Building sustainable public health data science and informatics capacity: possibilities for Eastern Europe and Central Asia

Abstract

Prevention, detection, and response to pandemic-prone diseases involve collecting, analyzing, and applying data swiftly at scale. Achieving these goals requires adequate information technology and a skilled public health workforce capable of translating data into action. While public health informatics and data science programs are established in parts of the Western world, Eastern Europe and Central Asia (EECA) face significant gaps in educational pathways. This study mapped the educational and labor market structures in Georgia, Kazakhstan, and Moldova, identified barriers to training and retaining professionals in public health informatics and data science, and proposed sustainable models for integrating training into national systems. A mixed-methods approach included a review of 111 policy and regulatory documents, labor-market analysis, 147 semistructured interviews, seven focus groups, and two expert workshops (n = 72) that validated findings and prioritized policy options. Although all three countries demonstrate strategic commitment to digital transformation, investment in human capital remains insufficient. Educational programs are isolated, with no accredited degrees in these professions, and accreditation processes delay educational innovations. Employers lack awareness of the value of these professions, and salary gaps between the health and information technology sectors, as well as between public and private sectors, weaken workforce retention. The absence of occupational recognition in national regulations further constrains workforce development. Stakeholders endorsed modular, competency-based training—integrated into Master of Public Health programs and in-service certificates—as the most feasible way to scale capacity. Building a sustainable workforce requires short-term measures such as problem-based and continuing training, along with long-term policies that (i) prioritize human capital development in national digital health strategies, (ii) adjust educational and occupational classifications, (iii) modernize accreditation processes for modular credentialing, and (iv) align incentives to attract and retain talent. Together, these actions could enable EECA health systems to harness digital innovation for better health outcomes.

Key messages.

• Digital transformation of health systems is occurring at a rapid pace worldwide and offers an opportunity to use this data more effectively for evidence-informed decision-making for public health.

• Taking advantage of this opportunity requires a skilled public health workforce that can turn data into action. However, in the studied countries, digital ambitions are not matched by current investments in workforce development, leaving these countries unable to reap the benefits of digital transformation.

• Education and labor-market structures in the studied countries are poorly aligned with workforce needs, with siloed academic programs, limited continuous professional development options, and wage gaps that drive talent away from the health sector.

• Sustainable workforce development requires coordinated, multisector, multipronged policy responses with short- and long-term perspectives, combining modular, competency-based training now with future reforms to educational program accreditation, occupational classification, and staffing standards.

Introduction

The COVID-19 (a contagious disease caused by the coronavirus SARS-CoV-2) pandemic and other (re-)emerging health threats, including the silent pandemic of antimicrobial resistance, highlight the urgent need for increased investment in the collection, analysis, and use of health data. The availability of extensive digital health data and advancements in artificial intelligence (AI) offer new opportunities to address these public health challenges. This includes the use of health information systems to enhance real-time surveillance of infectious diseases, especially across the human/animal and environmental sectors (under the One Health approach), which would enable early outbreak detection, timely reporting, and effective coordinated cross-sectoral response. It also entails integrating multiple data systems for more complete longitudinal records, harnessing clinical data for public health purposes, and using the power of “big data” to conduct implementation and comparative effectiveness research.

Aside from infectious disease threats, modernized data systems are critical to achieve universal health coverage goals, including tackling noncommunicable diseases (NCDs). In addition to the burden of morbidity and mortality from these conditions, there are also significant costs to economies and societies seeking to control them, straining health systems and economies through high treatment costs and lost productivity (Pan American Health Organization 2024). Controlling NCDs is a critical global development priority, and digital health systems could help focus on NCD prevention and control through people-centered care and universal coverage, build national research capacity to support NCD strategies, and monitor disease trends and progress to ensure effective prevention and control (WHO 2024).

Modernizing public health data solutions requires investment in a skilled workforce that can design appropriate information systems and manage large volumes of data utilizing public health informatics and data science (World Health Organization 2021, European Observatory on Health Systems and Policies 2025).

The need for professionals capable of designing adequately integrated information systems and handling and analyzing vast amounts of data has been further confirmed by the National Academies of Sciences, emphasizing the need for “data acumen”—the ability to derive meaningful and reliable answers from data. This skill goes beyond traditional math and statistics and becomes critical for public health professionals and must include a focus on “data literacy”—the ability to understand and critically evaluate data in real-world contexts, particularly in the field of public health (Mirin et al. 2022).

As the volume of digital data increases, two disciplinary/occupational fields discussed in this paper become crucial: (i) public health informatics and (ii) data science. These two fields are distinct, but related and mutually reinforcing, coexisting “upstream” and “downstream” within the data ecosystem.

Upstream

The first is “public health informatics,” which, according to Wan and Gurupur (2020), is still not clearly defined, with various experts providing differing perspectives. While multiple definitions exist, for the purposes of this paper, we adopt the definition provided by the Informatics and Data Science for Health (IDASH) Fellowship Program Handbook 2025. According to this source, public health informatics is defined as “the systematic application of information, computer science, and technology in areas of public health (surveillance, prevention, preparedness, and health promotion) to capture, manage, analyze, disseminate, and use information to improve population-level health outcomes”(Yasnoff et al. 2000, Aziz 2017).

Downstream

“Data science” leverages data to address crucial questions and enhance public health in a transparent, reproducible, and ethical manner. It encompasses study design using already collected data and data management tools, developing analytical methods, and generating and disseminating findings. This methodology builds upon existing knowledge from public health and related fields to safeguard and promote health while using large volumes of digital data generated by the digital systems. The term “data science” signifies the increasing significance of big data, predictive models, and interdisciplinary collaboration, which helps clarify its swift ascent in importance (Goldsmith et al. 2021).

Currently, few universities primarily in the USA and in some Western European countries offer educational courses in public health informatics and data science, and there is a significant shortage of such programs and professionals in many parts of the developing world, where little is being invested in the production of a skilled public health labor force (Curioso 2019, Kiosia et al. 2024, European Observatory on Health Systems and Policies 2025). Currently, technical and financial assistance from organizations, such as the US Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO), and others in collaboration with the national ministries of health and governments, aim to help build capacity through educational programs (short-term or long-term) in low- and middle-income countries (LMICs) (Kiosia et al. 2024). These programs were initiated when overseas development assistance (ODA) for health increased, reaching an all-time high of $84.0 billion in 2021. However, since the COVID-19 pandemic and particularly recent decisions by the US administration (Donor Tracker 2025) and other European governments (IHME 2025), ODA funding is expected to shrink due to fiscal challenges and shifting priorities in high-income countries (HICs). Between 2021 and 2023, estimates from the Institute for Health Metrics and Evaluation (IHME) indicate that health funding from donors decreased by 23.1%, reversing much of the progress made in 2000 and 2021, and is expected to decline even faster (IHME 2024). This poses a threat to externally funded programs, including those for education. Dependency on donor-funded trainings implies other challenges as they operate parallel to existing educational structures and faces sustainability and integration challenges. Without embedding these programs into formal systems, their long-term impact remains limited. Often, there is a disconnect between the training provided and the actual requirements of the job market, and this misalignment can lead to underutilization of acquired skills and reduced employability (WHO 2020). Donor-funded training programs offer valuable opportunities and can be catalytic to more durable solutions. However, without incorporating sustainability planning from the outset, including supply and demand considerations for an upskilled workforce, these programs may not achieve their intended goals.

This paper examines how sustainable educational pathways can be designed to produce a public health workforce with the knowledge and skills necessary in data science and public health informatics. Specifically, it aims to (i) assess current educational pathways for workforce development, (ii) identify barriers to producing and retaining a skilled workforce in public health informatics and data science, and (iii) propose sustainability models to integrate training into national systems. It draws insights from three countries to illustrate the challenges and explore potential solutions for workforce development across Eastern Europe and Central Asia (EECA). We hope the topic of sustainable workforce development in the stated disciplines and the approaches elaborated in this paper will resonate with public health education audiences among other LMICs.

Materials and methods

This study employed a mixed-methods approach to examine the challenges and opportunities in an LMIC setting for creating a sustainable educational path in the specified specialty areas. We integrated qualitative research methods with an extensive literature review and secondary data analysis to comprehensively understand the policy, education, and labor market barriers and facilitators that influence workforce development in Georgia, Kazakhstan, and Moldova. This was a purposive sample from seven countries from EECA that had participated in a fellowship training program supported by the US CDC and University of Washington—“IDASH.” The study countries were selected based on their interests and representation of each of the distinct former Soviet Union subregions of Central Asia, South Caucasus, and Eastern Europe and their varying stages in undertaking digital transformation of their health sectors. To ensure a robust analysis of primarily qualitative information, we followed a multiphase process.

First, a comprehensive desk review examined the current state of affairs related to public health informatics workforce development in selected countries. This review analyzed 111 policy, regulatory, analytical, and other documents, including national workforce strategies and accreditation guidelines/rules, alongside reports from international organizations, such as WHO-EURO, the CDC, and the World Bank. Additionally, relevant academic literature and statistical data on workforce supply, digital health adoption, and public health training initiatives were obtained and reviewed. The findings from this phase helped shape the interview and discussion guides for subsequent data collection.

Qualitative research through key informant interviews (KIIs), identified through criteria-based website search (Search criteria were separately developed for (i) academic institutions, (ii) public/government sector institutions, and (iii) private sector representatives.), and focus group discussions (FGDs) yielded further insights into the research questions. A total of 147 semistructured interviews were conducted with stakeholders from various sectors, including government ministries, academic institutions, private companies involved in digital health, healthcare providers, and international organizations (see Table 1). Participants were chosen using a purposeful and snowball (with the help of KIIs) sampling approach to ensure diverse representation. The interviews were held in person or virtually, lasting about 45–60 minutes, and were transcribed verbatim and analyzed using NVivo™ software. Separate interview guides were used for academic institutions covering topics related to production of needed workforce and touching upon awareness about and experience in the disciplines investigated by this paper, availability and demand for relevant educational programs, competency profile of public health informaticians and data scientists and governing national regulations, effective forms of teaching these subjects, availability of educational resources/capacity, and main current challenges and considerations for going forward. Interview guides for public–private employers were used to evaluate existing and expected demand for these specialists and covered topics of existing human resource capacity and skills and expected future demand for these professionals, influence of state policies on the employers and employees and overall on labor market, need for and/or lack of data-driven decision-making in the health sector and in their institutions, current practices for data analysis and use by the institution, and challenges faced. Additionally, seven FGDs (participants selected through snowball approach with the help of KIIs) occurred in Georgia, Kazakhstan, and Moldova to gather perspectives from employers, educational institutions, and professional associations. Each discussion included 6–12 participants and lasted 80–100 minutes. The topics discussed included the role of public health informatics and data science professionals in these countries, hiring trends, skill gaps, and/or curriculum development and accreditation challenges for the educational programs. Researchers facilitated the discussions using a structured guide to ensure consistency across the FGDs and linkages of findings with the KII interviews.

Table 1.

Respondent distribution.

Country	Educational institutions	Public sector organizations (government and providers)	Private sector employers	Other international and civil society organizations	Total
Georgia	11	8	13	4	36
Kazakhstan	19	20	2	6	47
Moldova	12	42	8	2	64
Total	42	70	23	12	147

Ninety-seven (66%) female and 50 (34%) male respondents. Bold values reveal totals.

Two expert workshops (April and September 2024) validated the findings. The first workshop engaged 23 experts from all three countries to review preliminary results; the second included 49 participants (both in-person and remote) and utilized a Delphi-style survey with the Slido™ tool to rank and prioritize emerging policy or programmatic recommendations. Quantitative data from the desk review and the Delphi method supported cross-country comparisons, highlighting common challenges and country-specific nuances.

This multistep approach ensured the validity and reliability of the presented findings. For qualitative data analysis arising from interviews, focus groups, desk reviews, and workshops, we used both deductive and inductive coding, which two independent researchers validated. During data analysis, themes emerging from the interviews and FGDs were inductively coded and assigned to either market, supply, or demand factors or to suggested recommendations. Iterative analysis of these themes and their interactions were extensively discussed by the research team using mind maps. Obtained findings and possible solutions were organized as presentations and discussed in the workshops. Quantitatively, the Delphi method prioritized recommendations, while desk review statistics informed labor market assessments. Cross-country comparisons highlighted shared challenges and country-specific differences, helping reveal the best options for sustainable workforce development in LMICs.

The Institutional Review Board (IRB) of the Health Research Union approved the study in Tbilisi, Georgia. Ethical approval from Kazakhstan and Moldova was not sought. Nonetheless, to ensure uniform ethical standards across all three countries, all data collected were managed under a protocol approved by the IRB in Georgia, ensuring that participants’ rights and confidentiality were protected regardless of location. All participants provided written informed consent, with confidentiality maintained through deidentification and strict data security protocols. Finally, the non-native English-speaking authors have used an AI tool—ChatGPT3—to improve the flow of English and grammar.

Results

The study’s findings are organized around three main themes: (i) market factors, (ii) supply (education system) factors, and (iii) demand factors. Each section provides a comparative analysis across countries, highlighting similar and country-specific challenges and opportunities for integrating public health informatics and data science training into higher education and continuous professional development (CPD) systems in a sustainable way.

Market factors: the economic and policy landscape

Although Georgia, Kazakhstan, and Moldova differ in population size, land area, and levels of economic development, all three countries have experienced significant economic growth over the past decade with rising gross domestic product (GDP). On the path of economic development, these countries have prioritized digital transformation as a key driver for further economic growth, recognizing the importance of innovation ecosystems in fostering knowledge-based economic growth. Their national strategies reflect a commitment to harnessing digital technologies that offer the potential to fuel economic growth and enhance societal well-being (Government of the Republic of Kazakhstan 2017, Government of the Republic of Moldova 2018, Ministry of Finance of Georgia 2023). However, the healthcare sector has not been given the same priority level as other productive industries, and it was only in 2025 that countries began to develop digital health strategies/programs [Moldova completed its development toward the end of 2025 (M1 Moldova 2025), while Kazakhstan (SDG Fund 2025) and Georgia (based on personal communication with WHO office in Georgia) were still in the process of creating such documents]. While significant resources have been allocated to digital transformation in Kazakhstan and Georgia, investments have focused mainly on industrial and commercial sectors, leaving limited support for developing the health informatics and data science fields and/or workforce. Similarly, in their national strategic documents, Georgia and Moldova have emphasized the importance of advancing digital health, but available budgetary allocations do not support these claims (Parliament of Georgia 2023, Government of the Republic of Kazakhstan 2024, Ministry of Finance of the Republic of Moldova 2024). Thus, despite widespread acknowledgment of the importance of digital transformation in policy and strategy documents, developing human resources with digital skills specific to the health sector remains underemphasized in health sector strategies for all countries (Government of Georgia 2022, Government of the Republic of Kazakhstan 2022, Government of the Republic of Moldova 2023). For Georgia, Kazakhstan, and Moldova, embracing digital health transformation is not merely a technological upgrade but a strategic imperative to enhance health spending efficiency, reduce costs linked to growing economic burden caused by NCDs, and improve population health outcomes. Prioritizing investments in digital infrastructure and workforce development is necessary to position these countries to meet current healthcare challenges and future demands more effectively while reducing healthcare costs through efficiency gains (The State of Digital Health 2023, Jack et al. 2024).

This highlights the mismatch between the countries’ aspirations for digital transformation in healthcare and the prioritization and investment in developing a skilled labor force to lead these transformations. Without aligning investments in workforce development with strategic initiatives for digital health, the potential for digital transformation to improve health outcomes will remain limited.

Another structural factor that negatively impacts the demand for a skilled labor force is the fragmented nature of digital health and information systems (Semyonov et al. 2023, Gotsadze et al. 2024). Kazakhstan is more advanced in this area compared to others. The country made significant progress by establishing the Republican Center for Electronic Healthcare in 2013 (the Republican Center for Electronic Healthcare, Kazakhstan https://rcez.kz/aboutcompany); however, its system is not yet sufficient to meet the current needs. Existing data collection, management, and analysis processes remain weak, preventing the effective and timely use of data for public health decision-making. Without stronger integration, enhanced interoperability, and investment in enhanced digital health infrastructure, leveraging data-driven insights to improve population health and healthcare outcomes remains a significant hurdle.

Finally, FDGs and individual interviews revealed that while innovative approaches for processing clinical and public health data remain underdeveloped, traditional statistical methods are better understood and predominantly applied for financial tasks, such as budget planning. Software tools, such as Excel™ and SPSS™, are the most commonly used, reflecting a reliance on familiar tools and methods with limited capacity:

In terms of clinical data, at the moment, in Moldova, there is no such thing as taking this data from somewhere and processing some information. Not at any level, not in any specialty. (respondent from Moldova)

We are far from a predictive analysis, although some organizations that work on international projects may have some capacity, I don’t think there is any prospect for this [doing it in-house] at this stage. (respondent from Georgia)

Finally, many employers lack awareness of public health informatics and data science benefits, resulting in limited demand for specialists in the public and private healthcare sectors:

Awareness is the first that needs to be raised in this direction. They must understand the importance of data. Today it is less observable. (respondent from Georgia)

There is general lack of knowledge and awareness. To tell the truth, they don’t know much about that topic [public health informatics and data science], nor do they have a feeling of how it should be used. (respondent from Moldova)

There should be a clear understanding of the profession and benefits of data use. (respondent from Kazakhstan)

Government policies do not require healthcare institutions to integrate informatics professionals or data scientists into their teams. As a result, organizations feel little motivated to invest in training programs and/or hire well-qualified staff in these fields.

Supply factors: education programs and accreditation

Georgia, Kazakhstan, and Moldova all have higher education regulatory frameworks aligned with the Bologna Process (European Higher Education Area 2025), an initiative launched in 1999 to ensure compatibility with the European Higher Education Area (EHEA) (European Higher Education Area 1999). Following the Bologna Process, all countries standardized educational programs by specifying credit hours, setting minimum faculty qualifications—such as requiring a certain number of doctoral degree holders—and defining program content requirements.

However, despite this alignment, none of the countries offer accredited degree programs focused on public health informatics or data science (see Table 2). Public health and informatics (nonhealth-related) programs are delivered separately at the bachelor’s, master’s, and doctoral levels, but they are typically siloed and lack integration across disciplines. This separation limits the potential for interdisciplinary learning and collaboration.

Table 2.

Accredited programs in public health and informatics/data science.

	Bachelor	Master	PhD
Accredited programs in public health
Georgia	6	19	4
Moldova	1	1	0
Kazakhstan	7	15	10
Accredited programs in informatics and data science
Georgia	17 in informatics and information systems, 20 in computer science, 1 in data sciences	12 in informatics and information technologies, 7 in computer sciences, 1 in data science/data analysis	3 in informatics, 2 in computer science
Moldova	16 programs in information technologies	6 programs in information technologies	3 in computer science, 3 in information technologies
Kazakhstan	187 in informatics and information systems	35 in informatics (1 in English), 4 in data science/data analysis	12 in informatics, 2 in data science/big data analysis

In the national accreditation framework for higher education, there is a lack of recognition of these disciplinary areas, which further hinders the creation of degree programs that the governments could fund. This delays the progress in building a qualified workforce that is greatly needed. The absence of job classification codes (a mandatory prerequisite for public funding) for public health informaticians and data scientists in the national classification system compounds this issue. This creates uncertainty regarding the professional roles and career development pathways for public health informaticians and data scientists, making it even more challenging to establish a capable workforce equipped with the necessary skills and knowledge.

In all three countries, introducing new academic programs involves a lengthy bureaucratic approval process. Accreditation for new degree programs takes 6–12 months, which can delay addressing immediate training needs. According to respondents, for the immediate period, a more practical solution would be to enhance existing Master of Public Health (MPH) programs by integrating public health informatics and data science modules, because creating entirely new degree programs and/or adopting new regulations for the educational sector, where disciplinary requirements are spelled out in the national accreditation rules, will require longer time:

We should avoid lengthy accreditation procedures. Instead of creating new programs, let’s integrate relevant courses into existing MPH degrees [as an immediate step]. (respondent from Kazakhstan)

During interviews, we noticed a disconnect between employer needs and the desires of higher education representatives. Employers consistently call for flexible, short-term certificate programs that enable upskilling current staff without leaving the workforce. Yet, many educational institutions remain focused on developing longer, formal degree programs that may not meet employers’ immediate and practical demands. This misalignment underscores the urgent need for stronger collaboration between academia and both the public and private healthcare sectors to codevelop training pathways that are both academically sound and practically relevant. Modular, competency-based courses with stackable credentials have been mentioned as a promising approach—allowing learners to build qualifications progressively while responding to real-time workplace requirements.

For in-service training, Kazakhstan and Moldova have established CPD requirements for healthcare professionals, whereas Georgia lacks formal CPD regulations. In Kazakhstan and Moldova, CPD programs must align with national occupational classifications, which are also necessary for educational institutions/programs to receive public or private funding/support; however, the absence of recognition for public health informatics and data science in the national classification would prevent government funding for such training programs. While developing and offering short certificate programs in public health informatics could serve as a solution, current barriers to (and even absolute lack of) state funding for such training, as noted above, further diminish demand for this education.

For example, in the absence of nationally supported educational programs, and to fulfill the need for strong information systems and a public health workforce, the University of Washington’s International Training and Education Center for Health in partnership with the CDC and the WHO-EURO developed and launched the IDASH fellowship program in 2021 (International Training and Education Center for Health 2025). IDASH is a 12-month, competency-based fellowship designed to advance applied learning in innovative and effective data science methods and tools; strengthen public health informatics and information systems using multisectoral, multidisciplinary approaches tailored to country-specific needs; and build long-term, sustainable regional capacity. The program’s scale is limited to a few dozen fellows each year across all involved countries (Armenia, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Ukraine, and Uzbekistan), and its long-term funding prospects in a changing Global Health funding landscape (Rasanathan et al. 2025) remain unclear.

Demand factors: workforce needs and employment challenges

Most respondents across the three countries demonstrated a limited understanding of health informatics and data science in public health. Data science was often equated with producing statistical tables and reports, while public health informatics was associated with information and communication technology (ICT), software engineering, or cybernetics. (Cybernetics, a term widely used in the Soviet Union, is an interdisciplinary science that studies the general laws of receiving, storing, transforming, and transmitting information in complex control systems, whether they are machines, living organisms, or society.) Public health informaticians were frequently mislabeled during interviews and group discussions as developers, IT managers, programmers, or IT specialists. In some cases, their roles were described solely by their tasks without mentioning profession.

The lack of precise terminology for these professions in local languages further complicates discussions, often requiring lengthy explanations. Of the two terms, “data science” resonates more due to the historical presence of the “Doctor Statistician” role from the Soviet era, making analytics and statistics more familiar concepts:

Most of these specialists who work in statistics are doctors. Doctors, but doctors of a certain [old] age… Young doctors who graduated from the faculty, they will not work as statisticians…. (respondent from Moldova)

To support the development of public health informatics and data science, standardized terminology and greater awareness—potentially facilitated by multilateral organizations, such as the WHO—are needed.

Next, there is a salary disparity between public health and the private sector for ICT jobs, which serves as an important deterrent for professionals considering careers in public health informatics. In Kazakhstan, Georgia, and Moldova, wages in the ICT sector are 50%–70% higher than those in public health, making it difficult to retain talent in the healthcare field. This gap is also documented in national employment statistics (National Statistical Offices):

Salary is not competitive. If we train professionals in AI-driven data analytics, they will leave for better-paying industries. (respondent from Kazakhstan)

The big question is who will pay them or who will be able to pay them [trained staff]. Currently, we are fighting for every single IT guy, especially when talking about public institutions, because we are limited in how [much] we pay. We need to find different projects to keep them [employed] since the private sector is offering them more money. The general idea is how to finance and pay them. (respondent from Moldova)

Furthermore, the limited size of the public health informatics job market (both in public and private sectors) restricts career mobility, discouraging young professionals from pursuing these disciplinary fields:

The market is not big… If I were a business analyst, I would work everywhere, if I prepare for health care, I would only work for 20 companies… I am already very constrained in health sector. There may be demand in America and Europe, but there won't be that much here. (respondent from Georgia)

I would like to say that it's not the best option to make a master’s degree and then work only in this field [health]. This is not the best option. Because our country is small and there are not much demand for specialists in this field. (respondent from Moldova)

Employers believe that government mandates requiring informatics professionals in public as well as private healthcare settings could help stimulate demand for the workforce. These strategic recommendations will be further explored in the discussion and sustainment pathway sections, offering a structured and long-term approach to workforce development in this critical field.

Discussion

While the study provides a thorough assessment, some limitations must be recognized when interpreting results. The findings are from Georgia, Kazakhstan, and Moldova, although they might also relate to other countries in respective subregions and other LMICs. We focused on public health informatics and did not specifically inquire about clinical informatics, which usually emerges earlier than public health. However, as noted in the paper, due to limited understanding of the term and field, we believe this omission did not negatively impact our findings, as no respondent mentioned progress in clinical informatics. Selection bias is possible, as key informants were chosen based on their availability and expertise, despite the use of a snowball approach. Finally, the data on human resource needs for these specialty areas were not available from the countries. Neither international benchmark exists to adequately quantify such requirements. Considering the rapid pace of developments in the digital health field, we think actual quantification of needs should be addressed by countries, and estimation was beyond the scope of this research. Therefore, we relied on expert opinions and qualitative insights. Nonetheless, the study’s rigorous methodology and use of data triangulation within and across countries improve the reliability of the findings and recommendations.

Our study identified five critical and interrelated barriers slowing the development of a digital public health workforce in a purposive sample of three countries in EECA. Unless addressed urgently, these challenges risk undermining the production of the skilled workforce necessary to drive digital transformation in the health sector that is so much needed to effectively achieve Universal Health Coverage goals (Wilson et al. 2021).

First, there is an apparent mismatch between national ambitions for healthcare digitalization and the level of investment in building the human capital required to achieve it. Although national health strategies increasingly prioritize digital transformation, they often overlook the essential need for targeted capacity building in public health informatics and data science. Efforts to train and equip health professionals with necessary digital competencies remain insufficient, despite growing demand. Bridging this gap requires that national strategies explicitly prioritize human resource development, including creation of structured education pathways and providing CPD opportunities to ensure that healthcare systems can fully leverage emerging technologies.

Second, our findings reveal significant gaps in the conceptual understanding and public recognition of public health informatics and data science professions. In Kazakhstan, Georgia, and Moldova, these roles are frequently conflated with general ICT functions, and professionals are often mischaracterized as programmers, IT managers, or software developers. Their contributions to strengthening health information systems are poorly understood, with discussions focusing narrowly on operational tasks rather than profession or occupation. This conceptual confusion is exacerbated by the absence of standardized, locally adapted terminology with respective profession and/or occupation classifications, complicating workforce planning and policy dialogue. Addressing this foundational gap must be a priority for both national governments and multilateral organizations, such as the WHO, through efforts to standardize terminology and elevate awareness about the benefits these essential professions bring to the health sector. When clearly defined and explained, the demand for these professionals within the health sector (for both public and private providers), among public health institutions, universities, and insurance companies, is expected to grow significantly.

Third, structural labor market barriers further inhibit workforce development. Across all three countries, public health informatics specialists face a wage gap of 50%–70% compared to the private ICT sector, significantly constraining public health institutions’ ability to recruit and retain qualified professionals. This salary disparity fuels talent outflow and discourages young professionals from entering the healthcare field. Moreover, currently, the limited size of the health informatics labor market restricts career mobility in both public and private sectors, making these career paths even less attractive. Health systems will struggle to build and sustain their digital workforces without strategies to address these market imbalances. As with other highly skilled professions, there are costs associated with paying and retaining highly skilled workers. However, these costs could be offset by efficiency gains realized through effective use of digital health information, which is estimated to amount to about 7%–11.5% of health expenditure when effectively deployed (London and Dash 2016).

Fourth, the lack of specific qualifications and occupational codes for public health informatics and data science within national classification systems creates additional obstacles. Without formal recognition, allocating public budgets, developing new educational programs, or attracting students and professionals through national education grants is difficult. This regulatory gap further delays efforts to build a strong workforce foundation in these emerging fields.

Fifth, from a health system perspective, a critical barrier lies in the misalignment between the urgent needs of healthcare institutions and the traditional structure of higher education offerings. Health systems require flexible, short-term training programs that allow current staff to acquire new competencies quickly without disrupting their employment. However, many academic institutions remain focused on long-duration, formal degree programs that—while valuable—do not help address the immediate capacity gaps. To bridge this divide, greater collaboration between health system players and academia is needed to design modular, competency-based training pathways, including stackable certificates that allow for progressive skill building. Short-term certificate programs, in particular, offer a pragmatic solution for rapidly developing workforce capacity while longer-term investments in full-degree programs are being developed.

The implications of these findings for health workforce development and digital health system strengthening are profound. Successful digital transformation in healthcare—an increasingly vital pillar of resilient and responsive health systems—relies not only on deploying new technologies but also on cultivating a workforce capable of supporting transition and navigating and leveraging their knowledge and skills effectively. The interconnected nature of these barriers suggests that isolated interventions will be insufficient; instead, coordinated, system-wide actions are needed. Without deliberate, sustained, and significant investments in training public health informaticians and data scientists, health systems risk exacerbating existing workforce shortages and undermining their ability to realize the full potential of digital innovations in public health practice. Strategic, coordinated action is urgently needed to address these interconnected barriers and build a future-ready health workforce.

Conclusion

Building a strong digital health workforce is an urgent priority for health system resilience and modernization in EECA. Our study highlights the deeply interconnected barriers—ranging from insufficient prioritization and investment in human capital, conceptual misunderstandings of key professions, labor market disincentives, regulatory gaps, and misaligned educational models—slowing progress toward this goal. Without coordinated and systemic action, these challenges will continue to hinder the countries’ ability to realize the benefits of digital transformation in health systems fully.

Addressing these barriers requires an integrated policy approach that focuses on strengthening the foundations for the adequate development and deployment of the public health informatics and data science workforce. Based on our findings, countries may consider undertaking mutually reinforcing near-term and longer-term measures, in parallel:

Near-term: growing the value proposition for key stakeholders

• Fostering problem-based learning for practitioners, on-the-job. Upskilling can be conducted while tackling real-world problems, whereby countries form multidisciplinary teams to participate in time-bound problem-solving endeavors aimed at addressing a specific problem space. Through this approach, a common language around the principles and practices of public health informatics and data science can evolve as these teams are supported in identifying root-cause problems and discovering solutions that can be designed, tested, and evaluated. Moreover, as stakeholder institutions may need to be engaged at the outset to define the problem space and assemble the team to address it, organizational engagement could be factored in from the outset.

• Modernization of education and training modalities in CPD schemes. Relatedly, there could be opportunities for fostering collaborations between health system actors and academic institutions in the design of flexible, modular, and competency-based didactic training programs, including short-term certificates and stackable credentials, which meet the immediate needs of the health sector while allowing existing working professionals to upskill without interrupting employment. Targeting executive leadership would also be important for supporting data governance and an enabling environment for both systems and the practitioners that use them.

Together, these two approaches (problem-based learning and CPD schemes) can nurture the enabling absorptive environment while informing the design of pre-employment undergraduate and graduate-level academic programs and foster an enabling ecosystem for absorption of these future cadres into both the public and private sectors.

Medium- to long-term: policy opportunities

• Prioritization of human capital development in the national health sector and/or digital health strategies seems essential. This may involve explicitly recognizing workforce development needs as a fundamental part of countries’ digital health transformation plans and creating avenues for investments in education, training, and career development.

• Standardization of terminology. Efforts to develop standardized yet locally adaptable terminology for public health informatics and data science, along with the functions they entail, could help secure support for the demand. This can also help better define the professions needed to perform these functions, create institutional and policy environments that enable progress, guide workforce planning and employment efforts to integrate these capacities, and inform strategies for developing future professionals.

• Formalization of qualification frameworks. Governments and professional bodies could work toward formally recognizing public health informatics and data science within their national classification systems (qualification and occupational codes). This could facilitate clarification of career pathways and access to public sector funding for education and employment.

• Stimulating demand for informatics and data science expertise through regulatory mandates. This could include establishing minimum staffing standards (in terms of quantity and competency) for both the healthcare industry and public sector actors. Such mandates could help expand the job market and provide the scale necessary for sustained workforce development programs.

• Addressing labor market disparities. Strategies to make public sector careers more competitive, including salary adjustments or developing alternative incentive mechanisms—such as career development opportunities, scholarships, or secondment arrangements— could help make public health careers more competitive. This would be crucial to attracting and retaining qualified professionals in public health jobs.

Overall, countries may consider forming working groups to gather key stakeholders and explore options for addressing immediate workforce gaps among existing staff, as well as building a sustainable, future-ready health workforce that can leverage digital innovation for improved population health outcomes across the region.

Author contributions

G.G., S.B., J.G., and R.L. conceptualized the research. G.G., E.Z., T.B., and M.G. developed the research methodology. E.Z., T.B., and M.G. handled data collection and interpretation under G.G.’s leadership and drafted relevant sections of the paper. G.G. and M.G. worked on the final draft of the paper, which was critically reviewed, revised, and approved by all authors.

Funding

The US CDC provided funding for this work through a cooperative agreement (Award No: 5 NU2HGH000100-02-00) with the University of Washington, USA.

Disclaimer

The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the US CDC/Agency for Toxic Substances and Disease Registry.

Ethical approval

The IRB of the Health Research Union approved the study in Tbilisi, Georgia (Protocol #2024-02).

Reflexivity statement

The author group includes five females and two males and spans multiple levels of seniority, including an early-career female researcher from Georgia. Two authors specialize in higher education, two in public health informatics and data science, one is an evaluation and development specialist, one is a public health expert, and one is a health policy and systems researcher. Five authors have extensive experience conducting qualitative research, and one author has extensive experience working on the health systems of the former Soviet states.

Data availability

The data used for the preparation of this paper is fully included in the document. Data related to qualitative interviews can not be made available due to confidentiality issues.