Beyond diagnosis in general practice: Predictive processing, a novel framework for persistent physical symptoms

Persistent physical symptoms are among the most common and challenging presentations in general practice, whether investigations are normal or reveal pathology insufficient to explain their severity. Patients and clinicians alike often experience these presentations as a dead end. Traditional biomedical models assume that symptoms passively reflect structural pathology, and that normal test results imply the absence of illness.

To introduce predictive processing as a theoretical framework for understanding persistent physical symptoms in general practice, and to outline its implications for clinical explanation and management.

This opinion paper draws on the predictive processing and active inference literature to develop a clinically applicable account of symptom perception, illustrated with clinical scenarios from general practice.

Predictive processing proposes that symptoms are active perceptual constructions, generated by the brain as it infers the state of the body from incoming signals and prior beliefs. When prior beliefs of danger or irreversibility are strong, and peripheral signals are weak or ambiguous, these prior beliefs can dominate perception, producing substantial suffering in the absence of clear pathology. This framework explains symptom-test discordance and reframes normal results: they do not end clinical responsibility, but shift it towards modifying symptom-sustaining priors towards expectations of safety and recovery.

For the general practitioner, this framework provides a coherent scientific rationale for explanation, continuity of care, expectation management, cognitive approaches and graded activity. These interventions emerge as core clinical tools, not consolation, when "nothing is found".