A Critical Review of Patient-Reported Inputs, Clinical Workflows, and Machine Learning Opportunities for...

Spinal cord stimulation (SCS) is an established treatment for chronic pain, but long-term outcomes remain inconsistent. Failures may originate from poor therapeutic targeting and dosing, both of which can stem from low-resolution patient-reported inputs and the subjective, trial-and-error programming in clinical workflows. These challenges are especially evident in patient-preferred, nominally paresthesia-free stimulation, which lacks somatotopic feedback, thereby complicating effective targeting and dosing. This review identifies deficiencies in current SCS clinical workflows: (1) lack of high-resolution, spatially contextualized communication tools for patient-reported pain and paresthesia, and (2) subjectivity and inefficiency of manual programming. Common patient-to-provider communication tools, such as the numeric rating scale, compress complex pain experiences into single values, which limit their clinical utility and suggest an opportunity for higher-resolution, contextualized communication tools. Recent research suggests that graphical user interfaces for pain mapping, particularly those incorporating advanced spatial annotation tools or 3D capabilities, may overcome these communication barriers while also generating structured, traceable, spatial datasets. When combined with stimulation metadata, these datasets may provide a foundation for data-driven lead placement and therapy personalization, which is particularly important for the effective application of nominally paresthesia-free stimulation paradigms. Integrating machine learning (ML) into the SCS clinical workflow, made possible by leveraging a large dataset of these spatial inputs, has the potential to create an algorithm that automates programming. This integration has the capacity to improve the effectiveness of pain relief and expand access to care through remote services. However, challenges still exist in validating the accuracy of patient-reported maps, accounting for psychosocial influences, and navigating regulatory pathways for ML-driven SCS.