The Importance of Real-World Data and Real-World Evidence

Posted: 12th October 2022
Categories: Uncategorised

The Importance of Real-World Data and Real-World Evidence


Real world data (RWD) are data which relates to patient health and the care which they receive. It encompasses any data that is not sourced from randomised controlled trials within controlled settings. RWD provides a deeper understanding on patient pathways, treatments, and subsequently the patient experience (drug efficacy, confidence in the process etc). This patient generated data in combination with the findings from a randomised controlled trial (RCT) will often build a convincing argument to influence better healthcare decisions.

RWD comes from many different sources which can be grouped into 3 main categories:

Clinical data Patient data Public health data
For example: For example: For example:
  • Electronic health records
  • Diseases registry
  • Medical, genetic, lifestyle history
  • Biometrics
  • Government agency data
  • National health system data

Public health bodies such as NICE have accepted RWD and they frequently use it to inform NICE guidance to:

  • characterise health conditions, interventions, care pathways and patient outcomes and experiences
  • design, populate and validate economic models
  • develop or validate digital health technologies, including patient generated data and patient health status
  • identify, characterise and address health inequalities
  • understand the safety of medical technologies
  • assess the impact of interventions (including tests) on service delivery and decisions about care
  • assess the applicability of clinical trials to patients in the NHS.

Real world evidence (RWE) uses RWD to build credible substantiation for a particular course of action, argument or hypothesis.


Uses of RWE in The Pharmaceutical Industry

Real world evidence studies can be used pre and post launch of a product. A pre-launch RWE study could be used to identify the disease burden, patient population and current practices, pathways and treatments. Whereas post-launch RWE studies can be employed to monitor the effectiveness of products after they have reached the market. They can be used to understand impact of a treatment or approach to the patients and health system and can be used as evidence to support HTA submissions or can provide context or support for prescribing decisions. RWE studies can use the opinions of professionals working in the chosen therapy area to provide insights on treatment pathways of diseases.

RWE is becoming increasingly popular as a means of collecting data to drive changes in healthcare. In response to the COVID-19 pandemic, the healthcare community has shown an increased interest in the Delphi approach. This can be attributed to its ability to provide highly credible recommendations in a relatively short period of time, particularly in an area with limited understanding and evidence.

The Benefits & Limitations of RWE

Although RCTs will always have a firm place in healthcare research, RWE can often be used to support existing data and fill any gaps. The benefits of RWE include:

Quick. When compared with RCTs, RWE can be generated very quickly. This means evidence can be kept relevant and updated in response to changes in policy or pathways.
Provides Rich Insights. RWE provides credible insights into the current practices with the health system – this can include safety, efficacy and dosing.

RWE is not always the right method to use to generate data to support your product.

Inconsistencies of electronic data. Not all data is recorded which means the real world data (RWD) is not always representative of the population for which is claims.
Less valid than RCTs. It is subjective what data is included in a RWE study for analysis which means there is a risk of bias.


How Delphi consensus can generate RWE

A highly credible and adaptable way of generating real world evidence in the form of expert opinion is using the Delphi consensus method. The systematic and iterative approach to communication seeks to aggregate opinions from a diverse set of experts to generate clarity and support (evidence) based on collective wisdom.  It can be used to help forecast future impact, consolidate a position, or to define policy. The Delphi method is widely adopted and established as a credible research approach that is recognised in the evidence hierarchy.  Many treatments or management guidelines have been developed using a version of the Delphi method.

A feature of communication or evidence generated using Triducive’s amplified consensus method is peer-advocacy. Because outputs from Delphi programs are generated by stakeholders who represent an important constituent of the health system itself, they help create social proof for the direction recommended as a result of the work.

Triducive’s amplified Delphi consensus method follows the following steps:


How Delphi meets the NICE principles of conducting RWE studies:

NICE principle1 How Delphi addresses this
Ensure data is of good and known provenance, relevant and of sufficient quality to answer the research question A context review which is completed in the first stage of the Triducive process. This finds existing published data that can help direct discussion in the expert steering group meeting. These publications can also be referenced in the final communication. The experts who form the steering group are elected based on their knowledge and experience of working in the disease area.
Generate evidence in a transparent way and with integrity from study planning through to study conduct and reporting Expert Delphi consensus is ranked in the hierarchy of evidence at level 4. It is a credible methodology which is well recognised in healthcare. The final communication details Delphi methodology and the study findings objectively.
Use analytical methods that minimise the risk of bias and characterise uncertainty Stringent application of scientific research techniques such as modified Delphi consensus, allows obtaining expert opinion in a high quality and scientific manner. The process is robust and delivered by an independent facilitator to minimise bias.

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