From research to clinical practice, and back again: Real World Evidence

From research to clinical practice, and back again: Real World Evidence

Real world evidence (RWE) is the clinical evidence concerning benefits or risks associated with the use of a medical product. This evidence is based on analysis of real world data (RWD) which are related to the health status of patients and the delivery of care. RWD can be obtained from electronic health records, patient generated data by remote patient monitoring, product registries and disease libraries. RWE can thus be generated by a wide spectrum of clinical studies including randomized clinical trials, observational studies and retrospective analyses1.

There is a gap between clinical research and everyday clinical practice: expected outcomes based on research findings differ from what actually happens in the clinic. Health outcomes are multifactorial and depend on a combination of medical care, genetics, behavior, social circumstances and environmental factors2. Clinical research is primarily focused on the effect of treatment and does not include all of these aspects into the equation. RWE functions as the feedback from reality back to research, in order to improve both3. Increasingly, RWE plays a role in important healthcare decisions. Regulatory agencies use RWE to monitor the safety and side effects of medical innovations in postmarket studies in order to make regulatory decisions, including the approval of novel indications for previously approved drugs. In addition, reimbursement strategies and guidelines for clinical care are based on collected RWE4. Furthermore, product developers use RWE to conduct observational studies to investigate novel medical interventions5.

Recently, there has been an increased interest in RWE. The frequent use of mobile devices and wearables has resulted in a rapid generation of health-related data. Analysis of these data will provide valuable insights to improve clinical study design. The continuous developments in artificial intelligence and machine learning have resulted in improved tools to analyze RWD and in turn transfer this knowledge into improved medical product development6,7. As a result, the reliability and relevance of the collected RWD has improved, and thus their usability as a source for making improvements in clinical care, such as improved decision support tools and development of novel guidelines8.

Therefore, RWE is of high value to patients and clinicians, as well as pharmaceutical companies and biotech who develop new treatments. In addition, RWE can be used to conduct quality measurement of therapies, clinicians and clinics9. A next step is to use RWE for clinical decision making. For instance in the screening and diagnosis for cancer, choosing personalized treatment and management of diseases including dosing of drugs10.

However, caution needs to be made concerning the interpretation, sample size, study design and data quality management in order to prevent errors and secure proper use of RWE11. Biases may be easily introduced by unrecognized confounding factors. For example, the tendency of clinicians to prescribe medication to patients who are most likely to benefit from them (selection bias) or the tendency that patients who adhere to treatment are more likely to engage in other health behaviors (adherence bias)12.

It is therefore of the utmost importance to collect data of the highest quality. At PatchAi we facilitate the highest standard of data collection and analysis. How? Click here.

 

1. FDA, Office of the Commissioner. Real-World Evidence. https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence (2021).

2. The Relative Contribution of Multiple Determinants to Health. https://www.healthaffairs.org/do/10.1377/hpb20140821.404487/full/.

3. Tarkoff, K. Bringing real world evidence into the care setting. Managed Healthcare Executive https://www.managedhealthcareexecutive.com/view/bringing-real-world-evidence-into-the-care-setting (2021).

4. Pietri, G. & Masoura, P. Market Access and Reimbursement: The Increasing Role of Real-World Evidence. Value Health 17, A450–1 (2014).

5. Dhruva, S. S., Ross, J. S. & Desai, N. R. Real-World Evidence: Promise and Peril For Medical Product Evaluation. P T 43, 464–472 (2018).

6. Reinke, T. Digitized Health Opens RWE Floodgates. Can Artificial Intelligence Harness the Power? Manag. Care 28, 18–19 (2019).

7. Naidoo, P. et al. Real-world evidence and product development: Opportunities, challenges and risk mitigation. Wien. Klin. Wochenschr. 133, 840–846 (2021).

8. Bousquet, J. et al. MASK 2017: ARIA digitally-enabled, integrated, person-centred care for rhinitis and asthma multimorbidity using real-world-evidence. Clin. Transl. Allergy 8, 45 (2018).

9. Echeazu, B. 9 ways real-world evidence is changing healthcare. https://www.arbormetrix.com/blog/9-ways-real-world-evidence-is-changing-healthcare.

10. Petracci, F. et al. Use of real-world evidence for oncology clinical decision making in emerging economies. Future Oncol. 17, 2951–2960 (2021).

11. Kim, H.-S. & Kim, J. H. Proceed with Caution When Using Real World Data and Real World Evidence. J. Korean Med. Sci. 34, e28 (2019).

12. Klonoff, D. C. The Expanding Role of Real-World Evidence Trials in Health Care Decision Making. J. Diabetes Sci. Technol. 14, 174–179 (2020).