Keywords

1 Introduction

The global health landscape is evolving rapidly, and digitization is now penetrating almost every aspect of modern society, including biomedical research and healthcare (Jacobs et al. 2018). Healthcare delivery is becoming more technologically advanced with the focus of digital health initiatives shifting towards scalability, integration into the healthcare system and sustainability. In the coming decades, digital health technology will unquestionably play a significant part in assisting healthcare professionals in adopting some of these advances into routine practice. In general, due to the rapid pace of change and digital growth, low and middle-income countries (LMICs) need more resources to be invested in their healthcare development, and those that do exist are unevenly distributed. Therefore, LMICs continue to face considerable difficulties in providing high-quality, affordable, and universally accessible healthcare delivery and research, and in response to these challenges, different types of digital health initiatives have been launched (Labrique et al. 2018).

Biobanks, whose purpose is the long-term storage and distribution of biological samples, and associated data, have become key actors in the biomedical research fields, by allowing for the analysis of samples long after their collection and by digitally making available information about the sample and its donor. For example, most major biobanks and cohort studies rely on online application procedures for their samples and data through dedicated laboratory information management systems (Jacobs et al. 2018). This allows for a reliable tracking and monitoring of said applications, which improves the efficiency of the use and distribution process. Furthermore, numerous technologies, like electronic health records (EHRs), remote consultations, and mobile health (m-health) applications (apps), are already widely used in several countries in many fields, such as diagnostics, robot-assisted surgery, clinician decision support, and epidemic prediction, however all these remain largely independent activities and not systematically linked (World Health Organization (WHO) 2021).

Many digital initiatives focus on healthcare challenges in LMICs aiming to improve both access to healthcare and the quality of healthcare delivery (Aisyah et al. 2022; Labrique et al. 2018). Egypt highlights the importance of m-health and e-health (Moss et al. 2019) to overcome many long-standing barriers to providing healthcare to the underprivileged in LMICs, particularly barriers like access, quality, time, and resources (Kamel 2021). Moreover, the COVID-19 pandemic has highlighted and amplified the shortcomings of LMICs healthcare systems, especially in terms of health infrastructure. However, one of the positive effects was the growing and accelerated investment in digital health initiatives, such as teleconsultations, which improved access to quality healthcare (Hirko et al. 2020; European Observatory on Health Systems and Policies et al. 2022).

The aim of this chapter is to outline the benefits and challenges associated with the implementation of digital health initiatives in LMICs, as well as to suggest innovative solutions already showing promising results in some LMICs.

2 Digitization and Biobanking

Digitization is currently penetrating all fields of modern sciences and has consequently become a critical aspect of modern biobanking operations, as biobanks constitute part of the foundational research infrastructures. Biobanks that specialize in the long-term storage of biological samples, such as tissues, blood, and DNA, have recently been established in several low- and middle- income countries (LMICs) in the Arab region of the Middle East, such as Egypt, Jordan, and Sudan (Abdelhafiz et al. 2022). According to Abdelhafiz et al. biobank managers in these countries faced similar difficulties for the establishment of their biobanks, for example, lack of trained and skilled staff, limited financial resources, and lack of knowledge about biobanking in the general population and among physicians leading to their reluctance to support these projects. For their training, the staff often has to enroll abroad, at European or USA institutions for instance, however, this training is often quite short, lasting a month/a few months, which might not be enough to gain full knowledge and experience (Abdelhafiz et al. 2022). In that context, digitalization might be part of the solution to this problem, by allowing for some of the training to be undertaken remotely, through virtual conferences and courses, as well as online educational resources.

Prospective biomedical research commonly requires storing tissue and liquid biological samples for long periods. Thus, biobanks have supported several research developments such as mapping the human genome, and the cancer genome atlas project (Abdelhafiz et al. 2022; Coppola et al. 2019) and others, thus are considered as gateways for precision medicine. Biobanks have also gained importance in the last few years due to increased quality requirements for biological samples. Indeed, processing and storage of biological samples with reliable and extensive pre-analytical history plays a key role for reproducibility in scientific research and due to the ever-increasing demand for samples, particular attention must be paid to sample acquisition and preparation in order to guarantee the highest possible sample quality (Baber and Kiehntopf 2019). Specifically, the time that follows the collection of a given sample and precedes its analysis is called the pre-analytical phase, during which the quality and stability of samples is strongly influenced, making it essential to have accurate information on the sampling time (Lippi et al. 2019). This leads to the role that digital solutions can play, for instance, on the integration of electronic data sources, and with automation of the technical processes, such as the retrieval of samples from storage, and their treatment for analysis.

The automation of biobanks’ complete life cycle, from early collection and pre-analytical processing through the storage, freeze-thaw cycles, to its final analysis allows to sharpen their competitive advantage, harmonization and standardization and allow for eventual accreditation (Baber and Kiehntopf 2019). Furthermore, the increasing complexity and amount of data generated by biobanking activities, makes digitalization necessary to manage these resources efficiently. With digital tools, such as virtual databases and software specifically designed for biobanking operations, it becomes easier to manage large amounts of data and to track the movement of samples from the point of collection to their destination after distribution. Digitalization can bring major improvements to the way biobanks manage their activity by allowing for an increase in speed and accuracy of data processing. With digitized information, researchers can quickly search for and access specific samples or data points, speeding up the research process significantly (Arrighi and Hofman 2022). Furthermore, digitization helps reduce the probability of errors and inaccuracies that are inevitable and quite common with manual data entry and handling (Arrighi and Hofman 2022). Another key benefit of digitalization for biobanks is improved data security. With advanced security and access controls, data is less susceptible to be tampered with or handled by unauthorized individuals. Therefore, sensitive data, which could identify a specific or group of donors for instance, can be adequately protected.

Conclusively, digitalization provides numerous potential benefits, including faster and more accurate sample tracking, improved data security, and increased operational efficiency, but can also be of great value for sharing information, networking, and collaboration between biobanks in LMICs. One such project is identified, the Zipline project, which consists in using drones to transport blood from storage units to hospitals. This technology has allowed hospitals in Rwanda to adapt to the lack of transportation infrastructure in rural areas, and minimize transportation time, therefore, addressing the concerns around the pre-analytical phase, which is an essential part of the life cycle of a biospecimen (Ackerman and Strickland 2018). Another example is the DxConnect Virtual Biobank that operates as a collaborative resource. Hosted by FIND, the global alliance for diagnostics, this open-access platform enables researchers across academic, non-profit and industry sectors to view collections- even if not self-identified as biobanks- registered by any institution worldwide, search by disease and other characteristics, and connect with those holding samples of interest (Ongarello et al. 2022). This virtual arrangement is anticipated to bridge the gap between LMICs (where most infectious diseases occur, and samples are collected) and high-income settings (where most samples are analyzed). Having said that, the above initiatives that rely heavily on digitalization of biobanks in LMICs currently constitute the exception rather than the rule.

2.1 Documentation of the Life Cycle of Biological Samples

Perhaps the greatest impact of digitalization in biobanking is on the documenting of the life cycle of biological samples. The life cycle of a biological sample can be divided in three main phases: the pre-analytical, the analytical, and the post-analytical phase. In the context of international clinical studies, an increasing number of biological samples are needed and being collected, which reveals the need for optimal management of these resources, so their quality can be guaranteed to researchers (Betsou 2017). However, health institutions and researchers tend to focus on the performance and efficiency of the analytical and post-analytical phases, during which typically 15–20% of all errors occur. On the other hand, the frequency of pre-analytical errors is generally between 60% and 70%, but despite these results, sampling time information is often missing (Vermeersch et al. 2021; Plebanis 2012). In that context, the problem can be addressed in different ways, including through information technology. Indeed, the ability to follow a biological sample’s complete life cycle, from its initial collection and pre-analytical processing through the intermediate storage conditions, including freeze-thaw cycles, to its final scientific usage is made possible by digital information technologies.

Nanni et al. were able to map and track the entire life cycle of stored biological samples using Radio Frequency Identification (RFID) technology. This technology allowed, through communication with radio-waves, to identify samples and their data by reading an electronic tag attached to the samples’ container, either manually or through an automated process using special cryotubes and racks. It also allowed us to keep track of every movement of a given sample and the time between each step by recording time stamps (Nanni et al. 2011). The RFID technology showed promising results in high-income settings and allowed for a better management of the technical process of a biobank with better tracking of samples. However, the evidence didn’t show a significant improvement on the quality of the samples, while the costs associated with the implementation of this technology are not compatible with the financial challenges and competing financial priorities faced by LMICs.

That being said, a thorough digital history of each biological sample can be created and used in research by combining the collected data within a so-called integrated ‘Biomaterial Information and Management System’ (BIMS) (Parajuli et al. 2022).

2.2 Biobank Information Management System (BIMS) Interaction with Other Digital Data Sources

Integrating biological sample-derived data (such as ‘-omics’ data) with the broad range of phenotypic data gathered in other specialized research contexts or retrieved from EHRs, or patients themselves could be made significantly easier by digitization. To this end, the Information Management System of a Biobank (BIMS) supervises all the relevant data related to the biobank’s activity, including sample movements and exact location, patient data, storage conditions, and governance-related documents.

The BIMS can be connected to multiple software systems and databases, such as the Laboratory Information Management System (LIMS) which handles data related to the life cycle of samples, the hospital information system (HIS) for patient data, and the monitoring system (MS) which keeps track and regulates temperature and liquid nitrogen levels (Fig. 1). BIMS are often a subset of LIMS, repurposed and customized to fit the needs of a biobank, however, customization and staff training as a consequence, can be too costly for LMICs. Therefore, LMICs may need the support of BIMS providers through the development of open access software (Ezzat et al. 2022).

Fig. 1
A schematic diagram depicts the concepts of the bio-bank information management system. It deals with L I M S, M S, H I S, and Q M S.

An illustration of the data input streams for Biobank Information Management Systems. Data streams can be integrated from Laboratory Information Management Systems (LIMS); from Monitoring and Surveillance systems (MS); from Healthcare Information Systems (HIS); as well as Quality Management Systems (QMS)

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The BIMS allows for a better management of the large amounts of data generated by biobanking and research activities, in addition, it can provide a user interface that does not require programming skills, and the centralization of sensitive and confidential data can create a more secure digital environment in which security breaches can be prevented more easily (Arrighi and Hofman 2022; Olund et al. 2007). Finally, manual handling of data is time-consuming, and errors are likely to appear, but the BIMS can automatically integrate data from different sources and reduce the likelihood of errors to occur (Arrighi and Hofman 2022). Therefore, biomaterial-data sources should be connected to a core BIMS using standard data sharing formats to support and facilitate local biomedical research and enhance networking with more biobanks and research institutions (Pote et al. 2021).

2.2.1 Data Quality

Data is commonly perceived and defined as a set of collected facts, and the International Organization for Standardization (ISO) defines it as “reinterpretable representation of information in a formalized manner suitable for communication, interpretation, or processing.” Biobanks store data about patients, samples, analyses and even sometimes drugs. The data quality depends on the degree to which reliable and accurate information can be extracted. Therefore, high-quality data will accurately represent a situation and support interpretations that would be obtained with error free data, as defined by the Institute of Medicine (IOM) (Eder and Shekhovtsov 2021). For that reason, the implementation of a quality management system (QMS) is essential for an organization to be able to monitor the quality of their activities. A QMS is a set of processes and procedures that help coordinate and guide an organization’s activities to continually meet quality standards and achieve greater levels of efficiency (Betsou 2017). In that context, to handle biological material, it is crucial for biobanks to develop standard operating procedures (SOP), so a given sample’s data can be documented accurately throughout its entire life cycle from collection or reception to distribution. In a low resource setting, the implementation of SOPs can be a challenge, but for example the Golestan Cancer Biobank in northern Iran, was able to develop and put in place SOPs according to internationally accepted standards and protocols with some modifications because of their limited resources (Ghasemi-Kebria et al. 2021).

The quality of a given data point can be appreciated across multiple dimensions, including, accuracy, traceability, reliability, confidentiality, and impartiality. For instance, data accuracy refers to the representation of reality with the highest degree of truthfulness, meaning that it is objectively and measurably correct, as well as precise (Arrighi and Hofman 2022). It is important to consider these different dimensions to determine the relevance and usefulness of a data point, which is a challenge for biobanks in LMICs with limited financial and skilled human resources (Chowdhury and Pick 2019).

3 Challenges in Digital Health in LMICs

Despite the many optimistic views on potential benefits from the digitalization of biobanking in LMICs, severe challenges remain. These many challenges hinder the potential benefits of digitalization in healthcare research, including regulatory and ethical dilemmas and policies, as well as the difficulties associated with integrating digital technology into routine practices, while implementing digital health programs (Al Knawy et al. 2022). The main challenges that need to be addressed in that context are as follows (summarized in Table 1):

  1. 1.

    The most common challenge encountered is a lack of appropriate technical infrastructure, for example basic telecommunications infrastructure, such as reliable internet access, electricity, and mobile networks. In the context of a biobank’s operation, high volumes of data are generated, and thus LMICs may lack the necessary IT infrastructure and expertise to effectively handle and analyze them (Parajuli et al. 2022).

  2. 2.

    Limited financial and human resources, making it difficult to invest in staff training, and equipment when digital health programs are implemented. In return, it is difficult to maintain and manage these programs when faced with a lack of skilled staff, such as IT specialists who could provide technical training to health professionals. This can limit the scope and scale of biobank projects, for instance, and make it difficult to sustain them over the long term (Chowdhury and Pick 2019).

  3. 3.

    Data privacy and security, in many LMICs, regulations to protect patient data and ensure privacy and security are limited. Therefore, the trust between patient and healthcare professional, and the trust between patient and digital health services are more difficult to foster. In that context, ethical and legal considerations, related to informed consent, data privacy and intellectual property rights must be considered.

  4. 4.

    The geographical context of a given region can have an impact on the development of digital health programs. Indeed, the geographical distribution and topology, as well as the presence or absence of proper roadways could make transportation operations and infrastructure maintenance more difficult and costly (Parajuli et al. 2022).

Table 1 Summary of main challenges and some potential solutions for digitalization in LMICs
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3.1 Access to Internet and Electricity

Reliable access to electricity increases services availability, readiness, and quality of care, especially for patients under critical care (Alhadi et al. 2022). On the contrary, a lack of access to electricity is associated with negative health outcomes, for example increased mortality, lower quality of care, and reduced utilization of health services (Irwin et al. 2020). Digital technologies typically rely on having access to the internet and electricity, both of which depend on various factors, including region, socioeconomic status, and others. The implementation of digital health programs requires a stable and strong internet connection and electricity supply for the equipment to function effectively, however, reliable and affordable access to these resources is a significant challenge for LMICs, affecting equally significantly digital and laboratory aspects of healthcare (Vounba et al. 2022). Furthermore, the cost, which is still considerable in many LMICs, is another prohibitive parameter (Kiehntopf and Krawczak 2011). For that reason, some manufacturers are investing in research around low-cost innovations that could benefit health institutions in low resource settings (Eder and Shekhovtsov 2021).

3.2 Technical Challenges

Biobanks require specialized infrastructure, such as cold storage facilities with −80°C freezers and liquid nitrogen tanks, as well as laboratory equipment and machinery for sample analysis. Basic infrastructure, such as electricity supply and a stable internet connection, necessary for any facility to function properly are also essential for biobanks to be and remain operational. Technical difficulties encountered as network issues, blurry images, poor sound during video consultations owing to a slow internet connection, and service interruptions due to poor network quality are just a few of the technical challenges encountered by healthcare professionals, researchers, patients and users of health services. Indeed, in a study from 2014, Mendy et al. reported that only 55% of biobanks had access to reliable and uninterrupted electricity supply, which is a key component of biobank infrastructure to operate equipment, freezers, and computers. This means that 45% of these facilities face a major challenge in maintaining the quality of their biological samples (Mendy et al. 2014). Therefore, digital health programs and solutions can be technically much more difficult to implement in LMICs. Unfortunately, there is little benchmarking done on this aspect, beyond isolated anecdotal evidence relating to facilities and/or scientific initiatives

3.3 Skill Shortage

LMICs experience severe skilled personnel shortages, and according to the WHO, with current population needs, there is a global shortage of about 7.2 m healthcare workers, which is expected to rise to 12.9 m by 2035 (Chowdhury and Pick 2019). LMICs are particularly affected by this phenomenon, which is amplified by the departure of medical practitioners to more developed nations. Therefore, to combat this issue, it is crucial to offer incentives to physicians for them to remain in their home countries, such as improving their working conditions and recognizing the value of their work, particularly in remote and rural areas (Scheffler et al. 2016). This may require investing in better healthcare facilities and promoting collaboration with healthcare providers operating in rural areas (Chowdhury and Pick 2019). In addition, ongoing training with IT experts will be necessary in a rising digital era, considering the increasing rate of change associated to health technologies, and digital literacy will be a necessary skill for health professionals to acquire for them to be able to address the concerns about technology divide affecting service accessibility (Holland and Davies 2020), between rural and urban areas for example.

3.4 Ethical and Legal Challenges

The ethical issues relevant to using digital technologies include data privacy, confidentiality, transparency, and ownership. The WHO emphasized that digital health interventions must consider individuals’ data privacy, security and its appropriate use and ownership (World Health Organization (WHO) 2021). Digital health technologies collect and store sensitive personal health data, which must be protected from unauthorized access or use. For this reason, a trusted and safe environment for health data has to be put in place, for example, with a data access model that enables research in a trusted digital space and doesn’t allow sharing data outside this digital space. However, this requires robust protocols to be established, which can be a challenge in LMICs where there may be limited resources or expertise in this area (Zatloukal et al. 2022).

That being said, patients must be fully informed of the purpose, risks, and benefits of digital health technologies and provide their consent for their data to be collected and used. In LMICs, where the population might not be educated sufficiently or where access to information is limited, this may affect the consenting rates. Another aspect to consider are the cultural barriers and how these can affect the overall approach to digital health. For instance, it may be considered disrespectful, in some cultures, to question or challenge an authority figure, such as a researcher or healthcare professional. Therefore, the necessary steps to ensure that prior informed consent is discussed and obtained, need to be taken, to ensure that samples and data can legally and ethically be used and shared (Vodosin et al. 2021).

Digital health has the potential to revolutionize healthcare delivery and improve health outcomes in LMICs, but also the potential to amplify pre-existing health inequities, for instance, between urban and rural areas in terms of access to the necessary infrastructures to deploy digital technologies (Hirko et al. 2020). Limited internet access and lack of digital education make it difficult to ensure equitable access to these technologies. Finally, on the legal side, there may be unclear or inadequate regulatory frameworks in place to govern the use of digital health technologies in LMICs, particularly in the context of biobanking (Biobank and Population Cohort Building Network (BCNet) 2022). This can lead to concerns around the quality and safety of these technologies, as well as the ethical use of patient data.

3.4.1 Policy and Data Security Challenges

The 2018 WHO resolution on digital health puts an emphasis on the necessity for LMICs to develop frameworks that address concerns around privacy, security, data ownership and consent (World Health Organization 2018). Indeed, a key element of appropriate and secure data management in biobanks should be that clinical data, sample-related data, and identifying data are physically stored in separate databases under different administrative power and using different identifiers, thus reducing the likelihood of a security breach (Scheffler et al. 2016; Zatloukal et al. 2022). However, the BIMS is generally connected to the internet, and data transfer operations are often conducted online which makes biobanks vulnerable to cyberattacks and confidentiality breaches. It is therefore essential for biobanks to be equipped with reliable IT infrastructures closely monitored to prevent data theft (Chowdhury and Pick 2019).

Policy challenges include, keeping pace with evolving digital technologies in healthcare, the establishment of standards for international data sharing (Vodosin et al. 2021). Also, to these challenges can be added, political instability, frequent transfers of skilled health professionals, and a lack of consistent official support (Scheffler et al. 2016). However, according to Vodosin et al. digital health programs have seen some increase in LMICs, as have regulatory frameworks. However, most LMICs still currently lack governance guidance/regulation, thus, long-term leadership and support at a very high level is necessary for them to succeed in this endeavor.

3.5 Funding Challenges

Biobanks in LMICs face several financial, operational, and social challenges in establishing sustainability. These challenges include developing a business plan that relies on dependable funding sources, enhancing operational efficiency, and building trusting governance arrangements with researchers and potential donors (Vaught 2011). Funding challenges in terms of buying costly equipment, high installation charges, and training staff were identified (Abdelhafiz et al. 2022; Van der Stijl and Eijdems 2019). Indeed, costs are an essential part of sustainable biobanking, and are highly variable and specific to the type of biobank, but overall, the initial starting investment at the creation of these structures consists essentially of capital investments in buildings, space and equipment. Across time as a biobank becomes operational, costs associated with sample and data collection, processing, storage and distribution start to rise (Van der Stijl and Eijdems 2019). During this phase, costs can be divided into different categories, including, human resources, equipment and infrastructure, as well as sample handling and data management (Van der Stijl and Eijdems 2019; Sqalli et al. 2020). Generally, human resources are the biggest source of expenses for biobanks which is a major challenge for LMICs facing skilled staff shortages and difficulties to generate revenue and secure long-term funding (Van der Stijl and Eijdems 2019). Digital health programs depend on funding and volunteers; when funding stops, the whole program gets disturbed and terminated. However, in LMICs the distribution of resources is extremely unequal, and it is widely agreed that funding aligns poorly with global health needs.

For biobanks to achieve longevity, they must be financially sustainable, but the access to secure long-term funding is very difficult. In Egypt, for example, numerous national funding agents are available to fund biobanking and research, Science, Technology Development Fund (STDF) and The Academy of Scientific Research and Technology (ASRT). Furthermore, the Ministry of Higher Education and the Ministry of Planning and Social Development, along with some non-governmental organizations, share in supporting planned research. According to van der Stijl and Eijdems, academic biobanks struggle to get access to enough revenue to sustain their activity, therefore, multiple sources of funding and income need to be considered. This diversification of income streams can include a mix of public funding, such as research grants, commercialization of services, and private funding through collaboration with industry (Van der Stijl and Eijdems 2019).

3.6 Regional Challenges

Accessibility of healthcare refers to the relative ease with which services can be reached from a given location, and one of the difficulties in establishing digital health services is the complex landscape in LMICs. Indeed, uneven geographic distribution and topography such as mountains and hills make the establishment of and access to health facilities difficult (Eder and Shekhovtsov 2021). It is a major barrier that is often underestimated despite its importance since a significant part of the populations in LMICs live in rural areas with no direct access to healthcare facilities but by traveling long distances by foot or public transport (Sqalli et al. 2020). In addition, many LMICs don’t have sufficiently developed infrastructure for logistics and transport, which can make it difficult for patients to access health facilities and difficult for health facilities, to move essential equipment and supplies, or in the context of a biobank, to move biological samples and data between different sites.

4 Conclusion

Throughout the world, information and communication technologies are increasingly used in healthcare. Many of the current issues facing health systems in LMICs, such as the non-availability of healthcare professionals in rural areas, the inconsistent quality of care and low patient compliance, may be mitigated through the widespread use of e-health, especially as technologies continue to develop. However, for e-health to spread and deliver its promises, a strong foundation needs to be put in place in LMICs by engaging all stakeholders (physicians, researchers, patients, ethics boards, governments, public and private institutions), and putting in place policies that provide a legal and ethical framework for digital health. Furthermore, investment in technologically advanced infrastructures (reliable electricity supply, stable internet connection, etc.) is essential for biobanks to maintain and develop their operations.

Digitalization offers biobanks a wealth of opportunities to enhance their effectiveness, including the:

  1. 1.

    Better documentation of the quality, life cycle, and scientific application of biological samples

  2. 2.

    Ability to scale-up the use of biological samples from LMICs.

  3. 3.

    Improved interoperability with other sources of donor-related data.

  4. 4.

    Easier and faster access for researchers to biological samples and their associated data, for example, through online applications.

  5. 5.

    Greater link with the automation of the technical processes for the retrieval and analysis of samples.

  6. 6.

    Ability to manage, track, and analyze large volumes of data.

However, for the implementation of digitalization to be effective in LMICs, technical, financial, legal, ethical, and geographical contexts need to be taken into account. These constitute a complex background of competing priorities against which any biobanking operations needs to survive. Thus, while digitalization of biobanking in LMICs is full of potential, the complexities outlined above maintain the high implementation barriers within LMICs.