Traffic Lights Intervention Reduces Therapeutic Inertia: A Randomized Controlled Trial in Multiple Sclerosis Care

Abstract

Background: Therapeutic inertia (TI) is a common phenomenon among physicians who care for patients with chronic conditions. We evaluated the efficacy of the traffic light system (TLS) educational intervention to reduce TI among neurologists with MS expertise. Methods: In this randomised, controlled trial, 90 neurologists who provide care to MS patients were randomly assigned to the TLS intervention (n = 45) or to the control group (n = 45). The educational intervention employed the TLS, a behavioral strategy that facilitates therapeutic choices by facilitating reflective decisions. The TLS consisted in a short, structured, single session intervention of 5-7 min duration. Participants made therapeutic choices of 10 simulated case-scenarios. The primary outcome was a reduction in TI based on a published TI score (case-scenarios in which a participant showed TI divided by the total number of scenarios where TI was possible ranging from 0 to 8). Results: All participants completed the study and were included in the primary analysis. TI was lower in the TLS group (1.47, 95% CI 1.32-1.61) compared to controls (1.93; 95% CI 1.79-2.08). The TLS group had a lower prevalence of TI compared to controls (0.67, 95% CI 0.62-0.71 vs. 0.82, 95% CI 0.78-0.86; p = 0.001). The multivariate analysis, adjusted for age, specialty, years of practice, and risk preference showed a 70% reduction in TI for the TLS intervention compared to controls (OR 0.30; 95% CI 0.10-0.89). Conclusions: In this randomized trial, the TLS strategy decreases the incidence of TI in MS care irrespective of age, expertise, years for training, and risk preference of participants, which would lead to better patient outcomes.

Keywords: decision making; disease-modifying therapy; educational intervention; multiple sclerosis; randomized clinical trial.

Figures

Figure 1

Consort flow diagram. Of 117 eligible participants, 90 participants were randomized to the educational intervention (n = 45) and control (n = 45). All participants completed the intervention and contributed a complete set of data to the analysis.

Figure 2

Experiments to assess ambiguity in the financial and health domains. Participants were told to imagine two different types or urns. For urn type A, they knew that 50% of the balls were red and the other 50% were blue. For urn type B, they did not know the exact proportion of blue to red balls, with the grey bar representing the unknown proportion of balls. For the financial domain (Panel A), participants knew that if they drew a blue ball, they would win the full amount of $400. If they drew a red ball, they would win $ 0. For the health domain (Panel B), participants decided between two treatments for a patient. With “Treatment A,” the patient had a 50% probability of survival. With “Treatment B,” the exact probability of survival was unknown, with the grey bar representing the unknown probability. In our tasks, participants were asked to choose between one option (presented as two-colored bar) with known 50/50 probability of winning 400 or 0 American dollars (urn A) versus an option with unknown probability of the same outcomes (urn B). Participants who chose the 50/50 options were classified as averse to ambiguity; the remaining participants were classified as tolerant to ambiguity. A similar approach was used to determine aversion to ambiguity in the health domain (Panel B).

Figure 3

The TLS intervention. Panel A illustrates background information on the TLS and application to therapeutic decisions. Panel B illustrates how the TLS facilitates the decision-making process using traffic light terminology, which creates a link between a color, representing a risk level, and an action: red light (“high risk”/“stop and think”), yellow light (“intermediate risk”/“reassess soon”), and green light (“low risk”/“continue the same strategy”). Panel C provides a case scenario as an example of those given the participants.

Figure 4

TLS intervention decreased therapeutic inertia (TI). (A) Comparison of adjusted TI scores in the intervention and control groups. This graph was derived from the multivariate linear regression analysis adjusted for age, years of practice, participants risk preference, and specialty (general neurologist v. MS expert). TI scores were significantly higher in the control group compared to the intervention group (*P value <0.001). (B) Comparison of adjusted prevalence of TI between the intervention and control groups. This graph was derived from the multivariate logistic regression analysis adjusted for age, years of practice, participants risk preference, and specialty (general neurologist v. MS expert). The prevalence of TI was significantly higher in the control group compared with the intervention group (*P value <0.001).

Figure 5

Adjusted probability of therapeutic inertia (TI). (A) Adjusted probability of TI as a function of TI scores. (B) Adjusted TI score categories stratified by intervention assignment group. The x-axis represents categories of the TI to evaluate whether the intervention had a different effect among participants with low, medium, and high TI scores. The y-axis represents the TI scores to be able to show the lack of overlap of 95% CI between TLS and controls for each TI category (P value TLS v. controls = 0.02). Data derived from multivariate linear regression with TI score as the dependent variable. “I” represent 95% CI error bars.