ChatGPT's Performance in Cardiac Arrest and Bradycardia Simulations Using the American Heart Association's Advanced Cardiovascular Life Support Guidelines: Exploratory Study.

BACKGROUND: ChatGPT is the most advanced large language model to date, with prior iterations having passed medical licensing examinations, providing clinical decision support, and improved diagnostics. Although limited, past studies of ChatGPT's performance found that artificial intelligence could pass the American Heart Association's advanced cardiovascular life support (ACLS) examinations with modifications. ChatGPT's accuracy has not been studied in more complex clinical scenarios. As heart disease and cardiac arrest remain leading causes of morbidity and mortality in the United States, finding technologies that help increase adherence to ACLS algorithms, which improves survival outcomes, is critical. OBJECTIVE: This study aims to examine the accuracy of ChatGPT in following ACLS guidelines for bradycardia and cardiac arrest. METHODS: We evaluated the accuracy of ChatGPT's responses to 2 simulations based on the 2020 American Heart Association ACLS guidelines with 3 primary outcomes of interest: the mean individual step accuracy, the accuracy score per simulation attempt, and the accuracy score for each algorithm. For each simulation step, ChatGPT was scored for correctness (1 point) or incorrectness (0 points). Each simulation was conducted 20 times. RESULTS: ChatGPT's median accuracy for each step was 85% (IQR 40%-100%) for cardiac arrest and 30% (IQR 13%-81%) for bradycardia. ChatGPT's median accuracy over 20 simulation attempts for cardiac arrest was 69% (IQR 67%-74%) and for bradycardia was 42% (IQR 33%-50%). We found that ChatGPT's outputs varied despite consistent input, the same actions were persistently missed, repetitive overemphasis hindered guidance, and erroneous medication information was presented. CONCLUSIONS: This study highlights the need for consistent and reliable guidance to prevent potential medical errors and optimize the application of ChatGPT to enhance its reliability and effectiveness in clinical practice.

Competencies for the Use of Artificial Intelligence in Primary Care.

The artificial intelligence (AI) revolution has arrived for the health care sector and is finally penetrating the far-reaching but perpetually underfinanced primary care platform. While AI has the potential to facilitate the achievement of the Quintuple Aim (better patient outcomes, population health, and health equity at lower costs while preserving clinician well-being), inattention to primary care training in the use of AI-based tools risks the opposite effects, imposing harm and exacerbating inequalities. The impact of AI-based tools on these aims will depend heavily on the decisions and skills of primary care clinicians; therefore, appropriate medical education and training will be crucial to maximize potential benefits and minimize harms. To facilitate this training, we propose 6 domains of competency for the effective deployment of AI-based tools in primary care: (1) foundational knowledge (what is this tool?), (2) critical appraisal (should I use this tool?), (3) medical decision making (when should I use this tool?), (4) technical use (how do I use this tool?), (5) patient communication (how should I communicate with patients regarding the use of this tool?), and (6) awareness of unintended consequences (what are the "side effects" of this tool?). Integrating these competencies will not be straightforward because of the breadth of knowledge already incorporated into family medicine training and the constantly changing technological landscape. Nonetheless, even incremental increases in AI-relevant training may be beneficial, and the sooner these challenges are tackled, the sooner the primary care workforce and those served by it will begin to reap the benefits.

Salary Differences Between Schools of Medicine and Schools of Public Health for Nonclinical PhD Faculty: A Case Study of One Large Multicampus University System.

There are no evidence-based findings to assist professionals with advanced public health and social science degrees in choosing the appropriate academic location. A cross-sectional case study in 2019 was conducted using publicly available online data of full-time, nonclinical, doctoral-level academic faculty in schools of public health (SOPHs) and schools of medicine (SOMs), within one large university system. Analyses included descriptive statistics and generalized linear regression models comparing salaries between school types by academic rank, after gender and race/ethnicity adjustment. The study included 181 faculty members, 35.8% assistant, 34.1% associate, and 30.1% full professors. After accounting for race/ethnicity and gender, SOM assistant and associate professors had 9% (P = .03) and 14% (P = .008) higher mean salaries than SOPH counterparts. Findings suggest slight salary advantages for SOM faculty for early- to mid-career PhDs in one university system. Factors such as start-up packages, time to promotion, and grant funding need further exploration.

Shared Language for Shared Work in Population Health.

People working on behalf of population health, community health, or public health often experience confusion or ambiguity in the meaning of these and other common terms-the similarities and differences and how they bear on the tasks and division of labor for care delivery and public health. Shared language must be clear enough to help, not hinder people working together as they ultimately come to mutual understanding of roles, responsibilities, and actions in their joint work. Based on an iterative lexicon development process, the authors developed and propose a definitional framework as an aid to navigating among related population and community health terms. These terms are defined, similarities and differences clarified, and then organized into 3 categories that reflect goals, realities, and ways to get the job done. Goals include (a) health as well-being for persons, (b) population health as that goal expressed in measurable terms for groups, and (c) community health as population health for particular communities of interest, geography, or other defining characteristic-groups with shared identity and particular systemic influences on health. Realities are social determinants as influences, health disparities as effects, and health equity as both a goal and a design principle. Ways to get the job done include health care delivery systems for enrollees and public health in population-based civic activities-with a broad zone of collaboration where streams of effort converge in partnership with served communities. This map of terms can enable people to move forward together in a broad zone of collaboration for health with less confusion, ambiguity, and conflict.

Early Impacts of the COVID-19 Pandemic on Telehealth Patterns in Primary Care, Mental Health, and Specialty Care Facilities in Texas.

OBJECTIVES: Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, many US clinics have shifted some or all of their practice from in-person to virtual visits. In this study, we assessed the use of telehealth among primary care and specialty clinics, by targeting healthcare administrators via multiple channels. METHODS: Using an online survey, we assessed the use of, barriers to, and reimbursement for telehealth. Respondents included clinic administrators (chief executive officers, vice presidents, directors, and senior-level managers). RESULTS: A total of 85 complete responses were recorded, 79% of which represented solo or group practices and 63% reported a daily patient census >50. The proportion of clinics that delivered ≥50% of their consults using telehealth increased from 16% in March to 42% in April, 35% in May, and 30% in June. Clinics identified problems with telehealth reimbursement; although 63% of clinics reported that ≥75% of their telehealth consults were reimbursed, only 51% indicated that ≥75% of their telehealth visits were reimbursed at par with in-person office visits. Sixty-five percent of clinics reported having basic or foundational telehealth services, whereas only 9% of clinics reported advanced telehealth maturity. Value-based care participating clinics were more likely to report advanced telehealth services (27%), compared with non-value-based care clinics (3%). CONCLUSIONS: These findings highlight the adaptability of clinics to quickly transition and adopt telehealth. Uncertainty about reimbursement and policy changes may make the shift temporal, however.

Using Machine Learning to Predict Primary Care and Advance Workforce Research.

PURPOSE: To develop and test a machine-learning-based model to predict primary care and other specialties using Medicare claims data. METHODS: We used 2014-2016 prescription and procedure Medicare data to train 3 sets of random forest classifiers (prescription only, procedure only, and combined) to predict specialty. Self-reported specialties were condensed to 27 categories. Physicians were assigned to testing and training cohorts, and random forest models were trained and then applied to 2014-2016 data sets for the testing cohort to generate a series of specialty predictions. Comparing the predicted specialty to self-report, we assessed performance with F1 scores and area under the receiver operating characteristic curve (AUROC) values. RESULTS: A total of 564,986 physicians were included. The combined model had a greater aggregate (macro) F1 score (0.876) than the prescription-only (0.745; P <.01) or procedure-only (0.821; P <.01) model. Mean F1 scores across specialties in the combined model ranged from 0.533 to 0.987. The mean F1 score was 0.920 for primary care. The mean AUROC value for the combined model was 0.992, with values ranging from 0.982 to 0.999. The AUROC value for primary care was 0.982. CONCLUSIONS: This novel approach showed high performance and provides a near real-time assessment of current primary care practice. These findings have important implications for primary care workforce research in the absence of accurate data.

Clinicians' Overestimation of Their Geographic Service Area.

In this study, we evaluated family physicians' ability to estimate the service area of their patient panel-a critical first step in contextual population-based primary care. We surveyed 14 clinicians and administrators from 6 practices. Participants circled their estimated service area on county maps that were compared with the actual service area containing 70% of the practice's patients. Accuracy was ascertained from overlap and the amount of estimated census tracts that were not part of the actual service area. Average overlap was 75%, but participants overestimated their service area by an average of 166 square miles. Service area overestimation impedes implementation of targeted community interventions by practices.

Loneliness in Primary Care Patients: A Prevalence Study.

PURPOSE: Loneliness has important health consequences. Little is known, however, about loneliness in primary care patient populations. This study describes the prevalence of loneliness in patients presenting for primary care and associations with self-reported demographic factors, health care utilization, and health-related quality of life. METHODS: We conducted cross-sectional surveys of adults presenting for routine care to outpatient primary care practices in 2 diverse practice-based research networks. The 3-item University of California, Los Angeles Loneliness Scale was utilized to determine loneliness. RESULTS: The prevalence of loneliness was 20% (246/1,235). Loneliness prevalence was inversely associated with age (P <.01) and less likely in those who were married (P <.01) or employed (P <.01). Loneliness was more common in those with lower health status (P <.01), including when adjusting for employment and relationship status (odds ratio [OR] = 1.05; 95% CI, 1.03-1.07). Primary care visits (OR = 1.07; 95% CI, 1.03-1.10), urgent care/emergency department visits (OR = 1.24; 95% CI, 1.12-1.38), and hospitalizations (OR = 1.15; 95% CI, 1.01-1.31) were associated with loneliness status. There was no significant difference in rates of loneliness between sexes (P = .08), racial categories (P = .57), or rural and urban respondents (P = .42). CONCLUSIONS: Our findings demonstrate that loneliness is common in primary care patients and is associated with adverse health consequences including poorer health status and greater health care utilization. Further work is needed to understand the value of screening for and using interventions to treat loneliness in primary care.

Geographic Characteristics of Loneliness in Primary Care.

Loneliness is associated with poor health outcomes, and there is growing attention on loneliness as a social determinant of health. Our study sought to determine the associations between community factors and loneliness. The Three-Item Loneliness Scale and zip codes of residence were collected in primary care practices in Colorado and Virginia. Living in zip codes with higher unemployment, poor access to health care, lower income, higher proportions of blacks, and poor transportation was associated with higher mean loneliness scores. Future studies that examine interventions addressing loneliness may be more effective if they consider social context and community characteristics.

Predictors of Attrition From Family Medicine Board Certification.

Board certification is associated with higher quality care. We sought to determine the rates and predictors of attrition from certification among family physicians who achieved initial certification with the American Board of Family Medicine from 1980 through 2000. In this period, 5.6% of family physicians never attempted recertification, with the rate increasing from 4.9% between 1990 and 1995 to 5.7% from 1996 to 2000. Being male, an international medical graduate, or 30 years of age or older at initial certification was associated with not recertifying. With information about those likely to leave certification, the board can design and implement interventions that minimize attrition.

Impact of Gaps in Merit-Based Incentive Payment System Measures on Marginalized Populations.

As the United States enters a new era of value-based payment heavy in emphasis on primary care measurement, careful examination of selected measures and their potential impact on outcomes and vulnerable populations is essential. Applying a theoretical model of health care quality as a coding matrix, we used a directed content analysis approach to categorize individual Merit Based Incentive Payment System (MIPS) measures. We found that most MIPS measures related to aspects of clinical effectiveness, whereas few, if any, related to aspects of access, patient experience, or interpersonal care. These gaps suggest that MIPS may fail to measure the broader aspects of health care quality and even risk worsening existing disparities.

When Do Primary Care Physicians Retire? Implications for Workforce Projections.

PURPOSE: Retirement of primary care physicians is a matter of increasing concern in light of physician shortages. The joint purposes of this investigation were to identify the ages when the majority of primary care physicians retire and to compare this with the retirement ages of practitioners in other specialties. METHODS: This descriptive study was based on AMA Physician Masterfile data from the most recent 5 years (2010-2014). We also compared 2008 Masterfile data with data from the National Plan and Provider Enumeration System to calculate an adjustment for upward bias in retirement ages when using the Masterfile alone. The main analysis defined retirement as leaving clinical practice. The primary outcome was construction of a retirement curve. Secondary outcomes involved comparisons of retirement interquartile ranges (IQRs) by sex and practice location across specialties. RESULTS: The 2014 Masterfile included 77,987 clinically active primary care physicians between ages 55 and 80 years. The median age of retirement from clinical activity of all primary care physicians who retired in the period from 2010 to 2014 was 64.9 years, (IQR, 61.4-68.3); the median age of retirement from any activity was 66.1 years (IQR, 62.6-69.5). However measured, retirement ages were generally similar across primary care specialties. Females had a median retirement about 1 year earlier than males. There were no substantive differences in retirement ages between rural and urban primary care physicians. CONCLUSIONS: Primary care physicians in our data tended to retire in their mid-60s. Relatively small differences across sex, practice location, and time suggest that changes in the composition of the primary care workforce will not have a remarkable impact on overall retirement rates in the near future.

Solo and Small Practices: A Vital, Diverse Part of Primary Care.

PURPOSE: Solo and small practices are facing growing pressure to consolidate. Our objectives were to determine (1) the percentage of family physicians in solo and small practices, and (2) the characteristics of and services provided by these practices. METHODS: A total of 10,888 family physicians seeking certification through the American Board of Family Medicine in 2013 completed a demographic survey. Their practices were split into categories by size: solo, small (2 to 5 providers), medium (6 to 20 providers), and large (more than 20 providers). We also determined the rurality of the county where the physicians practiced. We developed 2 logistic regression models: one assessed predictors of practicing in a solo or small practice, while the other was restricted to solo and small practices and assessed predictors of practicing in a solo practice. RESULTS: More than one-half of respondents worked in solo or small practices. Small practices were the largest group (36%) and were the most likely to be located in a rural setting (20%). The likelihood of having a care coordinator and medical home certification increased with practice size. Physicians were more likely to be practicing in small or solo practices (vs medium-sized or large ones) if they were African American or Hispanic, had been working for more than 30 years, and worked in rural areas. Physicians were more likely to be practicing in small practices (vs solo ones) if they worked in highly rural areas. CONCLUSIONS: Family physicians in solo and small practices comprised the majority among all family physicians seeking board certification and were more likely to work in rural geographies. Extension programs and community health teams have the potential to support transformation within these practices.

Estimating the residency expansion required to avoid projected primary care physician shortages by 2035.

PURPOSE: The purpose of this study was to calculate the projected primary care physician shortage, determine the amount and composition of residency growth needed, and estimate the impact of retirement age and panel size changes. METHODS: We used the 2010 National Ambulatory Medical Care Survey to calculate utilization of ambulatory primary care services and the US Census Bureau to project demographic changes. To determine the baseline number of primary care physicians and the number retiring at 66 years, we used the 2014 American Medical Association Masterfile. Using specialty board and American Osteopathic Association figures, we estimated the annual production of primary care residents. To calculate shortages, we subtracted the accumulated primary care physician production from the accumulated number of primary care physicians needed for each year from 2015 to 2035. RESULTS: More than 44,000 primary care physicians will be needed by 2035. Current primary care production rates will be unable to meet demand, resulting in a shortage in excess of 33,000 primary care physicians. Given current production, an additional 1,700 primary care residency slots will be necessary by 2035. A 10% reduction in the ratio of population per primary care physician would require more than 3,000 additional slots by 2035, whereas changing the expected retirement age from 66 years to 64 years would require more than 2,400 additional slots. CONCLUSIONS: To eliminate projected shortages in 2035, primary care residency production must increase by 21% compared with current production. Delivery models that shift toward smaller ratios of population to primary care physicians may substantially increase the shortage.