Designing and Implementing Real World Patient Reported Outcomes (RW- PROs) - Emerging Recommendations

Published Feb 2026

Citation

Rylands AJ, Maruszczyk K, Aiyegbusi OL, et al. Designing and implementing real world patient reported outcomes (RW-PROs) - emerging recommendations: a Good Practices Report of an ISPOR Task Force. Value Health. In Press.  DOI: 10.1016/j.jval.2026.02.018 

Abstract

The increasing use of real-world evidence (RWE) in regulatory, reimbursement, and clinical decision making has highlighted the need for high quality patient reported outcomes (PROs) collected outside traditional trial environments. Although PROs are well established in controlled clinical trials, their application in prospective real-world studies introduces methodological and operational challenges not fully addressed in existing guidance. As a result, stakeholders face uncertainty about how to generate real world PRO (RW-PRO) data that are both feasible to collect in routine care and sufficiently robust for decision use. 

This Task Force reviewed emerging methodological considerations relevant to the prospective collection of PROs in real world settings and examined how RW-PROs can complement clinical outcomes to capture patients lived experience and treatment effectiveness in everyday practice. The Task Force identified 9 interrelated methodological considerations when planning or implementing RW-PRO studies: defining clear study objectives; engaging patients as partners; ensuring representativeness; selecting fit for purpose instruments; supporting data quality; implementing feasible assessment strategies; applying rigorous analytical approaches; using technology responsibly; and engaging early with regulatory and HTA bodies. 

Best practices for RW-PRO research will vary according to study context, intended evidentiary use, and characteristics of the patient population and care setting. RW-PRO approaches necessarily involve balancing scientific rigor with feasibility, patient burden, and operational constraints. Rather than prescribing a single model, this report outlines key considerations, tradeoffs, and methodological options, describing their respective strengths and limitations.

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