Proving clinical superiority of personalized care models in interventional and surgical pain
management is challenging. The apparent difficulties may arise from the inability to standardize
complex surgical procedures that often involve multiple steps. Ensuring the surgery is performed
the same way every time is nearly impossible. Confounding factors, such as the variability of the
patient population and selection bias regarding comorbidities and anatomical variations are also
difficult to control for. Small sample sizes in study groups comparing iterations of a surgical protocol
may amplify bias. It is essentially impossible to conceal the surgical treatment from the surgeon and
the operating team. Restrictive inclusion and exclusion criteria may distort the study population
to no longer reflect patients seen in daily practice. Hindsight bias is introduced by the inability to
effectively blind patient group allocation, which affects clinical result interpretation, particularly if
the outcome is already known to the investigators when the outcome analysis is performed (often a
long time after the intervention). Randomization is equally problematic, as many patients want to
avoid being randomly assigned to a study group, particularly if they perceive their surgeon to be
unsure of which treatment will likely render the best clinical outcome for them. Ethical concerns
may also exist if the study involves additional and unnecessary risks. Lastly, surgical trials are costly,
especially if the tested interventions are complex and require long-term follow-up to assess their
benefit. Traditional clinical testing of personalized surgical pain management treatments may be
more challenging because individualized solutions tailored to each patient’s pain generator can vary
extensively. However, high-grade evidence is needed to prompt a protocol change and break with
traditional image-based criteria for treatment. In this article, the authors review issues in surgical
trials and offer practical solutions