Impact of industry 4.0 on healthcare systems of low- and middle- income countries: a systematic review

Abstract

Purpose

A growing body of empirical research has emerged, focused on leveraging Industry 4.0 technologies to develop and optimise systems within various operational contexts, including healthcare delivery. However, even though a significant number of studies have been published on application of digital technologies in enhancing delivery and health outcomes of health systems, systematic studies that review how extensively these technologies have been applied within a low- and middle-income economies’ context remain scarce in the literature. This work attempts to close that gap by investigating the impact of industry 4.0 on healthcare systems in emerging economies.

Methods

The study follows a systematic review approach and uses PRISMA guidelines to conduct the research and synthesise its findings. A final sample of 72 articles is selected for in-depth review following a systematic screening from an initial list of 597 results.

Results

The study successfully synthesises the latest research in the subject area and reveals that, hitherto, approaches to use of digital tools have been fragmented and thus unable to provide holistic optimisation solutions for healthcare systems in low-resource settings. The analysis exposes a heavy skew towards adoption of mobile health and telemedicine technologies, with conspicuous research gaps in the use of augmented reality, additive manufacturing as well as simulation and digital twin technologies.

Conclusions

The study provides researchers, health-care practitioners and systems engineers with knowledge on the state-of-the-art in healthcare systems optimisation and points out research gaps that may be addressed through future empirical studies.

Introduction

One of the World Health Organisation’s sustainable development goals, of attaining global universal health coverage (WHO SDG 3.8), requires a strengthening of health systems in all countries [1]. However, despite relatively high universal health coverage (UHC) indices [2], many low- and middle-income countries continue to suffer healthcare services that remain largely inaccessible to a majority of their communities due to high costs and a shortage of facilities. Effective delivery of healthcare services in emerging economies is hindered by several major challenges that include inadequate facilities, limited financial resources, inefficient planning, shortage of skilled personnel, sub-optimal medical supply chains and non-reliability of energy sources [3, 4].

As early as 2010, the major challenges facing healthcare systems were already well-researched and documented. A 2010 report prepared by [5] discussed in detail the critical challenges that health care systems were facing, noting that current systems were costly despite offering only little value, and were not robust enough to adequately respond to any sudden changes within the operating environment. The report further pointed out that, due to the high level of complexity, optimisation of healthcare systems required a multi-dimensional, multi-level stakeholders approach, which had to include development of consumer-facing health IT solutions, real-time data collection and integration, as well as the building of predictive and prescriptive models based on collaborative frameworks between public and private stakeholders [5]. Implementation of these digital solutions are not a one-size-fits-all however, as the challenges to tackle differ markedly between developed economies and resource-poor settings.

Healthcare systems in low- and middle-income countries (LMICs) differ from those in developed countries in several ways, including in negative aspects such as having insufficiently developed infrastructure, constrained funding, skills’ scarcity; and in positive aspects such as having a stronger presence of naturopathic healthcare in addition to conventional medicine, and less need for hospice, old age and chronic-care institutions [4]. The challenges in LMICs also include the relative inaccessibility of populations to healthcare workers during disease outbreaks, haphazard communal and peri-urban settlements set-ups, as well as a reliance on out-of-pocket payments for the delivery of health services [4]. There is also a wider gap in the quality of care between private institutions and public health facilities in LMICs, unlike in developed countries where the quality gap is either non-existent or insignificant [3, 6]

To date, significant progress has been made in addressing some of the challenges identified by [5], particularly in the developed world. The rapid development and roll-out of Industry 4.0 (4IR) technologies has brought with it opportunities to address many of the long-standing challenges that have been obstacles to the effective delivery of healthcare services and the attainment of universal health coverage goals. In developed economies in particular, there has been a marked growth in the volume of research focusing on application of 4IR technologies in developing more reliable business models as well as in optimising systems in various operational environments, including in manufacturing, transportation, supply chain and healthcare [4]. However, still relatively little is known about the extent to which digital technologies have been applied in optimising healthcare delivery systems in LMICs and the challenges thereof.

This study uses the systematic review approach in order to attempt to answer the following research questions:

1. What is the state of research on the application of 4IR technologies in optimising service delivery in hospitals and other healthcare facilities?

2. How is 4IR technologies currently improving health outcomes in LMICs?

3. What major challenges do health systems of LMICs still face that application of specific 4IR technologies may help tackle?

These central research questions are further broken down into the following specific research goals. The study’s objectives are to:

• (i)Establish the current state of research in the general use of industry 4.0 technologies in healthcare management systems of low-income economies.
• (ii)Identify the 4IR technologies most adopted in LMICs healthcare systems and their main areas of application.
• (iii)Identify, for further research, the knowledge gaps and opportunities that may still exist in the digital optimisation of healthcare systems.

The authors achieve the preceding objectives by carrying out a systematic evaluation of relevant high-quality literature and then synthesising the findings in order to establish the current state of research in the use of 4IR technologies in the optimisation of healthcare management systems within developing economies.

The paper is organised into seven sections. This first section introduces the research study; it discusses the background of the study and states the central research questions, as well as the objectives of the study. In Sect. 2, the authors discuss the study’s key concepts such as defining healthcare systems and describing 4IR technologies. In order to give context and background, we also present a brief literature review of previous systematic reviews related to the topic. Section 3 deals with the methodology, detailing the steps followed in conducting this systematic review study, including discussing the PRISMA and the VOSviewer tools and how they are applied. We explain the rationale for why certain databases were chosen ahead of others, as well as how papers were selected for analysis and synthesis. In Sect. 4, we use descriptive statistics to categorise the papers and identify dominant and non-dominant research themes. Section 5 presents a detailed discussion on the review’s findings regarding the key 4IR technologies in the LMICs healthcare context and highlights the most prominent technologies in application. In addition, we assess the overall impact of 4IR on healthcare. We also discuss the challenges that LMIC heath systems face, as well as the key ideas that may help address these challenges. Section 6 concludes the paper with a recap of the study’s contributions, as well as constraints, and future research opportunities.

Literature review

For context, we begin by discussing the two main concepts involved, namely healthcare systems and Industry 4.0 technologies. Healthcare systems, also referred to as Health Systems or as Healthcare Delivery Systems, may comprise of one or more hospitals, clinics, doctors, hospices and other infrastructure that provide communities with services that help people restore, maintain and improve their health and wellness [7]. They may also include less prominent roles such as the prevention and management of communicable disease outbreaks, palliative care, communal health awareness, as well as promotion of environmental and social conditions that facilitate good health [8]. As healthcare systems are complex socio-economic entities, their strengthening often requires combining public and social interest elements with market factors in both setup and management. As a result, they are often operationalised through a combination of private and public institutions [9]. Healthcare systems in LMICs suffer from a less developed infrastructure, inadequate funding, a critical shortage of skilled human resource, as well as a relative scarcity of palliative care institutions [6]. In addition, challenges in LMICs include inaccessibility of populations during outbreaks, communal setup and out-of-pocket payments for health services. There is also a wider gap between private institutions and public facilities in terms of the quality of service, much unlike in developed countries, where this gap is either insignificant or non-existent [10].

The fourth industrial revolution, also known as Industry 4.0, or simply as 4IR, is characterised by the application of technologies that integrate the digital and real worlds in the customisation and optimisation of industrial and business systems and processes [11]. 4IR mainly comprises of nine closely related and interdependent advanced digital technologies, namely: (i) Internet of Things, (ii) Cloud Computing, (iii) Big Data Analytics, also known as Big Data Informatics (iv) Cyber-security and Cyber Physical Systems, (v) Automation and Robotics, (vi) Additive Manufacturing, (vii) Augmented or Virtual Reality, (viii) Horizontal and Vertical System Integration and (ix) Simulation & Digital Twinning [12]. Whereas the majority of researchers regard these nine as the core 4IR technologies, some scholars also consider Artificial Intelligence (AI) as the tenth key technology of 4IR [11], while yet other researchers consider Block-chain to be another core technology [13]. 4IR has its roots in the preceding phases of worldwide industrialisation, namely the first, second and third industrial revolutions [14].

With the two key concepts discussed above now in context, we now identify and refine the area of focus for the intended study. The starting point is to explore existing literature review studies that touch on healthcare and 4IR. Accordingly, we look for published reviews on the subject by performing a title search in SCOPUS, PubMed and Google Scholar sources using the search term: [(healthcare OR health) AND (4IR OR “Industry 4.0” OR “fourth industrial revolution”) AND review]. This search could only yield an aggregate of 11 results; Scopus produced six results and Google Scholar had five, while PubMed did not produce any, of which only eight results were found to be unique and relevant after deduplication. We then studied the full text of these eight articles in detail in order to establish what the major research themes in the field have been to date, and to identify any research gaps.

Of the eight papers, three offer a general discussion on overall application of 4IR technologies within the Healthcare sector [15–17]. Noting an absence of any preceding systematic reviews on the subject, the paper by [15] offers a critical analysis on how 4IR technologies have been applied in the healthcare industry and discuss the impact thereof. However, the work focuses only on the Italian context and thus most of its findings may not fully apply to an LMIC context. The second paper [16] attempts to give a qualitative and descriptive bibliographic review of the 4IR technologies that have been applied within healthcare. Focusing on the interface between 4IR and Brazil’s health sector, the study identifies Big Data Informatics, Cloud Computing, Internet of Things (IoT) or Internet of medical Things (IomT), Health Monitoring Systems and Wearables & Smartphones as the main 4IR technologies that have found application in addressing LMIC-specific healthcare system delivery challenges. The challenges include improvement of healthcare equity and accuracy of diagnoses, as well as mitigation against skills’ shortage. The third study takes a holistic approach in analysing relevant publications on the topic for the period 2012 to 2021 [17]. It classifies previous research into three major categories, namely; Healthcare Systems, Cloud Computing and Digital Technologies. Based on the review, the authors conclude that 4IR has indeed enhanced standards of healthcare in general. However, the paper fails to identify clearly any research gaps. Nor does it discuss the other 4IR technologies, including Horizontal and Vertical Systems Integration, Cyber-security, IoT and Virtual Reality.

The remainder of the articles do not attempt an overall review of all the 4IR technologies as they are applied in healthcare, but rather consider specific use cases. For example, [18] consider the opportunities that come with implementing 4IR to reduce medical waste at hospitals. Their research indicates that although 4IR has had an impact towards more efficient use of resources, more research is required focused on reduction of medical waste. The research by [19] considers the state of the art of 4IR implementations within the healthcare context, and highlights the downside of these implementations insofar as they affect management and ethical issues. The paper investigates the implications of 4IR on health service quality and effectiveness, concluding that 4IR has enhanced and revolutionised the quality and effectiveness of healthcare services. Meanwhile, [20] accumulate what they consider to be the most important research works in the field of 4IR and psychiatric health, and do a comprehensive analysis of these studies. They consider the technical factors and challenges involved in the implementation of Virtual Reality, AI, Machine Learning, IoT and Big Data Analytics in the mental healthcare industry, observing that these technologies have improved the quality and accuracy of diagnoses for patients over time [20], thereby leading to significant clinical benefit. The eighth and final article that we reviewed, by [21], did not actually deal with health systems as such, but with occupational health and safety instead, which is a different research area altogether. As such, it was not considered any further.

We conclude therefore, based on the preceding review, that despite numerous publications of empirical research studies on the application of Industry 4.0 technologies in health systems, there is a paucity of systematic reviews in that research area in general, and particularly so within the context of LMICs. Extant research indicates that the main research questions thus far have been inclined towards identifying which of the 4IR technologies have been applied in Healthcare and on the technicalities of how to go about implementing specific technologies in different cases. However, the approaches have hitherto been rather fragmented, looking at different core applications in contexts that are not easy to co-relate one to the other. Furthermore, no studies have taken the particular context of LMICs and their peculiarities and studied how 4IR technologies may have contributed directly towards enhancing their health outcomes. There are enough substantial differences between LMICs and developed economies to warrant a review study specifically focused on the former.

Research methodology

The authors adopt a systematic literature review (SLR) approach for this study. The SLR methodology uses systematic and explicit methods to identify, select and analyse relevant literature in order to answer a set of clearly formulated research questions, in a manner that guarantees repeatability of findings [22, 23]. The study considers empirical research generated and published in the period between the years 2013 and 2022, and accessible through searches in established digital libraries of academic literature. Scopus, PubMed and ScienceDirect are selected as the main sources for their strong presence of scientific and health related literature. In order to mitigate against the risk of publication bias, Google Scholar is also used as a source in order to include any additional potentially relevant and important scholarly literature that may not have been indexed in any of the three main sources above. The PRISMA guidelines are followed in conducting the research and synthesising its findings. The PRISMA approach is now the most preferred among standard methodologies for doing systematic reviews. The VOSviewer tool is used for text mining and for identifying the major themes emerging from the selected literature. The identified themes are then classified using descriptive statistics, after which a detailed analysis and discussion follow.

Inclusion and exclusion criteria

We aim to include, in this study, all relevant qualitative and quantitative original research published in peer-reviewed journals between 2013 and 2022 that focus on the application of 4IR technologies in the field of healthcare with an impact on health outcomes within LMICs. This period is chosen due to the contemporaneousness of 4IR technologies. Expanding the search period beyond 2013 was judged counterproductive given that the advent of Industry 4.0 dates back only to 2011–2012 [12]. The studies may be journal articles, book chapters or conference papers, as long as they are published in peer-reviewed journals, and in the English language. However, in order to enhance our search strategy’s ability to exclude irrelevant results, also known as the search’s specificity [24], studies whose titles neither mention the words INDUSTRY 4.0 and HEALTH or HEALTHCARE or HOSPITAL, nor contain the words LOW- AND MIDDLE-INCOME ECONOMY or COUNTRY in their title or abstract are not considered. In addition, reviews and meta-analyses are excluded, as they are not primary research. Studies that focus on application of 4IR technologies in the development of disease specific devices are also excluded since the aim is to understand overall impact on healthcare delivery system rather than on technical details of the development of any specific device or gadget. Studies that are to do with 4IR but not specific to the LMIC demographic are also excluded. Finally, in this context, health refers only to the health of people and not of animals. Studies that discuss the impact of 4IR technologies on health of animals are out of the study’s scope.

Information sources

The authors look to Scopus, ScienceDirect and PubMed digital libraries for published scholarly literature in order to identify and select all relevant and eligible studies that help to answer the research questions. We use multiple sources to ensure that the search is as comprehensive as possible, and to maximise the search’s sensitivity, that is, the chance of finding all the important works relevant to the study [24]. In addition to these 3 sources, Google Scholar is also searched in order to make the search as exhaustive as is practicable. All these electronic sources use a variety of advanced tools to track and analyse research output, thereby ensuring that all relevant and important research is found if properly structured search queries are used. Accordingly, it is unlikely that any important published work in the area of study is missed in this study. The last search on all the sources was on 26 February 2022.

Search strategy

First, we develop search phrases based on keywords carefully selected from the research question and the research objectives. This is done by picking keywords and key phrases in the research questions and then identifying equivalent alternative terms that other researchers might have used. The keywords and their synonyms are selected in a way that gives a broad enough coverage that ensures no important studies are missed, while at the same time focused enough that it does not produce results that are too competitive. We consider a competitive search result to occur when the keyword search yields over 1000 results. Table 1 presents the keywords and key phrases used in the search, together with their alternative terms.

Table 1.

Keywords and synonyms used for building the search queries

Key word or phrase	Synonyms
Industry 4.0	4IR, Digital, Smart
Healthcare	Health, healthcare delivery, Hospital
Low- and middle-income	Developing, Emerging
Countries	Economies, Nations

We search for articles in which INDUSTRY 4.0 or its synonyms and HEALTHCARE or its synonyms appear within the article title, and in which LOW- AND MIDDLE-INCOME COUNTRIES or synonymous phrases appear in either the article’s title or abstract. However, when searching Google Scholar, this approach was returning too competitive an outcome. The allintitle operator was therefore employed in a revised search in order to maximise relevance of search results. We also developed the search queries further in order to take into account the inclusion and exclusion criteria as already specified, namely:

• Year of publication: 2013—2022,

• Language: English,

• Excluding: Reviews and other non-empirical studies

Due to slight differences in how the various search engines are configured, the actual search strings are slightly modified to suit the limitations of each source. The specific search queries applied and results count obtained per database source are as shown in Table 2.

Table 2.

Search queries and results count

Source	Search String	Results
SCOPUS	( TITLE ( "industry 4.0" OR digital) AND TITLE ( health OR healthcare OR hospital) AND TITLE-ABS-KEY ( ( ( low AND middle AND income) OR developing OR emerging) AND ( countries OR economies))) After inclusion/exclusion criteria: years 2013–2022, language English, type “j” or “d”	192 137
PubMed	("Industry 4.0"[Title] OR "digital"[Title]) AND ("health"[Title] OR "healthcare"[Title] OR "hospital"[Title]) AND (("low"[Title/Abstract] AND "middle income"[Title/Abstract]) OR "developing"[Title/Abstract] OR "emerging"[Title/Abstract]) AND ("countries"[Title/Abstract] OR "economies"[Title/Abstract]) After inclusion/exclusion criteria: years 2013–2022, language English	111 107
ScienceDirect	Title, abstract, keywords: (industry 4.0 OR digital) AND (health OR healthcare OR hospital) AND ((low middle income) OR developing) AND (countries OR economies) After inclusion/exclusion criteria: years 2013–2022, type research, review articles	262 185
Google Scholar	intitle:"Industry 4.0" OR intitle:digital AND intitle:health OR intitle:healthcare OR intitle:hospital AND intitle:(low and middle income) OR intitle:developing OR intitle:emerging AND intitle:countries OR intitle:economies After inclusion/exclusion criteria: 2013–2022	32 28

Selection process

The initial list of candidate articles after automated exclusion of ineligible results is 457, comprising of 137 results from Scopus, 111 from PubMed, 185 from ScienceDirect and 28 from Google Scholar. We import these 457 results into Zotero software, which allows for automatic detection and removal of duplicates. After removal of duplicates, mainly due to titles that appeared across multiple libraries, and to conference papers later published in journals as journal articles, a list of 327 de-duplicated articles remains. In removing duplicate files, we chose to retain whichever version had more metadata, particularly digital object identifiers (DOIs), abstracts, and full names for authors.

Following the de-duplication step, the authors independently carried out a parallel screening based on the titles, after which the resultant lists were compared and only unanimously retained titles were consolidated into a single list for the next step. In this step, each author considered the article titles and judged their relevance to the research study and objectives. This independent screening eliminated author bias as only those studies independently selected for retention by all authors were considered for further steps in the study. After this step, the number of eligible documents is reduced to 124 articles. Two hundred and three articles are removed, of which four are written in languages other than English, while 53 are either systematic, scoping or narrative reviews and not empirical studies, while the remainder either discuss 4IR technology implementation in healthcare but in a developed economy context or are irrelevant altogether. The next step involved further screening the retained 124 articles based on the abstracts in order to determine relevance of content and context. Subsequently, 52 items are judged to fall outside the objectives of the study and are therefore dropped. A final list of 72 documents is thus retained for full text review. The diagram in Fig. 1 illustrates the screening steps while appendix 1 contains a DOI listing of all the 72 studies included in the full text review.

Fig. 1.

PRISMA flow diagram for identification and screening of studies

Results and analyses

We offer descriptive analyses of the search results and of selected papers in order to identify trends and patterns of how research in the subject area of 4IR and healthcare has evolved over time. On all search results, we analyse the trend of publications over time and the distribution of studies by publisher. On the final list of selected papers, we determine the key research areas, keywords and the impact based on number of citations.

Publications by year

First, all the 327 search results from the various databases (after deduplication) are aggregated and then grouped by year of publication. Figure 2 indicates the trend of publications over the period 2013 to 2022. It is observed that, as Industry 4.0 is a relatively new area of research, let alone its application in healthcare, there is only a few studies published at the onset of this period, between the years 2013 and 2015. However, the rate of publications doubles between 2015 and 2016, and also between 2017 and 2018. There are also significant jumps in number of annual publications in 2020 and 2021. Even though 2022 publications are only for the first 2 months of the year, they give indication that, should the monthly publication rate continue for the remainder of the year, we could project yet another increase as the subject area’s research matures and widens. This sharp upward trend indicates how the research area is growing even more rapidly.

Fig. 2.

Number of publications by year 2013 – 2022

Leading publications in field

We analyse the distribution of published research by journal title or publication title to identify the leading publications in the field. The 327 articles are published amongst 207 journals and proceedings. Of these, 158 publications have just a single published study each, while 22 have two published studies each, 13 publications have three articles each and nine publications have four published articles each. Only five publications have five or more articles each. The leading journals publishing in the field of 4IR and Healthcare are as indicated in Fig. 3.

Fig. 3.

Graph of published studies by journal or proceedings with three publications or more

Research themes

We use VOSviewer to get an overview of the main research themes under 4IR and health and to visualise how they link to each other. We are interested in identifying the key technologies, the health services that they affect, as well as the interrelatedness of the technologies. The main 4IR technologies applied in healthcare systems of LMICs are as indicated in Fig. 4.

Fig. 4.

VOSviewer output showing 4IR technologies and key application areas

The leading technologies are Internet of Things (IoT) and Internet of medical Things (IomT), Telemedicine, Artificial Intelligence, Big Data Informatics, and Cloud Computing. Key areas of application include Health Information Systems (HIS) and Electronic Health Records (EHR), Decision Support Systems, Telemedicine, e-Health and m-Health, healthcare planning, disease surveillance and medical computing.

The VOSviewer output in Fig. 4 also shows that the emergent themes may be grouped into 7 clusters as indicated by the different cluster colours. The related research themes within each cluster are as follows:

• Cluster 1—Algorithms, AI, BDI, cloud computing, EHR, smartphones & internet access

• Cluster 2—Decision support systems, health information systems, interoperability, patient monitoring, intersectoral collaboration, mobile apps

• Cluster 3—Disease surveillance, health services accessibility, online systems, population surveillance, global health

• Cluster 4—data security, government, telemedicine/teleconsultation and videoconferencing, patient participation and self-care

• Cluster 5—health care facilities, health care services, community health workers, healthcare 4.0, hospitals, IomT, resilient health care, public health

• Cluster 6—integrated health care systems, health care planning, medical computing, preventive health services, primary health care

• Cluster 7—personalised medicine, precision medicine, deep learning, remote sensing

Research trend

Industry 4.0 as a concept has its inception in 2011 and some of its earliest applications in healthcare appear in the period up to 2016, in the areas of mobile phone use in communication and disease surveillance in order to improve global health in general, and rural healthcare in particular. Health information systems, electronic health records, decision support systems and m-Health have received more focus in the period 2017 to 2019. In the years 2020 to 2021, most of the studies have focused on AI, data security, big data informatics, healthcare planning, integrated healthcare systems, and deep learning. We observe that the overall trend has been such that in the early years, much focus was on harnessing the new technologies to improve global reach of healthcare services, whereas in the intervening years more emphasis was on improving the capacity to generate, store and retrieve medical data more efficiently. The cutting edge is now more invested in developing advanced digital tools that are able to harness the big medical data and use it to generate intelligent insights that improve not only patient outcomes but also overall efficiency of healthcare systems. The latest research in the period 2021 to 2022 is predominantly in areas such as teleconsultation, community health workers, the building of resilient healthcare models, hospital systems, and health 4.0.

4IR categories

We analysed each of the 72 papers to determine which 4IR technology or technologies it focused on and the results are in Fig. 5 below. Many of the papers deal with multiple 4IR technologies as focus is on area of application rather than on the specific technology applied. It was also observed that while digital technologies such as artificial intelligence, big data informatics, as well as automation and robotics are generally well applied, there is relatively less focus on IoT, Cloud Computing, Block-chain and Cybersecurity, and little to no application, within an LMIC context, of Augmented Reality, Digital twin & simulation, as well as Additive manufacturing. Less than 10 studies, out of 72, deal with any of these latter technologies. The non-application of these latter 4IR technologies leaves an opportunity for addressing some of the peculiar needs of healthcare systems within LMICs, including improving healthcare equity, making up for limited infrastructure, reducing cost of healthcare, as well as mitigating against skills shortages, among other challenges.

Fig. 5.

Number of studies, out of 72, dealing with each 4IR technology

Impact

The citation count of the studies can be used as a measure of the reach and impact of a work of research. In this analysis, we consider the impact of the research after selection of relevant studies, that is, from the final list of 72 papers. The combined citation count of these papers is 854, or 11.86 citations per paper on average. The 22 leading studies, with a citation count of 10 or more, are given in Table 3.

Table 3.

Details of highly cited studies

Year	Author(s)	Title	Publication Title	Keywords or key concepts	Cited
2020	Coccia, Mario	Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence	Technology in Society	Artificial intelligence; Deep learning; Cancer imaging; Emerging technology; Technological paradigm	126
2018	Asi, Yara M.; Williams, Cynthia	The role of digital health in making progress toward Sustainable Development Goal (SDG) 3 in conflict-affected populations	International Journal of Medical Informatics		78
2018	Istepanian, Robert S. H.; Al-Anzi, Turki	m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics	Methods		67
2017	Miah, Shah J.; Hasan, Jahidul; Gammack, John G	On-Cloud Healthcare Clinic: An e-health consultancy approach for remote communities in a developing country	Telematics and Informatics	e-health; Developing countries; Cloud computing; Healthcare model; Patients; Rural healthcare	63
2015	Pahl, Christina et. al	Role of Open EHR as an open source solution for the regional modelling of patient data in obstetrics	Journal of Biomedical Informatics	Knowledge management; Electronic health; Health record; Information system; Semantic interoperability	51
2017	Miah, Shah Jahan et. al	Healthcare support for underserved communities using a mobile social media platform	Information Systems	Decision support; m-Health; Healthcare information	48
2021	Mukesh, Soni; Singh, Dileep Kumar	Block-chain-based security & privacy for biomedical and healthcare information exchange systems	Materials Today: Proceedings	Privacy; Block-chain; Healthcare; Electronic Health Records (EHRs); Electronic Medical Records (EMRs)	33
2019	Gebre-Mariam, Mikael; Bygstad, Bendik	Digitalization mechanisms of health management information systems in developing countries	Information and Organization		29
2020	Wasil, Akash R. et. al	Harnessing single-session interventions to improve adolescent mental health and well-being in India: Development, adaptation, and pilot testing of online single-session interventions in Indian secondary schools	Asian Journal of Psychiatry	Digital mental health	27
2021	Chauhan, Ankur et. al	The interplay of circular economy with industry 4.0 enabled smart city drivers of healthcare waste disposal	Journal of Cleaner Production	Decision making; IoT; Health care; Industry 4.0; Circular economy; Healthcare waste; Multi-criteria decision making; Connected healthcares; Healthcare workers	26
2018	Carswell, Kenneth et. al	Step-by-Step: a new WHO digital mental health intervention for depression	m-Health	developing countries; telemedicine; Mental health; digital divide	24
2017	Flahault, A. et. al	Precision global health in the digital age	Swiss Medical Weekly	mobile phone; Internet; global health; health care delivery system; Public Health; Precision Medicine; medical informatics; machine learning; remote sensing; personalized medicine; disease surveillance	19
2021	Schiavone, Francesco et. al	Digital business models and ridesharing for value co-creation in healthcare: A multi-stakeholder ecosystem analysis	Technological Forecasting and Social Change	Healthcare services; Digital business model; Multi-stakeholder ecosystems; Ridesharing	19
2018	Konduri, Niranjan et. al	Digital health technologies to support access to medicines and pharmaceutical services in the achievement of sustainable development goals	Digital Health	digital health; e-Health; low- and middle-income countries; Access to medicines; decision-making	14
2016	Thapa, Amit; Kc, Bidur; Shakya, Bikram	Cost Effective Use of Free-to-Use Apps in Neurosurgery (FAN) in Developing Countries: From Clinical Decision Making to Educational Courses, Strengthening Health Care Delivery	World Neurosurgery	Telemedicine; Communication; Smart phones; Free-to-use apps; Neurosurgery	14
2018	Lee, Elizabeth C. et. al	Deploying digital health data to optimize influenza surveillance at national and local scales	PLoS computational biology		13
2019	Malhotra, Savita; Chakrabarti, Subho; Shah, Ruchita	A model for digital mental healthcare: Its usefulness and potential for service delivery in low- and middle-income countries	Indian Journal of Psychiatry	mental health care; telepsychiatry; clinical decision support system; middle income country; online system; secondary health care	12
2018	Shah, Hirav; Sengupta, Amit	Designing mobile based computational support for low-literate community health workers	International Journal of Human–Computer Studies	Health care; Rural; Data entry; Digital signature; Mobile user interface	12
2021	Tortorella, Guilherme Luz et. al	Impacts of Healthcare 4.0 digital technologies on the resilience of hospitals	Technological Forecasting and Social Change		12
2018	Ganiga, Raghavendra et. al	Private cloud solution for Securing and Managing Patient Data in Rural Healthcare System	Procedia Computer Science	Healthcare; cloud encryption; Private; Rural	11
2020	Muinga, Naomi et. al	Digital health Systems in Kenyan Public Hospitals: a mixed-methods survey	BMC medical informatics and decision making	Digital health; electronic health record; medical informatics; health; government; public hospital; Health information systems	10
2018	Sahney, Ruby; Sharma, Mukesh	Electronic health records: A general overview	Current Medicine Research and Practice	Electronic health record; Security; Hospital information system	10

Discussion

Current state of research

Digital technologies have found wide and innovative application in healthcare, including within low-resource settings. Due to the nature of challenges in the key areas of application, the most adopted and leveraged 4IR technologies within healthcare in the period 2013 to 2022, are AI, IomT, big data informatics, remote sensing, deep learning, cloud computing and cybersecurity. Leading areas of industry 4.0 application in healthcare within the LMIC context include the use of the technologies listed above in the following areas: EHR, telemedicine and teleconsultation, healthcare planning, decision support systems, integrated healthcare systems, resilient health systems, medical computing, disease surveillance, rural healthcare, personalised and precision medicine as well as preventive health services. Since many of these areas of application extend the digital technologies rather than fit snugly into existing 4IR categories, this intersection between 4IR technologies and customised human health application may be referred to more appropriately as Health 4.0 [25]. Technologies that have had limited application are Augmented/Virtual Reality, Simulation and Digital Twin, Block-chain, and Cloud Computing. The Additive Manufacturing technology has largely remained irrelevant to healthcare so far (Fig. 5).

The cutting edge of the research has predominantly been in the application of technologies such as IomT, big data, AI and deep learning in areas such as tele-consultation, the capacitation of community health workers, as well as the building of resilient healthcare models and hospital systems. In particular, IomT and remote sensing have been found to be helpful in circumventing challenges associated with physical appointments. This has, in turn, lessened the burden on the already severely limited equipment and insufficiently developed infrastructure. Along with electronic health records (EHR) and eHealth, IomT and remote sensing lead to improved disease surveillance and hence proactive health policies and interventions for the prevention and management of disease outbreaks. Adoption of mHealth and telemedicine technologies has also contributed to this reduction of demand on an already constrained infrastructure such as hospitals, hospices and other chronic care institutions, as more patients can now be monitored virtually and treated from their homes. Another positive for LMICs from this development has been the increased capacity of the healthcare system as the number of patients that can be assisted within a given period is higher than what is possible with the traditional hospital or healthcare model.

In addition to helping address the challenges of a constrained infrastructure and low capacity, digital technologies have also helped to improve access to healthcare services for populations. For example, electronic health records (EHR), remote sensing, and disease surveillance technologies have been demonstrated to help improve accessibility of healthcare by delivering healthcare to rural populations despite poor road networks and non-streamlined settlement patterns [26]. The most critical infrastructure required in this regard has been mobile network coverage and power supply. Finally, the applicability of deep learning in areas like cancer imaging has been demonstrated to be capable of improving the quality of diagnosis and treatment [27]. This is an important area of application for digital technologies since cancer prevalence in LMICs has seen a recent rise. However, further studies are still required in order to prove the efficiency of deep learning technologies in improving overall regional and national healthcare sectors [27].

Hospital and healthcare systems optimisation

Different needs have inspired adoption of digital health systems in LMICs hospitals, from the need for financial accountability, to managing clinical data, human resources management, facilities management, inventory management, and etcetera. However, lack of interoperability of these systems has remained a significant barrier to a maximisation of the benefits of adopting digital systems in healthcare [28]. Accordingly, studies that explore how interoperability may be enhanced have also gained traction. Holistic hospital management systems that combine patient registration, billing, outpatient clinical data, pharmacy and laboratory, finance, HR, and inpatient admin are being adopted [28]. Interoperability remains a significant challenge for LMICs however, in addition to system support, inadequate infrastructure, training as well as connectivity.

Within the context of hospitals, resilient healthcare systems have been developed to be capable of anticipating system changes and responding intelligently, as well as to be capable of automated monitoring and learning from them. Findings indicate at least five digital Health 4.0 technologies that have had a strong impact on a healthcare system’s resilience abilities, namely: teleconsultations, real time health care planning, digital non-invasive care, integrated medical emergency support, and collaborative sharing of patient health records [29]. Resilient healthcare systems reduce reliance on human skill and offer expanded opportunities for performance. Another model that has been demonstrated to be a cost effective alternative to the traditional hospital model is the Home Hospitalisation (HH) model, where remote monitoring systems are used and where tailored educational content is required to ensure patient safety [30].

Electronic health records are also playing a role in the development of optimised healthcare systems or of digital hospital models. An example is the development of a multi-centre digital trauma registry across 10 public hospitals in Malawi, a low-resource setting [31]. The project demonstrated feasibility of implementing a large-scale registry operation. Electronic health records and big data informatics form the backbone of robust Health information systems. Current research efforts in the past two years have been focused towards leveraging health 4.0 to build resilient health systems for integrated networks of public and private hospitals. Health 4.0 deals more with technologies at the interface between digital technologies and human health.

Opportunities and challenges

The 4IR technologies that have not yet been fully exploited within healthcare, such as additive manufacturing, augmented reality, as well as digital twin and simulation, represent an opportunity for solving various healthcare challenges within LMICs. For example, additive manufacturing, also known as rapid prototyping, may find application in the development of cost-effective prosthetic limbs, particularly needful in areas ravaged by wars and civil unrest. It may also be exploited for the low cost manufacture of medical devices. Another opportunity lies in the use of simulation and digital twin technology for developing and optimising digital healthcare systems, not just for hospitals but also for end-to-end healthcare delivery systems, comprising the entire health value chain.

Despite these opportunities, there may also be a few challenges that 4IR creates for healthcare systems. These challenges include vulnerability of patient data (privacy) and security. Nevertheless, block-chain is being actively researched with the aim of developing solutions to address this particular concern. Another challenge that 4IR technologies create for healthcare systems relates to implementation; most of the technologies require technical knowledge in order to implement competently and there may be a steep learning curve for users. Yet another problem stems from the fact that precision medicine and self-care programmes do need active monitoring systems that are responsive enough to pick up deviations in medication compliance as soon as they occur, or even pre-empt them, and then institute corrective protocols in order to maintain safety to patients. However, it is hard to have systems that are completely independent of people and people skills. Therefore, human dependency may be regarded as a weakness, since people are still required to implement, oversee, maintain and improve the systems. Other emergent key challenges for LMICs that largely remain unresolved, however, include enhancing accessibility of healthcare services, improving capacity and quality of rural healthcare, developing integrated hospital models, and improving healthcare facilities management.

This review has shown that indeed there is room for use of specific 4IR techs such as digital twin and simulation, and additive manufacturing in LMIC healthcare. Simulation and digital twin may lead to more harmonised healthcare supply chains with less resource wastage such as due to expired medicines or under-utilised equipment and personnel. This would directly address the challenges of limited funding as well as inadequate infrastructure. Additive manufacturing aka rapid prototyping is already being used in the production of prosthetics in developed economies and use could be extended to LMICs. Augmented reality could help address the challenge of delivering quality healthcare diagnosis and patient monitoring to rural communities if the equipment needed at the receiving end could be made available. This could be by, for example, setting up virtual consultation/examination/check-up rooms at local clinics where doctors could ‘meet’ patients virtually in the presence of nurses who would facilitate and ensure that prescribed medications and care are administered. In this manner, doctors previously limited to one small hospital or clinic will be able to take care of an expanded area without physically moving from one point to another.

Conclusions

In this review, the authors have successfully synthesised findings from key studies of the past ten years in the application of 4IR to healthcare systems of LMICs. In addressing the first research question, which sought to establish the state of research on the application of 4IR technologies within healthcare systems of LMICs, the authors have put together a coherent picture of how 4IR technology is finding application in various healthcare areas, including EHR, Healthcare Information Systems, diagnostics, telehealth and mHealth. This study has also revealed that, to date, there has only been limited application of 4IR technologies in healthcare systems of LMICs. It has been established that although some 4IR technologies, such as Systems Integration, Big Data Analytics, Artificial Intelligence, and Automation and Robotics have found some application within LMIC healthcare systems, others including Digital Twin and Simulation, Blockchain, Virtual Reality, IoT and Cloud Computing have remained largely unexploited. We have established the trend of how the research areas have evolved over time and shown where things are now, as well as pointed out the most likely future research directions. We have also presented the leading journals that are publishing in the research area, together with the leading research studies. Despite the limited application of 4IR technologies within the health sectors of LMICs, it has been noted that these digital tools have resulted in improved health outcomes where they have been applied. We have established that 4IR technologies have helped address challenges related to a constrained infrastructure and low capacity, improved access to healthcare particularly for rural populations, and through technologies such as remote sensing and disease surveillance, helped improve the quality of primary healthcare. However, it has also emerged that, hitherto, most studies have only focused on application of 4IR technologies to solve disease specific problems, so-called digital health solutions, and that there is not yet enough work that considers the holistic improvement of entire healthcare systems. We have therefore highlighted the many opportunities that exist for LMICs to leverage 4IR technologies in order to address their health systems challenges including digital twin, simulation, blockchain, cybersecurity and EHR. The final research question sought to identify the major challenges that health systems of LMICs still face that application of specific 4IR technologies may help tackle. In this regard, we identified that there are still significant opportunities for the application of 4IR tools such as simulation and digital twin, cloud computing and cybersecurity in solving persistent healthcare challenges within LMICs. These challenges include limited access to healthcare for poorer populations, privacy and security risks, as well as a lack of systems optimisation. The use of smart technologies to develop, for example, a digital model (digital twin—symbiotic simulation) of a hospital's healthcare delivery system, will likely improve system integration and thereby enhance optimisation of healthcare delivery in low- and middle-income countries. Furthermore, governments could invest in IomT for specific demographics, for example, in the elderly, or for patients requiring chronic care such as cancer, hypertension and diabetes, as well as mental illness. Even healthy populations could benefit from IomT use for routine check-ups and proactive monitoring as well as to inform health policy. It is hoped that governments, health practitioners and researchers are persuaded and assured of the gains that stand to be made from investing further work and effort in this area of digital technology applications in healthcare.

Appendix 1

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Author contributions

Joseph Mwanza: Writing—Original draft preparation, Methodology, Article Screening, Full text review. Arnesh Telukdarie: Conceptualisation, Article Screening, Formal Analysis, Writing—Reviewing and Editing, Supervision. Tak Igusa: Formal Analysis, Writing—Reviewing and Editing, Supervision.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Availability of data and material

Not applicable.

Code availability

Not applicable.

Declarations

Competing interests

The authors have no competing interests to declare.