research Performance Evaluation of the Generative Pre-trained Transformer (GPT-4) on the Family Medicine In-Training Examination Read Performance Evaluation of the Generative Pre-trained Transformer (GPT-4) on the Family Medicine In-Training Examination
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Home Research Research Library Which family physicians work routinely with nurse practitioners, physician assistants or certified nurse midwives Which family physicians work routinely with nurse practitioners, physician assistants or certified nurse midwives 2014 Author(s) Peterson, Lars E, Blackburn, Brenna E, Petterson, Stephen M, Puffer, James C, Bazemore, Andrew W, and Phillips, Robert L Topic(s) Role of Primary Care, and What Family Physicians Do Keyword(s) Maternity Care, and Teams Volume Journal of Rural Health Source Journal of Rural Health Purpose: Facing rising numbers of insured with implementation of the Affordable Care Act, policy makers are interested in building teams of providers that can accommodate a growing demand for primary care services. Nurse Practitioners (NPs), Physician Assistants (PAs), and Certified Nurse Midwives (CNMs) already augment the physician workforce, particularly in rural areas. Our objective was to determine what physician and areal-level characteristics were associated with working with NPs, PAs or CNMs. Methods: The sample consisted of a convenience sample of physicians through the American Board of Family Medicine (ABFM) website in the fall of 2011. We linked these data to demographic and practice information collected by the ABFM and with provider information supplied from the National Provider Identifier file aggregated at the Primary Care Service Area level. Hierarchical logistic regression models were used to determine variables associated with working with NPs, PAs, or CNMs. Findings: Of the 3,855 family physicians in our sample, 60% reported routinely working with NPs, PAs, or CNMs. In regression analysis, characteristics positively associated with working with NPs, PAs, or CNMs were providing gynecological care (Odds Ratio = 1.23 [95% confidence interval, 1.06-1.42]), multispecialty group practice (OR = 1.72 [1.36-2.18]), any rural setting, and higher availability of PAs (OR = 1.40 [1.10-1.79]). Restrictive NP scope of practice laws failed to reach significance (OR = 0.86 [0.71-1.05]). Conclusions: This study suggests that the number of family physicians routinely working with NPs, PAs, and CNMs continues to increase, which may allow for improved access to health care, particularly in rural areas. Read More ABFM Research Read all 2020 Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE) Go to Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE) 2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments 2011 Rewarding family medicine while penalizing comprehensiveness? Primary care payment incentives and health reform: the Patient Protection and Affordable Care Act (PPACA) Go to Rewarding family medicine while penalizing comprehensiveness? Primary care payment incentives and health reform: the Patient Protection and Affordable Care Act (PPACA) 2022 Barriers to care for perinatal patients with opioid use disorder: family physician perspectives Go to Barriers to care for perinatal patients with opioid use disorder: family physician perspectives
Author(s) Peterson, Lars E, Blackburn, Brenna E, Petterson, Stephen M, Puffer, James C, Bazemore, Andrew W, and Phillips, Robert L Topic(s) Role of Primary Care, and What Family Physicians Do Keyword(s) Maternity Care, and Teams Volume Journal of Rural Health Source Journal of Rural Health
ABFM Research Read all 2020 Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE) Go to Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE) 2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments 2011 Rewarding family medicine while penalizing comprehensiveness? Primary care payment incentives and health reform: the Patient Protection and Affordable Care Act (PPACA) Go to Rewarding family medicine while penalizing comprehensiveness? Primary care payment incentives and health reform: the Patient Protection and Affordable Care Act (PPACA) 2022 Barriers to care for perinatal patients with opioid use disorder: family physician perspectives Go to Barriers to care for perinatal patients with opioid use disorder: family physician perspectives
2020 Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE) Go to Integrating Community and Clinical Data to Assess Patient Risks with A Population Health Assessment Engine (PHATE)
2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments
2011 Rewarding family medicine while penalizing comprehensiveness? Primary care payment incentives and health reform: the Patient Protection and Affordable Care Act (PPACA) Go to Rewarding family medicine while penalizing comprehensiveness? Primary care payment incentives and health reform: the Patient Protection and Affordable Care Act (PPACA)
2022 Barriers to care for perinatal patients with opioid use disorder: family physician perspectives Go to Barriers to care for perinatal patients with opioid use disorder: family physician perspectives