Figure 4.

Diagnostic journeys for two individuals through a healthcare system. Left: an ‘ideal’ non-biased diagnostic journey. Visit 1 produces a preliminary diagnosis (D_x1), with some uncertainty (σ₁); a second visit refines this diagnosis (D_x2), resulting in reduced diagnostic uncertainty (σ₂ < σ₁) and treatment (T_x2). Visit 3 results in an increasingly appropriate diagnosis and treatment, and uncertainty continues to reduce (σ₃ < < σ₁). Right: modelling potential compound bias throughout a diagnostic journey. Here, an initial visit results in a diagnosis (D_x1) influenced by both uncertainty (σ₁) and bias (B). Bias compounds across visits; D_x2 is influenced by both current physician bias (B) and bias relating to the previous diagnosis (B | D_x1). Similarly, treatments assigned at visit 3 will be influenced by compound bias based on knowledge of previous treatment options (B | T_x1) and response to those treatments (B | R | T_x1). H_x, hospital system; D_x, diagnosis; T_x, treatment; σ, uncertainty; B, bias; B | D_x, bias regarding previous diagnosis D_x; B | T_x, bias regarding previous treatment T_x; R | T_x, response to treatment T_x.