Karen A. Scherr

Physician Recommendations Trump Patient Preferences in Prostate Cancer Treatment Decisions

Objective. To assess the influence of patient preferences and urologist recommendations in treatment decisions for clinically localized prostate cancer. Methods. We enrolled 257 men with clinically localized prostate cancer (prostate-specific antigen <20; Gleason score 6 or 7) seen by urologists (primarily residents and fellows) in 4 Veterans Affairs medical centers. We measured patients’ baseline preferences prior to their urology appointments, including initial treatment preference, cancer-related anxiety, and interest in sex. In longitudinal follow-up, we determined which treatment patients received. We used hierarchical logistic regression to determine the factors that predicted treatment received (active treatment v. active surveillance) and urologist recommendations. We also conducted a directed content analysis of recorded clinical encounters to determine if urologists discussed patients’ interest in sex. Results. Patients’ initial treatment preferences did not predict receipt of active treatment versus surveillance, Δχ2(4) = 3.67, P = 0.45. Instead, receipt of active treatment was predicted primarily by urologists’ recommendations, Δχ2(2) = 32.81, P < 0.001. Urologists’ recommendations, in turn, were influenced heavily by medical factors (age and Gleason score) but were unrelated to patient preferences, Δχ2(6) = 0, P = 1. Urologists rarely discussed patients’ interest in sex (<15% of appointments). Conclusions. Patients’ treatment decisions were based largely on urologists’ recommendations, which, in turn, were based on medical factors (age and Gleason score) and not on patients’ personal views of the relative pros and cons of treatment alternatives.

Treatment Availability Influences Physicians’ Portrayal of Robotic Surgery During Clinical Appointments

ABSTRACT In order to empower patients as decision makers, physicians must educate them about their treatment options in a factual, nonbiased manner. We propose that site-specific availability of treatment options may be a novel source of bias, whereby physicians describe treatments more positively when they are available. We performed a content analysis of physicians’ descriptions of robotic prostatectomy within 252 appointments at four Veterans Affairs medical centers where robotic surgery was either available or unavailable. We coded how physicians portrayed robotic versus open prostatectomy across specific clinical categories and in the appointment overall. We found that physicians were more likely to describe robotic prostatectomy as superior when it was available [F(1, 42) = 8.65, p = .005]. We also provide initial qualitative evidence that physicians may be shaping their descriptions of robotic prostatectomy in an effort to manage patients’ emotions and demand for the robotic technology. To our knowledge, this is the first study to provide empirical evidence that treatment availability influences how physicians describe the advantages and disadvantages of treatment alternatives to patients during clinical encounters, which has important practical implications for patient empowerment and patient satisfaction.

The Physician Recommendation Coding System (PhyReCS): A Reliable and Valid Method to Quantify the Strength of Physician Recommendations During Clinical Encounters.

Background. Physicians’ recommendations affect patients’ treatment choices. However, most research relies on physicians’ or patients’ retrospective reports of recommendations, which offer a limited perspective and have limitations such as recall bias. Objective. To develop a reliable and valid method to measure the strength of physician recommendations using direct observation of clinical encounters. Methods. Clinical encounters (n = 257) were recorded as part of a larger study of prostate cancer decision making. We used an iterative process to create the 5-point Physician Recommendation Coding System (PhyReCS). To determine reliability, research assistants double-coded 50 transcripts. To establish content validity, we used 1-way analyses of variance to determine whether relative treatment recommendation scores differed as a function of which treatment patients received. To establish concurrent validity, we examined whether patients’ perceived treatment recommendations matched our coded recommendations. Results. The PhyReCS was highly reliable (Krippendorf’s alpha = 0.89, 95% CI [0.86, 0.91]). The average relative treatment recommendation score for each treatment was higher for individuals who received that particular treatment. For example, the average relative surgery recommendation score was higher for individuals who received surgery versus radiation (mean difference = 0.98, SE = 0.18, P < 0.001) or active surveillance (mean difference = 1.10, SE = 0.14, P < 0.001). Patients’ perceived recommendations matched coded recommendations 81% of the time. Conclusion. The PhyReCS is a reliable and valid way to capture the strength of physician recommendations. We believe that the PhyReCS would be helpful for other researchers who wish to study physician recommendations, an important part of patient decision making.

Poor Consumer Comprehension and Plan Selection Inconsistencies Under the 2016 HealthCare.gov Choice Architecture

Background: Many health policy experts have endorsed insurance competition as a way to reduce the cost and improve the quality of medical care. In line with this approach, health insurance exchanges, such as HealthCare.gov, allow consumers to compare insurance plans online. Since the 2013 rollout of HealthCare.gov, administrators have added features intended to help consumers better understand and compare insurance plans. Although well-intentioned, changes to exchange websites affect the context in which consumers view plans, or choice architecture, which may impede their ability to choose plans that best fit their needs at the lowest cost. Methods: By simulating the 2016 HealthCare.gov enrollment experience in an online sample of 374 American adults, we examined comprehension and choice of HealthCare.gov plans under its choice architecture. Results: We found room for improvement in plan comprehension, with higher rates of misunderstanding among participants with poor math skills (P < 0.05). We observed substantial variations in plan choice when identical plan sets were displayed in different orders (P < 0.001). However, regardless of order in which they viewed the plans, participants cited the same factors as most important to their choices (P > 0.9). Limitations: Participants were drawn from a general population sample. The study does not assess for all possible plan choice influencers, such as provider networks, brand recognition, or help from others. Conclusions: Our findings suggest two areas of improvement for exchanges: first, the remaining gap in consumer plan comprehension and, second, the apparent influence of sorting order—and likely other choice architecture elements—on plan choice. Our findings inform strategies for exchange administrators to help consumers understand and select plans that better fit their needs.

The Impact of Cost Conversations on the Patient-Physician Relationship

ABSTRACT Previous research has suggested that fear of harm to the patient–physician relationship is an important barrier to conversations about cost of care. However, few experimental studies have investigated the effects of cost of care conversations on the patient–physician relationship, particularly from the patient’s perspective. In the current research, we take an experimental approach to investigate patients’ attitudes and preferences for a hypothetical physician who discusses cost versus one who does not. Across three studies, using data from both the general population and cancer patients, we find that people prefer a hypothetical physician who discusses cost over one who does not (Pilot Study, Studies 1 and 2). In addition, we find that people use cost information to inform their hypothetical treatment decisions without changing their attitudes toward the physician who includes this information (Study 1). Finally, we examine how and when cost information compares to more traditional medical information (e.g., side effects; Study 2). We discuss the implications of this research for cost communications and the patient–physician relationship, highlighting that cost conversations may not be as harmful as previously thought.