Literature Collection

BACKGROUND: Successfully combating the opioid crisis requires patients who misuse opioids to have access to affirming and effective health care. However, there is a shortage of physicians who are willing to work with these patients. We investigated novel predictors of what might be contributing to physicians' unwillingness to engage with this patient population to better identify and direct interventions to improve physician attitudes. METHODS: 333 physicians who were board certified in the state of Ohio completed a survey about their willingness to work with patients who misuse opioids. The hypothesized relationships between the proposed predictors and willingness to work with this patient population were tested using multivariate regression, supplemented with qualitative analysis of open-text responses to questions about the causes of addiction. RESULTS: Perceptions of personal invulnerability to opioid misuse and addiction, opioid misuse and addiction controllability, and health care provider blame for the opioid crisis were negatively associated with physician willingness to work with patients who misuse opioids after controlling for known predictors of physician bias toward patients with substance use disorders. Physicians working in family and internal medicine, addiction medicine, and emergency medicine were also more willing to work with this patient population. CONCLUSIONS: Distancing oneself and health care professionals from opioid misuse and placing blame on those who misuse are negatively associated with treatment willingness. Interventions to improve physician willingness to work with patients who misuse opioids can target these beliefs as a way to improve physician attitudes and provide patients with needed health care resources.

Introduction: The use of telemedicine (TM) for patient care greatly increased during the COVID pandemic. This study presents data from a single health system regarding physician's perspectives on TM, which could ultimately determine how it is used in the future. Methods: A questionnaire was distributed to physicians throughout the health system. Physicians were divided based on the standard level of patient interaction in each specialty, as well as practice locations and years in practice. Physician perspectives were categorized by their opinions on different aspects of telehealth visits. Results: Of 1,794 physicians, 379 (20.7%) responded to the survey. Psychiatrists used TM significantly more than other groups and project the most future use. Surgeons were least likely to incorporate TM in the future. Ability to perform a physical examination via TM differed significantly by specialty and practice environment, but not by years in practice. Frequency of being able to complete a treatment plan via TM differed significantly by specialty, but not by years in practice or practice environment. Overall, 76.3% of physicians reported feeling "satisfied" with performing TM visits. Satisfaction with TM varied significantly by specialty and practice environment, but not by years in practice. There were no significant differences regarding physician expectations on reimbursement or billing for TM visits based on specialty, age, or practice environment. Conclusions: Discrepancies exist among physicians with respect to their satisfaction and expected future use of TM. Consensus may be difficult to reach regarding reimbursement for these visits, and further work is needed to clarify the optimal practice setting for TM.

BACKGROUND: As artificial intelligence (AI) tools are integrated more widely in psychiatric medicine, it is important to consider the impact these tools will have on clinical practice. OBJECTIVE: This study aimed to characterize physician perspectives on the potential impact AI tools will have in psychiatric medicine. METHODS: We interviewed 42 physicians (21 psychiatrists and 21 family medicine practitioners). These interviews used detailed clinical case scenarios involving the use of AI technologies in the evaluation, diagnosis, and treatment of psychiatric conditions. Interviews were transcribed and subsequently analyzed using qualitative analysis methods. RESULTS: Physicians highlighted multiple potential benefits of AI tools, including potential support for optimizing pharmaceutical efficacy, reducing administrative burden, aiding shared decision-making, and increasing access to health services, and were optimistic about the long-term impact of these technologies. This optimism was tempered by concerns about potential near-term risks to both patients and themselves including misguiding clinical judgment, increasing clinical burden, introducing patient harms, and creating legal liability. CONCLUSIONS: Our results highlight the importance of considering specialist perspectives when deploying AI tools in psychiatric medicine.

IMPORTANCE: Universal adolescent depression screening is recommended as routine primary care, but it is unclear how best to implement it. Systematic evaluation of physician preferences can support optimal screening implementation. OBJECTIVE: To assess primary care physicians' preferences for different attributes of a universal adolescent depression screening strategy. DESIGN, SETTING, AND PARTICIPANTS: In this survey study, a discrete choice experiment was administered to a US physician panel maintained by Qualtrics. Five attributes were identified from prior qualitative work: screening modality, screening location, screening completion time, missed depression cases, and clinical examination time, each with 2 to 3 levels. The survey presented 13 discrete choice questions and physician characteristics' questions. The survey was pretested through cognitive debriefings, piloted to an independent sample, and fielded to the final sample from April to June 2024. MAIN OUTCOMES AND MEASURES: Physicians' preference and importance coefficients for different screening strategies using time to assess willingness to make trade-offs for changes in attribute levels were estimated. Data were analyzed using conditional logit and latent class models. RESULTS: Among the 181 physician respondents (96 males [53.0%]), 90 (49.7%) were in urban settings, 112 (61.9%) were in a pediatrics primary care specialty, and 68 (37.6%) were in private practice. Conditional logit analyses showed that respondents preferred the least missed depression cases (59.6% importance), shortest clinical examination time (21.0% importance), 3-minute screening time (12.9% importance), private area provision (3.8% importance), and electronic modality (2.7% importance). Physicians reported their willingness to spend 37.3 minutes (95% CI, 32.1-42.5 minutes) during examinations per patient to reduce missed diagnoses from 10% to 5%. In the latent class analysis, 3 subgroups were identified. The diagnostic accuracy-sensitive group (n = 66) prioritized the top important attribute, missed depression cases (75.1% importance), more than other groups. The clinic time-sensitive group (n = 33) prioritized shortening examination time (35.2% importance) more than other groups. The screener type-specific group (n = 82) prioritized 3-minute screening (25.8% importance) and electronic modality (10.2% importance) more than other groups. CONCLUSION AND RELEVANCE: In this survey study, primary care physicians preferred identifying adolescent depression accurately and shortening well-child examination time. Health systems and payers should consider these preferences for accuracy and efficiency by physicians, who are strongly positioned to identify adolescent depression early and to implement screenings. Further research is needed to better understand family and administrative staff preferences regarding pediatric integrated behavioral health care.

Physician workload is known to impact provider well-being and individual patient encounters, but less is understood about how provider availability affects broader community health outcomes. Primary care physicians (PCPs) often serve as de facto mental health providers, particularly in underserved communities. This study evaluated whether PCP and mental health provider workload, measured by provider-to-resident ratios, predict population-level physical and mental health outcomes. County-level data from the 2024 Robert Wood Johnson Foundation County Health Rankings dataset (N = 3142 counties) were analyzed using two path analysis models; such models are used to estimate both direct and indirect relationships among multiple predictors and outcomes simultaneously. Predictor variables included provider ratios, percent uninsured (mediator), and self-reported physically and mentally unhealthy days (outcomes). Higher PCP workload was significantly associated with greater numbers of poor physical and mental health days. Mental health provider ratios were not directly associated with either outcome. Indirect effects through the percent uninsured were also significant, particularly for physical health outcomes. These findings suggest that PCPs play a disproportionate role in shaping both mental and physical health at the community level. The analysis supports the conclusion that addressing provider shortages and improving insurance coverage can enhance health outcomes, particularly when efforts are integrated into collaborative care models that distribute workload across providers and align treatment approaches with the diverse psychosocial and medical needs of the populations they serve.