TOP RATED ABSTRACTS I
Method: We conducted an online survey experiment in which participants imagined receiving hemoglobin A1c test results in-between clinical visits for management of Type 2 diabetes. Adults (N=1,785) viewed their mock results in one of three formats: (1) standard table that included the test result and a standard range, (2) table with an indicator for whether a result was high or low, or (3) a horizontal line graph that visually showed the test result and standard range. We also varied whether A1c was within the standard range (5.4%), or one of three higher levels (6.4%, 7.1%, or 8.4%). Our primary outcome measure was participants’ ratings of how good or bad they thought the test value was (or whether they marked “don’t know”). Secondary measures included 4 questions related to graph preferences. We also assessed participant numeracy using both subjective and objective scales as well as graphical literacy.
Result: Controlling for numeracy and graphical literacy, significantly more respondents marked “don’t know” for how good or bad the test result was when they viewed the table without markers (OR=2.42, p<.001) or table with markers (OR=2.23, p<.001) than when they viewed the line graph. Subjective numeracy strongly predicted “don’t know” responses (p<.001), but neither objective numeracy (p=.09) nor graphical literacy (p=.14) were significant predictors. Mean risk perceptions varied by A1c level (p<.001) and were significantly predicted by subjective numeracy, objective numeracy, and graphical literacy (all p’s<.001), but were not significantly different across formats. The 4 graph perception questions were highly correlated (Cronbach’s alpha=0.89), and respondents significantly preferred the line graphs compared to either table format (p=.003).
Conclusion: Presenting laboratory test results in the commonly-used table formats, even those with high/low markers, can confuse patients. Visual line graph displays improve patient understanding and satisfaction and could be easily implemented into patient electronic health record portals.
Purpose: The threshold model represents one of the most important advances in medical decision-making but it has never been empirically tested in real-life setting. We aimed to empirically test the regret-based threshold model in end of life care where patients face choices between hospice and curative treatment.
Method: According to the regret-based threshold model there must be some probability of death (pDeath) at which patients should be indifferent (Pt) between hospice care (Hospice) and continuing treatment targeted at their disease (Rx). The model predicts that if pDeath>Pt, patients should choose hospice; if pDeath<Pt, they should opt for Rx. We tested these predictions by interviewing 134 terminally ill patients facing Rx vs. Hospice decisions. We determined Pt by eliciting regret of omission (i.e. losing benefits of hospice care) and regret of commission (i.e. incurring harms from unnecessary treatment) using a dual visual analogue scale1. We estimated pDeath over 6-months using the Palliative Performance Scale (PPS) and adjusted PPS prognostic models. We compared the regret-based threshold model recommendation to the patients' choice at two different time frames: immediately after the interview and one month after the interview to study the patients' preferences and actual choice of care. We used Cramer V (effect size) to calculate the strength of agreement between the model recommendations and the patients' preferences and actual choice, respectively.
Result: We observed statistically significant agreement between the model recommendations and the patients' stated preferences (p<0.0001). Out of 134 patients 111 (83%) agreed with the model recommendations immediately after the interview, 6 patients (4%) disagreed, and 17 (13%) were unsure about their preferences (figure). This converts into very large effect size (0.84). 111/134 patients were approached one month after the interview to determine what type of care the patients actually chose: 59 (53%) chose according to the model recommendations; 39 (35%) chose a different option than the model's recommendation; and 13 (12%) patients remained unsure. While the association remains statistically significant (p=0.0067), the effect size dropped to 0.21 indicating medium effect.
Conclusion: The regret-based threshold model strongly predicts what patients think they would want (preferences) and moderately predicts the patients' actual choice. This is the first empirical study testing the threshold model in a real-life setting.
Agreement between patient preferences and recommendation of regret threshold model.
Making a good decision about surgery requires a patient to predict how she will feel in the future, with or without the procedure. However, people often mis-predict how they will feel, tending to overestimate the impact of life events. We hypothesized that breast cancer patients undergoing mastectomy would overestimate the negative impact of mastectomy and the positive impact of reconstruction on well-being. We also hypothesized that prediction accuracy would be associated with satisfaction with decisions.
Adult women undergoing mastectomy for stage I-III breast cancer, DCIS, or prophylaxis were enrolled at a single site. Before surgery, participants were asked to predict their 12-month happiness, quality of life (QOL), body image, sexual attractiveness, physical sensations, and pain. 12 months after surgery, actual scores and satisfaction with decisions were measured. Prediction accuracy was calculated as the difference in predicted and actual 12 month scores, and compared between groups using t-tests. Associations of prediction accuracy with satisfaction with decisions were evaluated by linear regression.
131 patients completed the baseline survey (72% participation rate) and 111 completed the 12 month survey (88% participation rate). 15 patients were excluded due to delayed reconstruction, leaving 54 who had mastectomy-only and 42 who had mastectomy-with-reconstruction. Mastectomy-only patients generally predicted poorer outcomes than they actually experienced, and mastectomy-with-reconstruction patients generally predicted better outcomes than they actually experienced (Figure 1). Prediction accuracy differed by treatment for QOL (6.3 v -2.3, p=0.01), satisfaction with breast clothed (0.4 v -0.1, p=0.04) and unclothed (0.1 v -0.5, p=0.02), sexual attractiveness clothed (0.2 v -0.4, p=0.03) and unclothed (-0.3 v -1.0, p=0.01) (Table 1). Both groups predicted poorer outcomes after mastectomy-only than after mastectomy-with-reconstruction, with the mastectomy-with-reconstruction group predicting significantly larger differences, for all but the physical sensation and pain items. Prediction accuracy was not associated with satisfaction with decisions (all p≥0.10).
Women undergoing mastectomy made mis-predictions about their future well-being after surgery. On average, they overestimated the negative impact of mastectomy and the positive impact of reconstruction. Prediction accuracy was not associated with decisional satisfaction.
Method: Newly diagnosed breast cancer patients in the Georgia and Los Angeles SEER registries were surveyed approximately 6 months post-diagnosis. A validated 5-item decision satisfaction scale adapted for breast cancer surgery was used to assess women’s satisfaction with their surgical decision-making process. Items were combined into a composite decision satisfaction score (scale: 0-5). A 3-item subjective measure of decision quality was used (Resnicow et al, 2014) that assessed respondents’ satisfaction with the information, involvement and time to make their surgical decision. These items were summed into a composite decision quality score (scale: 0-5). Decision style was assessed with three measures: 1) degree to which decisions are typically motivated by anticipatory regret, 2) rational vs. intuitive decision style, and 3) degree of deliberative decision-making. Multivariable linear regression was used to examine the adjusted differences in decision satisfaction and decision quality by the three participant-reported decision style items. Models adjusted for sociodemograhpic and clinical factors, including type of surgery received.
Result: Among the 2,020 women in the sample, decision-making was appraised positively: the mean decision satisfaction score was 4.4 (SD:0.81) and the mean decision quality score was 4.5 (SD:0.84) out of 5. In the adjusted regression models, greater orientation towards anticipatory regret was significantly associated with worse appraisal of decision-making (P<0.001 for decision satisfaction and decision quality). Conversely, more deliberation was significantly associated with positive appraisal of decision-making (P<0.001 for decision satisfaction and P=0.029 for decision quality) Rational/intuitive decision style was not significantly associated with either decision-making outcome.
Conclusion: Decision quality and satisfaction were high in our sample overall. Greater deliberation in decision--making was associated with better appraisal of both decision quality and satisfaction. Identification of women who typically make decisions based on anticipatory regret may assist clinicians in supporting their breast cancer surgical decision-making process.
Method: An analytic sample of 32,671 respondents with a usual source of care (USC) was identified using the 2007-2012 Medical Expenditure Panel Survey (MEPS). Patient satisfaction with provider’s communication skills was measured on four dimensions; how often the provider listened carefully, explained medical care in an understandable way, showed respect and spent enough time during consultation. Patients and physician’s race and ethnicity were categorized as non-Hispanic white, non-Hispanic black, Hispanic and other race. SES was defined using income at federal poverty level (FPL); “low SES” (<200%), “middle SES” (200%-400%) and “high SES” (>400%). Logistic regression models were used to examine the effect of SES on perception of communication skills in racially concordant interactions.
Result: Approximately 30% of the respondents indicated being racially discordant with their provider; racial discordance was more common among minority and low to middle SES patients. Racial concordance did not have a statistically significant association with higher satisfaction on any measure. Compared to high SES patients, low SES patients were more likely to be dissatisfied in all four domains of provider’s communication skills. The largest differences were detected in satisfaction with provider’s ability to explain medical care (4.5 percentage points, p<0.001). However, perceptions of communication skills did not differ between middle and low SES patients. Additionally, no significant differences were found in the association between race concordance and satisfaction across SES categories.
Conclusion: Vulnerable low SES populations may experience ineffective patient-provider communication even when they have a USC. This can result in greater dissatisfaction with the care received relative to more advantaged populations. Concordance is multidimensional and patient’s perception of similarity to their provider extends to aspects beyond demographic characteristics like personal beliefs and values. With growing emphasis on patient satisfaction scores, a key policy challenge is enhancing physician skills to elicit patient communication preferences that can transcend issues of race and sex to foster positive experiences of care.
Method: The Office-Guidelines Applied in Practice (Office-GAP) intervention included: 1) patient SDM training in one 90 min group visit 2) clinician SDM training in one 60-90 min session 3) patient decision aid & clinical encounter decision checklist. Two site intervention/control design. Main outcome measure: change in blood pressure (BP) control by chart review. Logistic regression analysis with propensity scoring to control for confounders was used to examine change over time in the rate of BP control in two clinical sites.
Result: Participants were low-income patients with diabetes and coronary heart disease (CHD) in two Federally Qualified Healthcare Centers (FQHCs). 120 patients were in the intervention arm; 123 in the control arm. Medication use was not different statistically at baseline. Results show that program elements were consistently used with > 98% clinician attended training and the checklist present in the patient chart. Patient attendance at the group visit was > 80% in the intervention. After controlling for confounders, the model showed that the Office-GAP intervention significantly increased the probability of getting BP under control (p=0.0122, OR=2.36). For diabetic patients, those with Medicaid insurance were more likely to have their BP under control compared to those without insurance (p=0.0590, OR=1.67). Patients whose BP was under control at baseline were more likely to have their BP controlled at 12 months (p<0.0001, OR=5.01). Diabetic patients were less likely to have BP controlled compared to non-diabetic patients (p=0.0041, OR=0.312).
Conclusion: Use of the Office-GAP program to teach SDM and use of DAs in clinical practice was demonstrated to be feasible in FQHCs. This two group intervention showed that the Office-GAP intervention led to higher rates of BP control among underserved patients with CHD and diabetes. The Office-GAP program combines previously developed interventions into a brief, efficient approach to improved communication and collaborative decision making in clinical practice. Further research is needed to reproduce our results and describe the mechanism that appears to improve shared decision making.