Category Reference for Presentations | |||||
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AHE | Applied Health Economics | DEC | Decision Psychology and Shared Decision Making | ||
HSP | Health Services, and Policy Research | MET | Quantitative Methods and Theoretical Developments |
* Candidate for the Lee B. Lusted Student Prize Competition
Methods: Using freely available online news items, we created a composite news article about a health decision that evokes diverse views: home birth. We then selected comments that had been posted on the original articles and classified them as either (a) positive or negative about the topic of home birth and (b) fact-oriented or story-oriented. We conducted a between-subjects factorial online experiment in a demographically diverse, US-based sample population: N = 1703; mean age 49 (SD 16); 51% female; 80% white; 49% no college. All participants read the same mock news article, but we experimentally manipulated whether or not comments were posted to the article and what types of comments were shown. After participants viewed the materials, we asked them to indicate their overall opinion of home birth on a scale from ‘extremely negative opinion’ (value 0) to ‘extremely positive opinion’ (value 100). We also asked their likelihood of planning a home birth if they or a partner were to be expecting a child, and of recommending home birth to others (secondary outcomes). To account for the potential influence of prior opinions and knowledge, we asked them to indicate how much they felt they knew about home birth before reading the article: ‘Nothing’, ‘A little bit’, or ‘A lot’.
Results: Opinions of home birth ranged widely (median 51, IQR 46) and were significantly influenced by the presence of positive versus negative comments (means: 63 for positive versus 39 for negative, p<.0001). Story-oriented comments exerted greater influence on opinions than fact-oriented comments (differences between positive and negative: 27 for story-oriented versus 19 for fact-oriented, p<.0001). Although people who felt more knowledgeable about home birth had more positive opinions overall, effects of experimental factors were consistent regardless of prior knowledge. Results for secondary outcomes followed similar patterns.
Conclusions: Comments posted on an online health news article can powerfully influence readers’ opinions and potential decision intentions, regardless of their prior knowledge on the topic. Last year, 72% of US-based Internet users sought health information online, suggesting that this finding may be very broadly applicable.
Method: We created an engaging, interactive decision support guide for women of varying literacy levels that addresses these issues, and conducted a randomized trial of an “informed free choice” (IFC) approach to prenatal testing among English- or Spanish-speaking pregnant women. Women randomized to IFC viewed the guide and were told they could have any of the tests described, free of charge, while women randomized to usual care received no intervention and were not offered testing free of charge. At 24-36 weeks gestation, participants completed a telephone interview to assess patient-reported outcomes. After delivery, charts were reviewed to ascertain which prenatal tests, if any, the participant underwent.
Result: We recruited a diverse population of 710 women with varying numeracy and literacy levels. Half (47.6%) of the participants were Latina, 25.4% had poor literacy (REALM-R scores < 6), and 44.5% had low numeracy scores (<2 on a 0-to-5 scale). Compared to women randomized to usual care, women randomized to IFC had higher knowledge scores (9.4 versus 8.6, p=.001) and were less likely to undergo invasive testing (5.6% versus 12.4%, p=.004). No differences emerged in decisional conflict, pregnancy worry, or depression.
Conclusion: Providing women of varying literacy levels the opportunity to use an engaging decision support guide and to choose between differing prenatal screening and diagnostic testing strategies (including no testing) without financial barriers enabled them to make more informed choices that led to lesser use of invasive testing options.
Method: The field study consisted of 243 doctor-patient interactions that were recorded from 4 VA hospitals as part of a study on prostate cancer and in which the treatment outcome of the patient was known. Transcripts from the recordings were coded to record the presence or absence of a bias statement—for example, “I’m a surgeon so I’m biased towards recommending surgery,”—and we examined whether these statements were more likely to be present when patients opted for surgery. The lab experiment consisted of 377 male participants who watched a series of video clips in which a surgeon explained two treatment options for prostate cancer: radiation therapy and surgery. The men were randomized into two conditions—those that heard their urologist admitting their specialty bias in the video clips and those who did not. These men then decided on which treatment they would prefer and their level of trust in the doctor
Result: Patients across the 4 VA hospitals who heard their surgeon admit to a specialty bias (n = 58 out of 243 transcripts) were more likely to take surgery (43%) than those who did not hear their surgeon admit a bias (27%), p = .001. The lab experiment found similar results; participants were more likely to choose surgery if their urologist admitted a specialty bias (87% vs. 68%, p < .01), and these participants also felt increased their trust in the surgeon (p < .05) and felt that their surgeon was more competent (p < .05).
Conclusion: Hearing a doctor openly admit to their specialty bias alters the patients’ perception of the doctor. It increases the patients’ evaluation of the doctor’s skill and competence, their trust in the doctor, and their compliance with the doctor’s biased recommendation.
Purpose : Compare the relative importance of shared decision making to other factors that influence a patient's choice of a specialist for consultation.
Methods: We recruited a national sample designed to roughly parallel US population demographics using an Internet survey vendor. Adults who reported having a visit to a healthcare provider in the past year were invited to complete a web based adaptive conjoint analysis (ACA) survey consisting of 8 attributes with 3 levels each. We performed data quality control by excluding participants that did not spend an adequate time on each page or who consistently selected one response. We estimated individuals' utility (overall preference) for each level of each attribute using hierarchical Bayesian analysis and then normalized the weights based on the observed ranges of utilities for the attributes. We simulated patient choice for different types of specialists using a randomized first choice method. Survey data collection and analysis was completed using SSIWeb and ACA/HB (Sawtooth Software).
Results: 706 patients completed the survey. Of these 530 had adequate data quality as defined by time on page for responses and variability in response items. Subject demographics paralleled those of the US population (53.5% female, 31.1% minority, 9.4% Latino) but were better educated (82.5% with some college or higher). Their health was somewhat lower than typical of the population (17.6% reported poor to fair health). Not surprisingly, the most important factor in patients' choice of a specialist was cost of out of pocket cost (insurance coverage). However, among the non financial factors, EHR interoperability and communication between specialist and generalist had greatest weight (P <0.001) followed by the specialist's decision making style (P< 0.001 for differences, see figure). In model simulations, two thirds of patients (67%) were willing to trade two weeks of time waiting for an appoint with a specialist that participates in shared decision making.
Conclusion: Coordination of care with the primary care providers and decision making style (shared decision making) are highly valued by patients in the choice of a specialist for a referral—more valued than attributes such as specialist availability, expertise, and travel time to the specialist. Generalists should consider patients' preferences when recommending a specialist to patients for a referral.
Clinical Benefits of Contralateral Prophylactic Mastectomy for Women with Unilateral Early-stage Breast Cancer
Purpose:
To examine the survival benefits of contralateral prophylactic mastectomy (CPM) in women with early-stage breast cancer without a BRCA mutation or additional risk factors.
Methods:
We developed a Markov model to compare CPM with no CPM among women with early-stage breast cancer without a BRCA mutation. Probabilities for developing contralateral breast cancer (CBC), dying from CBC, dying from the primary breast cancer, and the reduction in CBC due to CPM were estimated from the published literature. From the parameterized model, we estimated the life expectancy (LE) gain and absolute 20-year survival difference of CPM among cohorts of women newly diagnosed with unilateral breast cancer defined by age (40-60 years), estrogen receptor (ER) status (positive or negative) and stage of cancer (I or II).
Results:
LE gain from CPM ranged from 0.13 to 0.59 years for women with stage I breast cancer and 0.08 to 0.29 years for those with stage II breast cancer (table). Twenty-year absolute survival differences ranged from 0.56% to 0.94% for women with stage I breast cancer and 0.36% to 0.61% for women with stage II breast cancer. CPM was more beneficial among younger women, stage I, and ER negative breast cancer.
Conclusions:
Although CPM dramatically reduces the risk of CBC, the maximum life expectancy gain is about 7 months with a less than 1% 20-year survival difference for all age, ER status, and cancer stage groups. Decision models may be helpful for physicians when counseling patients on prophylactic breast cancer risk reduction strategies.
Table: LE Gains from CPM
|
Life Expectancy (yrs) |
||
Characteristics |
CPM |
No CPM |
LE Gain |
40 yo, ER+, Stage I |
36.77 |
36.30 |
0.47 |
40 yo, ER-, Stage I |
36.76 |
36.17 |
0.59 |
40 yo, ER+, Stage II |
24.16 |
23.92 |
0.24 |
40 yo, ER-, Stage II |
24.15 |
23.86 |
0.29 |
50 yo, ER+, Stage I |
29.72 |
29.45 |
0.27 |
50 yo, ER- , Stage I |
29.71 |
29.38 |
0.33 |
50 yo, ER+, Stage II |
20.89 |
20.74 |
0.15 |
50 yo, ER-, Stage II |
20.88 |
20.70 |
0.18 |
60 yo, ER+, Stage I |
22.54 |
22.41 |
0.13 |
60 yo, ER-, Stage I |
22.53 |
22.37 |
0.16 |
60 yo, ER+, Stage II |
16.98 |
16.90 |
0.08 |
60 yo, ER-, Stage II |
16.98 |
16.88 |
0.10 |