RWD MAKES UP 95% OF PATIENT DATA, WHILE ACTUAL CLINICAL TRIALS MAKES UP JUST 5%

- acc. To pharmaboardroom.com (1)

Real-World Evidence creates data?

As defined by the FDA (2), “Real-world evidence (RWE) is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of Real-World Data (RWD).

RWE is based on data, thus RWD is playing an increasing role in health care decisions.

Since RWE became such a powerful tool in health science, doctors, and other healthcare professionals in order to understand a drug, device or treatment’s impact need to understand the data behind actual practice settings. The “real world” is a strong contrast to the highly controlled environment of clinical trials, which often have narrow, predefined patient criteria.

While researchers agree (3) that Real-World Evidence (RWE) could close evidence gaps by providing insight into the actual value of medicines in real-world conditions, data experts have identified certain challenges:

  1. heterogeneous perspectives and variations in outcome measures used for RWE generation
  2. a lack of practical experience with Real-World Data (RWD) collected via mandatory registries e.g. the German Benefit Assessment due to an unclear implementation of the GSAV (4).

If addressed appropriately, these challenges could lead to a better understanding of how medicines can benefit patients in real-world settings.

Unlike clinical trials, RWE does not require any specific criteria for patients and includes data from a broad population of all patients, regardless of their pre-treatment history. With RWE, researchers can easily assess data and make better decisions about treatments within larger populations, yielding a more comprehensive understanding of the actual burdens of disease, care patterns and population health.

Extracting information from the data set with RWE enables physicians to better understand the effectiveness of the drug and make informed decisions about the appropriate therapy for their patients. For example, RWE can help to supplement and refine findings from traditional clinical trials and can even provide new evidence regarding the effectiveness of various treatments (5).

RWE and RWD offers significant advantages to all stakeholders

Doctors, researchers, and healthcare professionals can have access to data and in particular RWD regarding the use of various treatments and therapies, enabling them to make more informed and accurate decisions that are better tailored towards specific patient needs and expectations. From a larger research perspective, RWE brings research one step closer to utilizing existing data sets to improve patient care and understand the effects of treatments.

Researchers can now benefit from a more comprehensive understanding also of the potential benefits of a given treatment. By collecting data from a diverse set of real-world patients, rather than pre-selected samples, health authorities can more accurately assess the potential of the therapy. Patients too can anticipate results and outcomes with greater accuracy. Moreover, these insights provide invaluable data for study teams to analyse and use for future research projects.

How does the FDA utilize RWD/RWE in drug approvals?

The FDA review process for new drugs is undergoing a transformation, with faster and more efficient approvals. RWE is already being used to complement clinical-trial evidence and supplement available drug data. The 21st Century Cures Act (6) has increased the awareness around the potential use of RWE in new drug reviews, yet little is known about how the FDA is applying RWE in practice.

RWE can bridge two of the limitations of clinical trial data from a researcher's perspective. In effect, RWE holds the potential to accelerate drug development, increase confidence in outcomes, and, most importantly, benefit patients. This is of great value to both the biopharma industry and researchers (1,3,4,5).

Excellent data, but...

Until recently, important health data, often in the form of unstructured or 'free' text entered by healthcare professionals, had been trapped in the Electronic Health Record (EHR) or other Electronic Medical Records (eMRs).

With the advent of Artificial Intelligence (AI) techniques such as Natural Language Processing (NLP) and Machine Learning (ML), researchers and clinicians can now unlock valuable, unstructured information from even GxP archives.

biomedion explored the potential of AI to process vast amounts of data, stored in research records while always ensuring data compliance and GxP standards, and found how AI-driven data management and a new decentralized methodology to archiving can help researchers uncover the evidence in RWD and RWE.

In this way biomedion helps to drive innovation for researchers in the field of health and life sciences, enabling them to maximize the opportunities growing with AI technology. Follow us and learn how to unlock the potential of AI to conduct research with data protection and data privacy in mind.

No garbage out

If you're a researcher, you know the saying “garbage in, garbage out” certainly applies when considering RWD and RWE. But when it comes to data reliability, how do you ensure you get quality information?

At biomedion, we understand that data quality is essential for the success of any research project, especially ones reliant on Real World Data (RWD) and Real World Evidence (RWE). That’s why we offer a rigorous quality control process to ensure accuracy, timeliness, and utility for your research.

One of the challenges researchers can face is working with EHRs and eMRs which, though an exciting and useful data source, can draw major issues with data privacy and confidentiality. Also, if such records come from systems with a different scope i.e. from financial billing systems, formal errors and data gaps can be expected. Though patients might find inaccuracies on their own and attempt to correct them, physicians in the system may be out of the loop to fix this in time. This creates problems that may potentially lead to medical harm and cause research data to be inaccurate.

In order to be a success such data must be reliable so that subsequent evidence can be trusted by authorities. That’s why biomedion offers rigorous solutions from acquiring the Raw-Data, reviewing them until the retention in long term archives. All based on solid Quality Controls ready for AI support to gain full GxP compliance. This can only be achieved with valid data, to the highest degrees, which require non or just minimal corrections. biomedion’s solutions (iRAW, iRCS, iTMF, iPQS) are designed with metadata in mind and to bridge decentralized settings and providing data expert guidance and support as needed.

We all need reliable and trustworthy data to ensure successful research projects. At biomedion, we are committed to helping you achieve that. Contact us today to learn more about how we can help you.

  1. PharmaBoardroom - Real-World Evidence - A Game Changer for Pharma
  2. https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence Accessed March 2021
  3. Liu M, Qi Y, Wang W, et al Toward a better understanding about real-world evidence European Journal of Hospital Pharmacy 2022;29:8-11.
  4. Sievers H, Joos A, Hiligsmann M. Real-world evidence: perspectives on challenges, value, and alignment of regulatory and national health technology assessment data collection requirements. Int J Technol Assess Health Care. 2021 Feb 24;37:e40. doi: 10.1017/S0266462321000131. PMID: 33622423.
  5. Mahendraratnam N, Mercon K, Gill M, Benzing L, McClellan MB. Understanding Use of Real-World Data and Real-World Evidence to Support Regulatory Decisions on Medical Product Effectiveness. Clin Pharmacol Ther. 2022 Jan;111(1):150-154. doi: 10.1002/cpt.2272. Epub 2021 Jul 2. PMID: 33891318.
  6. 21st Century Cures Act: PUBLIC LAW 114–255—DEC. 13, 2016 130 STAT. 1033; SEC. 3022. REAL WORLD EVIDENCE.

 

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