INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 05,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 2420
IMPLEMENTATION OF DIGITAL PLATFORMS FOR REFERENCE PRICING OF
MEDICINES: GLOBAL EXPERIENCE AND METHODOLOGICAL APPROACHES
Obidova Ruxsorabonu Olimjon kizi
Central Asian Medical University, Department of Chemistry and Pharmacology, Assistant
Abstract.
Rising pharmaceutical costs have driven many countries to adopt reference pricing as
a tool for regulating medicine prices. However, the effectiveness of this approach depends
heavily on access to timely and accurate data from international markets. This article explores
the global experience in implementing digital platforms for calculating reference prices,
compares key country-level platforms, and presents methodological approaches to selecting and
analyzing reference data. Based on case studies and comparative analysis, the article identifies
core success factors and outlines recommendations for developing countries seeking to
implement or improve digital tools in this domain.
Keywords:
Reference pricing, digital platforms, pharmaceutical regulation, Euripid, PMPRB,
e-health, global medicine pricing, case study.
Introduction.
The continuous rise in medicine prices presents a major challenge for health
systems worldwide. Reference pricing has become a commonly used method to limit excessive
pharmaceutical spending. However, traditional (manual) reference price calculations are time-
consuming, error-prone, and lack transparency. In response, several countries have
implemented digital platforms to automate the collection, processing, and analysis of
international pricing data.
This study investigates global best practices in the implementation of such platforms and
provides methodological insights for countries considering digitalization of reference pricing
systems.
The aim of this research is to analyze the global experience of digital platforms for reference
price calculation and to propose methodological principles for selecting, comparing, and
applying reference data.
Methods.
This study employed a combination of research methods, including literature and
policy review, comparative analysis, case studies, SWOT analysis, and content analysis to
explore the implementation and effectiveness of digital platforms for reference pricing of
medicines.
Results.
A quantitative comparison of selected digital platforms for reference pricing is
presented in the table below, highlighting structural and operational attributes:
Country
Platform No. of Referenced
Countries
Avg.
Data
Update Interval
Year
Introduced
Public
Access (%)
EU
(Euripid)
Euripid
32
Monthly
(30
days)
2010
30%
Canada
PMPRB 7
Quarterly
(90
days)
1987
50%
South
Africa
MPR
N/A
(Domestic
only)
Weekly
2004
100%
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 05,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 2421
Country
Platform No. of Referenced
Countries
Avg.
Data
Update Interval
Year
Introduced
Public
Access (%)
Kazakhstan
DAR
9
Monthly
2018
20%
Table 1.
Overview of Country-Level Digital Platforms
Countries utilizing digital platforms tend to update pricing data 2 to 4 times more frequently
than those relying on manual systems, leading to improved regulatory responsiveness. In the
Euripid collaboration, over 70% of participating countries have fully digitized price registration
systems, promoting more accurate and standardized pricing decisions across borders. South
Africa’s MPR platform provides real-time access to over 15,000 retail medicine prices, updated
on a weekly basis, offering full public transparency. In Canada, the PMPRB monitors and
publishes data on more than 1,300 patented medicines, maintaining a historical dataset that
spans over 35 years. These numerical insights highlight how platform maturity, update
frequency, and public accessibility vary significantly, shaping their impact on national pricing
policy and stakeholder trust.
Figure 1.
Comparison of Digital Platforms for Reference Pricing Based on Key Performance
Metrics
Figure 1 illustrates a comparative analysis of four national and regional digital platforms for
reference pricing: Euripid (EU), PMPRB (Canada), MPR (South Africa), and DAR
(Kazakhstan). The platforms are evaluated across four performance metrics: update frequency
(per year), number of referenced countries, public access (%), and integration score (1–5).
The results show that South Africa’s MPR platform has the highest update frequency, reflecting
its weekly real-time functionality (52 updates per year), along with full public access (100%).
However, it does not use external reference countries, as it is intended solely for domestic price
transparency. In contrast, Euripid leads in the number of referenced countries (32), although its
update frequency is monthly (12 per year), and public access is limited (30%).
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 05,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 2422
Canada’s PMPRB platform references seven countries and provides moderate public access
(50%), but its update frequency is lower (quarterly). Kazakhstan’s DAR platform shows a
balanced profile, with monthly updates, nine referenced countries, and a relatively low public
access score (20%). Integration scores—evaluated on a 1 to 5 scale based on interconnection
with customs, tax, and health systems—are moderately high for Euripid, PMPRB, and DAR
(scores of 4–5), while MPR ranks slightly lower (3), as it is not connected to procurement
systems.
Overall, the chart highlights key structural and functional variations across platforms, revealing
how different countries prioritize update speed, international referencing, and public
transparency based on policy goals and system maturity.
Discussion.
The findings of this study confirm that the implementation of digital platforms for
reference pricing yields measurable improvements in accuracy, transparency, and timeliness.
According to WHO estimates (2022), countries that adopted automated pricing systems
experienced an average 12–18% reduction in price variation errors within just two years of
implementation. These platforms enable real-time updates, automatic validation, and
centralized access to verified international price benchmarks, reducing reliance on inconsistent
manual data entry and outdated references.
A notable example is Kazakhstan’s Drug Analytical Register (DAR), where integration with
customs and tax systems led to a 23% increase in procurement price transparency during the
first year of operation (Ministry of Health, 2019). Such outcomes demonstrate the potential of
digital tools to enhance market oversight and curb irregular pricing practices.
However, the transition to digital platforms requires substantial financial investment. Based on
OECD Health Working Paper No. 63, the estimated cost of developing and deploying a
national-level pricing registry ranges from $250,000 to $1 million USD, depending on system
complexity, integration with external databases, and automation levels. Despite the long-term
benefits, this initial cost may pose a barrier for low-income or under-resourced countries.
Furthermore, in countries where manual systems remain the norm, the delay between receiving
international pricing data and updating national reference prices can extend to 3–6 months,
significantly undermining the responsiveness and relevance of regulatory decisions. This lag
not only contributes to inefficiencies in procurement and reimbursement but may also lead to
higher out-of-pocket costs for patients.
In this context, global cooperation and knowledge-sharing—such as open-source tools, regional
digital infrastructure, and shared access to reference price databases—could provide feasible
pathways for lower- and middle-income countries to overcome these challenges and modernize
their pricing systems effectively.
References:
1. World Health Organization. (2020). Guidelines on Country Pharmaceutical Pricing Policies.
Geneva: WHO Press.
2. Euripid Collaboration. (2022). About the Euripid Database. Retrieved from
3. Patented Medicine Prices Review Board (PMPRB). (2022). Annual Report. Ottawa,
Canada: Government of Canada. Retrieved from https://www.pmprb-cepmb.gc.ca
4. OECD. (2021). Pharmaceutical Pricing Policies in a Global Market (Health Working Paper
No. 63). Organisation for Economic Co-operation and Development.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 05,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 2423
5. Department of Health, Republic of South Africa. (2021). Medicines Price Registry (MPR)
Database. Retrieved from https://mpr.code4sa.org
6. Ministry of Health, Republic of Kazakhstan. (2019). Drug Analytical Register:
Transparency Report on Public Procurement Prices. Astana: MoH Kazakhstan.
7. Vogler, S., Schneider, P., & Zimmermann, N. (2021). Policy options for external reference
pricing: Lessons learned from 30 countries. Health Policy, 125(1), 67–75.
https://doi.org/10.1016/j.healthpol.2020.10.001
8. World Health Organization. (2022). Access to Medicines and the Role of Digital Pricing
Platforms: Global Observations and Case Studies. WHO Technical Brief.
