FROM REAL WORLD TO REAL IMPACT

1. TRIAL DESIGN AND RIGOR: Real-world evidence (RWE) is increasingly shaping clinical trial design. In the Asia-Pacific (APAC) context, how can efficiency and scientific rigor be balanced when using real-world data for eligibility screening and protocol development?

DEEPAK MUKHERJEE: APAC houses almost 60 per cent of the world’s population. However, less than 20 per cent of global trials are conducted in this region due to barriers like heterogenous healthcare data management systems, variation in regulatory pathways, resource limitations and compromised focus on research.

To leverage RWE effectively for eligibility screening (identifying suitable patients) and protocol development (designing feasible, patient-centric trials), stakeholders can adopt targeted strategies informed by expert panels and industry practices. These emphasize pre-specification of study designs, multidisciplinary collaboration, and regulatory engagement to ensure transparent, auditable evidence.

YUN LU: In Asia-Pacific, real-world data (RWD) are increasingly used to inform eligibility criteria, refine endpoints and simulate protocol scenarios, but the heterogeneity of healthcare systems (from national health insurance databases in Korea and Taiwan to fragmented private networks in India and Southeast Asia) makes scientific rigor a constant concern.

A pragmatic way to balance efficiency and rigor is to treat RWD as a design laboratory:

Data-driven eligibility and feasibility

Large claims, EMR and registry datasets can be mined to understand disease incidence, comorbidity patterns and prior treatment pathways in specific geographies. This supports:

• More realistic and inclusive inclusion/exclusion criteria.
• Sample size estimates and site selection that reflect real care pathways rather than extrapolations from US/EU data.
• RWE-informed interventional trial design

RWD/RWE can help create a robust repository and provide access to well-characterised patient cohorts, facilitate the identification of breakthrough interventional studies with targeted outcomes, and make more informed decisions with the involvement of healthcare stakeholders. To avoid bias, RWE should inform a prospective design framework: key eligibility signals, endpoints and covariates are pre-specified based on exploratory RWD analyses and then locked into the protocol and SAP. This keeps the statistical backbone consistent with ICH E9/R1 principles.

Standardisation and curation

RWD in many countries in the region is still evolving and often presents challenges in standardisation and completeness, for example, variable coding using ICD vs local codes, incomplete lab data and fragmented longitudinal follow-up. Sponsors, therefore, invest in:

• Common data models to facilitate the use of RWD/RWE in clinical research
• Data quality rules, adjudication workflows and audit trails
• Clinician oversight committees that review RWD-based design choices.

Many leading sponsors in Asia-Pacific engage the multi-disciplinary biostatistics, data science and therapeutic experts into the RWE plan and design early. This allows efficiency gains while still meeting regulatory expectations for reproducibility and transparency.

KAVITA LAMROR: In the APAC context, balancing efficiency and scientific rigor with RWD requires a structured approach. Data access and quality is important. We need to invest in validating diverse data sources to ensure they are fit-for-purpose, addressing regional variability in coding and completeness. For eligibility screening, we can analyse real world patterns to identify potential sites and patient populations. This optimises recruitment without compromising rigor, provided the RWD cohort mirrors the trial criteria adequately. In protocol development, RWE can be leveraged to design representative trials using historical data to refine end-points, define comparator arms, and identify standard-of-care practices, making trials more efficient and generalisable. Finally, a robust governance framework that pre-specifies RWE analytical plans and acknowledges data limitations is key to maintaining scientific integrity while gaining operational speed.

2. OPERATIONAL CHALLENGES: What are the major barriers organisations in APAC face in operationalising RWE at scale, and how can digital transformation help overcome these challenges?

DEEPAK MUKHERJEE: In the APAC region, organisations and healthcare providers face significant hurdles in scaling RWE due to the region's diverse healthcare systems and infrastructure levels, exacerbated by heterogeneity across countries.

Some common barriers include:

Data fragmentation: EMR systems vary significantly not just across different countries but also across different centres within a country. It becomes challenging to design common datasets with which actionable data can be extracted seamlessly if multiple healthcare centres are involved.

Lack of uniform data entry: In many cases in spite of a good EMR system in place, HCPs prefer to enter key information in free text boxes instead of selecting the available options. This leads to difficulty in data extraction.

Skills and Capacity gaps: There is a shortage of data scientists, analysts, and trained regulators to handle RWE generation and validation. And in many cases due to the high burden in the healthcare system (due to the population size in APAC), existing resources fail to allocate sufficient time for better data handling.

Digital transformation including AI, cloud-based platforms, standardised EMR models, and interconnected systems can enhance efficiency, accessibility, and reliability.

Improving data standardisation: AI and machine learning tools can detect manual entries, convert handwritten notes to digital leading to automated data cleaning and harmonisation.

Interlinked EMRs: Integration of EMRs in different centres with central government registries and interlinking EMRs of different healthcare centres can create a robust network from where data can be accessed and downloaded readily.

Building capacity and fostering collaboration: Online training platforms and AI-assisted analytics upskill workforces, closing expertise gaps.

YUN LU: Organisations in the region typically face four operational barriers in scaling RWE:

• Fragmented data ecosystems: Multiple EMR vendors, public–private segmentation and limited interoperability.
• Variable data quality and governance: Missingness, nonstandard coding and inconsistent consent structures.
• Talent gaps: Shortage of people who understand both local clinical practice and advanced analytics/epidemiology.
• Regulatory and ethical uncertainty: Differing rules on secondary use of health data, cross-border transfers and de-identification.

Structure RWE framework and digital transformation help valid study design and implementation:

Fit-for purpose RWE workflows

It is critical to conduct systematic feasibility assessments and identify fit-for-purpose RWD/RWE data source throughout study design and development. Organisations should establish end-to-end data pipelines and AI-powered platform to streamline clinical trial execution from ingestion through curation, analysis and visualisation. Clinical data science and innovation help minimise manual effort, enhance consistency across studies, reduce manual effort and improve consistency across various data sources.

Embedded quality and compliance

Role-based access, de-identification pipelines, audit logs and integration with eTMF/GxP systems make it easier to defend RWE processes in inspections and health technology assessment (HTA) reviews.

Interoperable data platforms and common data models

Cloud-based data lakes that map local codes and formats into a common data model allow multi-country comparative RWE while preserving local nuances. This is increasingly seen in regional oncology and rare disease registries.

Augmented talent models

Hybrid delivery, combining in-house teams with specialised Asia-Pacific CROs and analytics partners, helps overcome local skill gaps, especially in advanced methods like causal inference and time-varying survival models.

KAVITA LAMROR: Operationalising RWE at scale in APAC faces major barriers including data fragmentation across siloed systems, inconsistent quality and standardisation, and a complex regulatory landscape with varying data privacy laws. Digital transformation directly addresses these challenges by implementing interoperability standards like FHIR and common data models to standardise data. AI and NLP tools can automate data curation, improving quality and consistency by structuring unstructured data. Federated analytics platforms (JMDC, NHIS, NHSA, NHIRD, IQVIA, FlatIron, etc.) enable research across institutions without sharing raw data, navigating privacy concerns. Integrated cloud-based platforms provide the scalable infrastructure needed for robust data management and analysis. By building this integrated digital backbone, organisations can transform disparate data into a reliable, high-quality evidence generation engine, overcoming the key hurdles to scaling RWE across the diverse APAC region.

3. PATIENT INCLUSIVITY: From a patient-centric perspective, how can RWE improve inclusivity and diversity in clinical trials, particularly for underrepresented and vulnerable populations across Asia Pacific’s diverse healthcare systems?

DEEPAK MUKHERJEE: As an instance, in APAC, where Asians bear over 60 per cent of the global cardiometabolic disease burden yet comprise less than 10 per cent of trial participants. RWE can address such gaps by informing trial design with insights into disease prevalence, treatment responses, and barriers among underrepresented cohorts.

RWE can identify ethnic pharmacogenomic differences and lifestyle factors to relax stringent inclusion/exclusion criteria, enabling participation from diverse groups like South Asian or Indigenous populations.

Furthermore, RWE can incorporate patient-reported outcomes (PROs) to personalize care, ensuring trials reflect real-life experiences across APAC's socioeconomic spectrum. This promotes health equity by filling data gaps in understudied areas. By prioritising patient needs, RWE transforms trials into inclusive platforms, bridging APAC's healthcare disparities and empowering underrepresented voices for equitable innovation.

YUN LU: Asia-Pacific’s burden of disease is heavily shaped by ethnic, socio-economic and geographic diversity: rural populations, Indigenous communities, migrants and older adults are often underrepresented in traditional trials. RWE can support inclusivity in several ways:

Mapping “who is missing” from trials

Linked claims/EMR data and disease registries can identify segments that rarey appear in clinical trials, e.g., older multimorbid patients with diabetes and CKD in India, or rural cancer patients in Southeast Asia. This helps sponsors design targeted outreach, site strategies and pragmatic trials that better mirror the treated population.

Decentralised and hybrid models

RWE-enabled decentralised trials, using ePROs, telemedicine visits, local labs and home nursing, can reduce travel and cost burden that disproportionately affects lower-income or remote participants. In several Asia-Pacific markets, smartphone penetration is high even where infrastructure is uneven, enabling mobile-first engagement.

Locally meaningful endpoints

Real world practice patterns reveal outcomes that matter to patients beyond classical clinical endpoints: treatment persistence, work productivity, economic burden or culturally specific functional measures. Capturing these through RWE makes trials more patient-centric and improves the relevance of benefit–risk assessments for local HTA bodies.

Engagement via patient organisations and foundations

Disease-specific registries run with non-profit and patient-group partners help to build trust in communities historically wary of research. In Asia-Pacific, these alliances are particularly important in rare diseases, oncology and chronic infectious diseases.

A CRO or consulting partner with both registry operations experience and field presence across Asia-Pacific can translate these insights into practical protocol adaptations and recruitment strategies, without over-burdening sites.

KAVITA LAMROR: RWE can significantly improve inclusivity and diversity in APAC clinical trials by addressing key barriers to participation. RWE derived from electronic health records and registries can identify where and how under-represented populations receive care. This allows sponsors to select trial sites that are more accessible and trusted by the target communities, reducing geographic and economic burden. Also, by analysing real-world treatment patterns and outcomes, RWE can help design more pragmatic and restrictive protocols. This accommodates the co-morbidities and lifestyle realities of vulnerable groups, who are often excluded by overly stringent eligibility criteria. Using RWE ensures that trials are designed around the actual patient journey within each healthcare system. This leads to research that is more relevant to the diverse populations of APAC, generating evidence that truly reflects those who will use the treatments.

4. POST-MARKET SURVEILLANCE: How do you foresee RWE’s role evolving in APAC for detecting rare adverse events, ensuring long-term safety, and demonstrating effectiveness in varied populations?

DEEPAK MUKHERJEE: With APAC's diverse population, yet minimal trial representation, RWE will bridge gaps left by randomised controlled trials (RCTs), fostering equitable outcomes amid heterogeneous healthcare systems.

In APAC, where rare events like vaccine-induced thrombosis were identified via RWE during COVID-19, future trends include proactive signal detection in pharmacovigilance, with regulators like Japan's PMDA and China's NMPA leveraging standardised databases (e.g., MID-NET) to spot low-incidence risks earlier, reducing reliance on underpowered RCTs. AI-driven tools could enhance detection by 30 per cent - 50 per cent in diverse ethnic groups, addressing underreporting in emerging markets.

In ensuring long-term safety, RWE will shift from reactive to predictive monitoring, using longitudinal data for post-marketing surveillance. Wearables and patient-reported outcomes (PROs) will personalize safety profiles, with guidelines in Taiwan and South Korea evolving to mandate RWE for rare diseases and chronic conditions.

YUN LU: RWE’s role in post-marketing safety in Asia-Pacific is moving from passive pharmacovigilance to active, analytics-driven surveillance:

Detection of rare and delayed adverse events

For biologics, oncology agents and advanced therapies, rare toxicities may only emerge years after launch. Linked longitudinal EMR claims data and disease registries allow signal detection in real-world practice, particularly where spontaneous adverse event (AE) reporting is under-developed.

Local risk–benefit profiling

Population genetics, co-morbidities (e.g., TB, hepatitis, malnutrition), and polypharmacy patterns differ across APAC. RWE can reveal risk modifiers that are not evident in global pivotal trials, supporting region-specific labelling, risk management plans, and guidance for clinicians.

Regulator-sponsored surveillance networks

Some APAC regulators are developing or planning multi-institution networks for active surveillance (e.g., vaccine safety monitoring, cardiovascular drug safety). Sponsors that can align their post-marketing studies and registries with these infrastructures will have more credible and accepted RWE outputs.

Effectiveness in routine practice 

Beyond safety, post-launch RWE supports demonstration of effectiveness under routine conditions, often informing reimbursement decisions and treatment guidelines in markets where HTA processes are maturing.

Organisations with capabilities spanning signal management, epidemiology and registry operations are well placed to design these post-market RWE programs in a way that meets both global standards and local regulatory expectations.

KAVITA LAMROR: I see RWE’s role in APAC evolving from a reactive to a proactive integral component of pharmacovigilance and effectiveness research. For rare adverse event detection, federated learning networks will pool data across multiple health systems, significantly increasing statistical power to identify safety signals that are invisible within a single country’s dataset. For long-term safety, RWE from registries and claims data will provide continuous post-market surveillance for chronic treatments, capturing real-world risks over a patient’s lifetime, far beyond the duration of the clinical trial. For demonstrating effectiveness, RWE can provide critical insights into how therapies perform across APAC’s genetically and clinically diverse subpopulations, ensuring safety and efficacy findings are relevant to local patients and strengthening the region’s voice in global health evidence.

5. ACCELERATING THERAPIES: Can the integration of RWE into trial design significantly shorten development timelines for novel therapies in APAC, especially in rare diseases and oncology? What models best support this acceleration?

DEEPAK MUKHERJEE: Yes, the integration of RWE into clinical trial design can significantly shorten development timelines for novel therapies in APAC, particularly in rare diseases, where patient recruitment challenges and ethical constraints limit traditional Randomised Controlled Trials (RCTs).

This acceleration addresses APAC's regulatory lags, with bodies like Japan's PMDA and China's NMPA increasingly accepting RWE for submissions, as in COVID-era approvals. By informing site selection, eligibility criteria, and endpoints, RWE minimises delays in heterogeneous systems from Singapore to India.

Best models supporting this include:

Synthetic control arms (SCAs): Use real-world data (RWD) to create virtual comparators, reducing enrollment needs by 50 per cent in rare oncology trials.
External Control Arms (ECAs): Historical RWD as benchmarks, accelerating single-arm trials for rare diseases.
Pragmatic and Hybrid Trials: Integrate RWE for real-time adaptations, shortening Phase III by embedding routine care data.
Basket/Umbrella Designs: RWE-guided multi-indication studies in oncology and other rare diseases, fostering efficiency in APAC's diverse cohorts.

YUN LU: Integrating RWE into trial design can shorten timelines in several ways, which is especially valuable in rare diseases and oncology:

External and hybrid control arms

When randomisation is difficult or ethically constrained, carefully curated external controls from registries or EMR datasets can supplement or partially replace traditional control arms. 

This can:

• Reduce required sample sizes.
• Shorten accrual periods.
• Make trials more acceptable to patients and investigators.

Enrichment and adaptive designs

RWD can identify subgroups with higher event rates or distinct biomarker profiles, enabling enriched or adaptive designs that reach endpoints sooner. For example, identifying high-risk molecular subtypes in hematologic malignancies or specific phenotypes in rare metabolic disorders.

Platform and basket trials anchored in RWE

In APAC, where patient numbers may be modest in each country, multi-country platform trials that leverage RWE-derived background rates and historical controls can be particularly efficient. Sponsors with strong regional data infrastructure can continuously update platform assumptions using incoming RWD.

However, acceleration is only credible if:

• External data sources meet stringent quality and provenance standards.
• Methods for bias adjustment (e.g., propensity scores, inverse probability weighting, synthetic controls) are transparent and pre-specified.
• Regulators are engaged early to agree on the role of RWE in the evidentiary package.

We need a network of subject matter experts and trusted experience necessary to fully implement the stated requirements as we apply data science technology and AI to integrate RWE into clinical research and drive quicker and better data and quality, reducing cost and increasing efficiency.

KAVITA LAMROR: RWE integration can significantly shorten development timelines in APAC by leveraging the clinical and genetic diversity. RWE from local registries and hospital data helps design trials that account for unique genetic markers and regional disease sub-types. This enables more efficient and faster trials by pre-identifying eligible patient pools and optimising site selection. RWE can also support regulatory approvals by providing external context for single-arm trials or supplementing long-term outcomes.

The most effective models supporting this acceleration are Hybrid or Pragmatic Trials, that embed trial procedures within routine care, making participation easier and faster. Synthetic Control Arms generate RWE from matched historical patients, avoiding the delay of recruiting a concurrent control group, especially for rare diseases. Master Protocol Platforms use a single infrastructure to test multiple therapies, with RWE continuously informing patient stratification and new trial arms. By adopting these models, sponsors can bring novel therapies to APAC’s diverse patients more rapidly.

6. AI/ML IN RWE: With the growing role of AI/ML in analysing RWE, how can APAC regulators and sponsors address concerns around algorithmic bias, data transparency, and reproducibility in clinical decision-making?

DEEPAK MUKHERJEE: Algorithmic Bias: Bias arises from unrepresentative datasets, potentially worsening disparities in ethnic or socioeconomic groups. APAC regulators can mandate diverse training data, bias audits, and stress testing, as outlined in AI/ML-based SaMD guidelines. Sponsors should adopt consensus recommendations emphasizing representative health datasets and impact assessments to mitigate discrimination in RWE-derived insights. For instance, South Korea's Basic AI Act requires safety documentation for high-impact systems, while Australia's AI Ethics Principles promote risk management in RWE analysis.

Data Transparency: To build trust, regulators can enforce disclosures on data sources and algorithm structures via frameworks such as Model AI Governance and AI Verify. Sponsors must ensure traceable data lineages in RWE platforms, using interoperable systems and patient-centric consent models.

Reproducibility: Ensuring repeatable results involves standardised metrics (e.g., sensitivity, specificity) and post-market surveillance. Regulators can require validation through retrospective RWE studies. Sponsors should document methodologies for independent audits, fostering consistency in hybrid trials and reducing variability in APAC's heterogeneous data.

YUN LU: AI/ML is increasingly used in the region’s RWE programmes, from NLP of unstructured data, risk prediction models to automated signal detection. Incorporation of AI and ML into RWE data implementation and analysis helps organisations make data-backed decisions with greater speed and accuracy. Technology advancements and tools can help identify the critical data to quality and really focus on insights. As we start to adopt AI in the highly regulated clinical research field, it is very important to consider the benefit of technology together with study participant safety, security, and compliance. Before implementing an end-to-end solution for a robust AI platform, we should start with modular and small-scale AI and Agentic AI pilot effort.

Organisations with combined AI/ML expertise and deep clinical, pharmacovigilance and regulatory knowledge are uniquely positioned to build AI-enabled RWE workflows that meet regulatory compliance and drive evidence-based decisions.

KAVITA LAMROR: APAC regulators and sponsors can address AI/ML concerns in RWE analysis through a multi-pronged approach focusing on governance and technical rigor. To combat algorithmic bias, diverse and representative training data from sub-populations across APAC is essential, including data from differing socio-economic and lifestyle subsets. Models must be routinely audited for performance disparities across ethnic, clinical, and demographic groups. For data transparency, sponsors should adopt established frameworks (FAIR principles) for documenting the origin, transformation, and lineage of the RWD used, to create an auditable trail. Ensuring reproducibility requires pre-specifying analysis plans and using version-controlled open-source code if possible. Regulators are increasingly expecting detailed model validation reports and white-box systems. Ultimately, collaboration is key. Developing region-specific regulatory guidelines for AI/ML, similar to the FDA and EMA discussion papers and guidelines, will build a common understanding of acceptable approaches. By prioritising these measures, stakeholders can foster trust in AI-driven clinical decisions.

7. COLLABORATIONS & CREDIBILITY: What role do cross-industry collaborations in Asia Pacific - between regulators, pharma, technology providers, and academia — play in ensuring the credibility and global acceptance of RWE-driven trial transformations?

DEEPAK MUKHERJEE: Cross-industry collaborations in Asia Pacific (APAC) between regulators, pharmaceutical companies, technology providers, and academia are pivotal in bolstering the credibility and global acceptance of Real-World Evidence (RWE)-driven trial transformations. These partnerships foster an ecosystem that addresses data fragmentation, regulatory inconsistencies, and skill gaps, transforming RWE from supplementary to integral in clinical development.

In trial transformations, these alliances enable efficient designs, such as hybrid trials integrating RWE for faster approvals and inclusivity. Policy investments like India's Ayushman Bharat Digital Mission and Singapore's health data systems support interoperable infrastructure, accelerating innovations while ensuring patient-centric, equitable outcomes. Ultimately, such collaborations position APAC as a leader in credible, globally accepted RWE-driven research.

YUN LU: In Asia-Pacific, cross-industry collaborations are crucial for making RWE globally credible:

Regulator–industry–academia consortia

Multi-stakeholder networks can define methodological standards, data models and quality benchmarks specific to APAC, analogous to Sentinel or PCORnet structures seen elsewhere. Such networks are particularly valuable for vaccine safety, oncology and chronic disease registries.

Shared registries and data platforms

For rare diseases and niche oncology indications, no single company can accumulate sufficient data. Shared or federated registries, where data stay within institutions but can be queried via common protocols, allow pooled analyses while respecting data sovereignty and privacy laws.

Methodological transparency and joint publications

Jointly authored publications and HTA submissions with academia and public agencies enhance the credibility of RWE studies. International co-authorship and alignment with emerging global best practices (e.g., ISPE, ISPOR task forces) further support global acceptance.

Technology providers and interoperability

Partnerships with technology companies that specialise in EMR integration, de-identification and secure data sharing are key to scaling RWE initiatives across diverse APAC healthcare IT landscapes.

CROs and consulting partners that are already embedded in regional networks, registries, and working parties can play an important convening role, bridging sponsors, providers, regulators and technology vendors.

KAVITA LAMROR: Cross-industry collaborations in APAC are fundamental to establishing the credibility and global acceptance of RWE-driven trials. They create the necessary trust and standardisation that single entities cannot achieve alone. These partnerships are crucial for aligning regulators (e.g., HSA, PMDA, TFDA) on acceptance criteria, enabling pharma to collect fit-for-purpose data, and leveraging academia’s analytical rigor. Technology providers can ensure scalable interoperable platforms and attempt regional harmonisation of common data models, like an Asian adaptation of OMOP CDM, specific to the region’s diverse populations and healthcare systems.

A prime example is Project Orbis, led by Singapore’s HSA (including Australia’s TGA), with other global regulators. This initiative facilitates concurrent submission and review of oncology drugs. When sponsors include RWE from APAC patients to support these applications, it demonstrates how regional data generated under aligned standards can directly inform regulatory decisions across multiple countries, thereby accelerating patient access and bolstering global confidence in APAC-generated evidence.

8. FUTURE OUTLOOK: Looking ahead to 2030, what would a fully RWE-integrated clinical trial ecosystem in Asia Pacific look like, and what milestones must the region achieve to get there?

DEEPAK MUKHERJEE: By 2030, a fully RWE-integrated clinical trial ecosystem in Asia Pacific (APAC) would feature seamless incorporation of Real-World Data (RWD) from electronic health records, wearables, registries, and patient-reported outcomes into all trial phases, complementing RCTs for faster, more inclusive research. AI-driven analytics would enable predictive modeling, synthetic control arms, and bias mitigation, shortening development timelines by 20-50% and enhancing precision medicine amid APAC's diverse populations.

To achieve this, APAC must hit key milestones: By 2026, harmonise regulatory guidelines across countries, building on existing frameworks in China, Japan, and Taiwan, with Australia and South Korea issuing formal policies. By 2027, invest in interoperable health IT infrastructure and public-private partnerships to improve data quality and access. Workforce upskilling in AI and data science should occur by 2028. Finally, by 2029, pilot hybrid trials in oncology and rare diseases to demonstrate RWE's value, paving the way for global acceptance and patient-centric innovation.

YUN LU: By 2030, a mature RWE-integrated clinical trial ecosystem in Asia-Pacific would feature:

Routine use of RWE across the product lifecycle 

From target identification and feasibility through pivotal trials, post-marketing safety and HTA submissions, RWE would serve as a continuous evidence stream.

Interoperable, privacy-preserving data networks

Most major markets would have:

• Common data models adopted across large hospital systems.
• Federated analytics capabilities allowing queries across borders without raw data leaving local servers.
• Standard frameworks for de-identification, consent and secondary use.

Normalised hybrid evidence packages

Regulators and payers would routinely see submissions where RCTs, pragmatic trials, external controls and observational RWE are integrated into a single coherent narrative of benefit risk and value.

Embedded patient voice and equity metrics

RWE would be routinely used to track inclusion of underserved populations, real-world access patterns, and long-term outcomes, informing policy interventions to reduce inequities.

Key milestones for Asia-Pacific to reach this state include:

1. Stronger data governance and harmonised regulations on health data use, cross-border flows and AI-enabled analytics. 
2. Broad adoption of common data models and interoperability standards across major markets and large health systems. 
3. Scalable training and capacity-building in pharmacoepidemiology, biostatistics, AI/ML and health economics, tailored to regional disease priorities. 
4. Demonstration projects that show clear value, e.g., accelerated approvals, improved safety outcomes or better HTA decisions based on high-quality RWE. 
5. Deep, long-term partnerships across sponsors, CROs, regulators, academia, technology providers and patient groups that institutionalise RWE best practices.

An organisation with long-standing regional operations, robust data science and registry capabilities, and a track record in both clinical development and safety would be well placed to help shape and operationalise such an ecosystem, enabling sponsors to move from experimental RWE pilots to a fully integrated, RWE-driven model of evidence generation in Asia-Pacific.

KAVITA LAMROR: Looking ahead to 2030, what would a fully RWE-integrated clinical trial ecosystem in Asia Pacific look like, and what milestones must the region achieve to get there?

By 2030, a mature RWE-integrated clinical trial ecosystem in APAC will function as a seamless learning network. Decentralised and pragmatic trials will be standard, with RWE from diverse regional health systems dynamically informing eligibility, creating external control arms, and providing long-term outcome data, making studies faster and more representative of real-world populations.

To reach this future, the region must achieve three critical milestones. Regulatory Harmonisation is needed for alignment on common data standards and clear, mutually recognised guidelines for using RWE in regulatory submissions and approvals. Establishment of a Federated Data Infrastructure for a secure, interoperable network that allows for cross-border analysis while maintaining data sovereignty and patient privacy is essential. Validated AI & Governance by developing robust, region-specific frameworks to ensure the algorithms analysing RWD are transparent, unbiased, and reproducible is indispensable for gaining overall trust. Achieving these will require collaboration, transforming APAC’s diversity from a challenge into its greatest asset for clinical innovation.

--PFA Issue 62--