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Review

Emerging Research Topics in Drone Healthcare Delivery

by
Hamish A. Campbell
1,*,
Vanya Bosiocic
1,
Aliesha Hvala
1,
Mark Brady
1,2,
Mariana A. Campbell
1,
Kade Skelton
1 and
Osmar J. Luiz
3
1
North Australia Centre for Autonomous Systems, Charles Darwin University, Casuarina, NT 0810, Australia
2
School of Law, Faculty of Arts and Society, Charles Darwin University, Casuarina, NT 0810, Australia
3
Research Institute for the Environment &Livelihoods, Charles Darwin University, Casuarina, NT 0810, Australia
*
Author to whom correspondence should be addressed.
Drones 2024, 8(6), 258; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8060258
Submission received: 25 April 2024 / Revised: 1 June 2024 / Accepted: 5 June 2024 / Published: 12 June 2024
Browse Figures
Review Reports Versions Notes

Abstract

:
The application of drones to assist with healthcare delivery has grown rapidly over the last decade. This industry is supported by a growing research field, and we have undertaken a systematic review of the published literature. Web-based searches returned 290 relevant manuscripts published between 2010 and 2024. We applied Topic Modelling to this corpus of literature, which examines word association and connectedness within the research papers. The modelling identified two emerging research themes with little connection between them: those who used drones to deliver time-critical medical items and those who used drones to deliver non-time-critical medical items. The former was in response to medical emergencies, while the latter was for enhancing resilience in the healthcare supply chain. The topics within these research themes exhibited notable differences. The delivery of time-critical medical items theme comprised the topics of ‘Emergency Response’, ‘Defibrillator and Organ Delivery’, and ‘Search and Rescue’, whilst non-time-critical delivery researched the topics of ‘Supply Chain Optimisation’ and ‘Cost-Effectiveness’, ‘Overcoming Remoteness’, and ‘Pandemic Response’. Research on ‘Engineering and Design Considerations’ and ‘Ethical and Social Considerations’ cut across both research themes. We undertook further analysis to assess research topic alignment and identify knowledge gaps. We found that efforts are needed to establish a more standardised terminology for better alignment across the two emerging research themes. Future studies should focus on evaluating the impact of drone delivery on patient health using systematic methods. Additionally, exploring the economic viability of drone-based health services and addressing regulatory barriers are crucial for efficient and effective drone deployment in healthcare delivery systems.

1. Introduction

The Sustainable Development Goal outlines equitable and universal health coverage as a critical global health priority [1]. Providing access to healthcare services for all people is essential to achieving this agenda. However, over half of the world’s population continues to experience inequitable healthcare access in developed and developing countries [2]. Remoteness and distance significantly limit a patient’s access to services [3], impacting overall community health and well-being [4].
Where access to quality health products is impacted by remoteness, poor supply chain systems, and inadequate transport infrastructure, emerging aviation technologies have been shown to improve the timeliness and efficiency of health product transport. Uncrewed Aerial Vehicles (UAVs) have been labelled a “leapfrog” technology, providing cost-effective and timely solutions to transporting blood, pathology samples, medicines, organs, and non-perishable medical supplies [5,6].
Most of the drone-based medical delivery occurring today is carried out by the manufacturers of the UAVs themselves (e.g., Zipline, Swoop Aero, Rigi-tech) and generally funded through aid agencies (e.g., UNICEF, USAID) [7]. Consequently, research into the benefits of drone-based healthcare delivery is still in its infancy. There is a need for data-driven, evidence-based research that can help inform decision-making outcomes and drive healthcare operational and organisational reforms [7,8].
To ensure that future research agendas provide the data and information needed by policymakers and health administrators, we must have a comprehensive baseline understanding of the current research trends and knowledge gaps. Furthermore, we must critique how well current research trajectories align with the information and data needs of policymakers, health systems, and society. To our knowledge, studies have yet to provide a baseline analysis of the key research topics in drone healthcare delivery literature.
Here, we searched the literature focused on drone healthcare delivery using keywords and applied Topic Modelling to identify the various research topics within the field. Topic Modelling enables the unbiased identification of critical ideas that can be classified into research topics within a collection of literature, also known as a corpus [9]. This method involves identifying topics through sets of co-occurring words to provide insight into the corpus components of a research field. The method is commonly applied to mature and broaden research fields [9]. However, the technique has also proven valuable in emerging research fields in identifying themes and knowledge gaps and prioritising future research [10]. This broad overview aims to guide future research and act as a diffusion catalyst for drone integration into the healthcare industry.

2. Materials and Methods

2.1. Data Collection

The literature review used the PRISMA Systematic Reviews guidelines to identify journal articles for the study. Pre-defined selection criteria were established to retrieve peer-reviewed articles from Scopus, Web of Science, and Google Scholar about drone technology’s use to transport healthcare-related items. Although the study specifically focused on assessing literature that studied uncrewed drones as a tool for transporting and delivering medicines and health-related items, the search string was kept broad due to the novelty of the research areas, the ranging terminology that is used to describe drones, and the lack of broadly established research terminologies (Supplementary Materials). The sample frame focused on English-written peer-reviewed articles published from 2010 to the end of 2022. We further excluded book chapters, literature reviews, and grey literature.
The following string was used to search for papers.
(drone* OR “unmanned aerial vehicle*” OR “unmanned aerial system*” OR “remotely piloted aircraft*” OR “uncrewed aerial vehicle*” OR “uncrewed aerial system*”) AND (“healthcare” OR “medic*” OR “health” OR “health care”)
The search was carried out in September 2023 and then updated in May 2024, resulting in the identification of 3254 articles. The manuscripts were then manually screened to ensure that they researched the application of aerial drone systems for the transportation and delivery of health-related products. Following this process, only 290 journal articles qualified for inclusion in the study. Bibliographic information on all articles included in the review is provided in Supplementary Materials.

2.2. Text Mining

We extracted each article’s titles, keywords, and abstracts from the Clarivate Web of Science and Scopus platforms. Due to the novelty of the research field and the lack of broadly established research terminologies, we also included grey literature captured in Google Scholar. We downloaded BibTeX files containing the article information and exported them as a data frame to R using the ‘convert2df’ function in the package ‘bibliometrix’ [11]. Next, we tokenised the content by breaking down sentences into individual words, also known as unigrams. We removed stop words and other irrelevant terms (e.g., “the”, “or”, “and”, and “which”), as well as numbers and punctuation.
Additionally, we set the minimum word length to three characters. After this pre-processing, we stemmed the remaining words, reducing them to their base or root form. For example, the words “flying”, “flew”, and “flies” would all be stemmed from their root form “fly”. Finally, we analysed the processed text for the presence of bi-grams and trigrams.
Bi-grams and trigrams are pairs or triplets of words that provide essential context, information, and meaning, which would otherwise be lost if the words were analysed separately. For example, in the context of drones in healthcare delivery, the following bi-grams and trigrams were identified: “unmanned_aerial_vehicles”, “cardiac_arrest”, “automated_external_defibrillator”, “first_aid”, “medical_supplies”, “response_time”, “drone_delivery”, and “supply_chain”. These words were connected by an underscore, creating unique and relevant terms for the research area of drone technology in healthcare. To process automated n-gramming in our dataset, we used the R package ‘ngram’ (https://cran.r-project.org/web/packages/ngram accessed on 11 October 2023), which searches for word pairs or triplets that occurred together more frequently than expected by chance, using a significance value of p < 0.05.

2.3. Topic Modelling

Notably, the themes identified are derived from a rigorous quantitative methodology rather than being determined by an evaluator. This paper uses Latent Dirichlet Allocation (LDA) modelling to identify topics within the manuscript collection [10]. The LDA process detects sets of co-occurring words that frequently appear together within the same phonological context, as opposed to the likelihood of the words occurring together by chance. Such co-occurring words are expected to convey similar meanings and pertain to a related subject, thus allowing for topics to be defined. The LDA model assumes that scientific publications within a research field are heterogeneous and that a research article may encompass multiple topics in varying proportions. For example, an article may consist of 40% Topic A, 30% Topic B, and 30% Topic C. The topic with the highest proportionality within an article is identified as the primary topic of the article. Topic Model predictions for each manuscript are provided in the Supplementary Materials.

2.4. Topic Similarity

The LDA model generates a matrix of word-topic probabilities, providing an overview of the distribution of words across the different topics (Table 1). We calculated the pairwise Bray–Curtis dissimilarity measure to examine the similarity between these topics, quantifying the compositional difference between two samples based on their word probabilities. By applying the Bray–Curtis distance metric to the word-topic probability matrix, we obtained a dissimilarity matrix, which offered insight into the similarity and dissimilarity of the topics.
To visualise the relationships between the topics more intuitively, we employed non-metric multidimensional scaling (NMDS), a widely used ordination method. NMDS projects the high-dimensional Bray–Curtis dissimilarity matrix onto a lower-dimensional space, typically two or three dimensions, while preserving the rank order of the distances between the data points. The resulting configuration of points in the lower-dimensional space represents the relative similarity or dissimilarity of the topics, allowing for a more straightforward interpretation and identification of potential clusters or patterns in the data.

2.5. Research Gap Analysis

To identify research gaps, we aimed to define pairs of topics that occurred separately within the thematic content of an article and among the articles in the corpus [9]. This was achieved by utilising LDA modelling to produce a matrix of the weight of each topic within each article. The matrix was then transposed so that a topic defined each row, while columns represented article weights. We subsequently calculated the Bray–Curtis distance between each pair of topics using the matrix of topics and article weights, creating an “article” distance matrix. We then computed the product of the “word” and “article” distance matrices, generating a “gap” distance matrix, which allowed us to compare the difference in topics based on “words” vs. “articles”. We plotted the dissimilarity metric from the “gap” distance matrix to investigate research gaps between topics. The greater the metric, the more significant the gap between topics, signifying topics containing different sets of words and rarely co-occurring in the same article. This approach enabled us to identify and characterise research gaps between topics, providing insights into potential areas for future exploration and investigation in drone technology for healthcare delivery.

2.6. Pressing Issues

We explored the interconnectedness of pressing issues, such as environmental impacts, safety concerns, and effectiveness of Uncrewed Aerial Vehicles (UAVs), plus economic, social, legal, and organisational aspects. Using a chord diagram, we hypothesised dominating aspects (technology and application) in UAV-based healthcare research. A comprehensive list of keywords representing pressing issues and hypothesised dominating aspects was generated using regular expressions, and their probabilities were summed up under each keyword label to construct the diagram. The keywords were identified by searching for specific terms and their variations within the word/topic matrix generated by the LDA model, using the following regular expressions: Environment: enviro|ecolog|sustainab|green)*; Safety: (safe|secure|protect|risk)*; Effectiveness: (effectiv|efficien|perform|outcome|success)*; Economic: (economic|financial|monetary|fiscal|cost)*; Social: (social|community|societal|socio|public|people)*; Legal: (legal|law|regulation|legislation|compliance)*; Organizational: (organisat|organizat|management|administration|corporate|hierarchy)*; Technology: (tech(nology)?|innovat|digital|software|hardware|platform)*; and Application: (app(lication)?|tool|implement|usage)*.
These regular expressions captured the essence of all terms connected to each pressing issue and hypothesised dominating aspects. The keywords were then attached to the identified latent topics in the literature based on their occurrence patterns and probabilities. The resulting chord diagram provided a visual representation of the relationships between the keywords and the latent issues, allowing for a comparative analysis of their presence in the literature and revealing the areas where pressing issues are more likely to emerge or be considered in drone-based healthcare research.

3. Results and Discussion

The literature search found 290 papers researching different aspects of integrating drones into healthcare applications. The first study was published in 2011 [12], with a significant year-on-year increase in the number of manuscripts published. This publication rate accelerated from 2018 onwards, highlighting a growing research field (Figure 1).
A qualitative review of the corpus found two emerging themes. These were research into the application of drones for the delivery of time-critical medical items [13,14] and research into drones for the delivery of non-time-critical medical items [15,16]. The difference between these two applications is that the delivery of time-critical medical items by drone is generally in response to an emergency [17]. As emergencies occur arbitrarily in space and time, the flight paths must be ad hoc and not scheduled [18]. In contrast, drone delivery of non-time critical medical items would be planned along a predetermined flight path, requiring different compliance, planning, and infrastructure [19]. Interestingly, the two themes show a similar volume of papers and publication trends over time. These data indicate that the two themes are progressing simultaneously; one is not leading or lagging the other.
The application of Topic Modelling to identify common co-occurring words within the titles and abstracts of these papers identified 11 different topics across the 290 scientific papers (multiple topics could co-occur within a single manuscript). The 11 topics were the most suitable representation of the body of research within those manuscripts, representing the application of drones in healthcare (Table 1). While most topics were exclusive to one of the emerging themes, ‘Engineering and Design Considerations’ and ‘Social and Ethical Considerations’ were shared across both themes. Here, we discuss the alignment of these topics within the two emerging themes and the level of association and disassociation between them. The purpose is to provide a guide for future areas of investigation and cooperation.

3.1. Drones in the Delivery of Time-Critical Medical Items

This research theme encompassed approximately 52% of the corpus of published research, delineated by three distinct but closely associated topics (Figure 2).
The topic ‘Defibrillator Deployment’ featured prominently in the literature, addressing the use of UAVs to rapidly deliver Automated External Defibrillators (AEDs) to out-of-hospital cardiac arrest (OHCA) cases [20]. This body of research primarily consisted of simulation studies to evaluate the feasibility and effectiveness of drone-based AED deployment [21,22,23,24,25,26,27]. These studies focused on rural, remote, and mountainous regions and assessed how geographical, topographical, and meteorological factors influence delivery times. They explored the development of UAV networks and applied optimal allocation strategies to enhance coverage and decrease the interval between cardiac arrest occurrence and AED application. These investigations frequently compared UAV-enabled response times with traditional transport services, highlighting improvements in medical service accessibility and potential increases in survival rates [22,28,29].
The topic ‘Blood and Organ Transport’ examined the efficacy, reliability, cost, and feasibility of using UAVs for transporting time-critical biomedical materials [18,30]. This research scrutinised the quality, stability, and integrity of biological time-sensitive materials like blood and organs under certain factors, such as vibration, temperature variations, and transport duration [31,32,33,34,35,36]. Studies involved rigorous laboratory testing to assess the impact of flight conditions on clinical specimens, evaluating parameters that affect the viability of organs and the quality of blood samples for transfusion purposes. The goal was to establish protocols and systems ensuring these materials’ safe and effective transportation, thereby contributing to improved clinical outcomes in transfusion medicine and organ transplantation.
The topic ‘Emergency Response’ aligned closely with ‘Defibrillator Deployment’, evaluating similar aspects but extending its focus to other time-critical medical items. This research emphasised the strategic use of UAVs to bolster emergency medical services (EMS), aiming to reduce intervention times in critical situations [20,21,37,38,39]. Studies under this theme often featured comparative analyses of UAV-assisted emergency response against conventional ambulance services, showcasing the potential of UAVs to save minutes and lives by navigating and mitigating challenging terrain and traffic conditions efficiently [4,8,39,40]. More papers likely describe the use of UAVs in emergency and disaster management than are collated here because this research examined studies focused on healthcare.
The ‘Search and Rescue’ topic focused on utilising UAV systems to enhance the efficiency (optimising the process to minimise resources) and effectiveness (accomplishing the desired outcomes) of search and rescue operations [40,41,42]. Although ‘Search & Rescue’ is not explicitly ‘Health Care Delivery’, it was featured as a topic within healthcare delivery manuscripts. These topics relate to autonomous control systems, GPS, and smart detection devices, presumably improving search operations in diverse environments and situations [39,43,44]. This research aimed to develop techniques that enable UAVs to locate and assist patients swiftly and accurately, often in challenging or hazardous terrain. Integrating Internet of Things (IoT)-guided landing systems and machine learning technologies was a significant theme, enhancing the UAVs’ capability to execute complex rescue missions autonomously and efficiently. The applications in this topic were not explicitly focused on delivering a medical item, and the Topic Modelling showed that these applications used different vocabulary and were, thus, separated within the NMDS plot (Figure 2).
The ‘Disaster Relief’ topic addressed using UAVs to provide medical and logistical support in disaster-stricken areas [44,45,46]. The applications under this topic focused on delivering essential medical supplies, including medicine and equipment, to remote and inaccessible zones affected by disasters. The topic vocabulary was more closely associated with the ‘Search and Rescue’ topic than the other drone delivery of time-critical medical items topics. The ‘Disaster Relief’ studies highlighted the role of UAVs in establishing impromptu aerial supply chains that can carry goods and provide care and assistance in humanitarian relief efforts [47]. The research underscored the logistical and infrastructural challenges faced in disaster relief operations and explored how UAV technology, alongside military and humanitarian coordination, could improve life-saving medical support in those scenarios [46]. These studies also pointed to the regulatory and operational hurdles that need to be navigated to leverage UAVs effectively in disaster response and relief operations.

3.2. Drone Delivery of Non-Time Critical Medical Items

Healthcare supply chains were represented in 32% of the published literature, spanning four topics. The topic ‘Optimisation of Supply Chains’ concentrated on the strategic placement, routing, and efficient use of drones for delivering healthcare services and supplies [7,8,48,49,50]. Research in this area focused on optimising the location, number, and distribution of UAV bases and developing routing algorithms to enhance resource distribution efficiency and energy utilisation. The studies proposed models and algorithms to solve logistical problems, ensuring optimal paths and facilities for the distribution of medical supplies, thereby improving the overall efficiency of healthcare delivery systems [51,52,53,54].
The topic ‘Cost Effectiveness’ scrutinised the economic viability of integrating UAVs into healthcare supply chains [6,55,56]. This research conducted comparative economic analyses between UAV implementations and traditional healthcare supply chain methods, assessing the cost implications and potential increases in efficiency. The analyses included the costs associated with establishing and maintaining communication networks necessary for long-range pilot-to-UAV communications, evaluating their impact on the overall cost-effectiveness of UAV deployment in healthcare logistics [52,57,58].
The topic ‘Overcoming Remoteness’ specifically addressed using UAVs to deliver medical supplies, testing kits, and pharmaceuticals to rural and hard-to-reach areas [3,25,55,59]. The focus was on the last-mile delivery challenges in these regions, where traditional delivery methods are often impeded by geographical and infrastructural barriers [60]. Studies highlighted the advantages of UAVs in overcoming these barriers, providing timely access to essential healthcare products in remote locations, often in developing countries with less stringent aviation regulations [61,62,63].
The topic ‘Pandemic Response’ emerged prominently in the context of global health emergencies like the COVID-19 pandemic. This area of research explored how UAVs could be deployed to support healthcare systems during pandemics, focusing on delivering medical supplies, vaccines, and testing kits [64,65,66,67]. The studies also examined the development and application of UAV technology in pandemic situations, assessing its role in enhancing the resilience and responsiveness of healthcare systems [68,69,70]. Research also delved into integrating emerging technologies, such as artificial intelligence, the Internet of Things (IoT), and blockchain, to improve the management and distribution of medical resources during health crises [71,72].

3.3. Empirical Research

Two topics were shared across both the delivery of time-critical and non-time-critical medical item themes (Figure 2). The ‘Engineering and Design Considerations’ topic delved into the technical aspects of UAV development, focusing on the design, safety, and performance requirements of drones for life-critical missions. Research in this field assessed the engineering challenges in creating reliable and efficient UAVs under the diverse conditions of healthcare delivery. Studies explored various design parameters, including flight dynamics, risk management, and the optimal configuration of multirotor and fixed-wing UAVs to ensure safe and effective operations [73]. Attention was also given to developing onboard equipment to maintain the integrity and quality of biomedical samples during transit, extending the drones’ applicability in medical logistics and at vertiports [17,51,64,74].
The ‘Ethical and Social Considerations’ topic examined the broader implications of drone integration into healthcare systems, focusing on the perspectives of healthcare managers, workers, and the public. This research highlighted the ethical dilemmas and social challenges of deploying UAVs in healthcare, such as privacy, informed consent, and the distribution of medical supplies [75,76,77]. This body of work also considered how societal factors like gender, age, and cultural norms influence the perception and acceptance of UAV technology in healthcare settings [78]. By investigating these anthropogenic aspects, the studies aimed to provide insights into the factors driving or hindering the adoption of UAVs in healthcare, emphasising the need for policy frameworks and practices that align with ethical standards and social expectations [79].

3.4. Research Field Alignment

Research gaps and misalignment in the literature were identified by analysing the frequency of 11 topics in the same articles (Figure 3). The findings revealed an apparent disconnect between research on UAV delivery of time-critical medical items and UAVs for non-time-critical medical items. Although both themes investigated the efficiency and cost-effectiveness of UAV delivery, the way these aspects were discussed in the literature differed. The distinction may stem from studies on UAVs in healthcare supply chains being grounded in real-world scenarios [16,27,80], whilst research on UAVs for time-critical medical deliveries has largely relied upon simulated data [80,81,82,83].
The application of UAVs in healthcare supply chains tends to operate along established routes with scheduled services. Meanwhile, applying UAVs for time-critical medical deliveries is typically in response to emergencies, leading to unplanned flights with unpredictable intervals between launches. Consequently, integrating UAVs for time-critical medical deliveries poses a more intricate challenge due to unfamiliar airspace, the potential lack of awareness among uncrewed aviation, and the complexity of establishing a safe regulatory framework. Moreover, the sporadic nature of emergencies hinders the development of a viable business case and the accumulation of sufficient data for research purposes. This disparity likely explains why the topic of ‘UAV Ethical, Social, and Public Perception’ is prevalent in studies on healthcare supply chains but is seldom addressed in research on the delivery of time-critical medical items.
The disparity in the occurrence of ‘Defibrillator Deployment’ and ‘Blood and Organ Transport’ in research papers (Figure 2), despite their linguistic similarity (Figure 1), indicates distinct exploration contexts for these topics in urgent medical UAV deliveries. ‘Defibrillator Deployment’ focuses on immediate life-saving responses during emergencies, while ‘Blood and Organ Transport’ deals with the intricate logistics of handling delicate biological materials for medical purposes. Though they share common urgency and drone transport technology terms, their unique research paths underscore the diverse operational, regulatory, and logistical obstacles specific to each domain. This distinction has shaped their progression in medical drone literature. While there is potential for interdisciplinary research due to lexical similarities, the separate research focuses demonstrate independent evolution, underscoring the opportunity for integration to enhance emergency medical services and healthcare logistics through cohesive drone delivery systems.
The identification of topic connectedness with keywords hypothesised to be essential for the safe, efficient, and effective adoption of any new technology is shown in Figure 4. This modelling showed the difference between the established nature of the two research areas. While topics such as ethics, environment, safety, and social impact are being well researched within the regions of UAVs in healthcare supply chains, the economics, legality, effectiveness, and organisational aspects are the main research topics within the areas for delivering time-critical medical items.
The strong link between ‘Safety’, ‘Engineering’, and ‘Design Considerations’ is unsurprising (Figure 4). Safety is essential in drone design, particularly in healthcare applications. This correlation signifies a research focus on ensuring that drones are operationally effective and meet strict safety standards to prevent mishaps and provide dependable performance. Engineering and design factors include drone structural stability, fail-safe mechanisms, redundancy systems, and compliance with safety regulations vital for safe drone usage in critical medical scenarios. The ‘Financial Aspect’ naturally corresponds with ‘Cost-Effectiveness’, as financial factors play a crucial role in assessing the feasibility and sustainability of medical drone delivery systems.
The alignment of ‘Legal’ and ‘Organisational Aspects’ with ‘Disaster Relief’ highlights their importance in situations requiring a swift, ad hoc, and coordinated response (Figure 4). Legal considerations, including regulatory adherence, airspace constraints, and privacy regulations, are critical in disaster relief settings where drones are utilised for rapid evaluations, medical supply deliveries, and search and rescue missions. Often in tension with operational requirements of immediate deployment in disaster situations, legal and regulatory compliance considerations were viewed as potential impediments. Interestingly, these legal connections are less prominently linked to other topics within the drone delivery of time-critical medical items. The lack of strong correlations in these contexts may indicate an underexplored research area, suggesting a need for a focused exploration of how legal and organisational frameworks intersect with various aspects of medical drone usage beyond disaster relief. As the field expands and more literature is produced, these pressing issues are expected to evolve into distinct topics.
The apparent link between ‘Environmental Considerations’ and ‘Search and Rescue’ could be misconstrued as addressing the environmental impacts of drone technology on wildlife, ecosystems, or carbon emissions (Figure 4). However, in this context, the term ”environment” likely refers to the operational surroundings of search and rescue missions, such as geographic and physical landscapes, rather than the environmental effects of drone usage itself. The literature appears to overlook the environmental impact of deploying drones, particularly concerning natural resources and ecological balance. This gap underscores a need to thoroughly examine the environmental repercussions of drone deployment, especially in sensitive or conservation areas. A comprehensive assessment of drones’ ecological footprint, from production and operation to disposal, is essential to ensure the sustainable and environmentally conscious application of drones in medical and rescue operations.

3.5. Study Limitations

While Topic Modelling is a valuable tool for scientific literature review, it is essential to be aware of its limitations when interpreting results. Like any model, the output quality is only as good as the data inputted. Whilst this study attempted to capture all the words that would likely be found in the title, abstract, and keywords of a manuscript researching drones for healthcare delivery, it also acknowledges that literature keyword searches could always be more comprehensive and likely missed several papers in the field. However, most published manuscripts researching the integration of drones into healthcare are expected to use the same search words in either the title, keywords, or abstract (see Section 2.1). Therefore, the findings and trends observed within this corpus of 290 manuscripts likely represent the field. Another limitation of the present study is that only manuscripts until the end of the year 2023 were included. This criterion ensured that only full years of manuscripts were added to the dataset for rigorous temporal analysis. The research field is growing, with an ever-greater number of publications produced yearly. Thus, the findings from this study may rapidly become outdated. Nevertheless, this review will provide a baseline for measuring the field’s research and development.
Topic Modelling focuses on identifying patterns within the text and has been criticised for not defining relationships between concepts and extracting meaningful insights [10]. Although this study followed up the Topic Modelling results with a research Gap Analysis and Topic Matrix, we remained careful not to overreach our findings and focused our observations and conclusions on the results from the Topic Modelling outputs rather than a qualitative review of the literature, which would capture the nuances of the literature rather than identify the knowledge gaps.

3.6. Recommendations

The rising trend in peer-reviewed research literature indicates the growth of a promising field that supports an emerging industry with substantial benefits for humanity. We propose the following considerations as the research field grows.
  • The development of more unified terminology to facilitate the identification and research alignment between studies upon the delivery of critical and non-critical medical items;
  • The implementation of studies to investigate the impacts of UAV medical item delivery on patient health outcomes. This could use established methods for systematically evaluating and synthesising evidence on the properties, effects, and impacts of health-related technologies [84,85];
  • The examination of the economic sustainability of drones for delivering health services through standard efficiency theory and a value-based economic assessment to weigh the benefits against the costs, considering that different solutions may compete for the limited financial resources available;
  • The improvement of the private sector to better collect and share operational data, especially in time-critical medical item deliveries, where data collection opportunities are infrequent and the field lacks real-world user cases;
  • Improved transparency and reporting of the technological and regulatory hurdles and compliance that drone operations had to overcome to be successful. Often, only the successes are reported;
  • Research into the legal and regulatory barriers to the effective deployment of drones for delivering health services to determine a practical framework that facilitates drone use while minimising time and cost.

4. Conclusions

In conclusion, the literature search revealed a significant increase in research on integrating drones into healthcare applications, with a notable rise in publications since 2018. Two primary themes emerged: drones for delivering time-critical and non-time-critical medical items. Topic modelling identified 11 topics across the 290 papers, highlighting the diverse research areas within drone integration in healthcare. Exploring both time-critical and non-time-critical medical item deliveries showcased distinct challenges and opportunities, emphasising the need for unified terminology, evaluation of patient health outcomes, economic sustainability assessments, enhanced data sharing, and transparent reporting of operational challenges. The future of this field of research looks bright and will be crucial in advancing drone healthcare applications.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/drones8060258/s1, Table S1 Manuscript Database, Table S2 Topic model predictions.

Author Contributions

Conceptualisation, H.A.C., V.B. and O.J.L.; methodology, H.A.C., V.B. and O.J.L.; formal analysis, O.J.L. investigation, V.B. and A.H.; resources, H.A.C.; data curation, V.B., K.S. and A.H.; writing—original draft preparation, H.A.C. and V.B.; writing—review and editing, all authors; visualisation, K.S. and O.J.L.; supervision, H.A.C.; project administration, H.A.C. and M.A.C.; funding acquisition, H.A.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Australian Commonwealth Government, Department of Infrastructure, Transport, Regional Development, Communication and the Arts, through the Emerging Aviation Technology Partnership Program, and a PhD stipend awarded to V.B. from the Developing the North Cooperative Research Centre.

Data Availability Statement

Data supporting the resorted results will be available via Dryad upon paper acceptance.

Acknowledgments

We would like to acknowledge all the authors of the research literature used within this review.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare no conflicts of interest.

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Figure 1. Publication rate of manuscripts returned by a Web of Science search (using the search string in Section 2.1) and then screened for content relevance. The graph displays the total annual number of publications (black) and then the breakdown of papers that focused on the delivery of critical medical items (dark grey) and those that concentrated on non-critical medical items (light grey).
Figure 1. Publication rate of manuscripts returned by a Web of Science search (using the search string in Section 2.1) and then screened for content relevance. The graph displays the total annual number of publications (black) and then the breakdown of papers that focused on the delivery of critical medical items (dark grey) and those that concentrated on non-critical medical items (light grey).
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Figure 2. An inter-topic distance map that shows a two-dimensional representation (via NMDS) of research topics in medical and health drone delivery based on vocabulary similarity. Each node signifies a distinct research topic derived from LDA Topic Modelling (definitions for the Topic numbering found in Table 1), with the proximity reflecting shared linguistic patterns. The layout illustrates the lexical diversity across the research landscape, where close topics share a common vocabulary, indicating thematic or methodological overlap. In contrast, distant topics represent distinct thematic areas. This visualisation aids in appreciating the full spectrum of research themes, from closely aligned to divergent, within the domain of medical drone delivery. Node colours correspond to different research areas, while the size reflects the topic’s prevalence in the literature.
Figure 2. An inter-topic distance map that shows a two-dimensional representation (via NMDS) of research topics in medical and health drone delivery based on vocabulary similarity. Each node signifies a distinct research topic derived from LDA Topic Modelling (definitions for the Topic numbering found in Table 1), with the proximity reflecting shared linguistic patterns. The layout illustrates the lexical diversity across the research landscape, where close topics share a common vocabulary, indicating thematic or methodological overlap. In contrast, distant topics represent distinct thematic areas. This visualisation aids in appreciating the full spectrum of research themes, from closely aligned to divergent, within the domain of medical drone delivery. Node colours correspond to different research areas, while the size reflects the topic’s prevalence in the literature.
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Figure 3. Research gap distance correlation plot for the literature focused on drone delivery of medical items. The research gap distance is derived from the dissimilarity analysis of the topic-per-article proportion distance matrix. A higher metric value indicates a more significant gap between topics (i.e., topics that rarely co-occur in the same article). Red intersections represent pairs with distance values greater than 0.5, indicating topic pairs that were poorly integrated in the literature (the darker the red, the more significant the gaps). Blue intersections represent pairs with distance values smaller than 0.5, indicating well-integrated topic pairs in the literature (the darker the blue, the closer the connection).
Figure 3. Research gap distance correlation plot for the literature focused on drone delivery of medical items. The research gap distance is derived from the dissimilarity analysis of the topic-per-article proportion distance matrix. A higher metric value indicates a more significant gap between topics (i.e., topics that rarely co-occur in the same article). Red intersections represent pairs with distance values greater than 0.5, indicating topic pairs that were poorly integrated in the literature (the darker the red, the more significant the gaps). Blue intersections represent pairs with distance values smaller than 0.5, indicating well-integrated topic pairs in the literature (the darker the blue, the closer the connection).
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Figure 4. Chord diagram illustrating the relationships between different research topics (definitions for the Topic numbering found in Table 1) and selected keywords representing critical pressing issues in medical and health drone delivery research. The width of the ribbons connecting the various topics and pressing issues corresponds to the strength of the association of keywords to the topics. Each topic description is detailed in Table 1.
Figure 4. Chord diagram illustrating the relationships between different research topics (definitions for the Topic numbering found in Table 1) and selected keywords representing critical pressing issues in medical and health drone delivery research. The width of the ribbons connecting the various topics and pressing issues corresponds to the strength of the association of keywords to the topics. Each topic description is detailed in Table 1.
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Table 1. Uncovered research topics from 290 research articles about medical and health drone delivery published from 2011 to 2023 using a Latent Dirichlet Allocation (LDA) model.
Table 1. Uncovered research topics from 290 research articles about medical and health drone delivery published from 2011 to 2023 using a Latent Dirichlet Allocation (LDA) model.
Topic #Topic NameResearch AreaTop 20 Words
1Optimisation of Supply ChainsHealthcare supply chainslocation, service, problem, propose, model, algorithm, optimisation, efficiency, provide, time, route, resource, solution, plan, path, facility, distribution, station, optimal, data
2Pandemic ResponseHealthcare supply chainshealthcare, technology, develop, COVID, application, pandemic, research, current, process, future, include, work, recent, learning, review, article, world, implement, blockchain
3Search and RescueDisaster relief and search operationssystem, patient, rescue, control, search, operation, environment, situation, use, present, autonomous, propose, GPS, device, monitor, technique, improve, smart, detection
4Defibrillator DeploymentDelivery of time-critical medical itemsdefibrillator, hospital, ohca, cardiac arrest, external, automated, simulation, survival, study, improve, medical service, bystander, ems, time, region, feasibility, response time, access, experience, minute
5Engineering and Design ConsiderationsEmpirical researchdesign, flight, requirement, safety, scenario, urban, performance, aircraft, reliable, risk, fix, wing, type, fast, landing, configuration, project, failure, multirotor, power
6Cost-effectivenessHealthcare supply chainscost, network, analysis, area, model, increase, effective, vaccine, comparison, improve, range, condition, life, year, coverage, existing, economic, number, communication
7Blood and Organ TransportDelivery of time-critical medical itemstransport, blood, sample, product, test, study, organ, quality, effect, flight, laboratory, temperature, significant, vibration, degrees, ground, specimen, evaluation, impact, parameter, clinical, transfusion
8Ethical and Social ConsiderationsEmpirical researchcare, human, acceptance, challenge, public, community, ethic, decision, sector, context, participant, user, interview, technology, value, impact, perception, adoption, sensitive, worker
9Overcoming RemotenessHealthcare supply chainsdelivery, medicine, supply, rural, distance, country, road, drug, timely, solution, barrier, item, deploy, essential, mile, fleet, pharmacy, access, overcome, battery
10Emergency ResponseDelivery of time-critical medical itemsemergency, time, response, vehicle, reduce, aid, significant, ambulance, fast, traffic, save, victim, real, travel, demonstrate, live, comparison, min, many, locate, data, accident, scene
11Disaster ReliefDisaster relief and search operationsmedical, disaster, area, remote, zone, relief, logistic, humanitarian, system, event, aerial, assistance, care, military, carry, goods, provision, infrastructure, challenge, life
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MDPI and ACS Style

Campbell, H.A.; Bosiocic, V.; Hvala, A.; Brady, M.; Campbell, M.A.; Skelton, K.; Luiz, O.J. Emerging Research Topics in Drone Healthcare Delivery. Drones 2024, 8, 258. https://0-doi-org.brum.beds.ac.uk/10.3390/drones8060258

AMA Style

Campbell HA, Bosiocic V, Hvala A, Brady M, Campbell MA, Skelton K, Luiz OJ. Emerging Research Topics in Drone Healthcare Delivery. Drones. 2024; 8(6):258. https://0-doi-org.brum.beds.ac.uk/10.3390/drones8060258

Chicago/Turabian Style

Campbell, Hamish A., Vanya Bosiocic, Aliesha Hvala, Mark Brady, Mariana A. Campbell, Kade Skelton, and Osmar J. Luiz. 2024. "Emerging Research Topics in Drone Healthcare Delivery" Drones 8, no. 6: 258. https://0-doi-org.brum.beds.ac.uk/10.3390/drones8060258

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