction
3. Results
3.1. Study Selection
3.2. Study Characteristics
3.3. Qualitative Summary and Synthesis
3.3.1. Transport of Biomedical Goods
3.3.2. AED Delivery
3.3.3. Healthcare Logistics
3.3.4. Air Ambulance
3.3.5. Other Medical Applications
3.3.6. Public Acceptance
3.3.7. Regulatory Framework
4. Discussion
4.1. Limitations of This Review
4.2. Directions of Future Research
- Weather dependency: Many studies emphasize the sensitivity of drone operations to adverse weather conditions, such as strong winds, rainfall, or extreme temperatures. Weather can disrupt drone services causing their occasional unavailability, which is a significant concern for critical healthcare deliveries. UAV technical design should take into account such environmental factors for a wider adoption of drone operations in healthcare on a daily basis.
- Battery lifespan: The limited battery life of drones is a critical technological constraint. Long-distance deliveries may require scheduled stops for battery replacement or recharging, which makes this a challenge that encompasses not only the battery and the device itself but also infrastructure and urban planning considerations. Optimization techniques to drone control should be implemented for a more efficient use of battery lifespan; however, a variety of constraints that could intervene in real-life situations, such as obstacles and path planning in uncertain environment, should be taken into account when developing such systems.
- Technical reliability: Ensuring the reliability of drone systems is paramount. Technical failures or malfunctions during transportation missions can have severe consequences, particularly when transporting life-saving medical supplies or organs, thus highlighting the importance of developing and adhering to technical standards.
- Transport capacity: The payload capacity of drones can be limited, affecting the volume and the variety of medical goods that can be transported. Finding the right balance between payload capacity and drone size is essential.
- Storage and temperature control: Maintaining temperature control and ensuring the stability of perishable goods during transport, such as blood samples or organs, is a technological challenge. Effective solutions for temperature control and maintaining the quality of medical items need to be developed.
- Public acceptance: despite Generally positive perceptions, some individuals express concerns of fear regarding the use of drones in healthcare. In light of this review, a general feeling of unsafety around drones according to societal perceptions needs to be overcome before considering the deployment of medical use cases in urban areas.
- Privacy and safety: Ensuring the privacy and safety of individuals during drone operations is a significant concern. Regulatory frameworks must include provisions for safeguarding privacy and addressing safety issues.
- Regulatory gaps: The absence of comprehensive and standardized regulations tailored to drone delivery operations in healthcare is a persistent issue. Regulatory gaps need to be filled to provide clear guidelines for safe and lawful drone operations. In particular, technical standards to demonstrate the conformity to DGR intended for traditional aviation need to be adapted to UAV operations.
- Human resources: Effective drone operations in healthcare require trained personnel. Ensuring adequate workforce with the skills to operate, maintain, and manage drones is essential.
- Cost-effectiveness: The cost-effectiveness of drone-based healthcare services is a topic of debate. While some studies suggest cost savings, the initial expenses associated with drone implementation, including equipment and training, can be a barrier for adoption. A comprehensive review on this topic would aid in pinpointing the most cost-effective business cases, which can function as trailblazers for the less cost-effective ones, paving the way for more efficient and economical practices in the future.
- Data security: managing and securing the data collected during UAV operations, especially in telemedicine, remote sensing, and remote monitoring applications, is crucial for protecting patient information and the safety of the operations.
- Environmental impact: the environmental impact of drone operations, including noise pollution and wildlife disruption, needs to be considered and mitigated in advance.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Delhomme, C.; Njeim, M.; Varlet, E.; Pechmajou, L.; Benameur, N.; Cassan, P.; Derkenne, C.; Jost, D.; Lamhaut, L.; Marijon, E.; et al. Automated External Defibrillator Use in Out-of-Hospital Cardiac Arrest: Current Limitations and Solutions. Arch. Cardiovasc. Dis. 2019, 112, 217–222. [Google Scholar] [CrossRef] [PubMed]
- Van de Voorde, P.; Gautama, S.; Momont, A.; Ionescu, C.M.; De Paepe, P.; Fraeyman, N. The Drone Ambulance [A-UAS]: Golden Bullet or Just a Blank? Resuscitation 2017, 116, 46–48. [Google Scholar] [CrossRef]
- Scalea, J.R.; Restaino, S.; Scassero, M.; Blankenship, G.; Bartlett, S.T.; Wereley, N. An Initial Investigation of Unmanned Aircraft Systems (UAS) and Real-Time Organ Status Measurement for Transporting Human Organs. IEEE J. Transl. Eng. Health Med. 2018, 6, 4000107. [Google Scholar] [CrossRef] [PubMed]
- Ling, G.; Draghic, N. Aerial Drones for Blood Delivery. Transfusion 2019, 59, 1608–1611. [Google Scholar] [CrossRef] [PubMed]
- Amukele, T.; Ness, P.M.; Tobian, A.A.R.; Boyd, J.; Street, J. Drone Transportation of Blood Products. Transfusion 2017, 57, 582–588. [Google Scholar] [CrossRef]
- Poljak, M.; Šterbenc, A. Use of Drones in Clinical Microbiology and Infectious Diseases: Current Status, Challenges and Barriers. Clin. Microbiol. Infect. 2020, 26, 425–430. [Google Scholar] [CrossRef] [PubMed]
- Stephan, F.; Reinsperger, N.; Grünthal, M.; Paulicke, D.; Jahn, P. Human Drone Interaction in Delivery of Medical Supplies: A Scoping Review of Experimental Studies. PLoS ONE 2022, 17, e0267664. [Google Scholar] [CrossRef]
- Rosser, J.C., Jr.; Vignesh, V.; Terwilliger, B.A.; Parker, B.C. Surgical and Medical Applications of Drones: A Comprehensive Review. J. Soc. Laparoendosc. Surg. 2018, 22, e2018.00018. [Google Scholar] [CrossRef]
- Roberts, N.B.; Ager, E.; Leith, T.; Lott, I.; Mason-Maready, M.; Nix, T.; Gottula, A.; Hunt, N.; Brent, C. Current Summary of the Evidence in Drone-Based Emergency Medical Services Care. Resusc. Plus 2023, 17, 100347. [Google Scholar] [CrossRef]
- Eichleay, M.; Evens, E.; Stankevitz, K.; Parker, C. Using the Unmanned Aerial Vehicle Delivery Decision Tool to Consider Transporting Medical Supplies via Drone. Glob. Health Sci. Pract. 2019, 7, 500–506. [Google Scholar] [CrossRef]
- Boutilier, J.J.; Brooks, S.C.; Janmohamed, A.; Byers, A.; Buick, J.E.; Zhan, C.; Schoellig, A.P.; Cheskes, S.; Morrison, L.J.; Chan, T.C.Y.; et al. Optimizing a Drone Network to Deliver Automated External Defibrillators. Circulation 2017, 135, 2454–2465. [Google Scholar] [CrossRef] [PubMed]
- Laksham, K.B. Unmanned Aerial Vehicle (Drones) in Public Health: A SWOT Analysis. J. Family Med. Prim. Care 2019, 8, 342–346. [Google Scholar] [CrossRef] [PubMed]
- Rejeb, A.; Rejeb, K.; Simske, S.; Treiblmaier, H. Humanitarian Drones: A Review and Research Agenda. Internet Things 2021, 16, 100434. [Google Scholar] [CrossRef]
- Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann. Intern. Med. 2018, 169, 467–473. [Google Scholar] [CrossRef]
- Peters, M.D.J.; Godfrey, C.M.; Khalil, H.; McInerney, P.; Parker, D.; Soares, C.B. Guidance for Conducting Systematic Scoping Reviews. Int. J. Evid.-Based Healthc. 2015, 13, 141–146. [Google Scholar] [CrossRef] [PubMed]
- Ouzzani, M.; Hammady, H.; Fedorowicz, Z.; Elmagarmid, A. Rayyan—A Web and Mobile App for Systematic Reviews. Syst. Rev. 2016, 5, 210. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
- Innocenti, E.; Agostini, G.; Giuliano, R. UAVs for Medicine Delivery in a Smart City Using Fiducial Markers. Information 2022, 13, 501. [Google Scholar] [CrossRef]
- Barnawi, A.; Chhikara, P.; Tekchandani, R.; Kumar, N.; Boulares, M. A CNN-Based Scheme for COVID-19 Detection with Emergency Services Provisions Using an Optimal Path Planning. Multimed. Syst. 2023, 29, 1683–1697. [Google Scholar] [CrossRef]
- Sylverken, A.A.; Owusu, M.; Agbavor, B.; Kwarteng, A.; Ayisi-Boateng, N.K.; Ofori, P.; El-Duah, P.; Yeboah, R.; Aryeetey, S.; Addo Asamoah, J.; et al. Using Drones to Transport Suspected COVID-19 Samples; Experiences from the Second Largest Testing Centre in Ghana, West Africa. PLoS ONE 2022, 17, e0277057. [Google Scholar] [CrossRef]
- Thakur, V.; Ganeshkumar, P.; Lakshmanan, S.; Rubeshkumar, P. Do Unmanned Aerial Vehicles Reduce the Duration and Costs in Transporting Sputum Samples? A Feasibility Study Conducted in Himachal Pradesh, India. Trans. R. Soc. Trop. Med. Hyg. 2022, 116, 971–973. [Google Scholar] [CrossRef] [PubMed]
- Mohd, S.A.; Gan, K.B.; Ariffin, A.K. Development of Medical Drone for Blood Product Delivery: A Technical Assessment. Int. J. Online Biomed. Eng. 2021, 17, 183. [Google Scholar] [CrossRef]
- Saeed, F.; Mehmood, A.; Majeed, M.F.; Maple, C.; Saeed, K.; Khattak, M.K.; Wang, H.; Epiphaniou, G. Smart Delivery and Retrieval of Swab Collection Kit for COVID-19 Test Using Autonomous Unmanned Aerial Vehicles. Phys. Commun. 2021, 48, 101373. [Google Scholar] [CrossRef]
- Oakey, A.; Waters, T.; Zhu, W.; Royall, P.; Cherrett, T.; Courtney, P.; Majoe, D.; Jelev, N. Quantifying the Effects of Vibration on Medicines in Transit Caused by Fixed-Wing and Multi-Copter Drones. Drones 2021, 5, 22. [Google Scholar] [CrossRef]
- Zhu, W.; Oakey, A.; Royall, P.G.; Waters, T.P.; Cherrett, T.; Theobald, K.; Bester, A.-M.; Lucas, R. Investigating the Influence of Drone Flight on the Stability of Cancer Medicines. PLoS ONE 2023, 18, e0278873. [Google Scholar] [CrossRef] [PubMed]
- Gan, K.B.; Mohd, S.A.; Ng, T.Y. Apps-Based Temperature Monitoring System with Location Services for Medical Needs Delivery Using Drone. Int. J. Interact. Mob. Technol. 2021, 15, 103. [Google Scholar] [CrossRef]
- Scalea, J.R.; Pucciarella, T.; Talaie, T.; Restaino, S.; Drachenberg, C.B.; Alexander, C.; Qaoud, T.A.; Barth, R.N.; Wereley, N.M.; Scassero, M. Successful Implementation of Unmanned Aircraft Use for Delivery of a Human Organ for Transplantation. Ann. Surg. 2021, 274, e282–e288. [Google Scholar] [CrossRef]
- Nisingizwe, M.P.; Ndishimye, P.; Swaibu, K.; Nshimiyimana, L.; Karame, P.; Dushimiyimana, V.; Musabyimana, J.P.; Musanabaganwa, C.; Nsanzimana, S.; Law, M.R. 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–e569. [Google Scholar] [CrossRef]
- Khan, S.I.; Qadir, Z.; Munawar, H.S.; Nayak, S.R.; Budati, A.K.; Verma, K.D.; Prakash, D. UAVs Path Planning Architecture for Effective Medical Emergency Response in Future Networks. Phys. Commun. 2021, 47, 101337. [Google Scholar] [CrossRef]
- Lv, Z.; Chen, D.; Feng, H.; Zhu, H.; Lv, H. Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics. IEEE Trans. Intell. Transport. Syst. 2022, 23, 25106–25114. [Google Scholar] [CrossRef]
- Munawar, H.S.; Inam, H.; Ullah, F.; Qayyum, S.; Kouzani, A.Z.; Mahmud, M.A.P. Towards Smart Healthcare: UAV-Based Optimized Path Planning for Delivering COVID-19 Self-Testing Kits Using Cutting Edge Technologies. Sustainability 2021, 13, 10426. [Google Scholar] [CrossRef]
- Zailani, M.A.H.; Raja Sabudin, R.Z.A.; Ismail, A.; Abd Rahman, R.; Mohd Saiboon, I.; Sabri, S.I.; Seong, C.K.; Mail, J.; Md Jamal, S.; Beng, G.K.; et al. Influence of Drone Carriage Material on Maintenance of Storage Temperature and Quality of Blood Samples during Transportation in an Equatorial Climate. PLoS ONE 2022, 17, e0269866. [Google Scholar] [CrossRef] [PubMed]
- Grote, M.; Cherrett, T.; Oakey, A.; Royall, P.; Whalley, S.; Dickinson, J. How Do Dangerous Goods Regulations Apply to Uncrewed Aerial Vehicles Transporting Medical Cargos? Drones 2021, 5, 38. [Google Scholar] [CrossRef]
- Hogan, W.; Harris, M.; Brock, A.; Rodwell, J. What Is Holding Back The Use of Drones for Medication Delivery in Rural Australia? Sustainability 2022, 14, 15778. [Google Scholar] [CrossRef]
- Talaie, T.; Niederhaus, S.; Villalongas, E.; Scalea, J. Innovating Organ Delivery to Improve Access to Care: Surgeon Perspectives on the Current System and Future Use of Unmanned Aircrafts. BMJ Innov. 2021, 7, 157–163. [Google Scholar] [CrossRef]
- Shi, Y.; Lin, Y.; Li, B.; Yi Man Li, R. A Bi-Objective Optimization Model for the Medical Supplies’ Simultaneous Pickup and Delivery with Drones. Comput. Ind. Eng. 2022, 171, 108389. [Google Scholar] [CrossRef]
- Abbas, S.; Ashraf, F.; Jarad, F.; Shoaib Sardar, M.; Siddique, I. A Drone-Based Blood Donation Approach Using an Ant Colony Optimization Algorithm. Comput. Model. Eng. Sci. 2023, 136, 1917–1930. [Google Scholar] [CrossRef]
- Röper, J.W.A.; Fischer, K.; Baumgarten, M.C.; Thies, K.C.; Hahnenkamp, K.; Fleßa, S. Can Drones Save Lives and Money? An Economic Evaluation of Airborne Delivery of Automated External Defibrillators. Eur. J. Health Econ. 2022, 24, 1141–1150. [Google Scholar] [CrossRef]
- Derkenne, C.; Jost, D.; Miron De L’Espinay, A.; Corpet, P.; Frattini, B.; Hong, V.; Lemoine, F.; Jouffroy, R.; Roquet, F.; Marijon, E.; et al. Automatic External Defibrillator Provided by Unmanned Aerial Vehicle (Drone) in Greater Paris: A Real World-Based Simulation. Resuscitation 2021, 162, 259–265. [Google Scholar] [CrossRef]
- Choi, D.S.; Hong, K.J.; Shin, S.D.; Lee, C.-G.; Kim, T.H.; Cho, Y.; Song, K.J.; Ro, Y.S.; Park, J.H.; Kim, K.H. Effect of Topography and Weather on Delivery of Automatic Electrical Defibrillator by Drone for Out-of-Hospital Cardiac Arrest. Sci. Rep. 2021, 11, 24195. [Google Scholar] [CrossRef]
- Schierbeck, S.; Nord, A.; Svensson, L.; Rawshani, A.; Hollenberg, J.; Ringh, M.; Forsberg, S.; Nordberg, P.; Hilding, F.; Claesson, A. National Coverage of Out-of-Hospital Cardiac Arrests Using Automated External Defibrillator-Equipped Drones—A Geographical Information System Analysis. Resuscitation 2021, 163, 136–145. [Google Scholar] [CrossRef] [PubMed]
- Chu, J.; Leung, K.H.B.; Snobelen, P.; Nevils, G.; Drennan, I.R.; Cheskes, S.; Chan, T.C.Y. Machine Learning-Based Dispatch of Drone-Delivered Defibrillators for out-of-Hospital Cardiac Arrest. Resuscitation 2021, 162, 120–127. [Google Scholar] [CrossRef] [PubMed]
- Yukun, J.; Yanmang, S.; Yan, W.; Bei, W.; Shurui, F. Improved Immune Algorithm for Sudden Cardiac Death First Aid Drones Site Selection. Int. J. Med. Inform. 2023, 173, 105025. [Google Scholar] [CrossRef]
- Scholz, S.S.; Wähnert, D.; Jansen, G.; Sauzet, O.; Latka, E.; Rehberg, S.; Thies, K.-C. AED Delivery at Night—Can Drones Do the Job? A Feasibility Study of Unmanned Aerial Systems to Transport Automated External Defibrillators during Night-Time. Resuscitation 2023, 185, 109734. [Google Scholar] [CrossRef] [PubMed]
- Purahong, B.; Anuwongpinit, T.; Juhong, A.; Kanjanasurat, I.; Pintaviooj, C. Medical Drone Managing System for Automated External Defibrillator Delivery Service. Drones 2022, 6, 93. [Google Scholar] [CrossRef]
- Gino, B.; Williams, K.-L.; Neilson, C.S.; d’Entremont, P.; Dubrowski, A.; Renouf, T.S. The PHOENIX: Design and Development of a Three-Dimensional-Printed Drone Prototype and Corresponding Simulation Scenario Based on the Management of Cardiac Arrest. Cureus 2022, 14, e21594. [Google Scholar] [CrossRef]
- Boutilier, J.J.; Chan, T.C.Y. Drone Network Design for Cardiac Arrest Response. Manuf. Serv. Oper. Manag. 2022, 24, 2407–2424. [Google Scholar] [CrossRef]
- Baumgarten, M.C.; Röper, J.; Hahnenkamp, K.; Thies, K.-C. Drones Delivering Automated External Defibrillators—Integrating Unmanned Aerial Systems into the Chain of Survival: A Simulation Study in Rural Germany. Resuscitation 2022, 172, 139–145. [Google Scholar] [CrossRef]
- Rees, N.; Howitt, J.; Breyley, N.; Geoghegan, P.; Powel, C. A Simulation Study of Drone Delivery of Automated External Defibrillator (AED) in Out of Hospital Cardiac Arrest (OHCA) in the UK. PLoS ONE 2021, 16, e0259555. [Google Scholar] [CrossRef]
- Leung, K.H.B.; Grunau, B.; Al Assil, R.; Heidet, M.; Liang, L.D.; Deakin, J.; Christenson, J.; Cheskes, S.; Chan, T.C.Y. Incremental Gains in Response Time with Varying Base Location Types for Drone-Delivered Automated External Defibrillators. Resuscitation 2022, 174, 24–30. [Google Scholar] [CrossRef]
- Schierbeck, S.; Hollenberg, J.; Nord, A.; Svensson, L.; Nordberg, P.; Ringh, M.; Forsberg, S.; Lundgren, P.; Axelsson, C.; Claesson, A. Automated External Defibrillators Delivered by Drones to Patients with Suspected Out-of-Hospital Cardiac Arrest. Eur. Heart J. 2022, 43, 1478–1487. [Google Scholar] [CrossRef] [PubMed]
- Bauer, J.; Moormann, D.; Strametz, R.; Groneberg, D.A. Development of Unmanned Aerial Vehicle (UAV) Networks Delivering Early Defibrillation for out-of-Hospital Cardiac Arrests (OHCA) in Areas Lacking Timely Access to Emergency Medical Services (EMS) in Germany: A Comparative Economic Study. BMJ Open 2021, 11, e043791. [Google Scholar] [CrossRef] [PubMed]
- Ryan, J.P. The Feasibility of Medical Unmanned Aerial Systems in Suburban Areas. Am. J. Emerg. Med. 2021, 50, 532–545. [Google Scholar] [CrossRef]
- Flemons, K.; Baylis, B.; Khan, A.Z.; Kirkpatrick, A.W.; Whitehead, K.; Moeini, S.; Schreiber, A.; Lapointe, S.; Ashoori, S.; Arif, M.; et al. The Use of Drones for the Delivery of Diagnostic Test Kits and Medical Supplies to Remote First Nations Communities during COVID-19. Am. J. Infect. Control. 2022, 50, 849–856. [Google Scholar] [CrossRef]
- Quintanilla García, I.; Vera Vélez, N.; Alcaraz Martínez, P.; Vidal Ull, J.; Fernández Gallo, B. A Quickly Deployed and UAS-Based Logistics Network for Delivery of Critical Medical Goods during Healthcare System Stress Periods: A Real Use Case in Valencia (Spain). Drones 2021, 5, 13. [Google Scholar] [CrossRef]
- Al-Rabiaah, S.; Hosny, M.; AlMuhaideb, S. An Efficient Greedy Randomized Heuristic for the Maximum Coverage Facility Location Problem with Drones in Healthcare. Appl. Sci. 2022, 12, 1403. [Google Scholar] [CrossRef]
- Al-Rabiaah, S.; Hosny, M.; AlMuhaideb, S. A Greedy Heuristic Based on Optimizing Battery Consumption and Routing Distance for Transporting Blood Using Unmanned Aerial Vehicles. Electronics 2022, 11, 3399. [Google Scholar] [CrossRef]
- Shankar, N.; Nallakaruppan, M.K.; Ravindranath, V.; Senthilkumar, M.; Bhagavath, B.P. Smart IoMT Framework for Supporting UAV Systems with AI. Electronics 2022, 12, 86. [Google Scholar] [CrossRef]
- Ghelichi, Z.; Gentili, M.; Mirchandani, P.B. Logistics for a Fleet of Drones for Medical Item Delivery: A Case Study for Louisville, KY. Comput. Oper. Res. 2021, 135, 105443. [Google Scholar] [CrossRef]
- Du, L.; Li, X.; Gan, Y.; Leng, K. Optimal Model and Algorithm of Medical Materials Delivery Drone Routing Problem under Major Public Health Emergencies. Sustainability 2022, 14, 4651. [Google Scholar] [CrossRef]
- Asadi, A.; Nurre Pinkley, S.; Mes, M. A Markov Decision Process Approach for Managing Medical Drone Deliveries. Expert Syst. Appl. 2022, 204, 117490. [Google Scholar] [CrossRef]
- Zailani, M.A.; Azma, R.Z.; Aniza, I.; Rahana, A.R.; Ismail, M.S.; Shahnaz, I.S.; Chan, K.S.; Jamaludin, M.; Mahdy, Z.A. Drone versus Ambulance for Blood Products Transportation: An Economic Evaluation Study. BMC Health Serv. Res. 2021, 21, 1308. [Google Scholar] [CrossRef] [PubMed]
- Johnson, A.M.; Cunningham, C.J.; Arnold, E.; Rosamond, W.D.; Zègre-Hemsey, J.K. Impact of Using Drones in Emergency Medicine: What Does the Future Hold? Open Access Emerg. Med. 2021, 13, 487–498. [Google Scholar] [CrossRef]
- Abouzaid, L.; Elbiaze, H.; Sabir, E. Agile Roadmap for Application-driven Multi-UAV Networks: The Case of COVID-19. IET Netw. 2022, 11, 195–206. [Google Scholar] [CrossRef]
- Munawar, H.S.; Akram, J.; Khan, S.I.; Ullah, F.; Choi, B.J. Drone-as-a-Service (DaaS) for COVID-19 Self-Testing Kits Delivery in Smart Healthcare Setups: A Technological Perspective. ICT Express 2022, 9, 748–753. [Google Scholar] [CrossRef]
- De Silvestri, S.; Pagliarani, M.; Tomasello, F.; Trojaniello, D.; Sanna, A. Design of a Service for Hospital Internal Transport of Urgent Pharmaceuticals via Drones. Drones 2022, 6, 70. [Google Scholar] [CrossRef]
- Qassab, M.S.; Ali, Q.I. A UAV-based Portable Health Clinic System for Coronavirus Hotspot Areas. Healthc. Technol. Lett. 2022, 9, 77–90. [Google Scholar] [CrossRef]
- Juned, M.; Sangle, P.; Gudheniya, N.; Haldankar, P.V.; Tiwari, M.K. Designing the Drone Based End-to-End Local Supply Chain Distribution Network. IFAC-Pap. 2022, 55, 743–748. [Google Scholar] [CrossRef]
- Gunaratne, K.; Thibbotuwawa, A.; Vasegaard, A.E.; Nielsen, P.; Perera, H.N. Unmanned Aerial Vehicle Adaptation to Facilitate Healthcare Supply Chains in Low-Income Countries. Drones 2022, 6, 321. [Google Scholar] [CrossRef]
- Johannessen, K.-A.; Comtet, H.; Fosse, E. A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory. Int. J. Environ. Res. Public Health 2021, 18, 4580. [Google Scholar] [CrossRef]
- Damoah, I.S.; Ayakwah, A.; Tingbani, I. Artificial Intelligence (AI)-Enhanced Medical Drones in the Healthcare Supply Chain (HSC) for Sustainability Development: A Case Study. J. Clean. Prod. 2021, 328, 129598. [Google Scholar] [CrossRef]
- Oakey, A.; Grote, M.; Smith, A.; Cherrett, T.; Pilko, A.; Dickinson, J.; AitBihiOuali, L. Integrating Drones into NHS Patient Diagnostic Logistics Systems: Flight or Fantasy? PLoS ONE 2022, 17, e0264669. [Google Scholar] [CrossRef] [PubMed]
- Lin, M.; Chen, Y.; Han, R.; Chen, Y. Discrete Optimization on Truck-Drone Collaborative Transportation System for Delivering Medical Resources. Discret. Dyn. Nat. Soc. 2022, 2022, 1811288. [Google Scholar] [CrossRef]
- Mihara, Y.; Nakamura, T.; Nakamoto, A.; Nakano, M. Department of System Design and Management, Keio University Collaboration Complex, 4-1-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8526, Japan Airframe Design Optimization and Simulation of a Flying Car for Medical Emergencies. IJAT 2022, 16, 183–196. [Google Scholar] [CrossRef]
- Maddry, J.K.; Arana, A.A.; Mora, A.G.; Perez, C.A.; Cutright, J.E.; Kester, B.M.; Ng, P.C.; Schauer, S.G.; Bebarta, V.S. Advancing Prehospital Combat Casualty Evacuation: Patients Amenable to Aeromedical Evacuation via Unmanned Aerial Vehicles. Mil. Med. 2021, 186, e366–e372. [Google Scholar] [CrossRef]
- Goyal, R.; Cohen, A. Advanced Air Mobility: Opportunities and Challenges Deploying EVTOLs for Air Ambulance Service. Appl. Sci. 2022, 12, 1183. [Google Scholar] [CrossRef]
- Sheng, T.; Jin, R.; Yang, C.; Qiu, K.; Wang, M.; Shi, J.; Zhang, J.; Gao, Y.; Wu, Q.; Zhou, X.; et al. Unmanned Aerial Vehicle Mediated Drug Delivery for First Aid. Adv. Mater. 2023, 35, 2208648. [Google Scholar] [CrossRef]
- Saitoh, T.; Takahashi, Y.; Minami, H.; Nakashima, Y.; Aramaki, S.; Mihara, Y.; Iwakura, T.; Odagiri, K.; Maekawa, Y.; Yoshino, A. Real-Time Breath Recognition by Movies from a Small Drone Landing on Victim’s Bodies. Sci. Rep. 2021, 11, 5042. [Google Scholar] [CrossRef]
- Kirkpatrick, A.W.; McKee, J.L.; Moeini, S.; Conly, J.M.; Ma, I.W.Y.; Baylis, B.; Hawkins, W. Pioneering Remotely Piloted Aerial Systems (Drone) Delivery of a Remotely Telementored Ultrasound Capability for Self Diagnosis and Assessment of Vulnerable Populations—The Sky Is the Limit. J. Digit. Imaging 2021, 34, 841–845. [Google Scholar] [CrossRef]
- Betriana, F.; Tanioka, R.; Kogawa, A.; Suzuki, R.; Seki, Y.; Osaka, K.; Zhao, Y.; Kai, Y.; Tanioka, T.; Locsin, R. Remote-Controlled Drone System through Eye Movements of Patients Who Need Long-Term Care: An Intermediary’s Role. Healthcare 2022, 10, 827. [Google Scholar] [CrossRef]
- Queirós Pokee, D.; Barbosa Pereira, C.; Mösch, L.; Follmann, A.; Czaplik, M. Consciousness Detection on Injured Simulated Patients Using Manual and Automatic Classification via Visible and Infrared Imaging. Sensors 2021, 21, 8455. [Google Scholar] [CrossRef] [PubMed]
- Askari, Z.; Abouei, J.; Jaseemuddin, M.; Anpalagan, A.; Plataniotis, K.N. A Q -Learning Approach for Real-Time NOMA Scheduling of Medical Data in UAV-Aided WBANs. IEEE Access 2022, 10, 115074–115091. [Google Scholar] [CrossRef]
- Comtet, H.E.; Johannessen, K.-A. The Moderating Role of Pro-Innovative Leadership and Gender as an Enabler for Future Drone Transports in Healthcare Systems. Int. J. Environ. Res. Public Health 2021, 18, 2637. [Google Scholar] [CrossRef] [PubMed]
- Sham, R.; Siau, C.S.; Tan, S.; Kiu, D.C.; Sabhi, H.; Thew, H.Z.; Selvachandran, G.; Quek, S.G.; Ahmad, N.; Ramli, M.H.M. Drone Usage for Medicine and Vaccine Delivery during the COVID-19 Pandemic: Attitude of Health Care Workers in Rural Medical Centres. Drones 2022, 6, 109. [Google Scholar] [CrossRef]
- Król-Całkowska, E.J.; Walczak, D. The Use of Drones in the Area of Minimizing Health Risk during the COVID-19 Epidemic. J. Intell. Robot Syst. 2022, 106, 40. [Google Scholar] [CrossRef]
- Royall, P.G.; Courtney, P.; Goodair, C.; Copeland, C.S. An Evaluation of Naloxone Transit for Opioid Overdose Using Drones: A Case Study Using Real-world Coroner Data; Wiley: New York, NY, USA, 2023. [Google Scholar]
- International Civil Aviation Organization (ICAO). Technical Instructions for the Safe Transport of Dangerous Goods by Air (Doc 9284), 2019–2020 ed.; International Civil Aviation Organization: Montreal, QC, Canada, 2018. [Google Scholar]
- European Union Aviation Safety Agency (EASA). Easy Access Rules for Unmanned Aircraft Systems (Regulations (EU) 2019/947 and (EU) 2019/945); Revision from September 2022; European Union Aviation Safety Agency: Cologne, Germany, 2022. [Google Scholar]
- Zatout, M.S.; Rezoug, A.; Rezoug, A.; Baizid, K.; Iqbal, J. Optimisation of Fuzzy Logic Quadrotor Attitude Controller—Particle Swarm, Cuckoo Search and BAT Algorithms. Int. J. Syst. Sci. 2022, 53, 883–908. [Google Scholar] [CrossRef]
- Razzaq, S.; Xydeas, C.; Mahmood, A.; Ahmed, S.; Ratyal, N.I.; Iqbal, J. Efficient Optimization Techniques for Resource Allocation in UAVs Mission Framework. PLoS ONE 2023, 18, e0283923. [Google Scholar] [CrossRef]
- Jiao, Q.; Liu, Y.; Zheng, Z.; Sun, L.; Bai, Y.; Zhang, Z.; Sun, L.; Ren, G.; Zhou, G.; Chen, X.; et al. Ground Risk Assessment for Unmanned Aircraft Systems Based on Dynamic Model. Drones 2022, 6, 324. [Google Scholar] [CrossRef]
Empirical Drone Research | Prototype Design | Retrospective Data Analysis | Computational Simulation | Social Research | Theoretical Modeling | |
---|---|---|---|---|---|---|
Transport of biomedical goods | [18,19,20,21,22,23,24,25] | [26,27] | [28] | [29,30,31,32] | [33,34,35] | [36,37] |
AED delivery | [38,39] | - | [40,41] | [42,43,44,45,46,47,48,49,50,51,52] | - | [53] |
Healthcare logistics | [54,55,56,57,58,59,60,61,62] | [63,64,65,66,67,68] | [69] | [70] | [71,72] | [73] |
Air ambulance | - | [74] | [75] | [76] | - | - |
Other medical applications | [77,78] | - | - | [79,80,81] | - | [82] |
Public acceptance | - | - | - | - | [83,84] | - |
Regulatory framework | - | - | [85] | - | - | - |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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De Silvestri, S.; Capasso, P.J.; Gargiulo, A.; Molinari, S.; Sanna, A. Challenges for the Routine Application of Drones in Healthcare: A Scoping Review. Drones 2023, 7, 685. https://doi.org/10.3390/drones7120685
De Silvestri S, Capasso PJ, Gargiulo A, Molinari S, Sanna A. Challenges for the Routine Application of Drones in Healthcare: A Scoping Review. Drones. 2023; 7(12):685. https://doi.org/10.3390/drones7120685
Chicago/Turabian StyleDe Silvestri, Sara, Pasquale Junior Capasso, Alessandra Gargiulo, Sara Molinari, and Alberto Sanna. 2023. "Challenges for the Routine Application of Drones in Healthcare: A Scoping Review" Drones 7, no. 12: 685. https://doi.org/10.3390/drones7120685
APA StyleDe Silvestri, S., Capasso, P. J., Gargiulo, A., Molinari, S., & Sanna, A. (2023). Challenges for the Routine Application of Drones in Healthcare: A Scoping Review. Drones, 7(12), 685. https://doi.org/10.3390/drones7120685