Review
1Department of Politics and International Relations, University of Oxford, Oxford, United Kingdom
2School of International Studies, Peking University, Beijing, China
Corresponding Author:
Hanyu Wang, BA, BEc, MPhil
School of International Studies
Peking University
No 5 Yiheyuan Road
Haidian District
Beijing, 100871
China
Phone: 86 13261712766
Email: wang.hanyu@outlook.com
Abstract
Background: Imbalanced health care resource distribution has been central to unequal health outcomes and political tension around the world. Artificial intelligence (AI) has emerged as a promising tool for facilitating resource distribution, especially during emergencies. However, no comprehensive review exists on the use and ethics of AI in health care resource distribution.
Objective: This study aims to conduct a scoping review of the application of AI in health care resource distribution, and explore the ethical and political issues in such situations.
Methods: A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). A comprehensive search of relevant literature was conducted in MEDLINE (Ovid), PubMed, Web of Science, and Embase from inception to February 2022. The review included qualitative and quantitative studies investigating the application of AI in health care resource allocation.
Results: The review involved 22 articles, including 9 on model development and 13 on theoretical discussions, qualitative studies, or review studies. Of the 9 on model development and validation, 5 were conducted in emerging economies, 3 in developed countries, and 1 in a global context. In terms of content, 4 focused on resource distribution at the health system level and 5 focused on resource allocation at the hospital level. Of the 13 qualitative studies, 8 were discussions on the COVID-19 pandemic and the rest were on hospital resources, outbreaks, screening, human resources, and digitalization.
Conclusions: This scoping review synthesized evidence on AI in health resource distribution, focusing on the COVID-19 pandemic. The results suggest that the application of AI has the potential to improve efficacy in resource distribution, especially during emergencies. Efficient data sharing and collecting structures are needed to make reliable and evidence-based decisions. Health inequality, distributive justice, and transparency must be considered when deploying AI models in real-world situations.
doi:10.2196/38397
Keywords
Introduction
Global responses to COVID-19 are converging with the use of digital health and algorithms based on artificial intelligence (AI), impacting health care systems around the world [van der Schaar M, Alaa AM, Floto A, Gimson A, Scholtes S, Wood A, et al. How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Mach Learn 2021 Dec 09;110(1):1-14 [https://europepmc.org/abstract/MED/33318723] [CrossRef] [Medline]1]. AI was partially founded by Alan Turing, and a machine or a process that could demonstrate intelligent behaviors in cognitive tasks, which can pass the Turing test, would be deemed as AI [Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol 2019 Apr 27;28(2):73-81 [CrossRef] [Medline]2]. Multiple AI techniques, such as fuzzy expert systems and Bayesian networks, have been applied both virtually and physically in the health care field [Amisha, Malik P, Pathania M, Rathaur V. Overview of artificial intelligence in medicine. J Family Med Prim Care 2019 Jul;8(7):2328-2331 [http://www.jfmpc.com/article.asp?issn=2249-4863;year=2019;volume=8;issue=7;spage=2328;epage=2331;aulast=Amisha%2C] [CrossRef] [Medline]3]. For example, clinical pathway analysis, a critical area in ensuring standard medical procedures, can be analyzed by pattern-mining procedures [Le HH, Edman H, Honda Y, Kushima M, Yamazaki T, Araki K, et al. Fast Generation of Clinical Pathways including Time Intervals in Sequential Pattern Mining on Electronic Medical Record Systems. 2017 Presented at: International Conference on Computational Science and Computational Intelligence (CSCI); December 14-16, 2017; Las Vegas, NV, USA [CrossRef]4]. Resource distribution includes the distribution of resources at strategic, tactical, and operational levels and is a key issue in health policy [Yu M, He S, Wu D, Zhu H, Webster C. Examining the Multi-Scalar Unevenness of High-Quality Healthcare Resources Distribution in China. Int J Environ Res Public Health 2019 Aug 07;16(16):2813 [https://www.mdpi.com/resolver?pii=ijerph16162813] [CrossRef] [Medline]5,Hulshof PJH, Kortbeek N, Boucherie RJ, Hans EW, Bakker PJM. Taxonomic classification of planning decisions in health care: a structured review of the state of the art in OR/MS. Health Systems 2017 Dec 19;1(2):129-175 [CrossRef]6].
Luengo-Oroz et al proposed that the application of AI during the COVID-19 pandemic can be broken down into 3 scales: molecular, clinical, and societal [Luengo-Oroz M, Hoffmann Pham K, Bullock J, Kirkpatrick R, Luccioni A, Rubel S, et al. Artificial intelligence cooperation to support the global response to COVID-19. Nat Mach Intell 2020 May 22;2(6):295-297 [CrossRef]7]. At the molecular level, protein structure prediction, novel nucleic acid testing, drug repurposing, and drug discovery all rely on AI and deep-learning algorithms [Luengo-Oroz M, Hoffmann Pham K, Bullock J, Kirkpatrick R, Luccioni A, Rubel S, et al. Artificial intelligence cooperation to support the global response to COVID-19. Nat Mach Intell 2020 May 22;2(6):295-297 [CrossRef]7-Cheng F, Desai RJ, Handy DE, Wang R, Schneeweiss S, Barabási AL, et al. Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat Commun 2018 Jul 12;9(1):2691 [https://doi.org/10.1038/s41467-018-05116-5] [CrossRef] [Medline]9]. At the clinical level, diagnosis, treatment, and prognosis all benefit from AI. For example, AI-based computed tomography diagnosis has been widely applied for identifying COVID cases [Luengo-Oroz M, Hoffmann Pham K, Bullock J, Kirkpatrick R, Luccioni A, Rubel S, et al. Artificial intelligence cooperation to support the global response to COVID-19. Nat Mach Intell 2020 May 22;2(6):295-297 [CrossRef]7,Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X, et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 2020 Oct 09;11(1):5088 [https://doi.org/10.1038/s41467-020-18685-1] [CrossRef] [Medline]10,Mei X, Lee H, Diao K, Huang M, Lin B, Liu C, et al. Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat Med 2020 Aug 19;26(8):1224-1228 [https://europepmc.org/abstract/MED/32427924] [CrossRef] [Medline]11], alongside robotics and telemedicine that facilitate clinical processes. At the societal level, AI is applied in epidemiological research and social policymaking. In particular, AI-based case forecasting has been in use since the beginning of the pandemic [Luengo-Oroz M, Hoffmann Pham K, Bullock J, Kirkpatrick R, Luccioni A, Rubel S, et al. Artificial intelligence cooperation to support the global response to COVID-19. Nat Mach Intell 2020 May 22;2(6):295-297 [CrossRef]7,Hu Z, Ge Q, Li S, Jin L, Xiong M. Artificial Intelligence Forecasting of Covid-19 in China. arXiv. 2020. URL: https://arxiv.org/abs/2002.07112 [accessed 2023-01-15] 12]. The application of AI at the societal level can stratify population risk, facilitate diagnosis and testing, support the design of trials and drugs, and inform policymaking, relieving the burden of COVID-19 on health care systems and helping the society to better respond to the pandemic [van der Schaar M, Alaa AM, Floto A, Gimson A, Scholtes S, Wood A, et al. How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Mach Learn 2021 Dec 09;110(1):1-14 [https://europepmc.org/abstract/MED/33318723] [CrossRef] [Medline]1].
The application of AI to decision-making processes in health care systems significantly precedes the COVID-19 pandemic [Luengo-Oroz M, Hoffmann Pham K, Bullock J, Kirkpatrick R, Luccioni A, Rubel S, et al. Artificial intelligence cooperation to support the global response to COVID-19. Nat Mach Intell 2020 May 22;2(6):295-297 [CrossRef]7,Reeve J, Richardson C. Artificial intelligence (AI) and Britons health: how can AI help to health in resource-based situations? International Journal of Modern Engineering Technologies 2020;2(2)13]. Health policy aims at providing health care to the population, and the decision-making process aims to address 2 core issues: screening and diagnosis, and treatment and monitoring [Luengo-Oroz M, Hoffmann Pham K, Bullock J, Kirkpatrick R, Luccioni A, Rubel S, et al. Artificial intelligence cooperation to support the global response to COVID-19. Nat Mach Intell 2020 May 22;2(6):295-297 [CrossRef]7]. These 2 tasks are essential to the entire health care system. The policymaking process includes hypothesis generation, hypothesis testing, and action (or policy). AI can learn from past data, including health records, past insurance claims, and disease incidence and prevalence, to improve hypothesis generation and testing, and thus improve the quality of health care policymaking [Luengo-Oroz M, Hoffmann Pham K, Bullock J, Kirkpatrick R, Luccioni A, Rubel S, et al. Artificial intelligence cooperation to support the global response to COVID-19. Nat Mach Intell 2020 May 22;2(6):295-297 [CrossRef]7].
In the health care system, resource distribution is an essential issue for policymakers, as resources are always scarce [Kong X, Ai B, Kong Y, Su L, Ning Y, Howard N, et al. Artificial intelligence: a key to relieve China's insufficient and unequally-distributed medical resources. Am J Transl Res 2019;11(5):2632-2640 [https://europepmc.org/abstract/MED/31217843] [Medline]14]. For example, Kong et al argued that the primary problem in China’s health care system is the lack of high-quality health resources and the consequent supply-demand imbalance. They maintain that AI could benefit from China’s enormous data and has the potential to improve this unequal distribution of health resources [Kong X, Ai B, Kong Y, Su L, Ning Y, Howard N, et al. Artificial intelligence: a key to relieve China's insufficient and unequally-distributed medical resources. Am J Transl Res 2019;11(5):2632-2640 [https://europepmc.org/abstract/MED/31217843] [Medline]14].
During the COVID-19 pandemic, imbalanced health care resource distribution has been one of the central issues causing unequal health outcomes and political tension [Tan-Torres Edejer T, Hanssen O, Mirelman A, Verboom P, Lolong G, Watson OJ, et al. Projected health-care resource needs for an effective response to COVID-19 in 73 low-income and middle-income countries: a modelling study. Lancet Glob Health 2020 Nov;8(11):e1372-e1379 [https://linkinghub.elsevier.com/retrieve/pii/S2214-109X(20)30383-1] [CrossRef] [Medline]15,Ji Y, Ma Z, Peppelenbosch MP, Pan Q. Potential association between COVID-19 mortality and health-care resource availability. Lancet Glob Health 2020 Apr;8(4):e480 [https://linkinghub.elsevier.com/retrieve/pii/S2214-109X(20)30068-1] [CrossRef] [Medline]16]. Ji et al observed that the higher COVID case-fatality rate in Wuhan city and Hubei province compared with other parts of China at the beginning of the pandemic could potentially be attributed to health care resource scarcity [Ji Y, Ma Z, Peppelenbosch MP, Pan Q. Potential association between COVID-19 mortality and health-care resource availability. Lancet Glob Health 2020 Apr;8(4):e480 [https://linkinghub.elsevier.com/retrieve/pii/S2214-109X(20)30068-1] [CrossRef] [Medline]16]. Edejer et al projected that the cost of health care resources to combat the pandemic would continue to rise in low- and middle-income counties, and concluded that a comprehensive system of resource distribution is necessary [Tan-Torres Edejer T, Hanssen O, Mirelman A, Verboom P, Lolong G, Watson OJ, et al. Projected health-care resource needs for an effective response to COVID-19 in 73 low-income and middle-income countries: a modelling study. Lancet Glob Health 2020 Nov;8(11):e1372-e1379 [https://linkinghub.elsevier.com/retrieve/pii/S2214-109X(20)30383-1] [CrossRef] [Medline]15].
Health care resource distribution is determined by the supply-demand relationship, logistics, and governance structure [Lane H, Sarkies M, Martin J, Haines T. Equity in healthcare resource allocation decision making: A systematic review. Soc Sci Med 2017 Feb;175:11-27 [CrossRef] [Medline]17,Kang J, Michels A, Lyu F, Wang S, Agbodo N, Freeman VL, et al. Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA. Int J Health Geogr 2020 Sep 14;19(1):36 [https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-020-00229-x] [CrossRef] [Medline]18]. Using the COVID-19 response as an example, the severity of the pandemic can determine the health care resources required in each location, but the resources might not be distributed according to need [Kang J, Michels A, Lyu F, Wang S, Agbodo N, Freeman VL, et al. Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA. Int J Health Geogr 2020 Sep 14;19(1):36 [https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-020-00229-x] [CrossRef] [Medline]18]. AI can be applied to study supply-demand, logistics, and patient characteristics, but the ethics and implications of the use of AI in policymaking remain important issues [Luengo-Oroz M, Hoffmann Pham K, Bullock J, Kirkpatrick R, Luccioni A, Rubel S, et al. Artificial intelligence cooperation to support the global response to COVID-19. Nat Mach Intell 2020 May 22;2(6):295-297 [CrossRef]7].
Currently, there are no comprehensive reviews to provide an overall picture of the literature on the application of AI in resource distribution in health care settings, particularly with regard to societal and ethical aspects. This study aims to conduct a scoping review on the application of AI in health care resource distribution, particularly during the COVID-19 pandemic and to explore the ethics and implications of AI in health policymaking with regard to resource distribution.
Methods
Scoping Review Design
This scoping review follows the framework proposed by Arksey and O’Malley [Arksey H, O'Malley L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology 2005 Feb;8(1):19-32 [CrossRef]19]. Briefly, the review has the following 5 stages: (1) identifying the research question, “What are the roles of AI and machine learning in the allocation of health care resources, before and during the COVID-19 pandemic?”; (2) identifying suitable studies; (3) selecting studies for review; (4) consolidating the data; and (5) summarizing and reporting the results. This study complies with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) [Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 2018 Oct 02;169(7):467-473 [https://www.acpjournals.org/doi/abs/10.7326/M18-0850?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub 0pubmed] [CrossRef] [Medline]20] for reporting scoping review results.
Data Source and Search Strategy
Searches were conducted in MEDLINE (Ovid), PubMed, Web of Science, and Embase from inception to February 2022. The search featured 2 key terms: (1) artificial intelligence, including related terms such as big data and algorithm, and (2) health care resource allocation. The search terms were used with the “explode” feature where applicable. For example, in MEDLINE and Embase, we used exp artificial intelligence/ and exp resource allocation/, and in PubMed, relevant MeSH (Medical Subject Heading) terms were used. The search was individually designed and adapted for each database.
Study Selection
Inclusion and exclusion criteria were defined a priori. This scoping review includes qualitative and quantitative studies investigating the application of AI in health care resource allocation. Studies that are not relevant to AI or health care resource allocation were excluded, as were duplicate studies. The inclusion and exclusion criteria are summarized in Table 1.
Selection was conducted in 2 steps. First, titles and abstracts were screened for topic relevance and study design. Second, full texts of the remaining studies were screened to check for eligibility. All of the study selection processes were conducted in EndNote X9 (Clarivate).
Criterion | Inclusion | Exclusion |
Type of study | Qualitative, quantitative, mixed method, and review studies in peer-reviewed journals | Letters, comments, conference abstracts, editorials, and theses |
Language | English | All other languages |
Study variables | Includes (1) artificial intelligence/machine learning and relevant terms and (2) allocation of health care resources | Does not include (1) artificial intelligence/machine learning and relevant terms or (2) allocation of health care resources |
Study context | Health care resource allocation at either the population level or hospital level | All other resource allocation scenarios |
Data Consolidation
Selected studies were input into NVivo 12 (QSR International) for labeling and coding. Authors coded data of interest from the articles in NVivo 12 and extracted information regarding study author, study design, location, context, aim, main result, AI method under study, resource allocation situation, and policymaking relevance into a standardized Excel (Microsoft Corp) form.
Summarizing the Results
We employed an inductive approach to summarize the results from the included studies. First, the selected papers were grouped into 2 types: (1) studies of model development and validation of AI-based algorithms applied to health care resource distribution, and (2) qualitative studies, theoretical discussions, and review studies of the application of AI in health care resource distribution. For studies of model development and validation, we extracted the study objectives, resource distribution situations, AI model input variables, and policy relevance. For studies in the second category, objectives, resource distribution situations, discussed topics, and policy relevance were extracted. We further divided the input variables of the studies of model development and validation into 2 predefined categories: (1) ecological variables or variables at the group level, which included variables depicting characteristics at the population level, such as infant mortality in a region, local economic development, or disease prevalence and incidence; and (2) individual variables, which included variables that define individual characteristics such as diagnosis and age.
Results
Selected Studies
In total, 298 studies were identified in 4 databases after removing duplicates. After 1 round of screening for titles and abstracts, 255 studies were excluded due to irrelevant topics and unsuitable study designs. This left 43 studies for full-text screening. Of these, 2 were excluded because they were not directly relevant to health care, 8 because they were not related to resource distribution, 7 because they did not feature applications of AI, and 4 because of an inappropriate study design. In the end, 22 studies remained for qualitative synthesis. The PRISMA flow diagram for study selection is presented in Figure 1.

Summary of the Characteristics of Studies on Model Development
The characteristics of the included studies on model development are summarized in Table 2. The included studies were published between 2013 and 2021. Of the 22 included studies, 9 focused on model development and validation [Bednarski B, Singh A, Jones W. On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic. J Am Med Inform Assoc 2021 Mar 18;28(4):874-878 [https://europepmc.org/abstract/MED/33295626] [CrossRef] [Medline]21-Gartner D, Padman R. Improving Hospital-Wide Early Resource Allocation through Machine Learning. In: MEDINFO 2015: eHealth-enabled Health. Amsterdam, the Netherlands: IOS Press; 2015:315-31930]. Of these, 5 studies were conducted in emerging economies, including 2 in China [Xu Y, Liu S, Wang B. Research on Computational Intelligence in Medical Resource Allocation Based on Mass Customization. Journal of Universal Computer Science 2018;24(6):753-77427,Zhang R, Xing Y, Wang J, Shang X, Zhu X. A Novel Multiattribute Decision-Making Method Based on Point⁻Choquet Aggregation Operators and Its Application in Supporting the Hierarchical Medical Treatment System in China. Int J Environ Res Public Health 2018 Aug 10;15(8):1718 [https://www.mdpi.com/resolver?pii=ijerph15081718] [CrossRef] [Medline]29], 2 in Brazil [Rosas MA, Bezerra AFB, Duarte-Neto PJ. Use of artificial neural networks in applying methodology for allocating health resources. Rev Saude Publica 2013 Feb;47(1):128-36; discussion 136 [https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102013000100017&lng=en&nrm=iso&tlng=en] [CrossRef] [Medline]25,Yousefi M, Yousefi M, Ferreira RPM, Kim JH, Fogliatto FS. Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments. Artif Intell Med 2018 Jan;84:23-33 [CrossRef] [Medline]28], and 1 in Ecuador [Velez AFJ, Rosas JDC, Fierro JMM. Geospatial model e-health planning collective intelligence. 2016 Presented at: Third International Conference on eDemocracy & eGovernment (ICEDEG); March 30, 2016-April 01, 2016; Sangolqui, Ecuador [CrossRef]26]. In developed countries, 3 studies were conducted. These included 1 in Germany [Gartner D, Padman R. Improving Hospital-Wide Early Resource Allocation through Machine Learning. Stud Health Technol Inform 2015;216:315-319 [Medline]23], 1 in the United Kingdom [Belciug S, Gorunescu F. Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation. J Biomed Inform 2015 Feb;53:261-269 [https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(14)00240-8] [CrossRef] [Medline]22], and 1 in the United States with a validation data set in China [McRae MP, Dapkins IP, Sharif I, Anderman J, Fenyo D, Sinokrot O, et al. Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation. J Med Internet Res 2020 Aug 24;22(8):e22033 [https://www.jmir.org/2020/8/e22033/] [CrossRef] [Medline]24]. One study was applied to a global context [Bednarski B, Singh A, Jones W. On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic. J Am Med Inform Assoc 2021 Mar 18;28(4):874-878 [https://europepmc.org/abstract/MED/33295626] [CrossRef] [Medline]21].
Of the 9 studies, 4 focused on resource distribution at the health system level, including financial resources for public health in Brazil [Rosas MA, Bezerra AFB, Duarte-Neto PJ. Use of artificial neural networks in applying methodology for allocating health resources. Rev Saude Publica 2013 Feb;47(1):128-36; discussion 136 [https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102013000100017&lng=en&nrm=iso&tlng=en] [CrossRef] [Medline]25], health care resource distribution in health planning in Ecuador [Velez AFJ, Rosas JDC, Fierro JMM. Geospatial model e-health planning collective intelligence. 2016 Presented at: Third International Conference on eDemocracy & eGovernment (ICEDEG); March 30, 2016-April 01, 2016; Sangolqui, Ecuador [CrossRef]26], medical resource allocation in the hierarchical health system in China [Zhang R, Xing Y, Wang J, Shang X, Zhu X. A Novel Multiattribute Decision-Making Method Based on Point⁻Choquet Aggregation Operators and Its Application in Supporting the Hierarchical Medical Treatment System in China. Int J Environ Res Public Health 2018 Aug 10;15(8):1718 [https://www.mdpi.com/resolver?pii=ijerph15081718] [CrossRef] [Medline]29], and medical equipment allocation in the global COVID-19 pandemic [Bednarski B, Singh A, Jones W. On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic. J Am Med Inform Assoc 2021 Mar 18;28(4):874-878 [https://europepmc.org/abstract/MED/33295626] [CrossRef] [Medline]21]. The remaining 5 studies focused on resource allocation at the hospital level, including bed allocation in a London hospital [Belciug S, Gorunescu F. Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation. J Biomed Inform 2015 Feb;53:261-269 [https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(14)00240-8] [CrossRef] [Medline]22], day resources and bed allocation in a hospital in Munich, Germany [Gartner D, Padman R. Improving Hospital-Wide Early Resource Allocation through Machine Learning. Stud Health Technol Inform 2015;216:315-319 [Medline]23], human resources and medical materials in a public hospital in China [Xu Y, Liu S, Wang B. Research on Computational Intelligence in Medical Resource Allocation Based on Mass Customization. Journal of Universal Computer Science 2018;24(6):753-77427], medical resource allocation in a hospital in the capital of State of Minas Gerais in Brazil [Yousefi M, Yousefi M, Ferreira RPM, Kim JH, Fogliatto FS. Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments. Artif Intell Med 2018 Jan;84:23-33 [CrossRef] [Medline]28], and medical resource allocation in clinics for COVID-19 patients in New York [McRae MP, Dapkins IP, Sharif I, Anderman J, Fenyo D, Sinokrot O, et al. Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation. J Med Internet Res 2020 Aug 24;22(8):e22033 [https://www.jmir.org/2020/8/e22033/] [CrossRef] [Medline]24].
Reference | Objectives | Resource allocation situation | Input variables |
Rosas et al (2013) [Rosas MA, Bezerra AFB, Duarte-Neto PJ. Use of artificial neural networks in applying methodology for allocating health resources. Rev Saude Publica 2013 Feb;47(1):128-36; discussion 136 [https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102013000100017&lng=en&nrm=iso&tlng=en] [CrossRef] [Medline]25] | To construct a financial resource allocation model using an artificial neural network | Financial resources for public health in Brazil | Mortality characteristics, proportion of teenage mothers, proportion of inadequate prenatal care, fertility rate, Gini index, proportion of elderly people in the population, literacy rate, financing capacity per capita, percentage of people with income below half minimum wage, percentage of urban households with basic sanitation, and proportion of urban households served by garbage collection |
Belciug & Gorunescu (2015) [Belciug S, Gorunescu F. Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation. J Biomed Inform 2015 Feb;53:261-269 [https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(14)00240-8] [CrossRef] [Medline]22] | To propose a bed allocation and financial resource utilization strategy through queuing modeling and evolutionary computation | Bed allocation and financial resource utilization in the geriatric department of a London hospital | Bed inventory, arrival rate, mean service time, patient flow parameters, and holding and penalty cost and other cost considerations |
Gartner & Padman (2015) [Gartner D, Padman R. Improving Hospital-Wide Early Resource Allocation through Machine Learning. Stud Health Technol Inform 2015;216:315-319 [Medline]23] | To evaluate how early determination of diagnosis-related groups can be used for better allocation of scarce hospital resources | Hospital resources, including day resources and overnight resources (beds), validated in a mid-sized hospital near Munich, Germany | Primary and secondary diagnoses, clinical procedures, age, gender, and weight in newborns |
Velez et al (2016) [Velez AFJ, Rosas JDC, Fierro JMM. Geospatial model e-health planning collective intelligence. 2016 Presented at: Third International Conference on eDemocracy & eGovernment (ICEDEG); March 30, 2016-April 01, 2016; Sangolqui, Ecuador [CrossRef]26] | To present an artificial intelligence–based health planning model based on data from geospatial systems | Health care resource distribution in health planning in Ecuador | Geospatial variables based on the social determinants of health and geospatial patterns of territorial distribution in the allocation of equipment, supplies, and health services in relation to the availability, accessibility, and need of the population |
Xu et al (2018) [Xu Y, Liu S, Wang B. Research on Computational Intelligence in Medical Resource Allocation Based on Mass Customization. Journal of Universal Computer Science 2018;24(6):753-77427] | To propose a health resource allocation model based on mass customization to maximize revenue and customization | Allocation of doctors and other medical resources in a public hospital system in China | Distribution of medical stations, professional level of doctors (salary and seniority), patient preferences and illness severity, medical cost, and revenue |
Yousefi et al (2018) [Yousefi M, Yousefi M, Ferreira RPM, Kim JH, Fogliatto FS. Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments. Artif Intell Med 2018 Jan;84:23-33 [CrossRef] [Medline]28] | To present a model based on agent-based simulation, machine learning, and a genetic algorithm for allocation of medical resources in emergency departments | Medical resource allocation in a teaching hospital in the capital of State of Minas Gerais in Brazil | Number of receptionists in the reception area; number of triage nurses in the triage room; number of laboratory technicians in the laboratory and X-ray room; and number of doctors, nurses, and nurse technicians in the suturing yellow zone, orthopedics department, surgical department, and clinical emergency area. |
Zhang et al (2018) [Zhang R, Xing Y, Wang J, Shang X, Zhu X. A Novel Multiattribute Decision-Making Method Based on Point⁻Choquet Aggregation Operators and Its Application in Supporting the Hierarchical Medical Treatment System in China. Int J Environ Res Public Health 2018 Aug 10;15(8):1718 [https://www.mdpi.com/resolver?pii=ijerph15081718] [CrossRef] [Medline]29] | To propose a framework introducing a novel approach to multi-attribute decision-making problems in the picture fuzzy context | Medical resource allocation in the hierarchical medical treatment system in China | Patient diagnostic characteristics and hospital tiers |
McRae et al (2020) [McRae MP, Dapkins IP, Sharif I, Anderman J, Fenyo D, Sinokrot O, et al. Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation. J Med Internet Res 2020 Aug 24;22(8):e22033 [https://www.jmir.org/2020/8/e22033/] [CrossRef] [Medline]24] | To present a clinical decision-support system and mobile app to assist in COVID severity assessment, management, and care | Resource allocation during COVID in New York, with validation data sets from Wuhan, China | Outpatient score (age, gender, diabetes, cardiovascular comorbidities, and systolic blood pressure) and biomarker score (C-reactive protein, procalcitonin, and age) |
Bednarski et al (2021) [Bednarski B, Singh A, Jones W. On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic. J Am Med Inform Assoc 2021 Mar 18;28(4):874-878 [https://europepmc.org/abstract/MED/33295626] [CrossRef] [Medline]21] | To study how reinforcement learning and deep-learning models can facilitate the redistribution of medical equipment during pandemics | Pandemics in the context of COVID | COVID risk factors by region, COVID mortality by region, and current demand for medical equipment |
Summary of the Characteristics of Studies Involving Reviews and Theoretical Discussions
The characteristics of studies involving reviews and theoretical discussions are summarized in Table 3. Of the 22 included studies, 13 were theoretical discussions, qualitative studies, or review studies [Adly AS, Adly AS, Adly MS. Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review. J Med Internet Res 2020 Aug 10;22(8):e19104 [https://www.jmir.org/2020/8/e19104/] [CrossRef] [Medline]31-Zou H, Shu Y, Feng T. How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19. Infect Dis Poverty 2020 Jul 10;9(1):89 [https://idpjournal.biomedcentral.com/articles/10.1186/s40249-020-00714-2] [CrossRef] [Medline]43]. Of those studies, 8 studies were qualitative discussions on the COVID-19 pandemic [Adly AS, Adly AS, Adly MS. Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review. J Med Internet Res 2020 Aug 10;22(8):e19104 [https://www.jmir.org/2020/8/e19104/] [CrossRef] [Medline]31,Bernardo TM, Perez Gutierrez E, Hachborn GF, Forrest RO, Sobkowich KE. Innovating at the human-technology interface in disasters and disease outbreaks. Rev Sci Tech 2020 Aug 01;39(2):491-501 [https://doi.org/10.20506/rst.39.2.3100] [CrossRef] [Medline]33,Huang M, Wang J, Nicholas S, Maitland E, Guo Z. Development, Status Quo, and Challenges to China's Health Informatization During COVID-19: Evaluation and Recommendations. J Med Internet Res 2021 Jun 17;23(6):e27345 [https://www.jmir.org/2021/6/e27345/] [CrossRef] [Medline]34,Laudanski K, Shea G, DiMeglio M, Rastrepo M, Solomon C. What Can COVID-19 Teach Us about Using AI in Pandemics? Healthcare (Basel) 2020 Dec 01;8(4):527 [https://www.mdpi.com/resolver?pii=healthcare8040527] [CrossRef] [Medline]36,Neves NMBC, Bitencourt FBCSN, Bitencourt AGV. Ethical dilemmas in COVID-19 times: how to decide who lives and who dies? Rev. Assoc. Med. Bras 2020;66(suppl 2):106-111 [CrossRef]38,Pereira NL, Ahmad F, Byku M, Cummins NW, Morris AA, Owens A, et al. COVID-19: Understanding Inter-Individual Variability and Implications for Precision Medicine. Mayo Clin Proc 2021 Feb;96(2):446-463 [https://europepmc.org/abstract/MED/33549263] [CrossRef] [Medline]39,Röösli E, Rice B, Hernandez-Boussard T. Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19. J Am Med Inform Assoc 2021 Jan 15;28(1):190-192 [https://europepmc.org/abstract/MED/32805004] [CrossRef] [Medline]41,Zou H, Shu Y, Feng T. How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19. Infect Dis Poverty 2020 Jul 10;9(1):89 [https://idpjournal.biomedcentral.com/articles/10.1186/s40249-020-00714-2] [CrossRef] [Medline]43], with 2 in a Chinese context [Huang M, Wang J, Nicholas S, Maitland E, Guo Z. Development, Status Quo, and Challenges to China's Health Informatization During COVID-19: Evaluation and Recommendations. J Med Internet Res 2021 Jun 17;23(6):e27345 [https://www.jmir.org/2021/6/e27345/] [CrossRef] [Medline]34,Zou H, Shu Y, Feng T. How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19. Infect Dis Poverty 2020 Jul 10;9(1):89 [https://idpjournal.biomedcentral.com/articles/10.1186/s40249-020-00714-2] [CrossRef] [Medline]43] and the rest in a global situation. The remaining 5 studies focused on other situations, with 1 focusing on resource allocation in intensive care units and hospital stay [Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring Fairness in Machine Learning to Advance Health Equity. Ann Intern Med 2018 Dec 18;169(12):866-872 [https://europepmc.org/abstract/MED/30508424] [CrossRef] [Medline]40], 1 on disease outbreaks and disasters [Bernardo TM, Perez Gutierrez E, Hachborn GF, Forrest RO, Sobkowich KE. Innovating at the human-technology interface in disasters and disease outbreaks. Rev Sci Tech 2020 Aug 01;39(2):491-501 [https://doi.org/10.20506/rst.39.2.3100] [CrossRef] [Medline]33], 1 on diabetic retinopathy screening [Xie Y, Gunasekeran DV, Balaskas K, Keane PA, Sim DA, Bachmann LM, et al. Health Economic and Safety Considerations for Artificial Intelligence Applications in Diabetic Retinopathy Screening. Transl Vis Sci Technol 2020 Apr;9(2):22 [https://europepmc.org/abstract/MED/32818083] [CrossRef] [Medline]42], 1 on human resource allocation in health systems [Jain M, Goel A, Sinha S, Dhir S. Employability implications of artificial intelligence in healthcare ecosystem: responding with readiness. FS 2021 Jan 04;23(1):73-94 [CrossRef]35], and 1 on medical information digitalization [Lu W, Tsai I, Wang K, Tang T, Li K, Ke Y, et al. Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization. Sustainability 2021 Jul 14;13(14):7888 [CrossRef]37].
Reference | Objective | Resource allocation situation | Reviewed/discussed methods for the application of AIa during the COVID-19 pandemic |
Rajkomar et al (2018) [Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring Fairness in Machine Learning to Advance Health Equity. Ann Intern Med 2018 Dec 18;169(12):866-872 [https://europepmc.org/abstract/MED/30508424] [CrossRef] [Medline]40] | To explore how model design, biases in data, and interactions of model predictions with clinicians and patients exacerbate health inequalities | Intensive care unit and in-hospital stay length |
|
Laudanski et al (2020) [Laudanski K, Shea G, DiMeglio M, Rastrepo M, Solomon C. What Can COVID-19 Teach Us about Using AI in Pandemics? Healthcare (Basel) 2020 Dec 01;8(4):527 [https://www.mdpi.com/resolver?pii=healthcare8040527] [CrossRef] [Medline]36] | To analyze the applications of AI during COVID using the WHOb framework of pandemic evolution | Global COVID-19 pandemic |
|
Adly et al (2020) [Adly AS, Adly AS, Adly MS. Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review. J Med Internet Res 2020 Aug 10;22(8):e19104 [https://www.jmir.org/2020/8/e19104/] [CrossRef] [Medline]31] | To discuss the potential of using AI to prevent and control COVID | Global COVID-19 pandemic |
|
Bernardo et al (2020) [Bernardo TM, Perez Gutierrez E, Hachborn GF, Forrest RO, Sobkowich KE. Innovating at the human-technology interface in disasters and disease outbreaks. Rev Sci Tech 2020 Aug 01;39(2):491-501 [https://doi.org/10.20506/rst.39.2.3100] [CrossRef] [Medline]33] | To present approaches for using technology to facilitate resource distribution in disasters and outbreaks | Disasters and disease outbreaks |
|
Neves et al (2020) [Neves NMBC, Bitencourt FBCSN, Bitencourt AGV. Ethical dilemmas in COVID-19 times: how to decide who lives and who dies? Rev. Assoc. Med. Bras 2020;66(suppl 2):106-111 [CrossRef]38] | To discuss the basic principles of medical resource allocation choices during COVID | Global COVID-19 pandemic |
|
Xie et al (2020) [Xie Y, Gunasekeran DV, Balaskas K, Keane PA, Sim DA, Bachmann LM, et al. Health Economic and Safety Considerations for Artificial Intelligence Applications in Diabetic Retinopathy Screening. Transl Vis Sci Technol 2020 Apr;9(2):22 [https://europepmc.org/abstract/MED/32818083] [CrossRef] [Medline]42] | To present an overview of the application of AI technology in ophthalmology, with a focus on deep-learning systems | Diabetic retinopathy screening |
|
Zou et al (2020) [Zou H, Shu Y, Feng T. How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19. Infect Dis Poverty 2020 Jul 10;9(1):89 [https://idpjournal.biomedcentral.com/articles/10.1186/s40249-020-00714-2] [CrossRef] [Medline]43] | To present the COVID response of Shenzhen, China and discuss the potential of a successful model for COVID prevention and control | COVID-19 pandemic in Shenzhen, China |
|
Basit et al (2021) [Basit MA, Lehmann CU, Medford RJ. Managing Pandemics with Health Informatics: Successes and Challenges. Yearb Med Inform 2021 Aug 21;30(1):17-25 [http://www.thieme-connect.com/DOI/DOI?10.1055/s-0041-1726478] [CrossRef] [Medline]32] | To discuss the data sharing and collection process and the ethical considerations around pandemic data | Global COVID-19 pandemic |
|
Huang et al (2021) [Huang M, Wang J, Nicholas S, Maitland E, Guo Z. Development, Status Quo, and Challenges to China's Health Informatization During COVID-19: Evaluation and Recommendations. J Med Internet Res 2021 Jun 17;23(6):e27345 [https://www.jmir.org/2021/6/e27345/] [CrossRef] [Medline]34] | To investigate China’s health informatization, especially during the COVID-19 pandemic | COVID-19 pandemic in China |
|
Jain et al (2021) [Jain M, Goel A, Sinha S, Dhir S. Employability implications of artificial intelligence in healthcare ecosystem: responding with readiness. FS 2021 Jan 04;23(1):73-94 [CrossRef]35] | To discuss the implications of AI for employability by analyzing issues in the health care sector | Human resources in health systems |
|
Lu et al (2021) [Lu W, Tsai I, Wang K, Tang T, Li K, Ke Y, et al. Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization. Sustainability 2021 Jul 14;13(14):7888 [CrossRef]37] | To establish barriers that affect medical information digitalization innovation and development through interviews and a literature review | Medical information digitalization |
|
Pereira et al (2021) [Pereira NL, Ahmad F, Byku M, Cummins NW, Morris AA, Owens A, et al. COVID-19: Understanding Inter-Individual Variability and Implications for Precision Medicine. Mayo Clin Proc 2021 Feb;96(2):446-463 [https://europepmc.org/abstract/MED/33549263] [CrossRef] [Medline]39] | To present interindividual variability and the roles it plays in the variability of COVID presentation and susceptibility. | Global COVID-19 pandemic |
|
Röösli et al (2021) [Röösli E, Rice B, Hernandez-Boussard T. Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19. J Am Med Inform Assoc 2021 Jan 15;28(1):190-192 [https://europepmc.org/abstract/MED/32805004] [CrossRef] [Medline]41] | To discuss possible bias in the application of AI during the COVID-19 pandemic | Global COVID-19 pandemic |
|
aAI: artificial intelligence.
bWHO: World Health Organization.
Summary of the Policy Implications of the Selected Studies
The policy implications of studies on model development are relevant on 2 levels: (1) health system level [Bednarski B, Singh A, Jones W. On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic. J Am Med Inform Assoc 2021 Mar 18;28(4):874-878 [https://europepmc.org/abstract/MED/33295626] [CrossRef] [Medline]21,Rosas MA, Bezerra AFB, Duarte-Neto PJ. Use of artificial neural networks in applying methodology for allocating health resources. Rev Saude Publica 2013 Feb;47(1):128-36; discussion 136 [https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102013000100017&lng=en&nrm=iso&tlng=en] [CrossRef] [Medline]25,Velez AFJ, Rosas JDC, Fierro JMM. Geospatial model e-health planning collective intelligence. 2016 Presented at: Third International Conference on eDemocracy & eGovernment (ICEDEG); March 30, 2016-April 01, 2016; Sangolqui, Ecuador [CrossRef]26,Zhang R, Xing Y, Wang J, Shang X, Zhu X. A Novel Multiattribute Decision-Making Method Based on Point⁻Choquet Aggregation Operators and Its Application in Supporting the Hierarchical Medical Treatment System in China. Int J Environ Res Public Health 2018 Aug 10;15(8):1718 [https://www.mdpi.com/resolver?pii=ijerph15081718] [CrossRef] [Medline]29] and (2) hospital level [Belciug S, Gorunescu F. Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation. J Biomed Inform 2015 Feb;53:261-269 [https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(14)00240-8] [CrossRef] [Medline]22-McRae MP, Dapkins IP, Sharif I, Anderman J, Fenyo D, Sinokrot O, et al. Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation. J Med Internet Res 2020 Aug 24;22(8):e22033 [https://www.jmir.org/2020/8/e22033/] [CrossRef] [Medline]24,Xu Y, Liu S, Wang B. Research on Computational Intelligence in Medical Resource Allocation Based on Mass Customization. Journal of Universal Computer Science 2018;24(6):753-77427,Yousefi M, Yousefi M, Ferreira RPM, Kim JH, Fogliatto FS. Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments. Artif Intell Med 2018 Jan;84:23-33 [CrossRef] [Medline]28], corresponding to situations where the models were applied. Detailed policy implications of the included studies on model development are summarized in Table 4. The qualitative and review studies focused largely on 2 issues: (1) how AI can promote the efficacy of resource allocation [Bednarski B, Singh A, Jones W. On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic. J Am Med Inform Assoc 2021 Mar 18;28(4):874-878 [https://europepmc.org/abstract/MED/33295626] [CrossRef] [Medline]21,Basit MA, Lehmann CU, Medford RJ. Managing Pandemics with Health Informatics: Successes and Challenges. Yearb Med Inform 2021 Aug 21;30(1):17-25 [http://www.thieme-connect.com/DOI/DOI?10.1055/s-0041-1726478] [CrossRef] [Medline]32,Huang M, Wang J, Nicholas S, Maitland E, Guo Z. Development, Status Quo, and Challenges to China's Health Informatization During COVID-19: Evaluation and Recommendations. J Med Internet Res 2021 Jun 17;23(6):e27345 [https://www.jmir.org/2021/6/e27345/] [CrossRef] [Medline]34-Lu W, Tsai I, Wang K, Tang T, Li K, Ke Y, et al. Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization. Sustainability 2021 Jul 14;13(14):7888 [CrossRef]37,Pereira NL, Ahmad F, Byku M, Cummins NW, Morris AA, Owens A, et al. COVID-19: Understanding Inter-Individual Variability and Implications for Precision Medicine. Mayo Clin Proc 2021 Feb;96(2):446-463 [https://europepmc.org/abstract/MED/33549263] [CrossRef] [Medline]39,Xie Y, Gunasekeran DV, Balaskas K, Keane PA, Sim DA, Bachmann LM, et al. Health Economic and Safety Considerations for Artificial Intelligence Applications in Diabetic Retinopathy Screening. Transl Vis Sci Technol 2020 Apr;9(2):22 [https://europepmc.org/abstract/MED/32818083] [CrossRef] [Medline]42,Zou H, Shu Y, Feng T. How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19. Infect Dis Poverty 2020 Jul 10;9(1):89 [https://idpjournal.biomedcentral.com/articles/10.1186/s40249-020-00714-2] [CrossRef] [Medline]43] and (2) the ethics and equality issues associated with using AI systems [Neves NMBC, Bitencourt FBCSN, Bitencourt AGV. Ethical dilemmas in COVID-19 times: how to decide who lives and who dies? Rev. Assoc. Med. Bras 2020;66(suppl 2):106-111 [CrossRef]38,Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring Fairness in Machine Learning to Advance Health Equity. Ann Intern Med 2018 Dec 18;169(12):866-872 [https://europepmc.org/abstract/MED/30508424] [CrossRef] [Medline]40,Röösli E, Rice B, Hernandez-Boussard T. Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19. J Am Med Inform Assoc 2021 Jan 15;28(1):190-192 [https://europepmc.org/abstract/MED/32805004] [CrossRef] [Medline]41]. One study highlighted the lack of AI studies on resource distribution during COVID-19 [Adly AS, Adly AS, Adly MS. Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review. J Med Internet Res 2020 Aug 10;22(8):e19104 [https://www.jmir.org/2020/8/e19104/] [CrossRef] [Medline]31].
Table 5 summarizes the policy implications of these studies.
Reference | Policy relevance |
Rosas et al (2013) [Rosas MA, Bezerra AFB, Duarte-Neto PJ. Use of artificial neural networks in applying methodology for allocating health resources. Rev Saude Publica 2013 Feb;47(1):128-36; discussion 136 [https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102013000100017&lng=en&nrm=iso&tlng=en] [CrossRef] [Medline]25] |
|
Belciug & Gorunescu (2015) [Belciug S, Gorunescu F. Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation. J Biomed Inform 2015 Feb;53:261-269 [https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(14)00240-8] [CrossRef] [Medline]22] |
|
Gartner & Padman (2015) [Gartner D, Padman R. Improving Hospital-Wide Early Resource Allocation through Machine Learning. Stud Health Technol Inform 2015;216:315-319 [Medline]23] |
|
Velez et al (2016) [Velez AFJ, Rosas JDC, Fierro JMM. Geospatial model e-health planning collective intelligence. 2016 Presented at: Third International Conference on eDemocracy & eGovernment (ICEDEG); March 30, 2016-April 01, 2016; Sangolqui, Ecuador [CrossRef]26] |
|
Xu et al (2018) [Xu Y, Liu S, Wang B. Research on Computational Intelligence in Medical Resource Allocation Based on Mass Customization. Journal of Universal Computer Science 2018;24(6):753-77427] |
|
Yousefi et al (2018) [Yousefi M, Yousefi M, Ferreira RPM, Kim JH, Fogliatto FS. Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments. Artif Intell Med 2018 Jan;84:23-33 [CrossRef] [Medline]28] |
|
Zhang et al (2018) [Zhang R, Xing Y, Wang J, Shang X, Zhu X. A Novel Multiattribute Decision-Making Method Based on Point⁻Choquet Aggregation Operators and Its Application in Supporting the Hierarchical Medical Treatment System in China. Int J Environ Res Public Health 2018 Aug 10;15(8):1718 [https://www.mdpi.com/resolver?pii=ijerph15081718] [CrossRef] [Medline]29] |
|
McRae et al (2020) [McRae MP, Dapkins IP, Sharif I, Anderman J, Fenyo D, Sinokrot O, et al. Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation. J Med Internet Res 2020 Aug 24;22(8):e22033 [https://www.jmir.org/2020/8/e22033/] [CrossRef] [Medline]24] |
|
Bednarski et al (2021) [Bednarski B, Singh A, Jones W. On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic. J Am Med Inform Assoc 2021 Mar 18;28(4):874-878 [https://europepmc.org/abstract/MED/33295626] [CrossRef] [Medline]21] |
|
Reference | Policy relevance |
Rajkomar et al (2018) [Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring Fairness in Machine Learning to Advance Health Equity. Ann Intern Med 2018 Dec 18;169(12):866-872 [https://europepmc.org/abstract/MED/30508424] [CrossRef] [Medline]40] |
|
Laudanski et al (2020) [Laudanski K, Shea G, DiMeglio M, Rastrepo M, Solomon C. What Can COVID-19 Teach Us about Using AI in Pandemics? Healthcare (Basel) 2020 Dec 01;8(4):527 [https://www.mdpi.com/resolver?pii=healthcare8040527] [CrossRef] [Medline]36] |
|
Adly et al (2020) [Adly AS, Adly AS, Adly MS. Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review. J Med Internet Res 2020 Aug 10;22(8):e19104 [https://www.jmir.org/2020/8/e19104/] [CrossRef] [Medline]31] |
|
Bernardo et al (2020) [Bernardo TM, Perez Gutierrez E, Hachborn GF, Forrest RO, Sobkowich KE. Innovating at the human-technology interface in disasters and disease outbreaks. Rev Sci Tech 2020 Aug 01;39(2):491-501 [https://doi.org/10.20506/rst.39.2.3100] [CrossRef] [Medline]33] |
|
Neves et al (2020) [Neves NMBC, Bitencourt FBCSN, Bitencourt AGV. Ethical dilemmas in COVID-19 times: how to decide who lives and who dies? Rev. Assoc. Med. Bras 2020;66(suppl 2):106-111 [CrossRef]38] |
|
Xie et al (2020) [Xie Y, Gunasekeran DV, Balaskas K, Keane PA, Sim DA, Bachmann LM, et al. Health Economic and Safety Considerations for Artificial Intelligence Applications in Diabetic Retinopathy Screening. Transl Vis Sci Technol 2020 Apr;9(2):22 [https://europepmc.org/abstract/MED/32818083] [CrossRef] [Medline]42] |
|
Zou et al (2020) [Zou H, Shu Y, Feng T. How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19. Infect Dis Poverty 2020 Jul 10;9(1):89 [https://idpjournal.biomedcentral.com/articles/10.1186/s40249-020-00714-2] [CrossRef] [Medline]43] |
|
Basit et al (2021) [Basit MA, Lehmann CU, Medford RJ. Managing Pandemics with Health Informatics: Successes and Challenges. Yearb Med Inform 2021 Aug 21;30(1):17-25 [http://www.thieme-connect.com/DOI/DOI?10.1055/s-0041-1726478] [CrossRef] [Medline]32] |
|
Huang et al (2021) [Huang M, Wang J, Nicholas S, Maitland E, Guo Z. Development, Status Quo, and Challenges to China's Health Informatization During COVID-19: Evaluation and Recommendations. J Med Internet Res 2021 Jun 17;23(6):e27345 [https://www.jmir.org/2021/6/e27345/] [CrossRef] [Medline]34] |
|
Jain et al (2021) [Jain M, Goel A, Sinha S, Dhir S. Employability implications of artificial intelligence in healthcare ecosystem: responding with readiness. FS 2021 Jan 04;23(1):73-94 [CrossRef]35] |
|
Lu et al (2021) [Lu W, Tsai I, Wang K, Tang T, Li K, Ke Y, et al. Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization. Sustainability 2021 Jul 14;13(14):7888 [CrossRef]37] |
|
Pereira et al (2021) [Pereira NL, Ahmad F, Byku M, Cummins NW, Morris AA, Owens A, et al. COVID-19: Understanding Inter-Individual Variability and Implications for Precision Medicine. Mayo Clin Proc 2021 Feb;96(2):446-463 [https://europepmc.org/abstract/MED/33549263] [CrossRef] [Medline]39] |
|
Röösli et al (2021) [Röösli E, Rice B, Hernandez-Boussard T. Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19. J Am Med Inform Assoc 2021 Jan 15;28(1):190-192 [https://europepmc.org/abstract/MED/32805004] [CrossRef] [Medline]41] |
|
aAI: artificial intelligence.
Case Study Comparison: China and Brazil
China and Brazil are both developing countries with a similar per capita gross domestic product (China: US $10,435 and Brazil: US $6797) [GDP per capita (current US$). World Bank. URL: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD [accessed 2022-03-16] 44]. During the COVID-19 pandemic, Brazil has had one of the highest national overall cases and mortalities, as well as per capita cases and mortalities, with 29.5 million cases and 656,000 deaths as of March 2022 [Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE. GitHub. URL: https://github.com/CSSEGISandData/COVID-19 [accessed 2022-03-16] 45]. China has had one of the lowest per capita infection rates in the world, with a total of 124,000 cases and 4636 deaths as of March 2022 [Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE. GitHub. URL: https://github.com/CSSEGISandData/COVID-19 [accessed 2022-03-16] 45]. Given the similarity between the 2 countries in economic development and the enormous difference in COVID cases and mortalities, the resource distribution situation in the 2 countries is worth exploring.
Rosas et al [Rosas MA, Bezerra AFB, Duarte-Neto PJ. Use of artificial neural networks in applying methodology for allocating health resources. Rev Saude Publica 2013 Feb;47(1):128-36; discussion 136 [https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102013000100017&lng=en&nrm=iso&tlng=en] [CrossRef] [Medline]25] proposed a financial resource allocation algorithm for the public hospital system in Brazil based on mortality, socioeconomic characteristics, and income inequality. They argued that the choice of input variables for health care policymaking should consider the vulnerability of the population to being manipulated by those who manage public policy, the true representation of the factors of need, exemption from the process of political choice, and the availability of reliable data. The focus of the model was regional economic characteristics.
Zhang et al [Zhang R, Xing Y, Wang J, Shang X, Zhu X. A Novel Multiattribute Decision-Making Method Based on Point⁻Choquet Aggregation Operators and Its Application in Supporting the Hierarchical Medical Treatment System in China. Int J Environ Res Public Health 2018 Aug 10;15(8):1718 [https://www.mdpi.com/resolver?pii=ijerph15081718] [CrossRef] [Medline]29] proposed a model for the allocation of medical resources and tier classification of patients in China’s health system, with the input variables of patient characteristics and hospital tiers, and a focus on differentiation into different tiers based on patients’ disease severity. Xu et al [Xu Y, Liu S, Wang B. Research on Computational Intelligence in Medical Resource Allocation Based on Mass Customization. Journal of Universal Computer Science 2018;24(6):753-77427] proposed a health resource allocation model for the allocation of doctors and other medical resources in a public hospital system in China that considered the distribution of medical stations, the professional level of doctors (salary and seniority), patient preferences and illness severity, medical cost, and revenue.
Overall, the allocation of medical resources based on the models from the 3 studies demonstrated that the key considerations proposed by studies from China were the hospital tier system, the professional level of doctors, the geographical distribution of medical resources, and cost-effectiveness [Xu Y, Liu S, Wang B. Research on Computational Intelligence in Medical Resource Allocation Based on Mass Customization. Journal of Universal Computer Science 2018;24(6):753-77427,Zhang R, Xing Y, Wang J, Shang X, Zhu X. A Novel Multiattribute Decision-Making Method Based on Point⁻Choquet Aggregation Operators and Its Application in Supporting the Hierarchical Medical Treatment System in China. Int J Environ Res Public Health 2018 Aug 10;15(8):1718 [https://www.mdpi.com/resolver?pii=ijerph15081718] [CrossRef] [Medline]29]. However, the model proposed for Brazil focused on the regional economic situation [Rosas MA, Bezerra AFB, Duarte-Neto PJ. Use of artificial neural networks in applying methodology for allocating health resources. Rev Saude Publica 2013 Feb;47(1):128-36; discussion 136 [https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102013000100017&lng=en&nrm=iso&tlng=en] [CrossRef] [Medline]25].
Discussion
Principal Findings
In this review, we compiled evidence on the application of AI in health resource distribution, especially regarding COVID-related policy. After synthesizing 22 articles, we found that AI-based models were proposed at both hospital (secondary care in inpatient settings) and health system (public health) levels and that theoretical discussions and reviews focused on the potential for AI to improve the efficacy of resource distribution and on the ethics of applying AI in health resource distribution. Two major themes emerged from the review. First, we found that AI-informed resource distribution strategies are impactful for health access and equality. Second, the approaches can be categorized ideologically into revisionist and conservative groups.
Impact of an AI-Informed Resource Distribution Strategy on Health Access and Equality
AI and machine learning have considerable potential to improve efficacy in resource distribution, especially during emergencies, such as the COVID-19 pandemic, where quick decisions are required based on evolving situations [Huang M, Wang J, Nicholas S, Maitland E, Guo Z. Development, Status Quo, and Challenges to China's Health Informatization During COVID-19: Evaluation and Recommendations. J Med Internet Res 2021 Jun 17;23(6):e27345 [https://www.jmir.org/2021/6/e27345/] [CrossRef] [Medline]34,Pereira NL, Ahmad F, Byku M, Cummins NW, Morris AA, Owens A, et al. COVID-19: Understanding Inter-Individual Variability and Implications for Precision Medicine. Mayo Clin Proc 2021 Feb;96(2):446-463 [https://europepmc.org/abstract/MED/33549263] [CrossRef] [Medline]39,Zou H, Shu Y, Feng T. How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19. Infect Dis Poverty 2020 Jul 10;9(1):89 [https://idpjournal.biomedcentral.com/articles/10.1186/s40249-020-00714-2] [CrossRef] [Medline]43]. For example, health informatization, particularly digital contact tracing and AI-informed response design, played an instrumental role in responding to COVID in China and helped local governments to improve efficacy in allocating limited resources [Huang M, Wang J, Nicholas S, Maitland E, Guo Z. Development, Status Quo, and Challenges to China's Health Informatization During COVID-19: Evaluation and Recommendations. J Med Internet Res 2021 Jun 17;23(6):e27345 [https://www.jmir.org/2021/6/e27345/] [CrossRef] [Medline]34,Zou H, Shu Y, Feng T. How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19. Infect Dis Poverty 2020 Jul 10;9(1):89 [https://idpjournal.biomedcentral.com/articles/10.1186/s40249-020-00714-2] [CrossRef] [Medline]43]. AI can also be used to interpret diagnostic results and patient characteristics in order to predict disease progression and allocation of medicines, hospital beds, and medical professionals at the hospital level [Bednarski B, Singh A, Jones W. On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic. J Am Med Inform Assoc 2021 Mar 18;28(4):874-878 [https://europepmc.org/abstract/MED/33295626] [CrossRef] [Medline]21,Pereira NL, Ahmad F, Byku M, Cummins NW, Morris AA, Owens A, et al. COVID-19: Understanding Inter-Individual Variability and Implications for Precision Medicine. Mayo Clin Proc 2021 Feb;96(2):446-463 [https://europepmc.org/abstract/MED/33549263] [CrossRef] [Medline]39].
However, very large amounts of data are necessary for AI algorithms to make reliable and evidence-based decisions [Chen M, Decary M. Artificial intelligence in healthcare: An essential guide for health leaders. Healthc Manage Forum 2020 Jan;33(1):10-18 [CrossRef] [Medline]46]. Health care institutions globally must therefore collect, record, and analyze data. This will help policymakers gather novel insights and translate the data into a prompt, equal, coordinated, and more successful response to the next pandemic [Basit MA, Lehmann CU, Medford RJ. Managing Pandemics with Health Informatics: Successes and Challenges. Yearb Med Inform 2021 Aug 21;30(1):17-25 [http://www.thieme-connect.com/DOI/DOI?10.1055/s-0041-1726478] [CrossRef] [Medline]32,Wirtz BW, Weyerer JC, Geyer C. Artificial Intelligence and the Public Sector—Applications and Challenges. International Journal of Public Administration 2018 Jul 24;42(7):596-615 [CrossRef]47]. As such, data collection must be institutionalized. The disparity in data collection capacity potentially exacerbates the gap in decision-making quality between countries [Zhang L, Wang H, Li Q, Zhao M, Zhan Q. Big data and medical research in China. BMJ 2018 Feb 05;360:j5910 [http://www.bmj.com/lookup/pmidlookup?view=long&pmid=29437562] [CrossRef] [Medline]48,Ferreira L, Okano MT, Aguiar F, dos Santos HDCL, Ursini E. A panorama of the implementation of the General Law for the Protection of Personal Data (LGPD) in Brazil: an exploratory survey. 2022 Presented at: 12th Annual Computing and Communication Workshop and Conference (CCWC); January 26-29, 2022; Las Vegas, NV, USA [CrossRef]49]. For example, from the literature, China’s information infrastructure and data-sharing agreements expedited the data-gathering process, a possible consequence of the centralized government system that facilitated gathering data, which in turn made the data set larger and more comprehensive [Zhang L, Wang H, Li Q, Zhao M, Zhan Q. Big data and medical research in China. BMJ 2018 Feb 05;360:j5910 [http://www.bmj.com/lookup/pmidlookup?view=long&pmid=29437562] [CrossRef] [Medline]48]. In contrast, a selected study showed that Brazil’s decentralized government system, with heterogeneous policies on data privacy and data sharing, made the collection and consolidation of data difficult [Ferreira L, Okano MT, Aguiar F, dos Santos HDCL, Ursini E. A panorama of the implementation of the General Law for the Protection of Personal Data (LGPD) in Brazil: an exploratory survey. 2022 Presented at: 12th Annual Computing and Communication Workshop and Conference (CCWC); January 26-29, 2022; Las Vegas, NV, USA [CrossRef]49]. However, caution should be taken in interpreting those results, as there is no evidence that the studies selected here are representative of the real situation in China or Brazil.
The included articles highlighted the importance of distributive justice and transparency in AI model design. The analysis conducted by Rajkomar et al emphasized that machine learning systems should be used proactively to advance health equality [Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring Fairness in Machine Learning to Advance Health Equity. Ann Intern Med 2018 Dec 18;169(12):866-872 [https://europepmc.org/abstract/MED/30508424] [CrossRef] [Medline]40]. They proposed that distributive justice should be a core principle in AI models, including during the design, deployment, and evaluation processes. This perspective would include equality in patient outcomes, performance for every sociodemographic group, and resource allocation for each group. As Neves et al noted, resource allocation by AI and in emergencies should build on basic ethical values, including the equal value of people, instrumental value, and priority for critical situations. Transparency is the key to gaining trust when distributing resources [Neves NMBC, Bitencourt FBCSN, Bitencourt AGV. Ethical dilemmas in COVID-19 times: how to decide who lives and who dies? Rev. Assoc. Med. Bras 2020;66(suppl 2):106-111 [CrossRef]38].
Revisionist and Conservative Approaches in AI-Derived Resource Distribution
The build-up of AI models and implementation plans can be broadly categorized into revisionist and conservative approaches. In revisionist approaches, the models aim to revise the disparity in resource distribution by actively correcting the biases in previous decision-making processes. For example, the models proposed by Rosas et al [Rosas MA, Bezerra AFB, Duarte-Neto PJ. Use of artificial neural networks in applying methodology for allocating health resources. Rev Saude Publica 2013 Feb;47(1):128-36; discussion 136 [https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102013000100017&lng=en&nrm=iso&tlng=en] [CrossRef] [Medline]25] for financial resource allocation in Brazil emphasized consideration of income inequality, vulnerable populations, political choices, and the availability of reliable data. In conservative approaches, the models rely on traditional metrics, including supply and demand, profitability, and, perhaps most notably, previous decisions. This was demonstrated in a proposed model for the allocation of medical resources and tier classification of patients in China’s health system by Zhang et al [Zhang R, Xing Y, Wang J, Shang X, Zhu X. A Novel Multiattribute Decision-Making Method Based on Point⁻Choquet Aggregation Operators and Its Application in Supporting the Hierarchical Medical Treatment System in China. Int J Environ Res Public Health 2018 Aug 10;15(8):1718 [https://www.mdpi.com/resolver?pii=ijerph15081718] [CrossRef] [Medline]29], where the input variables were patients’ characteristics and hospital tiers, and a model suggested by Xu et al [Xu Y, Liu S, Wang B. Research on Computational Intelligence in Medical Resource Allocation Based on Mass Customization. Journal of Universal Computer Science 2018;24(6):753-77427] for the allocation of doctors and other medical resources in a public hospital system in China, where the input variables included the distribution of medical stations, the professional level of doctors, patient preferences and illness severity, medical cost, and revenue. Doctor expertise, patient characteristics, hospital tier, and location are common variables in human decision-making, but AI has the potential to analyze the data more thoroughly.
However, despite the revisionist model proposed by Brazilian academics [Rosas MA, Bezerra AFB, Duarte-Neto PJ. Use of artificial neural networks in applying methodology for allocating health resources. Rev Saude Publica 2013 Feb;47(1):128-36; discussion 136 [https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102013000100017&lng=en&nrm=iso&tlng=en] [CrossRef] [Medline]25], health inequality is a prevailing issue in Brazil across states and social classes, both before [Santos JAF. Classe Social, território e desigualdade de saúde no Brasil. Saude Soc 2018 Jun 25;27(2):556-572 [CrossRef]50] and during the COVID-19 pandemic [Malta M, Murray L, da Silva CMFP, Strathdee SA. Coronavirus in Brazil: The heavy weight of inequality and unsound leadership. EClinicalMedicine 2020 Aug;25:100472 [https://linkinghub.elsevier.com/retrieve/pii/S2589-5370(20)30216-9] [CrossRef] [Medline]51]. Health inequality in Brazil increased across states from 1990 to 2016 [Zou H, Shu Y, Feng T. How Shenzhen, China avoided widespread community transmission: a potential model for successful prevention and control of COVID-19. Infect Dis Poverty 2020 Jul 10;9(1):89 [https://idpjournal.biomedcentral.com/articles/10.1186/s40249-020-00714-2] [CrossRef] [Medline]43]. Comparatively, the health care access and quality index in China was higher than that in Brazil in 2016, suggesting better equality and health care access in China [GBD 2016 Healthcare Access and Quality Collaborators. Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016. Lancet 2018 Jun 02;391(10136):2236-2271 [https://linkinghub.elsevier.com/retrieve/pii/S0140-6736(18)30994-2] [CrossRef] [Medline]52]. However, due to the limitation of the research method, this study could not show the policymaking processes in both countries. From the selected studies alone, we observed that although proposing revisionist AI models to address health inequality should be encouraged, the application and practicality of using those models to inform health policy decisions and improve inequality should also be important considerations for researchers.
Strengths and Limitations
This is one of the first reviews to incorporate all available evidence qualitatively and provide a comprehensive picture of the model development and theoretical discussion on AI in medical resource distribution. Our results contribute to the ongoing discussion of applying AI in medical resource distribution and add novel insights into the social and ethical implications. Nonetheless, this study has several limitations. First, due to the scope of the study, we focused on published journal articles but did not examine policy documents or grey literature. This could have led to incompleteness in the collected information. Further studies could examine policy statements and grey literature to better understand intercountry differences. Second, we included only articles published in English and therefore might have overlooked publications in other languages. Third, there are potential sources of meaningful heterogeneity in this scoping review, including the diverse use of AI technologies, different study designs, and different locations. The analyses in this study could be affected by such heterogeneities. Fourth, this study is a qualitative overview of the general application of AI in health care resource distribution and is exploratory. We did not compare different levels of resource distribution and distinguish various machine learning methods in detail. Further studies are needed to explore and contrast different AI approaches at various resource distribution levels in detail. Lastly, due to the availability of evidence, we only compared studies from China and Brazil. We were only able to compare the differences between the 2 countries based on a few studies, which could not represent the real situation in either country. The comparison should be interpreted as exploratory and demonstrative.
Conclusions
This scoping review synthesized evidence on the application of AI in health resource distribution, particularly during the COVID pandemic. The included studies suggested that AI and machine learning have high potentials to improve efficacy in resource distribution, especially during sudden and evolving situations. A coordinated and continuous data sharing and collecting mechanism is needed for better data input so that AI can make reliable and evidence-based decisions. Various issues, including health inequality, distributive justice, and transparency, should be considered when deploying AI models. Such considerations are required for implementing revisionist AI models that can correct distribution inequality in actual policy processes.
Conflicts of Interest
None declared.
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Abbreviations
AI: artificial intelligence |
Edited by B Malin, K El Emam; submitted 31.03.22; peer-reviewed by W Perveen, H Mehdizadeh, D Vurmaz, ER Khalilian, D Gartner; comments to author 14.06.22; revised version received 29.12.22; accepted 06.01.23; published 30.01.23
Copyright©Hao Wu, Xiaoyu Lu, Hanyu Wang. Originally published in JMIR AI (https://ai.jmir.org), 30.01.2023.
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