The effect of simulated narratives that leverage EMR data on shared decision-making: a pilot study

Shared decision-making can improve patient satisfaction and outcomes. To participate in shared decision-making, patients need information about the potential risks and benefits of treatment options. Our team has developed a novel prototype tool for shared decision-making called hearts like mine (HLM) that leverages EHR data to provide personalized information to patients regarding potential outcomes of different treatments. These potential outcomes are presented through an Icon array and/or simulated narratives for each "person" in the display. In this pilot project we sought to determine whether the inclusion of simulated narratives in the display affects individuals' decision-making. Thirty subjects participated in this block-randomized study in which they used a version of HLM with simulated narratives and a version without (or in the opposite order) to make a hypothetical therapeutic decision. After each decision, participants completed a questionnaire that measured decisional confidence. We used Chi square tests to compare decisions across conditions and Mann-Whitney U tests to examine the effects of narratives on decisional confidence. Finally, we calculated the mean of subjects' post-experiment rating of whether narratives were helpful in their decision-making.

In this study, there was no effect of simulated narratives on treatment decisions (decision 1: Chi squared = 0, p = 1.0; decision 2: Chi squared = 0.574, p = 0.44) or Decisional confidence (decision 1, w = 105.5, p = 0.78; decision 2, w = 86.5, p = 0.28). Post-experiment, participants reported that narratives helped them to make decisions (mean = 3.3/4).

We found that simulated narratives had no measurable effect on decisional confidence or decisions and most participants felt that the narratives were helpful to them in making therapeutic decisions. The use of simulated stories holds promise for promoting shared decision-making while minimizing their potential biasing effect.