This presentation defines RWD and RWE in the context of digital health, and looks at potential uses for RWD and RWE. It briefly sets out the current landscape in Malaysia and looks at the challenges in using RWE. In particular, the issues of access, governance and ensuring good quality are considered.

1. #OHEMasterclass
Adrian Towse
Emeritus Director OHE Visiting
Professor LSE
The role of real world data and
evidence in building a
sustainable & efficient
healthcare system
Monash Health Economics Forum 2019
An efficient and sustainable healthcare system
in Malaysia :
The challenges, lessons and future

2. Agenda: Use of RWD and RWE to improve health system efficiency
●Definitions of RWD and RWE
●Potential uses for RWD and RWE
●Current Landscape in Malaysia
●Meeting the challenges in using RWE
●Examples of use of RWE in HTA decision making
●Conclusion

3. Conceptual framework of therapeutic research by study design
Figure 1:
Source: Roche et al., 2013 Integrating real-life studies in the global therapeutic research framework, The Lancet, 1 (10), e29-e30. DOI: http://dx.doi.org/10.1016/S2213-2600(13)70199-
1.
Sherman et al. 2016. 2016. Real-World Evidence - What Is It and What Can It Tell Us? The New England Journal of Medicine, 2016, 375:2293-2297.
The setting is key,
“not the presence or
absence of
..randomization”
(Sherman et al. 2016)

4. What is RWD and RWE?
●“Real-world data (RWD) are data relating to patient health status and/or the delivery of
health care routinely collected from a variety of sources. Examples of RWD include data
derived from EHRs, claims and billing data, data from product and disease registries, patient-
generated data including in home-use settings, and data gathered from other sources that
can inform on health status, such as mobile devices.”
●“Real world evidence (RWE) is the clinical evidence regarding the usage, and potential
benefits or risks, of a medical product derived from analysis of RWD” (FDA, 2017 Use of Real-World
Evidence to Support Regulatory Decisions-Making for Medical
Devices:https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm513027.pdf.)
●We include pragmatic trials (PCTs) in this definition of RWE
●For the purposes of this presentation, however, I now focus on observational data and
studies

5. Potential uses of RWD and RWE
●Use of digital health
●Electronic health / medical records to can
improve the management of patients, reduce
administrative costs, improve patient outcomes
●Predictive analytics through use of big data to
identify patients who fall into categories of
absorbing a lot of resource and/ or achieving poor
health outcomes
●Wearable devices to enable patients at risk or
with a chronic disease to better monitor their own
health or be remotely monitored and managed
● Use of tele-health to enable remote
consultation
●Sources of RWD
●Includes clinical registry data, electronic
medical records (EMR), claims databases,
wearable devices, consumer behavioural
data and pharmacy dispensing data
●Uses of RWE
●Evidence on patterns of disease, patient
journeys
●Identify target groups of patients
●Drug utilisation and adherence
●Clinical performance of a new drug

6. Landscape in South East Asia

7. Landscape for use of RWD and RWE in Malaysia
●Health Minister has announced plans to implement an EMR system in stages at government
hospitals and clinics nationwide
●Plans for bringing together data from public and private systems
●No claims database but a number of patient registries and publications

8. Landscape for use of RWD and RWE in Malaysia (2)
● Source: Event Proceedings, IQVIA RWE Forum, Malaysia, 2019

9. Meeting the challenges: Governance and access issues
https://www.ohe.org/publications/data-governance-arrangements-real-world-evidence
https://www.ohe.org/publications/data-governance-arrangements-real-world-evidence-japan

10. Challenges in the use of observational RWE
1. Bias/
confounding
• Concerns around internal validity, selection bias and reporting bias.
• Techniques exist to adjust for/mitigate these concerns.
2. Incomplete
data
• Systematic omissions and misclassifications
• Gaps (for example test results).
3. Data mining • Re-examining existing datasets to generate new information
• Concern that organisations can reanalyse datasets until one that delivers preferential
outcomes is identified.
4. Access to data • Sharing of data across organizations is not common.
• Inhibited by legal frameworks playing ‘catch up’.
5. Lack of
accepted
standards
• Various sets of principles exist for design, conduct, analysis, reporting of RWE.
• None have been universally accepted/adopted.
6. Lack of
expertise
• Analysts must understand RWD well in order to be able to interpret it properly and
adjust for biases appropriately
7. Obsolete
evidence
hierarchies
• Traditional evidence hierarchies that promote RCTs were developed for a world
without RWD.
• In reality the most appropriate study design depends on the research question.
Source: Hampson et al. 2018. Real World Evidence for Coverage Decisions: Opportunities and Challenges. A Report from the 2017 ICER Membership Policy Summit. Available at
https://www.ohe.org/publications/real-world-evidence-coverage-decisions-opportunities-and-challenges

11. Improving the evidence from observational studies.
Research to improve good practice in the use of RWE
New methods for RWE collection and
synthesis
Research papers
1. Martina R, Jenkins D, Bujkiewicz S, Dequen P, Abrams K. The inclusion of real world
evidence in clinical development planning. Trials. 2018 August 29. doi: 10.1186/s13063-
018-2769-2. Abstract and full-text available
through https://www.ncbi.nlm.nih.gov/pubmed/30157904
2. Didden EM, Ruffieux Y, Hummel N, Efthimiou O, Reichenbach S, Gsteiger S, Finckh A,
Flecther C, Salanti G, Egger M. Prediction of Real-World Drug Effectiveness Prelaunch:
Case Study in Rheumatoid Arthritis. Medical Decision Making. 2018 August 3. doi:
10.1177/0272989X18775975. Abstract and full-text available
through https://www.ncbi.nlm.nih.gov/pubmed/30074882
3. Karcher H, Fu S, Meng J, Ankarfeldt MZ, Efthimiou O, Belger M, Haro JM, Abenhaim
L, Nordon C. The "RCT augmentation": a novel simulation method to add patient
heterogeneity into phase III trials. BMC Medical Research Methodology. 2018 July 6.
doi: 10.1186/s12874-018-0534-6. Abstract and full-text available
through https://www.ncbi.nlm.nih.gov/pubmed/29980181#
+ 35 other journal articles

12. Examples of use of RWE in post-adoption HTA
●Managed Entry Schemes in Australia
●Ipilimumab (2012) – Yes
● Eligible patients followed in a register. 2 year survival results exceeded Phase 3 trial results in the HTA. Price and reimbursement
maintained2.
●Crizotinib (2014) - Yes
● Collect 12 month survival data for 50 patients (expected to take 2 years). Results consistent with earlier estimate, so price
continued at initial MES entry price1
●Use of CDF in England
●Brentuximab vedotin (Adcetris) – Yes
● Observational data collected through the CDF on rates of stem cell transplant after treatment with brentuximab vedotin.
1 Based on Tuffaha & Scuffham Pharmacoeconomics. 2018 May;36(5):555-565
2Taken from Kim et al. J of Pharm. Policy and Practice (2018) 11:4

13. Conclusions
●Digital health offers great opportunity for efficiency improvements and better health outcomes
●RWE can use the data generated to generate evidence of the performance of drugs, pathways of
care, and on how particular groups of patients fare in the health system
●Malaysia has plans to significantly increase the RWD generated
●Turning that into opportunities for producing RWE raises a number of challenges
●Two of the most important are
●Governance issues arising from the need to make data available whilst protecting the privacy of
patients
●Raising the standards of analysis and reporting in RWE studies so that they can more readily be
used in decision making

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