37th Annual Meeting of the Society for Medical Decision Making

October 18 - 21, 2015

Purpose: With the advent of online patient portals to electronic health record systems, patients are increasingly receiving their laboratory test results directly. Other research from our lab highlights that presenting test results with a visual line graph reduces confusion about the results compared to the tables typically used on online portals. However, it is unclear what specific line graph features might improve understanding of test results.

Methods: Participants (N=4,833) completed an online survey experiment in which they were presented with a hypothetical scenario where they received hemoglobin A1c test results from a blood draw done between clinical visits for management of Type 2 diabetes. Test results were randomly presented in one of three line graph formats: (1) solid, single-color that only showed the standard range, (2) color gradient utilizing a stoplight color theme (green to red) to indicate risk categories or (3) solid-color blocks that indicated discrete risk categories utilizing the stoplight color theme. The A1c test value was also randomized, being within the standard range (5.4%) or one of three higher levels (6.4%, 7.1%, or 8.4%). Our primary outcome measures were 1) how good or bad the participant thought their test result was for their health, with “don’t know” as a response option and 2) graph preferences. Individual difference measures included subjective and objective numeracy, and graphical literacy.

Results: Controlling for numeracy and graphical literacy, both A1c test value and graph format had significant effects on risk perceptions (p’s<.05). More importantly, we observed a significant A1c test value by format interaction (p<.001): Participants were more sensitive to changes in A1c values when they viewed the stoplight-colored block design with discrete risk categories than when they saw either of the other line graph formats. Respondents also significantly preferred the stoplight-colored block design (p’s<.03). The A1c test value was not a significant predictor of “don’t know” responses (p=.88), but the stoplight-colored gradient design did result in higher rates of “don’t know” responses than either the solid single-color and stoplight-colored block designs (p=.01).

Conclusion: Presenting laboratory test results using a line graph that utilizes stoplight-colored blocks that clearly demarcate discrete risk categories decreases confusion, increases sensitivity to differences in test values, and is more preferred to line graphs that utilize a solid, single-color or a stoplight-colored gradient.

Purpose:  To identify multimedia features that have been integrated into computer-based decision aids (DAs), and to assess which features facilitate high-quality decision-making.

Method:  Relevant studies were located by searching MEDLINE, Embase, CINAHL and CENTRAL databases. The review focused on studies that tested multimedia DAs in adults faced with preference-sensitive decisions, and reported quality of decision-making outcomes, such as knowledge or decisional conflict scores. A thematic analysis was conducted to identify multimedia features and sub-classes. A meta-analysis was conducted based on standardized mean differences (SMDs) for improvements in quality of decision-making scores. Subgroup analyses compared pooled SMDs for DAs that incorporated a specific feature to other computer-based DAs that did not incorporate the feature to assess whether specific multimedia features were associated with improvements in quality of decision-making.

Result:  Of 3,541 unique publications, 58 articles met all inclusion criteria. The thematic analysis identified six multimedia features: content control, tailoring, implicit values clarification, explicit values clarification, feedback, and social support. Overall, multimedia DAs performed significantly better than simple aids or usual care (SMD=0.43; 95%CI=0.31-0.56; p<0.00001). DAs that provided content control performed better than DAs that did not incorporate the feature (SMD=0.48 vs. 0.32; p=0.21); however, the association was only significant when providing control over clarifying (SMD=0.60 vs. 0.28; p=0.01) or supplemental information (SMD=0.65 vs. 0.32; p=0.03). Although DAs that tailored information performed better than usual care or simple aids (SMD=0.32; 95%CI=0.17-0.48; p<0.0001), inclusion of this feature was associated with reduced quality of decision-making when compared to other computer-based DAs (SMD=0.32 vs. 0.61; p=0.03). DAs providing implicit values clarification performed worse overall (SMD=0.36 vs. 0.51; p=0.25); however, the association was significant only when incorporating role modeling (SMD=0.16 vs. 0.50; p=0.006). DAs incorporating explicit values clarification, feedback or social support performed similar to DAs not including the feature with SMDs of 0.42 (vs. 0.47; p=0.72), 0.36 (vs. 0.49; p=0.92), and 0.45 (vs. 0.42; p=0.80), respectively.

Conclusion:  Multimedia features should be integrated into DAs to improve quality of decision-making; however, some features perform better than others. Content control, specifically control over clarifying or supplemental information, should be integrated into DAs. Alternative implicit values clarification, such as patient testimonials, should be integrated over role modeling. Further analyses are necessary to assess why DAs that incorporated tailoring performed significantly worse than other computer-based DAs.

Purpose:   To compare the effects of brief and long International Patient Decision Aids Standard (IPDAS) compliant patient decision aids (PtDA) on decisional outcomes.

Method:   Rheumatoid arthritis (RA) patients were presented, via mail survey, with a hypothetical decision scenario where they were asked to consider adding Enbrel^TM (etanercept) to their current regimen. To prepare for the decision, each patient was randomized to review either an etanercept specific long, 24 page PtDA (LONG DA) or a short, 2 page PtDA (SHORT DA).   Each subject was evaluated for: their decision to intensify therapy, strength of preference of decision, pre and post intervention knowledge, and decisional conflict (DCS).

Result:   Equal number of patients were allocated to each intervention. The response rate was 52% with 266 participants.  With formative evaluation of the PtDA, there was no difference in patient rating of: having information needed, being well organized, and being helpful in making a decision.  123 (14.6%) of patients who reviewed the LONG DA and 143 (14.0%) of who reviewed the SHORT DA chose to take etanercept (χ²=.023; NS). There was no significant difference between intervention groups in mean strength of preference to intensify therapy.  Those who were randomized to the SHORT DA vs LONG DA had a greater increase in post-intervention etanercept related knowledge that 15.5% vs 10.5% (P< .02).   There was no difference between interventions in: overall decisional conflict (DCS), nor informed, values clarity, uncertainty, or effective decision subscales. However, subjects who reviewed the SHORT DA had significantly higher score on the DCS support subscale (P< .04).

Conclusion:

In this randomized clinical trial of decision support, a brief PtDA was not inferior to a traditional long format PtDA.  Rather there is evidence of increased knowledge gain and feeling more supported to make a decision to intensify therapy.  Brief PtDA are acceptable to patients and can effectively support patients preparing for complex medication decisions.   Implementing simple decision supports at the point of care deserves further evaluation.

Purpose: As patients are encouraged to become more involved in their healthcare decisions they may be confronted with how little they actually know about the decision options. People often think they have a relatively good understanding of how something works, a phenomenon known as the “illusion of explanatory depth”, an illusion that is disrupted when people are forced to try to explain how it actually works (see Rosenblit & Keil, Cogn Sci,2002). Whether people exhibit the illusion of explanatory depth for medical interventions and the impact of disrupting this illusion on interest in using or being involved in decisions about using those interventions is unknown.

Methods: Participants (N = 309) were recruited through Amazon’s Mechanical Turk to complete an online survey experiment. Participants began by indicating the perceived effectiveness of a 12 medical interventions (preventative medicines, screening tools, and treatments) and their subjective knowledge about how the medical interventions work. Participants were then randomly assigned to explain how one of the medical interventions worked. After providing their explanation, participants re-rated their subjective knowledge and perceived efficacy of the medical intervention, as well as their willingness to use and be involved with making a decision about using the intervention. After completing this process for a second medical intervention, participants indicated their familiarity and usage of each prevention, screening tool, and treatment.

Results: Overall, participants exhibited a decreased sense of understanding after attempting to explain how a medical intervention works (p < .001), providing evidence for the illusion of explanatory depth. Perceived efficacy of the medical intervention also decreased after providing an explanation (p < .001). More importantly, decreases in perceived efficacy were associated with decreased willingness to use the medical intervention (p < .001) and decreased desire for participation in decision making about the medical intervention (p = .01).

Conclusion: Disrupting the illusion of explanatory depth for medical interventions was associated with decreased interest in utilizing the medical intervention and less desire for involvement in decisions about whether to use the medical intervention. While disrupting the illusion of explanatory depth may reduce a patient’s interest in being involved in the decision-making process, it could be used as a tool for correcting misinformation or reducing the overutilization of low-value services.

Purpose: Many people come to medical decision-making unused to participating in high-stakes health decisions. This study aimed to test whether providing people with a practice decision in a more familiar, less frightening context might help them with a subsequent medical decision.

Method: We first developed a novel values clarification method by applying best practices in interface design, including user-centered design methods. The values clarification method uses dynamic visual feedback throughout a decision aid to help people explore how well or poorly their available options align with their expressed values. We then randomized half of study participants in an online experiment to go through a practice travel decision followed by a medical decision. The other half of study participants were assigned only the medical decision. Participants randomized to the practice travel decision were asked to imagine that they had won a free trip and needed to choose one of four available travel options. For the medical decision, we asked participants to imagine they were diagnosed with early-stage breast cancer (women) or early-stage prostate cancer (men) and needed to choose a treatment. We carefully constructed the travel decision to closely mimic the medical decisions. We presented all decisions using the same decision aid interface design and values clarification method, showing participants how their values for different decision attributes (e.g., length of flight for the travel decision, length of hospital stay for the medical decision) aligned with their available options. After all participants completed the medical decision aid, we asked them (1) which medical treatment option they would choose, and (2) how confident they were that it was the best choice for them. 

Result: Participants (n=445) were a diverse sample of US adults (mean age 51, SD 8, 57% female, 82% white). Exposure to the practice travel decision was not associated with any differences in participants’ medical treatment choices (men: Chi-squared(3)=3.15, p=0.37; women: Chi-squared(2)=1.35, p=0.51), but it was associated with increased confidence in their medical decisions (F(1,443)=5.62, p=.02).

Conclusion: Giving people a practice decision in a less threatening context may help them feel more confident in their abilities to make medical decisions that reflect what matters to them.