Health Technology Assessment (HTA): Why Reliable Data Matters

Health Technology Assessment (HTA) is an essential tool for making informed decisions in healthcare. However, without high-quality and comprehensive data, conducting adequate HTA is impossible. In Ukraine, few fully understand what data are needed, how to collect them, or how to use them. This material aims to explain the importance of data for HTA, analyze the challenges of data availability in Ukraine, and suggest ways to improve the situation.

What Data Are Needed for Quality HTA?

HTA is a comprehensive analysis of the effectiveness, safety, economic feasibility, and financial impact of medical technologies. The reliability and credibility of such analysis depend on various types of data, including up-to-date and real-time monitoring information:

• Demographic data: population size, age and gender structure, level of urbanization, socio-economic indicators (education, employment, income), birth/death rates, and migration. These data help identify which population groups need specific technologies, how many people there are, where they live, and how population structure changes over time.
• Epidemiological data: disease prevalence and incidence, complication rates, levels of disability and mortality, prevalence of risk factors (smoking, obesity, etc.), disease burden, and trends in infectious disease outbreaks. These data are necessary to assess the magnitude of a problem, determine needs for specific technologies, forecast system load, and estimate the potential health and systemic impact of introducing the technology.
• Clinical data: treatment outcomes, frequency of adverse reactions, survival rates, and improvements in patients’ quality of life.
• Consumption data: volumes of medicine and medical device use, distribution across regions and facilities. For example, antibiotic consumption data allow monitoring of prescribing trends, overall use, and indirectly, the dynamics of antimicrobial resistance.
• Financial data: direct medical and non-medical costs (drug prices, procedures, hospitalization, care) and indirect costs (loss of productivity, social support).

Completeness, timeliness, and quality of these data form the foundation for reliable HTA conclusions that can support public procurement, clinical guideline development, and budget planning.

Why Doesn’t Ukraine Have Quality Data for HTA?

The main challenges that complicate data collection and use include:

• System fragmentation: medical data are scattered across different institutions without centralized coordination or standardization.
• Absence of a comprehensive clinical patient registry: the only national patient registry — the electronic healthcare system (eHealth, or eCOS) — enables electronic records, prescriptions, and referrals, and contains basic information about patients, doctors, and facilities. From an HTA perspective, however, eHealth is not a full-fledged clinical registry because:
  • it does not allow comprehensive tracking of disease prevalence and incidence, types, stages, and complications;
  • it does not include treatment outcomes;
  • analytical access is limited, and sampling algorithms are not aligned with state HTA requirements or flexible enough to incorporate them;
  • it lacks integration with specialized registries (e.g., oncology or rare disease registries) that would enable assessment of therapy effects for specific groups.

eHealth is a valuable digital health infrastructure, but it does not provide the data required for HTA. The lack of dynamic patient data — such as treatment response and adverse reactions — limits its use for systemic analysis. Without disease-specific clinical registries or significant upgrades to eHealth, HTA in Ukraine inevitably relies on assumptions rather than facts, reducing the quality of public procurement decisions and resource allocation efficiency.

• Limited access for analysts: even existing data are often inaccessible to independent HTA experts due to bureaucratic barriers and lack of legal frameworks.
• Low data culture: healthcare workers and administrators are not always motivated or adequately resourced to maintain complete and accurate records.
• Lack of transparency and consistency in data collection standards.

As a result, HTA often relies on incomplete, outdated, or poor-quality data, which undermines the accuracy of findings and the effectiveness of resulting decisions.

Why the Data Issue Is Not Just Technical, but About Governance

In health policy and systems management, the concept of “data as infrastructure” is increasingly discussed. This means not just raw data (tables or reports), but a system that enables data collection, storage, exchange, processing, interpretation, and decision-making.

It’s important to understand that this is not only about servers, software, or digital platforms.

Data infrastructure also includes rules, standards, governance mechanisms, analytical capacity (who can draw conclusions and how), and trust. Without these elements, no complex system — whether HTA, clinical protocols, or outcome-based financing — can function effectively. That’s why data are not just “fuel” but a critical part of infrastructure, like roads or power grids.

As analysts from the UK’s National Health Service (NHS) highlight, technical infrastructure alone is insufficient without:

• clearly defined data governance (who is responsible, who authorizes access, who can update or correct data);
• a metadata and standards system that enables consistent interpretation (e.g., uniform definitions of “remission” or “complication”);
• transparent mechanisms of inter-institutional interaction, allowing data to “move” and be used by analysts for decision-making.

In Ukraine, these “foundations” of data infrastructure are often missing or informal. Therefore, even when eHealth functions, it does not create the analytical capacity needed for HTA, because:

• there is no institutional data owner for effectiveness and safety information;
• no established data exchange and access protocols for analysts;
• no conceptual framework for measuring treatment outcomes.

What Can Be Done Now?

Even in difficult circumstances, small and realistic steps can be taken:

• conduct local studies and pilot projects — even small amounts of high-quality data collected at the hospital or regional level can provide valuable insights into technology effectiveness;
• standardize data collection protocols by introducing unified forms and procedures;
• train healthcare personnel on the importance of quality documentation;
• develop regulatory mechanisms to allow independent analysts open access to anonymized data;
• invest in digital infrastructure and health information systems.

Only with high-quality and comprehensive data can an effective HTA system be built — one that ensures rational use of resources and improves population health.