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Possibility of introducing telemedicine services in Asian and African countries
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Possibility of introducing telemedicine services in Asian and African countries Teppei Suzuki a,c, Jyuri Hotta b, Tomomi Kuwabara d, Hiroko Yamashina c, Tomoki Ishikawa c,e, Yuji Tani f, Katsuhiko Ogasawara c,∗ a
Hokkaido University of Education, Iwamizawa Campus, Japan Hokkaido University, Japan Faculty of Health Sciences, Hokkaido University, N12 W5 Sapporo, Japan d Hokkaido Centre for Family Medicine, Japan e Institute for Health Economics and policy, Japan f Department of Medical Informatics and Hospital Management, Asahikawa Medical University, Japan b c
a r t i c l e Article history: Available online xxx Keywords: Telemedicine service mHealth Healthcare Marketing principal component cluster analysis
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a b s t r a c t Objectives: In some developing countries, despite advancements in Information Technology (IT), medical resources are scarce; hence, introduction of telemedicine services can solve this problem. In this study, we examined the possibility of introducing telemedicine-based services in developing countries utilizing the available data. Methods: In Asia, the study was conducted in nine developing countries, excluding those where data were unavailable. In Africa, thirteen countries whose per capita Gross Domestic Product (GDP) was less than USD 10 0 0, and where data were unavailable, were also excluded. We chose the number of doctors, nurses, and midwives as indicators of the healthcare environment. We used the number of internet contracts and mobile phone contracts as indicators of IT penetration, and set per capita GDP and its growth rate as economic indicators. We combined the two continents’ data and performed a principal component analysis (PCA) and cluster analysis. Results: We used cluster analysis to classify the target countries into the following five clusters: Cluster A: Algeria, Egypt, Morocco, Indonesia, Ghana, Tunisia, Madagascar, Nigeria, and Thailand; Cluster B: Bangladesh, Ethiopia, Kenya, Uganda, India, and Pakistan; Cluster C: Sudan, Malaysia, Vietnam, Tanzania, Philippines, and China; Cluster D: South Africa, and Cluster E: Japan and Singapore. As a result of conducting PCA, Cluster A emerged as the region with the highest progressiveness and development possibility. Conclusions: Introduction of telemedicine services has been visualized by using cluster analysis and PCA. However, it is necessary to incorporate future medical needs as indicators to make a more appropriate assessment of its potential. © 2020 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved.
Introduction Government agencies and private organizations in developed countries are currently providing substantial international aid to developing countries [1]. International aid covers maintenance of the educational environment as well as the construction of infrastructure, such as roads, which serve as a basis for the recipient country’s economic development. With respect to healthcare, there is a concern that the spread of infectious diseases caused ∗
Corresponding author. E-mail address: [email protected] (K. Ogasawara).
by poverty, may deteriorate sanitation even further. Until now, numerous types of assistance for maintaining the healthcare environment have been provided mainly by developed countries. For example, measures to control major infectious diseases, such as AIDS, tuberculosis, and malaria, have been carried out in Myanmar, and Cambodia has launched a project for improving maternal and child health by enhancing competence in midwifery [2]. Although the average number of physicians per population of 1,0 0 0 is 3.1 in the Organization for Economic Co-operation and Development (OECD)’s member countries, in most Asian and African developing countries this figure is less than 1.0, indicating a persistent shortage of medical staff along with facilities for medical care and pub-
https://doi.org/10.1016/j.hlpt.2020.01.006 2211-8837/© 2020 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved.
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lic health [3,4]. It is extremely time-consuming and costly to establish medical facilities and train medical staff, and it is also not easy to maintain the medical environment in good condition [5]. Additionally, due to technology acquisition and income problems, doctors in developing countries are not being placed appropriately, so they are serving as physicians in other countries leading to brain drain [6]. In recent years, IT (Information Technology) has been rapidly spreading globally. In developing countries too, examples abound of services that have succeeded due to the use of IT. In many African countries, the absence of physical infrastructure has prevented people and merchandise from entering and exiting the market and slowed down development. However, various IT-related services have been spreading since IT can satisfy people’s needs even in the absence of physical infrastructure. One such example is mobile banking services that facilitate money transfers and encourage savings using mobile phones. Mobile banking services, such as the M-Pesa in Kenya, have been introduced in 37 out of 54 African countries [7]. The diffusion of such IT services is expected to support medical environment improvements through the use of telemedicine technology. The American Telemedicine Association defines telemedicine as the use of medical information exchanged from one site to another via electronic communication to improve a patient’s clinical health status [8]. Telemedicine is operated by a telecommunication system, examples of which are the telephone network, the radio broadcasting system, computer networks, and the internet. The nodes in the system used for communication comprise devices like phones and computers. Telemedicine includes both synchronous and asynchronous communications. Examples of synchronous communications are video conferences and text messages, while social media is an example of asynchronous communication. Both of these elements are required in the telecommunication system [25]. There are two major types of telemedicine [9]. The first is the doctor-to-patient type carried out between patients and medical staff members. It includes the use of videotelephony (videoconferencing) by a physician to examine a patient. Ye et al. conducted telemedicine field trials with smartphones and smart glasses using a 4G line environment [10]. Gadkari et al.’s study proposed a screening method for diabetic retinopathy using a camera device connected to a smartphone [11]. The second is the doctor-to-doctor type between medical staff members when a physician who has treated a patient consults a specialist either for diagnosis or treatment advice. Communication between the skin image and the skin pathological image was performed in a previous study by Nguyen et al. [12]. According to Njoroge et al., although Kenya has many eHealth projects, most of them focus on primary care and AIDS/HIV, and not many on telemedicine [13]. Advanced medical devices have not been able to spread in developing countries where basic medical facilities are not in place and consequently have not been found very useful. Hence, when considering the practice of telemedicine in developing countries, its use is proposed only if the task consists of medical consultation using mobile phones and smartphones between medical staff members and patients, or between medical staff members and their peers. Hsu et al. report that in China, market analyses have revealed the high demand for mobile health (mHealth) for primary care management of diabetes, hypertension, hepatitis, etc. [14]. While maintenance of advanced medical equipment is often financially challenging in developing countries, previous studies have shown that most cases can be handled through mobile devices, such as smartphones. Although such trends are expected to continue in the future, many studies analyzing the possibility of dissemination of telemedicine services to developing countries and their associated problems have been reported [15,16]. From
a global perspective, research has not yet been done on the kind of country that would benefit from the prospect of developing telemedicine services. By utilizing the existing IT infrastructure in developing countries, telemedicine services like the internet and mobile phones can be used to maintain a good healthcare environment. This study, therefore, examined the status of both IT adoption and the healthcare environment, as well as the economic situation in developing countries. Methods Although the survey included developing countries in Asia and Africa, for comparison purposes, two developed countries (Japan and Singapore) were added to the list. The study was conducted in nine Asian developing countries, excluding countries where data were unavailable. Similarly, thirteen African countries were excluded either because their per capita GDP was less than USD 10 0 0, or no data were available [17]. The surveyed countries are listed in Table 1. The following were considered as survey indices: healthcare environment, the status of progress in IT, and economic status. The survey indices are listed in Table 2 [17–19]. As for data on the healthcare environment, we used the latest data that could be collected from the World Health Organization (WHO)’s website. Based on Khanapi et al.’s report, we considered it necessary to secure sufficient medical staff for the spread of telemedicine. Therefore, we used the number of doctors, nurses, and midwives as survey indicators [16]. Additionally, to implement telemedicine services, the spread of the internet and communication devices, along with economic indicators for establishing such infrastructure was considered necessary. Hence, IT penetration indicators (number of mobile phone contracts, internet usage rate) and the economic situation index (GDP per capita and its growth rate) were developed for this research using data from 2012 and 2015. For the indicators of the medical environment, we used the medical staff numbers from WHO’s web page and divided it by each country’s population (taken from the World Bank’s web page). The countries were classified as Asian and African. A scatter diagram of the relationship between the status of IT adoption and the healthcare environment was created for each indicator; data for 2012 and 2015 were compared. PCA can extract the direction in which the fluctuation is the largest among multivariate data. The PCA is an analysis method that explains the original data fluctuations to the best possible extent with as few main components as possible. In this study, by performing PCA, factor loads of six indicators used in the healthcare environment, the IT diffusion situation, and economic situation were calculated for every two principal components. Next, the thirteen countries were plotted on a graph with the two principal components extracted by PCA as axes. Cluster analysis is a method of grouping objects having similar values as a group based on values contained in a plurality of variables, and forming a cluster. The ward method was chosen in this study to define the distance between clusters. A cluster analysis was performed for the thirteen countries using the six indices, then plotted on the graph obtained by PCA. For PCA and cluster analysis, we used the JMP Pro 12.2.0 software. Use of the analysis methods made visualization of the introduction of telemedicine services possible. Results Fig. 1 shows the internet usage rate for 2012 and 2015 and the percentage of physicians in eleven Asian countries. The results show that Malaysia had the highest internet usage rate after Singapore and Japan, and China had the highest percentage of physi-
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Table 1 List of surveyed countries. Asia Africa
China, Pakistan, Bangladesh, India, Thailand, Viet Nam, Philippine, Malaysia, Indonesia (Japan, Singapore) Morocco, Tunisia, Algeria, Egypt, Sudan, Nigeria, Ghana, Uganda, Ethiopia, Kenya, Tanzania, South Africa, Madagascar
Table 2 Survey indices. Healthcare environment Status of adoption of IT Economic status indicators
Doctors, Nurses and Midwives Number of mobile phone contracts, Internet usage rate GDP per capita, GDP growth rate
Fig. 1. Internet usage rate for 2012 and 2015 and the percentage of physicians in 11 countries.
cians. The results also showed that the internet usage rate was growing considerably in Thailand, India, and Vietnam. Fig. 2 shows the internet usage rate and the percentage of nurses/midwives in eleven Asian countries. The results show that in Bangladesh, Pakistan, Indonesia, and India, both the internet usage rates and the percentage of nurses/midwives were low. Fig. 3 shows the number of mobile phone contracts and the percentage of physicians in eleven Asian countries. In Thailand and Indonesia, the percentage of physicians was low, but the mobile phone penetration rate exceeded 100%. In Bangladesh, Pakistan, and India, the number of mobile phone contracts, as well as the percentage of physicians, were low.
Fig. 4 shows the number of mobile phone contracts and the percentage of nurses/midwives in eleven Asian countries. Thailand, Indonesia, and Vietnam had a low percentage of nurses/midwives but their rate of mobile phone contracts exceeded 100% of their population. In Bangladesh, China, Pakistan, and India, the number of mobile phone contracts as well as the percentage of nurses/midwives were low. Fig. 5 shows the internet usage rate and the percentage of physicians in the thirteen African countries. Compared with similar data for eleven Asian countries shown in Fig. 1, the data revealed an overall shortage of physicians in all the African countries studied. Morocco, South Africa, Tunisia, Kenya, and Nigeria were countries where the internet usage rate exceeded 40%, but
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Fig. 2. Internet usage rate and the percentage of nurses/midwives in 11 countries.
in Kenya and Nigeria, the percentage of physicians was only 0.02% and 0.04%, respectively. Additionally, from 2012 to 2015, internet usage has greatly improved in many countries. For example, in Algeria, it was at 22.97%, Nigeria 14.64%, Kenya 13.52%, Egypt 11.42%, and South Africa 10.92%. Fig. 6 shows the internet usage rate and the percentage of nurses/midwives in thirteen African countries. Compared with the data on Asia in Fig. 2, the percentage of nurses and midwives in Africa was lower than that in the eleven Asian countries studied. The results revealed that in Tunisia and South Africa, the percentage of nurses/midwives, as well as the internet usage rates, were higher than those in the other African countries studied. Fig. 7 shows the number of mobile phone contracts and the percentage of physicians in thirteen African countries. For the indicators of internet usage rate, our results show that in Algeria, Ghana, Morocco, South Africa, Tunisia, and Egypt, the number of mobile phone contracts per person in the population was 10 0 0 or higher. Fig. 8 shows the number of mobile phone contracts and the percentage of nurses/midwives in thirteen African countries. As shown in the figure, in Morocco, Ghana, Egypt, and Algeria, the rate of mobile phone contracts exceeded 100%, whereas their percentage of nurses/midwives was 0.2% or less. Table 3 shows the results of PCA on the healthcare environment, the IT dissemination situation, and the economic situation in 2015. The total of 69.336% was explained through the two principal components that accounted for 47.157% and 22.179% of the variance, respectively. Table 4 and Fig. 9 depict the results of PCA
and cluster analysis, wherein twenty-four countries were classified into the following five clusters: Cluster A: Algeria, Egypt, Morocco, Indonesia, Ghana, Tunisia, Madagascar, Nigeria, and Thailand; Cluster B: Bangladesh, Ethiopia, Kenya, Uganda, India, and Pakistan; Cluster C: Sudan, Malaysia, Vietnam, Tanzania, Philippines, and China; Cluster D: South Africa; and Cluster E: Japan and Singapore. Cluster E had the highest GDP per capita (43,434 USD); Cluster B the highest GDP growth rate (6.72%), and Cluster D had the most internet and mobile phones. The level of fulfillment of doctors, nurses and midwives were highest in cluster E and lowest in cluster B. The results suggest that internet usage and GDP growth rates had influenced the second principal component’s indicators. Since the y-axis represented the possibility of development, it is evident that the internet usage rate had the greatest factor loading. The more elevated it was, the higher was the possibility of development, and vice versa. Additionally, the x-axis indicated that the number of nurses/midwives, per capita GDP, the number of mobile phone contracts, and factor loading of the number of physicians were higher. Thus, these factors represented advancedness or progressiveness that was higher when they were to the right side of the graph and became lower as they moved further toward the graph’s left. This finding suggests that advancedness and the possibility of development were both high for countries in the first quadrant. Table 5 shows the results of an examination of the possibility of introducing telemedicine services based on PCA and cluster analysis results. The high or low criterion of that possibility was determined from the positional relationship of each cluster, as seen in Fig. 9.
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Fig. 3. Number of mobile phone contracts and the percentage of physicians in 11 countries. Table 3 Factor loading of each principal component.
GDP per capita GDP growth rate Percentage of individuals using the internet Mobile-cellular telephone subscriptions per 1,000 inhabitants Doctors Nurses Eigenvalue Contribution rate(%) Cumulative percent(%)
Z1 (Progressiveness)
Z2 (Development Possibility)
0.4757 −0.34796 0.18684 0.41853 0.40441 0.52923 2.8294 47.157 47.157
−0.23439 0.51476 0.71588 −0.05966 0.40354 0.03471 1.3307 22.179 69.336
Table 4 Results of cluster analysis.
GDP per capita (USD) GDP growth rate (%) Percentage of individuals using the internet (%) Mobile-cellular telephone subscriptions (per 1,000 population) Doctors (per 1,000 population) Nurses and midwives (per 1000 population)
Cluster A (n = 9)
Cluster B (n = 6)
Cluster C (n = 6)
Cluster D (n = 1)
Cluster E (n = 2)
3091.00 3.40 23.44 1,068.00 0.43 1.19
1143.33 6.72 42.79 668.00 0.38 0.89
4283.83 6.07 48.67 1,138.17 1.63 2.73
5773.00 1.30 91.06 1,645.00 0.76 5.01
43,434.00 1.60 36.95 1,365.00 2.11 8.22
Table 5 Possibility to introduce telemedicine service by cluster analysis.
A B C D E
Country
Progressiveness
Development Possibility
Algeria, Egypt, Morocco, Indonesia, Ghana,Tunisia, Madagascar, Nigeria, Thailand, Bangladesh, Ethiopia, Kenya, Uganda, India, Pakistan Sudan, Malaysia, Viet Nam, Tanzania, Philippines, China South Africa Japan, Singapore
high low high very high very high
high very high very high very high high
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Fig. 4. Number of mobile phone contracts and the percentage of nurses/midwives in 11 countries.
Fig. 5. Internet usage rate and the percentage of physicians in 13 countries. F.
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Fig. 6. Internet usage rate and the percentage of nurses/midwives in 13 countries.
Fig. 7. Number of mobile phone contracts and the percentage of physicians in 12 countries.
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Fig. 8. Number of mobile phone contracts and the percentage of nurses/midwives in 13 countries.
Fig. 9. Results of principal component analysis and cluster analysis. 24 countries were classified into 5 clusters.
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Table 6 Possibility to introduce telemedicine service from the viewpoint of Internet usage rate. Countries with high possibility of introducing telemedicine service in Asia Doctor-related services Services related to nurses and midwives
Thailand Thailand, Viet Nam
Countries with high possibility of introducing telemedicine service in Africa Doctor-related services Services related to nurses and midwives
Discussion Table 6 shows the Asian and African countries where there is a high possibility of introducing telemedicine services. Thailand was the only Asian country where physicians were fewer in number, and the rate of mobile phone contracts was 30% or more. In Indonesia and Thailand, physicians were fewer in number, but their mobile phone penetration rate exceeded 100%. The rapid increase in Thailand’s internet penetration rate suggests that it has the highest probability of introducing telemedicine-based services to compensate for the physicians’ shortage. In Indonesia, a future increase in its internet usage rate will elevate the probability of its introducing telemedicine services involving physicians. Vietnam, Thailand, and China had few nurses and a high internet usage rate. In Indonesia, Thailand, and Vietnam, nurses were fewer in number, but the mobile phone penetration rate exceeded 100% (although Vietnam’s number of mobile phone contracts had decreased from 2012 to 2015). These results predict that amongst Asian countries, Thailand has the highest likelihood of introducing telemedicine-based services for compensating its shortage of physicians. While the challenge for China is the mobile phone penetration rate, for Indonesia, it is the lack of a substantial increase in internet usage. Although globally China has the largest number of mobile phone contracts, its National Bureau of Statistics figures indicate a difference in the penetration of mobile phones between urban and rural areas [18]. Therefore, the spread of mobile phones to China’s rural areas is necessary for the spread of telemedicine services [19]. Similarly, despite India having a high GDP, it is amongst the countries with large differences between urban and rural areas. To investigate these differences, obtaining information at the state or prefectural levels is necessary, and the lack of such information is a limitation of this research. The African countries with few physicians and an internet usage rate of 30% or higher were Kenya, Nigeria, Egypt, Morocco, South Africa, and Algeria. In Ghana, Egypt, Morocco, and South Africa, physicians and nurses were few, but the mobile phone penetration rate exceeded 100%. These findings suggest that South Africa, Egypt, and Morocco would be the frontrunners amongst countries most likely to introduce telemedicine services to compensate for the shortage of physicians. Kenya and Nigeria have an issue with the penetration rate of mobile phones, whereas Ghana has an issue with the increase in internet usage. Kenya, Nigeria, Egypt, and Morocco have few nurses but since their internet usage rate is 30% or higher, it is highly feasible for them to introduce telemedicine services involving nurses. PCA and cluster analyses result using data from 2015 suggest that amongst the Cluster D countries, South Africa, has the highest likelihood of accommodating telemedicine services. Whilst those in Clusters C also have a high likelihood, they need to address issues affecting their advancedness such as increase in the number of physicians, nurses, and midwives, and their cell phone penetration rates. Cluster A and E countries’ probability of developing telemedicine services was relatively low being considerably affected by rates of GDP growth and internet usage. Improving their
South Africa, Egypt, Morocco, Algeria Egypt, Morocco, Algeria
internet usage rate was an issue for Cluster A countries (Algeria, Egypt, Morocco, Indonesia, Ghana, Tunisia, Madagascar, Nigeria, and Thailand), whilst Cluster E (Singapore and Japan), were believed to be influenced by the economic growth rate. However, Thailand is expected to shift from Cluster A to E since its internet usage rate has been increasing rapidly. Hence the likelihood of its introducing telemedicine services would be high. Limitations Ronald S et al. report that mHealth has been growing rapidly in recent years and there is a possibility of disruptive innovation transforming the future of healthcare. They have also pointed out that telemedicine service reimbursement, interstate medical licensure, and hospital credentialing are barriers to the future introduction of telemedicine [20,21]. Therefore, standardization is difficult since such problems vary in degree depending on the country. Also, for considering the introduction of telemedicine services in the future, it will be necessary to analyze variables excluded in this research like the level of national health systems and the size of private insurance markets (particularly related to payment methods), religious and cultural issues and political environment. Besides, it would be necessary to discuss the kind of data to be used specifically for these variables. In this research, we investigated the possibility of using IT by expressing the number of mobile phone subscribers and the internet usage rate. However, it is also necessary to select and analyze the target country by using more specific figures on IT availability. Another important issue that future research should consider is the appropriateness of selecting indices such as the spread of smartphones, types of communication lines, and information and communication technologies (ICT) literacy rate for clinicians, etc. Nonetheless, mobile terminals connected to the internet will become important devices for telemedicine in the future. Boissin et al. instructed specialists in surgery and emergency medicine to interpret medical images using smartphones, tablets, and computer screens, and then evaluated the quality of the images. The results showed that images viewed on smartphones and tablets had a higher evaluations than those viewed on computer screens. This suggests that mobile terminals, such as smartphones and tablets, could replace computer screens when used in telemedicine in cases of emergencies, worldwide[22]. Furthermore, when predicting future demand for telemedicine, we suggest that the analysis should include the number of future doctors and nurses in the target country. For example, Ishikawa et al. have predicted the future number of physicians and gynecologists in Japan, while JP Ansah et al. predicted the number of ophthalmologists in Singapore [23,24]. By integrating such results into future research, we believe that the possibility of introducing telemedicine services can be evaluated more appropriately. Conclusions The use of cluster analysis and PCA has made it possible to visualize the possibility of introducing telemedicine services. The
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results of this research suggest that there is a good possibility of introducing remote telemedicine services in South Africa and that the possibility for its introduction is also high in Thailand where internet spread is more advanced. Based on these results, we find it necessary to first grasp the region’s economic situation and then implement the introduction plan. Funding None. Ethical approval Not required. Declaration of Competing Interest None declared. CRediT authorship contribution statement Teppei Suzuki: Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Jyuri Hotta: Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Tomomi Kuwabara: Conceptualization, Writing - review & editing. Hiroko Yamashina: Conceptualization, Writing - review & editing. Tomoki Ishikawa: Conceptualization, Writing - review & editing. Yuji Tani: Conceptualization, Writing - review & editing. Katsuhiko Ogasawara: Conceptualization, Writing - review & editing. Acknowledgements We gratefully acknowledge the work of past and present members of our laboratory.
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Health Policy and Technology journal homepage: www.elsevier.com/locate/hlpt
Commentary
Possibility of introducing telemedicine services in Asian and African countries Teppei Suzuki a,c, Jyuri Hotta b, Tomomi Kuwabara d, Hiroko Yamashina c, Tomoki Ishikawa c,e, Yuji Tani f, Katsuhiko Ogasawara c,∗ a
Hokkaido University of Education, Iwamizawa Campus, Japan Hokkaido University, Japan Faculty of Health Sciences, Hokkaido University, N12 W5 Sapporo, Japan d Hokkaido Centre for Family Medicine, Japan e Institute for Health Economics and policy, Japan f Department of Medical Informatics and Hospital Management, Asahikawa Medical University, Japan b c
a r t i c l e Article history: Available online xxx Keywords: Telemedicine service mHealth Healthcare Marketing principal component cluster analysis
i n f o
a b s t r a c t Objectives: In some developing countries, despite advancements in Information Technology (IT), medical resources are scarce; hence, introduction of telemedicine services can solve this problem. In this study, we examined the possibility of introducing telemedicine-based services in developing countries utilizing the available data. Methods: In Asia, the study was conducted in nine developing countries, excluding those where data were unavailable. In Africa, thirteen countries whose per capita Gross Domestic Product (GDP) was less than USD 10 0 0, and where data were unavailable, were also excluded. We chose the number of doctors, nurses, and midwives as indicators of the healthcare environment. We used the number of internet contracts and mobile phone contracts as indicators of IT penetration, and set per capita GDP and its growth rate as economic indicators. We combined the two continents’ data and performed a principal component analysis (PCA) and cluster analysis. Results: We used cluster analysis to classify the target countries into the following five clusters: Cluster A: Algeria, Egypt, Morocco, Indonesia, Ghana, Tunisia, Madagascar, Nigeria, and Thailand; Cluster B: Bangladesh, Ethiopia, Kenya, Uganda, India, and Pakistan; Cluster C: Sudan, Malaysia, Vietnam, Tanzania, Philippines, and China; Cluster D: South Africa, and Cluster E: Japan and Singapore. As a result of conducting PCA, Cluster A emerged as the region with the highest progressiveness and development possibility. Conclusions: Introduction of telemedicine services has been visualized by using cluster analysis and PCA. However, it is necessary to incorporate future medical needs as indicators to make a more appropriate assessment of its potential. © 2020 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved.
Introduction Government agencies and private organizations in developed countries are currently providing substantial international aid to developing countries [1]. International aid covers maintenance of the educational environment as well as the construction of infrastructure, such as roads, which serve as a basis for the recipient country’s economic development. With respect to healthcare, there is a concern that the spread of infectious diseases caused ∗
Corresponding author. E-mail address: [email protected] (K. Ogasawara).
by poverty, may deteriorate sanitation even further. Until now, numerous types of assistance for maintaining the healthcare environment have been provided mainly by developed countries. For example, measures to control major infectious diseases, such as AIDS, tuberculosis, and malaria, have been carried out in Myanmar, and Cambodia has launched a project for improving maternal and child health by enhancing competence in midwifery [2]. Although the average number of physicians per population of 1,0 0 0 is 3.1 in the Organization for Economic Co-operation and Development (OECD)’s member countries, in most Asian and African developing countries this figure is less than 1.0, indicating a persistent shortage of medical staff along with facilities for medical care and pub-
https://doi.org/10.1016/j.hlpt.2020.01.006 2211-8837/© 2020 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved.
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lic health [3,4]. It is extremely time-consuming and costly to establish medical facilities and train medical staff, and it is also not easy to maintain the medical environment in good condition [5]. Additionally, due to technology acquisition and income problems, doctors in developing countries are not being placed appropriately, so they are serving as physicians in other countries leading to brain drain [6]. In recent years, IT (Information Technology) has been rapidly spreading globally. In developing countries too, examples abound of services that have succeeded due to the use of IT. In many African countries, the absence of physical infrastructure has prevented people and merchandise from entering and exiting the market and slowed down development. However, various IT-related services have been spreading since IT can satisfy people’s needs even in the absence of physical infrastructure. One such example is mobile banking services that facilitate money transfers and encourage savings using mobile phones. Mobile banking services, such as the M-Pesa in Kenya, have been introduced in 37 out of 54 African countries [7]. The diffusion of such IT services is expected to support medical environment improvements through the use of telemedicine technology. The American Telemedicine Association defines telemedicine as the use of medical information exchanged from one site to another via electronic communication to improve a patient’s clinical health status [8]. Telemedicine is operated by a telecommunication system, examples of which are the telephone network, the radio broadcasting system, computer networks, and the internet. The nodes in the system used for communication comprise devices like phones and computers. Telemedicine includes both synchronous and asynchronous communications. Examples of synchronous communications are video conferences and text messages, while social media is an example of asynchronous communication. Both of these elements are required in the telecommunication system [25]. There are two major types of telemedicine [9]. The first is the doctor-to-patient type carried out between patients and medical staff members. It includes the use of videotelephony (videoconferencing) by a physician to examine a patient. Ye et al. conducted telemedicine field trials with smartphones and smart glasses using a 4G line environment [10]. Gadkari et al.’s study proposed a screening method for diabetic retinopathy using a camera device connected to a smartphone [11]. The second is the doctor-to-doctor type between medical staff members when a physician who has treated a patient consults a specialist either for diagnosis or treatment advice. Communication between the skin image and the skin pathological image was performed in a previous study by Nguyen et al. [12]. According to Njoroge et al., although Kenya has many eHealth projects, most of them focus on primary care and AIDS/HIV, and not many on telemedicine [13]. Advanced medical devices have not been able to spread in developing countries where basic medical facilities are not in place and consequently have not been found very useful. Hence, when considering the practice of telemedicine in developing countries, its use is proposed only if the task consists of medical consultation using mobile phones and smartphones between medical staff members and patients, or between medical staff members and their peers. Hsu et al. report that in China, market analyses have revealed the high demand for mobile health (mHealth) for primary care management of diabetes, hypertension, hepatitis, etc. [14]. While maintenance of advanced medical equipment is often financially challenging in developing countries, previous studies have shown that most cases can be handled through mobile devices, such as smartphones. Although such trends are expected to continue in the future, many studies analyzing the possibility of dissemination of telemedicine services to developing countries and their associated problems have been reported [15,16]. From
a global perspective, research has not yet been done on the kind of country that would benefit from the prospect of developing telemedicine services. By utilizing the existing IT infrastructure in developing countries, telemedicine services like the internet and mobile phones can be used to maintain a good healthcare environment. This study, therefore, examined the status of both IT adoption and the healthcare environment, as well as the economic situation in developing countries. Methods Although the survey included developing countries in Asia and Africa, for comparison purposes, two developed countries (Japan and Singapore) were added to the list. The study was conducted in nine Asian developing countries, excluding countries where data were unavailable. Similarly, thirteen African countries were excluded either because their per capita GDP was less than USD 10 0 0, or no data were available [17]. The surveyed countries are listed in Table 1. The following were considered as survey indices: healthcare environment, the status of progress in IT, and economic status. The survey indices are listed in Table 2 [17–19]. As for data on the healthcare environment, we used the latest data that could be collected from the World Health Organization (WHO)’s website. Based on Khanapi et al.’s report, we considered it necessary to secure sufficient medical staff for the spread of telemedicine. Therefore, we used the number of doctors, nurses, and midwives as survey indicators [16]. Additionally, to implement telemedicine services, the spread of the internet and communication devices, along with economic indicators for establishing such infrastructure was considered necessary. Hence, IT penetration indicators (number of mobile phone contracts, internet usage rate) and the economic situation index (GDP per capita and its growth rate) were developed for this research using data from 2012 and 2015. For the indicators of the medical environment, we used the medical staff numbers from WHO’s web page and divided it by each country’s population (taken from the World Bank’s web page). The countries were classified as Asian and African. A scatter diagram of the relationship between the status of IT adoption and the healthcare environment was created for each indicator; data for 2012 and 2015 were compared. PCA can extract the direction in which the fluctuation is the largest among multivariate data. The PCA is an analysis method that explains the original data fluctuations to the best possible extent with as few main components as possible. In this study, by performing PCA, factor loads of six indicators used in the healthcare environment, the IT diffusion situation, and economic situation were calculated for every two principal components. Next, the thirteen countries were plotted on a graph with the two principal components extracted by PCA as axes. Cluster analysis is a method of grouping objects having similar values as a group based on values contained in a plurality of variables, and forming a cluster. The ward method was chosen in this study to define the distance between clusters. A cluster analysis was performed for the thirteen countries using the six indices, then plotted on the graph obtained by PCA. For PCA and cluster analysis, we used the JMP Pro 12.2.0 software. Use of the analysis methods made visualization of the introduction of telemedicine services possible. Results Fig. 1 shows the internet usage rate for 2012 and 2015 and the percentage of physicians in eleven Asian countries. The results show that Malaysia had the highest internet usage rate after Singapore and Japan, and China had the highest percentage of physi-
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Table 1 List of surveyed countries. Asia Africa
China, Pakistan, Bangladesh, India, Thailand, Viet Nam, Philippine, Malaysia, Indonesia (Japan, Singapore) Morocco, Tunisia, Algeria, Egypt, Sudan, Nigeria, Ghana, Uganda, Ethiopia, Kenya, Tanzania, South Africa, Madagascar
Table 2 Survey indices. Healthcare environment Status of adoption of IT Economic status indicators
Doctors, Nurses and Midwives Number of mobile phone contracts, Internet usage rate GDP per capita, GDP growth rate
Fig. 1. Internet usage rate for 2012 and 2015 and the percentage of physicians in 11 countries.
cians. The results also showed that the internet usage rate was growing considerably in Thailand, India, and Vietnam. Fig. 2 shows the internet usage rate and the percentage of nurses/midwives in eleven Asian countries. The results show that in Bangladesh, Pakistan, Indonesia, and India, both the internet usage rates and the percentage of nurses/midwives were low. Fig. 3 shows the number of mobile phone contracts and the percentage of physicians in eleven Asian countries. In Thailand and Indonesia, the percentage of physicians was low, but the mobile phone penetration rate exceeded 100%. In Bangladesh, Pakistan, and India, the number of mobile phone contracts, as well as the percentage of physicians, were low.
Fig. 4 shows the number of mobile phone contracts and the percentage of nurses/midwives in eleven Asian countries. Thailand, Indonesia, and Vietnam had a low percentage of nurses/midwives but their rate of mobile phone contracts exceeded 100% of their population. In Bangladesh, China, Pakistan, and India, the number of mobile phone contracts as well as the percentage of nurses/midwives were low. Fig. 5 shows the internet usage rate and the percentage of physicians in the thirteen African countries. Compared with similar data for eleven Asian countries shown in Fig. 1, the data revealed an overall shortage of physicians in all the African countries studied. Morocco, South Africa, Tunisia, Kenya, and Nigeria were countries where the internet usage rate exceeded 40%, but
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Fig. 2. Internet usage rate and the percentage of nurses/midwives in 11 countries.
in Kenya and Nigeria, the percentage of physicians was only 0.02% and 0.04%, respectively. Additionally, from 2012 to 2015, internet usage has greatly improved in many countries. For example, in Algeria, it was at 22.97%, Nigeria 14.64%, Kenya 13.52%, Egypt 11.42%, and South Africa 10.92%. Fig. 6 shows the internet usage rate and the percentage of nurses/midwives in thirteen African countries. Compared with the data on Asia in Fig. 2, the percentage of nurses and midwives in Africa was lower than that in the eleven Asian countries studied. The results revealed that in Tunisia and South Africa, the percentage of nurses/midwives, as well as the internet usage rates, were higher than those in the other African countries studied. Fig. 7 shows the number of mobile phone contracts and the percentage of physicians in thirteen African countries. For the indicators of internet usage rate, our results show that in Algeria, Ghana, Morocco, South Africa, Tunisia, and Egypt, the number of mobile phone contracts per person in the population was 10 0 0 or higher. Fig. 8 shows the number of mobile phone contracts and the percentage of nurses/midwives in thirteen African countries. As shown in the figure, in Morocco, Ghana, Egypt, and Algeria, the rate of mobile phone contracts exceeded 100%, whereas their percentage of nurses/midwives was 0.2% or less. Table 3 shows the results of PCA on the healthcare environment, the IT dissemination situation, and the economic situation in 2015. The total of 69.336% was explained through the two principal components that accounted for 47.157% and 22.179% of the variance, respectively. Table 4 and Fig. 9 depict the results of PCA
and cluster analysis, wherein twenty-four countries were classified into the following five clusters: Cluster A: Algeria, Egypt, Morocco, Indonesia, Ghana, Tunisia, Madagascar, Nigeria, and Thailand; Cluster B: Bangladesh, Ethiopia, Kenya, Uganda, India, and Pakistan; Cluster C: Sudan, Malaysia, Vietnam, Tanzania, Philippines, and China; Cluster D: South Africa; and Cluster E: Japan and Singapore. Cluster E had the highest GDP per capita (43,434 USD); Cluster B the highest GDP growth rate (6.72%), and Cluster D had the most internet and mobile phones. The level of fulfillment of doctors, nurses and midwives were highest in cluster E and lowest in cluster B. The results suggest that internet usage and GDP growth rates had influenced the second principal component’s indicators. Since the y-axis represented the possibility of development, it is evident that the internet usage rate had the greatest factor loading. The more elevated it was, the higher was the possibility of development, and vice versa. Additionally, the x-axis indicated that the number of nurses/midwives, per capita GDP, the number of mobile phone contracts, and factor loading of the number of physicians were higher. Thus, these factors represented advancedness or progressiveness that was higher when they were to the right side of the graph and became lower as they moved further toward the graph’s left. This finding suggests that advancedness and the possibility of development were both high for countries in the first quadrant. Table 5 shows the results of an examination of the possibility of introducing telemedicine services based on PCA and cluster analysis results. The high or low criterion of that possibility was determined from the positional relationship of each cluster, as seen in Fig. 9.
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Fig. 3. Number of mobile phone contracts and the percentage of physicians in 11 countries. Table 3 Factor loading of each principal component.
GDP per capita GDP growth rate Percentage of individuals using the internet Mobile-cellular telephone subscriptions per 1,000 inhabitants Doctors Nurses Eigenvalue Contribution rate(%) Cumulative percent(%)
Z1 (Progressiveness)
Z2 (Development Possibility)
0.4757 −0.34796 0.18684 0.41853 0.40441 0.52923 2.8294 47.157 47.157
−0.23439 0.51476 0.71588 −0.05966 0.40354 0.03471 1.3307 22.179 69.336
Table 4 Results of cluster analysis.
GDP per capita (USD) GDP growth rate (%) Percentage of individuals using the internet (%) Mobile-cellular telephone subscriptions (per 1,000 population) Doctors (per 1,000 population) Nurses and midwives (per 1000 population)
Cluster A (n = 9)
Cluster B (n = 6)
Cluster C (n = 6)
Cluster D (n = 1)
Cluster E (n = 2)
3091.00 3.40 23.44 1,068.00 0.43 1.19
1143.33 6.72 42.79 668.00 0.38 0.89
4283.83 6.07 48.67 1,138.17 1.63 2.73
5773.00 1.30 91.06 1,645.00 0.76 5.01
43,434.00 1.60 36.95 1,365.00 2.11 8.22
Table 5 Possibility to introduce telemedicine service by cluster analysis.
A B C D E
Country
Progressiveness
Development Possibility
Algeria, Egypt, Morocco, Indonesia, Ghana,Tunisia, Madagascar, Nigeria, Thailand, Bangladesh, Ethiopia, Kenya, Uganda, India, Pakistan Sudan, Malaysia, Viet Nam, Tanzania, Philippines, China South Africa Japan, Singapore
high low high very high very high
high very high very high very high high
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Fig. 4. Number of mobile phone contracts and the percentage of nurses/midwives in 11 countries.
Fig. 5. Internet usage rate and the percentage of physicians in 13 countries. F.
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Fig. 6. Internet usage rate and the percentage of nurses/midwives in 13 countries.
Fig. 7. Number of mobile phone contracts and the percentage of physicians in 12 countries.
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Fig. 8. Number of mobile phone contracts and the percentage of nurses/midwives in 13 countries.
Fig. 9. Results of principal component analysis and cluster analysis. 24 countries were classified into 5 clusters.
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Table 6 Possibility to introduce telemedicine service from the viewpoint of Internet usage rate. Countries with high possibility of introducing telemedicine service in Asia Doctor-related services Services related to nurses and midwives
Thailand Thailand, Viet Nam
Countries with high possibility of introducing telemedicine service in Africa Doctor-related services Services related to nurses and midwives
Discussion Table 6 shows the Asian and African countries where there is a high possibility of introducing telemedicine services. Thailand was the only Asian country where physicians were fewer in number, and the rate of mobile phone contracts was 30% or more. In Indonesia and Thailand, physicians were fewer in number, but their mobile phone penetration rate exceeded 100%. The rapid increase in Thailand’s internet penetration rate suggests that it has the highest probability of introducing telemedicine-based services to compensate for the physicians’ shortage. In Indonesia, a future increase in its internet usage rate will elevate the probability of its introducing telemedicine services involving physicians. Vietnam, Thailand, and China had few nurses and a high internet usage rate. In Indonesia, Thailand, and Vietnam, nurses were fewer in number, but the mobile phone penetration rate exceeded 100% (although Vietnam’s number of mobile phone contracts had decreased from 2012 to 2015). These results predict that amongst Asian countries, Thailand has the highest likelihood of introducing telemedicine-based services for compensating its shortage of physicians. While the challenge for China is the mobile phone penetration rate, for Indonesia, it is the lack of a substantial increase in internet usage. Although globally China has the largest number of mobile phone contracts, its National Bureau of Statistics figures indicate a difference in the penetration of mobile phones between urban and rural areas [18]. Therefore, the spread of mobile phones to China’s rural areas is necessary for the spread of telemedicine services [19]. Similarly, despite India having a high GDP, it is amongst the countries with large differences between urban and rural areas. To investigate these differences, obtaining information at the state or prefectural levels is necessary, and the lack of such information is a limitation of this research. The African countries with few physicians and an internet usage rate of 30% or higher were Kenya, Nigeria, Egypt, Morocco, South Africa, and Algeria. In Ghana, Egypt, Morocco, and South Africa, physicians and nurses were few, but the mobile phone penetration rate exceeded 100%. These findings suggest that South Africa, Egypt, and Morocco would be the frontrunners amongst countries most likely to introduce telemedicine services to compensate for the shortage of physicians. Kenya and Nigeria have an issue with the penetration rate of mobile phones, whereas Ghana has an issue with the increase in internet usage. Kenya, Nigeria, Egypt, and Morocco have few nurses but since their internet usage rate is 30% or higher, it is highly feasible for them to introduce telemedicine services involving nurses. PCA and cluster analyses result using data from 2015 suggest that amongst the Cluster D countries, South Africa, has the highest likelihood of accommodating telemedicine services. Whilst those in Clusters C also have a high likelihood, they need to address issues affecting their advancedness such as increase in the number of physicians, nurses, and midwives, and their cell phone penetration rates. Cluster A and E countries’ probability of developing telemedicine services was relatively low being considerably affected by rates of GDP growth and internet usage. Improving their
South Africa, Egypt, Morocco, Algeria Egypt, Morocco, Algeria
internet usage rate was an issue for Cluster A countries (Algeria, Egypt, Morocco, Indonesia, Ghana, Tunisia, Madagascar, Nigeria, and Thailand), whilst Cluster E (Singapore and Japan), were believed to be influenced by the economic growth rate. However, Thailand is expected to shift from Cluster A to E since its internet usage rate has been increasing rapidly. Hence the likelihood of its introducing telemedicine services would be high. Limitations Ronald S et al. report that mHealth has been growing rapidly in recent years and there is a possibility of disruptive innovation transforming the future of healthcare. They have also pointed out that telemedicine service reimbursement, interstate medical licensure, and hospital credentialing are barriers to the future introduction of telemedicine [20,21]. Therefore, standardization is difficult since such problems vary in degree depending on the country. Also, for considering the introduction of telemedicine services in the future, it will be necessary to analyze variables excluded in this research like the level of national health systems and the size of private insurance markets (particularly related to payment methods), religious and cultural issues and political environment. Besides, it would be necessary to discuss the kind of data to be used specifically for these variables. In this research, we investigated the possibility of using IT by expressing the number of mobile phone subscribers and the internet usage rate. However, it is also necessary to select and analyze the target country by using more specific figures on IT availability. Another important issue that future research should consider is the appropriateness of selecting indices such as the spread of smartphones, types of communication lines, and information and communication technologies (ICT) literacy rate for clinicians, etc. Nonetheless, mobile terminals connected to the internet will become important devices for telemedicine in the future. Boissin et al. instructed specialists in surgery and emergency medicine to interpret medical images using smartphones, tablets, and computer screens, and then evaluated the quality of the images. The results showed that images viewed on smartphones and tablets had a higher evaluations than those viewed on computer screens. This suggests that mobile terminals, such as smartphones and tablets, could replace computer screens when used in telemedicine in cases of emergencies, worldwide[22]. Furthermore, when predicting future demand for telemedicine, we suggest that the analysis should include the number of future doctors and nurses in the target country. For example, Ishikawa et al. have predicted the future number of physicians and gynecologists in Japan, while JP Ansah et al. predicted the number of ophthalmologists in Singapore [23,24]. By integrating such results into future research, we believe that the possibility of introducing telemedicine services can be evaluated more appropriately. Conclusions The use of cluster analysis and PCA has made it possible to visualize the possibility of introducing telemedicine services. The
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results of this research suggest that there is a good possibility of introducing remote telemedicine services in South Africa and that the possibility for its introduction is also high in Thailand where internet spread is more advanced. Based on these results, we find it necessary to first grasp the region’s economic situation and then implement the introduction plan. Funding None. Ethical approval Not required. Declaration of Competing Interest None declared. CRediT authorship contribution statement Teppei Suzuki: Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Jyuri Hotta: Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Tomomi Kuwabara: Conceptualization, Writing - review & editing. Hiroko Yamashina: Conceptualization, Writing - review & editing. Tomoki Ishikawa: Conceptualization, Writing - review & editing. Yuji Tani: Conceptualization, Writing - review & editing. Katsuhiko Ogasawara: Conceptualization, Writing - review & editing. Acknowledgements We gratefully acknowledge the work of past and present members of our laboratory.
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