Dual Processing Model for Medical Decision-Making: An Extension to Diagnostic Testing

Abstract

Dual Processing Theories (DPT) assume that human cognition is governed by two distinct types of processes typically referred to as type 1 (intuitive) and type 2 (deliberative). Based on DPT we have derived a Dual Processing Model (DPM) to describe and explain therapeutic medical decision-making. The DPM model indicates that doctors decide to treat when treatment benefits outweigh its harms, which occurs when the probability of the disease is greater than the so called "threshold probability" at which treatment benefits are equal to treatment harms. Here we extend our work to include a wider class of decision problems that involve diagnostic testing. We illustrate applicability of the proposed model in a typical clinical scenario considering the management of a patient with prostate cancer. To that end, we calculate and compare two types of decision-thresholds: one that adheres to expected utility theory (EUT) and the second according to DPM. Our results showed that the decisions to administer a diagnostic test could be better explained using the DPM threshold. This is because such decisions depend on objective evidence of test/treatment benefits and harms as well as type 1 cognition of benefits and harms, which are not considered under EUT. Given that type 1 processes are unique to each decision-maker, this means that the DPM threshold will vary among different individuals. We also showed that when type 1 processes exclusively dominate decisions, ordering a diagnostic test does not affect a decision; the decision is based on the assessment of benefits and harms of treatment. These findings could explain variations in the treatment and diagnostic patterns documented in today's clinical practice.

Fig 1. Relation between the probability of disease and the threshold probabilities for testing and treatment (adopted from [3]).

Fig 2. Decision tree describing a typical scenario in which a physician is considering administering (Rx) / withholding treatment (NoRx) to/from his patient.

x _i represents an outcome; γ is the involvement of type 1 in the decision process; p is the probability of disease; U _I,i is the utility of the outcome x _i under type 1 process; U _II,i is the utility of outcome x _i under type 2 processes; The valuation of an outcome x _i under type 1 is estimated as the regret associated with the outcome x _i; the valuation of an outcome x _i under type 2 is estimated as the utility of the outcome x _i ([12] for details).

Fig 3. Decision tree describing a typical scenario in which a physician is considering one the following three strategies: administering treatment (Rx); withholding treatment (NoRx); and performing a diagnostic test before deciding on treatment (Test).

x _i represents an outcome; γ is the involvement of type 1 in the decision process; p is the probability of disease; U _I,i is the utility of the outcome x _i under type 1 and U _II,i is the utility of outcome x _i under type 2 cognitive processes; H _I,T denotes the harms of test as realized by type 1 and H _II,T denotes the harms of test as realized by type 2 processes; P ₁ = pS+(1-p)(1-S _p); P ₁₁ = pS/P ₁; P ₁₂ = (1-p)(1-S _p)/P ₁; P ₂ = (1-p)S _p+p(1-s); P ₂₁ = p(1-s)/P ₂; P ₂₂ = (1-p)S _p/P ₂; S is the test’s sensitivity and S _p the test’s specificity. The valuation of an outcome x _i under type 1 is estimated as the regret associated with the outcome x _i; the valuation of an outcome x _i under type 2 is estimated as the utility of the outcome x _i.

Fig 4. EUT and DPM testing thresholds as functions of type 1 benefits/harms of prostatectomy ratio.

The chart progression (Fig 4a–4d) shows the effect of increasing type 1 harms of biopsy on the values of testing thresholds. Unlike the EUT threshold, as harms of biopsy (H _I,T) increase (Fig 4b, 4c and 4d), the DPM testing threshold increases to the maximum indicating that a decision maker will never choose a biopsy. When benefits of prostatectomy are higher than its harms (B _I>H _I), the decision maker opts for prostatectomy at practically 0% of disease. Note that the DPM model allows for the treatment threshold to be lower than the testing threshold. This is rationally not possible within the EUT framework, but has been observed in clinical practice. As an illustration consider the case where B _I<H _I. The DPM testing threshold (p _tt) is always higher than the EUT testing threshold (p _tt,EUT). This is because the DPM testing threshold considers the decision maker’s attitudes towards treatment according to which the benefits of treatment are higher than its harms (e.g. B _II>H _II). The same holds for the case of B _II<H _II, but only when H _I,T>0 (i.e. the diagnostic test is harmful) (Fig 4b, 4c and 4d). If H _I,T = 0 and B _II>H _II (Fig 4a), the decision maker may choose test or treatment at the same probability of disease. Also, for most B _I/H _I, the DPM treatment threshold (p _rx) is lower than the EUT treatment threshold (p _rx,EUT). Again, this is because the decision maker values treatment benefits higher that its harms.

Fig 5. EUT and DPM testing thresholds as functions of type 1 benefits/harms of prostatectomy ratio.

The chart progression (Fig 5a–5d) shows the effect of increasing type 1 harms of biopsy to the values of testing thresholds. The value of treatment threshold decreases as the ratio benefit/harms of prostatectomy increases (Fig 5a, 5b, 5c and 5d). The value of testing threshold also decreases as the ratio benefit/harms of prostatectomy increases but only when the harms of biopsy are zero (Fig 5a). If the decision maker perceives biopsy as harmful (Fig 5b, 5c and 5d) the testing threshold increases to the point that he will never choose biopsy. A prostatectomy becomes the preferred choice when B _I > 2H _I in Fig 5b; B _I > H _I in Fig 5c; B _I > 0.8H _I in Fig 5d.