Helena Roy

Patients increasingly select physicians through online “find a doctor” tools, which display information such as physician gender and patient ratings, rather than relying on physician referral, word-of-mouth, or soonest availability. Medical literature documents female patients' stated preferences for female physicians in certain specialties, but whether and how patients act on these preferences when given the tools to do so remains an open question. Using a randomized hypothetical choice experiment that mirrors the information presented in typical online physician-search platforms, we find that female patients have strong preferences for gender-concordant physicians in gender-sensitive specialties. Women are willing to wait 1.6 additional weeks for a female urologist and 2.2 additional weeks for a female psychiatrist, two specialties with more gender-sensitive components, but show no statistically significant concordance preference in orthopedics, a more gender-neutral specialty. Men exhibit no significant concordance preference in any specialty. Among women, the gender concordance preference is stronger among those who report prior negative healthcare experiences due to gender. We complement these experimental findings with evidence from Medicare claims data on bladder cancer patients. We find that female patients are more likely than male patients to see female urologists, and women who match with a female urologist wait longer than women who match with a male urologist. Male patients exhibit no corresponding wait-time difference by physician gender. These findings suggest that patient preferences over physician characteristics, and patients' growing capacity to act on those preferences, mean that the availability of female healthcare providers may affect patterns of health-seeking behavior such as provider choice and timing of care.

We estimate the causes and consequences of regional variation in healthcare utilization in the setting of Cesarean sections (C-sections), the most common inpatient surgery in the United States. C-section rates differ up to 10-fold across hospitals, with substantial variation even for clinically similar patients. Using nationwide Medicaid administrative claims data, we leverage physician mobility across hospitals to disentangle the role of physician practice style from hospital environment. We find that differences in physician practice style can explain approximately 20% of the across-hospital differences in C-section rates. This variation in practice style has meaningful consequences for patient health: low-risk patients quasi-randomly assigned to more C-section-intensive physicians are 28% more likely to deliver via unplanned C-section, leading to higher rates of maternal and infant health complications and worse maternal mental health postpartum. Our findings highlight physician practice style as an important driver of variation in obstetric care with direct consequences for maternal and infant health.

Early detection of cardiovascular disease is a global public health priority. Artificial intelligence (AI)-enabled stethoscopes offer robust performance characteristics in point-of-care detection of heart failure, atrial fibrillation, and valvular heart disease (VHD). We conducted a pragmatic, cluster-randomised controlled implementation trial to determine the real-world effect and implementation challenges of AI-stethoscopes. We found that implementation of an AI stethoscope in routine primary care did not significantly increase detection of heart failure or increase community-based diagnosis after 12 months of implementation. However, AI stethoscope use was independently associated with significantly higher detection rates of heart failure, as well as atrial fibrillation and VHD. This randomised controlled implementation trial establishes a pragmatic design with randomisation that generates real-world data essential for understanding and overcoming the barriers to implementation of innovation in health care.