Introduction

In spite of the global advancements in science and technology, the disparity in the quality of life across the globe has continuously broadened over the past centuries. This is especially so with respect to the availability of cutting-edge medical technologies. Smart phones and their apps, fast-processing computers, and automobiles may have penetrated every corner of the planet, but the difference between the types of medical treatments available to patients in the rich parts of the world and the quality of treatment available to patients in poor parts of the world continues to be staggeringly high.

Transferability of technologies across the rich-poor divide is rarely taken into account in the R&D sector despite its having an evident, albeit indirect, effect on the depth of this divide. Simply, technologies with a low degree of transferability would have a harder time crossing this gap and would tend to stay within the limits of the wealthier regions of the world, as opposed to transferable technologies, which could readily cross this divide, thereby diminishing its extent. Earlier, it was inferred that models for assessing the potential of technologies to aggravate or heal this divide could be created using qualitative methods (Uskoković, 2021a). Such models are built on the premise that no technologies are neutral from the socioeconomic and cultural standpoints, as each of them affects the totality of the social space. To illustrate this, a simple thought experiment could suffice, such as that of imagining one world promoting the development of therapeutic medical devices, and another fostering the development of preventative medical devices; whereas the diseases would be tackled only after their symptoms become manifested in the former scenario, proliferation of causes leading to these symptoms would be stood in the way of in the latter scenario, thus demonstrating how the choice of technologies affects the existential bases a top of which human cultures, sciences, healthcare, and economies develop.

Moreover, as pointed out by E. F. Schumacher in the 1970s (Schumacher, 1998), not all technologies are viably transferable to any given underdeveloped setting in the world. Rather, some technologies are better and some less suited to be integrated into a poor communal setting—hence, the different degrees of “appropriateness” ascribable to them. Ideally, as Schumacher held it, technologies are to be adapted to the new settings rather than inertly exported thereto, with little forethought involved in the process. Still, quantitative methods for assessing the transferability of advanced technologies have been thoroughly lacking, especially such that would take into account humanistic parameters alongside the technological and economic alone. And given that technologies are not neutral and do dramatically affect the cultural bedrock of the society (Heidegger, 1954), these effects extending beyond the standardly considered, economic ones should be taken into account by all means. Economic decisions in the developed world, on the other hand, are being increasingly made through sole reliance on computational models, which take into account free market competition, trends, and other conditions, along with governmental regulations, production capacities, and user base proclivities. Problematically, however, these models are rooted in obvious fundamental fallacies, one of which, identified exhaustively earlier (Farley & Daly, 2006), has been the treatment of the natural resource depletion as a source of income rather than an intrinsic cost. Another major deficiency has come from their not including the effects of social segregation caused by the economic activities assessed. Neither do they take into account the spiritual enrichment or impoverishment due to these activities. Therefore, the intrinsic difficulties of quantifying the qualitative aside, creating technological models that take into account humanistic parameters and are performed to mitigate the adverse effects of life standard segregation are of paramount importance for sustainability of the planet and of human civilization.

Here, an attempt has been made to formulate one such quantitative and more holistic model for assessing the transferability of new technologies across the rich-poor divide. For that purpose, twelve technologies of the author’s choice are being rated using a number of distinct parameters with respect to three comparatively impoverished regions of choice.

Model

Individual parameters included in the transferability score evaluation are listed in Table 1. Parameters were assigned different score ranges, being as low as 3 to 5 for the less decisive parameters or as high as 0 to 5 for the most critical ones. Within the broadest scale of 0–5, scores 5, 4, 3, 2, 1, and 0 corresponded to “high”, “moderately high”, “average”, “moderately low”, “low”, and “impermissibly low”, respectively, while the scores became narrowed, albeit preserving the same range from high to low, for scales reduced in range, e.g., 1–5, 2–5, 3–5, or 4–5. Each parameter in Table 1 was assigned a single score with respect to a particular technology in question and the region where the given technology is to be transferred to. The individual parameters were averaged with the geometric, not arithmetic means, so as to allow parameters assigned 0 in theory to completely annul the transferability and deem the given technology intrinsically nontransferable to a given region. Hence, the expression for the transferability score, T, is given as:

Table 1 Parameters included in transferability score evaluation and their corresponding score ranges
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$$T=\sqrt[n]{x(1)x(2)\dots x(n)}$$

where x(1) to x(n) refer to the n number of transferability parameters listed in Table 1. For the model implemented here, n = 19.

Regarding the individual parameters, they were deduced via free thinking on the subject, without using any explicit literature on transferability, notwithstanding that many of the proposed parameters could be backed with appropriate literature references. Some of these parameters are self-explanatory, such as the demands for low production, installation, training, usage, and repair costs (Table 1, A–E), considering that the affordability of resources is a critical limitation for innovation in poor settings. In fact, a prior literature meta-analysis (Bauer & Brown, 2014) ranked “affordability” as the second most cited among 49 emergent indicators of appropriateness of technologies in a new social setting, right after “the community input”. Clearly, innovation in the medical sector has to display a finite degree of improvement in terms of diagnostic precision or therapeutic outcome relative to the existing solutions (Table 1, F), lest there be no incentives for replacing the old with the new. This is one of the parameters assignable 0 as the score and given the ability to annul the net transferability score in cases where technologies do not provide even an incremental improvement over the technologies already in place. In addition to the direct financial costs of implementing the new technology, the environmental costs associated with setting it in place and using it must be accounted for (Table 1, G). Understandably, technologies that fare better from the environmental standpoint should always be given a priority over equally effective technologies with a more adverse ecological footprint. Despite the fact that green technologies frequently lack the efficiency and, thus, the financial incentive of their more traditional counterparts and the fact that poor countries are often typified by a paucity of environmental standards and regulations (Nguyen et al., 2022), studies have shown that given the supportive technology acquisition policies in place (Fu & Zhang, 2011), green technologies may have an edge over the more environmentally polluting ones when it comes to their transfer to less developed social settings (Hamhami et al., 2020). What is more, various global economic stimuli schemes are likely to emerge in the near future, which would allow develo** countries to subsidize the transition to cleaner and more sustainable technologies (Ng et al., 2021; Bai et al., 2020).

Table 2 Data of interest for the three regions of the world chosen for the analysis
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Table 3 Twelve advanced medical technologies of choice rated for their transferability scores
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Next, standalone advanced technologies have a nil long-term prospect, as their only way to thrive is within a pre-existing technological base. For this reason, the implementation of some new technologies is critically dependent on a specific infrastructure, and when this infrastructure is missing, the usage value of the technology gets annulled; hence, the ability of the infrastructure parameter to receive 0 as a value (Table 1, H). The traditional maxim according to which “the poor should be taught how to fish rather than given the fish” is best summed in the parameter measuring the potential of the technology to foster further innovation in the new setting with the use of modest resources (Table 1, I). A prior study conducted in the context of China’s green economic efficiency, for example, demonstrated that increasing the level of independent innovation associated with the newly introduced technologies facilitates this and a whole plethora of other industrial efficiencies, along with the economic growth (Zheng et al., 2022). Also, it has been recognized that the more labor-intensive and the less capital-intensive technologies, the greater their transferability to low-income and develo** settings (Menck, 1973). Massive and bulky technologies often disable such innovative alterations, especially so in the current times when the ecological consciousness, unexplainably, is regressing and disposability is the new norm. In contrast, simpler, low-cost technologies often have a natural open-source structure to themselves, allowing for innovation spanning from ad hoc DIY to more sophisticated amendments, depending on the amender’s preferences and capabilities. Correspondingly, technologies that are more compact and easily transportable between different points of care are favored over the bulky ones that must stay in place (Table 1, J). Such technologies with sufficient degrees of miniaturization and portability are often titled point-of-care technologies and are intended for use at or near the sites of patient presentation (Haney et al., 2017). So far, these technologies have mostly been used for diagnostic purposes, as exemplified by mobile mammography, pulse oximeters or blood glucose monitors, and very rarely as a treatment option. Relatedly, technologies that could be fabricated in whole or in part locally must be favored over those that require international importation (Table 1, K). A plethora of prior studies on international technology transfer have shown that the success of technological acquisition critically depends on the existence of domestic capacities to fabricate and maintain the technology through local manufacture (Schmidt & Huenteler, 2016).

Technologies vary in terms of the level of good laboratory and/or manufacturing practices (GLP and GMP, respectively) that they are in demand of (Table 1, L). Clearly, technologies that do not require higher level biosafety procedures, clean rooms, or intense sterilizations are in favor when it comes to transfer to settings where even the most elementary sanitations are a challenge. The fact that fully detailed GLPs accompany protocol transferability documents even in the pre-validation stage (Southee & Curren, 1997), in fact, is sufficient to infer that the complexity of laboratory processes associated with a new technology is directly reflected in the complexity of its transfer. Patient-friendliness is a universal criterion (Table 1, M) because technologies such as a transdermal microneedle patch or an orally consumed device would always be welcomed by patients more than surgically inserted implants, just as well as noninvasive diagnostic tools, such as a salivary lateral flow assay, will be more desirable than nuclear magnetic resonance or excision biopsy, regardless of whether the implementation milieu is rich or poor. Co-creation (Table 1, N) is a concept with extraordinarily diverse semantics (Uskoković, 2011; Uskoković, 2015b; Uskoković, 2018), which predicts that technologies developed and applied within a broader multidisciplinary niche will fare better than those stemming and operating under narrow specialization conditions. Accordingly, the development of affordable medical technologies is shown to be directly proportional to the degree of involvement of engineers, scientists, health professionals, and businessmen (DePasse et al., 2016). The necessity of bringing experts from different professions together is a natural way of fostering networks of cross-disciplinary cooperation, which has positive repercussions at different levels of the local scientific and technological multiverse. This participatory mode of technological development (Patnaik & Bhowmick, 2022) has been considered a critical grassroots component of efforts to mutually adapt new technologies and develo** countries to one another. Another key parameter with the ability to boost or annul the proposed innovation is that of the net financial gain for the healthcare system and the local economy per quality adjusted life year (QALY) saved for patients subjected to the treatment with the new technology (Table 1, O). QALY analyses are a form of cost-benefit analyses in the medical realm (Uskoković, 2021b) and are especially important in cases where the affordability of the treatment is a critical notion. The results of such analyses can disprove the long-term viability of particular treatments and call for the search for less costly and/or more effective ones. Many eastern European countries, for example, implement the 3 times per capita GDP/QALY threshold to determine which therapies or diagnostic procedures will be reimbursed by the national insurers and which will not (Gulácsi et al., 2014). One caveat of the use of QALY as a parameter in the model proposed here is that for diseases that are universally challenging to treat, it intrinsically favors the therapeutic approaches over the diagnostic ones in endemically poor regions where no sophisticated therapies are possible to match the positive diagnostic findings.

The lower section of Table 1 contains less palpable parameters, which are hardest to evaluate because of their more humanistic character. The first among them is that measuring cultural congruence between the new technology and the social setting in which it is to be integrated (Table 1, P). Indigenous people are often resistant to the introduction of instruments that appear ostensibly foreign to their milieu, fearing their acting to disrupt the local state of social harmony. In fact, the receptiveness of the local communities, which may be at odds with that of national governments, is said to be a key factor preventing a smooth transfer of technologies (Huh & Kim, 2018). Some technologies also could be said to resonate better with the characteristics of the local culture than others (Uskoković, 2021a). Here, it is instructive to discover analogies between traits of technologies and features of other cultural products—be it music, movies, literature, fashion, or other technologies—that are either well received or rejected by the local communities. Governed by the adage that “what people want and what people need is rarely the same thing”, it could be concluded that this parameter measuring cultural congruence may or may not have the similar value as the parameter measuring the public approval of the new technology (Table 1, Q), which is yet another parameter capable of annulling the transferability of the invention in scenarios where the public is overwhelmingly against the intrusion of such or similar products into their lives. Now, just as cultural congruence and public reception may not equate, so it is with the public approval and the state administration approval, which need not be aligned at all times. Since governmental constraints often do not coincide with the public disapproval, the public approval and the smoothness of the regulatory path (Table 1, R) correspond to two separate parameters in the model. These constraints can often be unsurmountable, which is why this parameter may have 0 as the value. One example of a technology that is not transferable due to social factors may be that of copper-bearing intrauterine devices for emergency contraception in countries such as Honduras or Costa Rica, where emergency contraception has been prohibited by law. Likewise, efforts to export hypersensitive mercury thermometers to Uruguay or sphygmomanometers to Cuba would provoke similarly intractable barriers because these countries have already phased out these mercury-containing medical devices from the healthcare practice (Rustagi & Singh, 2010).

A word of caution is needed here, given that whenever a nil value is assigned to a specific parameter in the model, this may change, either through an external intervention or through ad hoc amendments to the invention. For example, the transfer of wireless glucose sensors to regions without the required telecommunication networks in place may be given 0 for the infrastructural parameter at first, but more in-depth analyses might show that one such technology should not be automatically discarded as nontransferable to the given region because of the possible ad hoc solutions, such as connection to the satellite signal. Finally, the last parameter in Table 1 (Table 1, S), referring to the systemic improvement in the quality of life, is the finickiest and most difficult to narrow down or measure because it does not correspond to a sole improvement in the physical wellbeing of the individual subjected to the use of the device, but rather to an overall improvement of the social state of welfare across different strata, including the cultural, communicational, psychological, economic, health-wise, and, last but not least, spiritual. In short, the closer the effect of the medical technology approaches quiescent healing modalities, of both body and spirit, the closer it will be to earning a perfect score for this parameter. In contrast, the more the use of the technology disrupts the social fabric of communion and tears the social connections apart, degrading the culture and diminishing the spirituality of the system, the closer its score will be to 1.

Three Regions Chosen for the Technology Transfer

Rural areas of three different regions of Eurasia were chosen for the test transferability score evaluation: West Bengal in eastern India, ** regions of interest. These technologies will be briefly discussed in this section.

Paper-based diagnostic assays have emerged as an economical version of more expensive enzyme-linked immunosorbent assay (ELISA) or even bulkier techniques such as gas/liquid chromatography or mass spectrometry, capitalizing on the ability of porous and hydrophilic cellulose in paper to drive the liquid flow through capillary action without any external power sources (Yan et al., 2022). Folding such papers, like origamis, brings reactants loaded in different compartments of the device into contact and activates the reaction, which eventually provides a colorimetric result. Here, one such blotted paper assay is chosen as an exemplary low-cost, small-sample-volume, rapid-output diagnostic device (Table 3.1), but the score for it would be similar as that for many other paper-based assays. It is contrasted by higher-cost self-powered microfluidic chips for electrochemical immuno-biosensing (Table 3.2), as constructed by traditional photolithography or soft lithography (Haghayegh et al., 2022). Another high-tech tool proposed for use in medicine are drones, that is, unmanned aerial vehicles (Table 3.3). They have been either used in the recent years or proposed for use as vehicles for the transportation of therapeutics and microbiological samples between clinical centers and remote areas, but also as telecommunication means for diagnosis and perioperative patient evaluation (Rosser Jr et al., 2018). Their ability to facilitate a medical intervention without a direct human-to-human contact led to the surge of interest in their use during the COVID-19 pandemic (Maity et al., 2022).

Multimodal diagnostic tools, such as those combining magnetic resonance imaging (MRI), positron emission tomography (PET), and optical computed tomography (CT) scanning (Galgano et al., 4), for example, multimodal MRI/PET/CT imaging displayed the lowest acceptable score for record five different parameters, the major reason being the inadaptability of the technology to rural clinics whose resources permit mostly general practice and critical care, without any elaborate diagnostics. In contrast, the origami paper-based diagnostic assay and the topical nanoparticle-based antimicrobial displayed the highest scores for record eight different parameters, which was largely due to the portability and low cost of these technologies, their ability to be self-applied, and also the great need for affordable antibiotic creams among rural populations where the incidence of infected superficial wounds is comparatively high (Mahato et al., 2019; Chakraborty et al., 2012). For ** and adapting to the local infrastructure and needs of the populace. One way to solve this paradox is to create bridges of communication and trust between researchers in the rich and the poor parts of the world and engage them in translational thinking in the earliest stages of the design of technological blueprints and proofs of concept. The second important impetus should come in the form of the awareness that simple, elegant, and resource-effective technologies have a tendency to score better on the transferability test such as the one devised here. Whenever possible, researchers should resort to such creative and resourceful ideas, as opposed to indulging in the modern-day idea that the more complex and expensive is always the better.

In the end, learning from technologies that either overly ripened or never matured is essential for allowing the develo** societies to learn from the developed ones—notwithstanding the arrogance implied in the term “developed”—and perform what is often christened a “leapfrog” effect (Uskoković et al., 2010). In a sense, they would learn from the failures of the developed world and catch up with it over a finite period of time, thus diminishing the gap between those who live in abundance and those who struggle in poverty. Therefore, like all assessments, the one presented here should ideally be seen as an opportunity to learn and evolve rather than to provide judgments set in stone. This is especially so because the aforementioned lack of neutrality of technologies implies not only their effect on the totality of the social space surrounding them, but also the reverse effects of this space on the prospect of these technologies. As a result, divorcing the effects of the intrinsic nature of technologies from the effects of the tangled network of commerce, insurance policies, and regulations on the translatability of the given technologies is, strictly speaking, an impossible task. The example of blueprints for perfectly mature technologies that remain indefinitely locked in corporate files simply because of the lack of financial interest or capacity to push the product to the market may be invoked here to remind us of how substantial and decisive these nonscientific factors determining the fate of technologies are. For these reasons, breaking down the process of the technology transfer into numerous factors, such as that attempted here, cannot be denounced as reductionist in essence so long as these individual factors are not viewed as definite and factual. Rather, they are a guide for highlighting the factors that stand in the way of an effective transfer and devising strategies for their improvement.

Are the Most Transferable also the Most State-Of-The-Art?

As a top** on the cake in this discussion comes the question whether the most transferable technologies deduced from the analysis described here are also the ones given the most priority and room for publication in the world’s most prestigious publication platforms. To answer this question, a plot was constructed (Fig. 3), showing the average impact factors of scientific journals publishing research on each of the twelve technologies analyzed here in 2021 as a function of their transferability scores. The trend evidently shows that the most transferable medical technologies are not discoverable in high-impact journals, which appear to favor the research of comparatively low potential for transfer across the rich-poor divide. Research reports pertaining to the most transferable technologies are not discoverable either in journals on the low end of impact factors; rather, they are found most prominently around the middle of the impact factor range. This demonstrates that technologies that make up all the fad of a science era are not the most transferable ones. Instead, the search for the technologies with the largest potential for transfer to the poor regions of the world should bind us to less expected of places. To put it simply, things that heal the world, as ever, are to be looked for not in spotlights, but in the darker corners.

Fig. 3
figure 3

Transferability scores for the twelve test technologies averaged for the three regions of interest and represented as a function of the 2-year impact factors of journals publishing research on them in 2021 as per the Scopus database. Bibliographic search was carried out by inputting three keywords for each technology (Table 1). The average number of hits per technology was 30 ± 24. Dashed red line represents the best nonlinear fit of the data points

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Conclusion

A proof-of-concept method for quantifying the transferability of technologies was devised in the form of an assessment sheet containing nineteen independent parameters. The model was tested with respect to twelve state-of-the-art medical technologies and three comparatively impoverished regions of the world, namely, West Bengal in India, **njiang in China, and the former Yugoslav state of Montenegro. The results of the analysis demonstrate that neither the gross economic productivity nor the degree of poverty can be the sole determinants of the transferability of technologies. Rather, a complex network of scientific, technological, infrastructural, socioeconomic, and cultural factors defines the extent of transferability of new technologies across the rich-poor divide. The proposed model helps in discerning which of these factors represent the most critical hindrances in the transfer of technologies. For many of them, cultural factors assumed the dominant role in determining the transferability scores, indicating that they should be more commonly incorporated into models for assessing the potential of new technologies to create social impact. It is argued that the most dependable technologies to transfer are old and proven ones, but the best ones for ameliorating the rich-poor divide are juvenile technologies in formative stages of their development, which also happen to be employing simplistic ingenuity and resourcefulness in their design. It is also argued that transferability of technologies should be considered early on in their design, ideally upon their very inception. These findings reiterate that models for assessing the social value of technologies should inextricably tie the scientific factors with the socioeconomic and humanistic. Countless technical models of various natures could be devised with this holistic principle in mind.