Garrison, Gregory M.

Objectives: To assess how social risk factors and socioeconomic status (SES) affect health care costs at a large, integrated health system of 45 clinics in Iowa, Minnesota, and Wisconsin. Methods: We retrospectively reviewed administrative patient cost data from January 1 through December 31, 2022. Social risk factors (lack of financial resources, food insecurity, intimate partner violence, unmet transportation needs, and housing instability) were determined with patient questionnaires. Cost z -scores were compared across all risk-factor quartiles. We used a housing-based measure to objectively determine SES. Results: We analyzed data from 410 624 patients empaneled with 689 family medicine clinicians. Self-reported risk factor data were incomplete for 219 321 patients. Costs were higher for those with higher social risk (mean cost z -score: high risk = 0.38, low risk = 0.11; P < .001). In addition, higher costs were associated with increasing age, greater medical complexity (higher Adjusted Clinical Group scores), White race, and lack of financial resources. Although we observed geographic variations in cost, urban and rural locations did not have significant cost differences. Costs were similar across all quartiles of SES, and the absolute difference in cost z -scores between the highest and lowest SES quartiles was only 0.04. The multivariate analysis, which adjusted for age, medical complexity, race, sex, and objective SES, showed that these specific associations were substantially decreased and no longer significantly associated with mean cost z -scores. Conclusions: Patient-reported social risk factors were significantly associated with increased cost of care in a large, multispecialty health system in the midwestern US. These associations were decreased in the multivariate analysis. Results should be interpreted cautiously for readers outside the midwestern US, given potential responder bias and the racial homogeneity (92% White) of the study cohort.

Prostate-specific antigen (PSA) screening has played a critical role in reducing prostate cancer mortality but remains controversial due to concerns regarding overdiagnosis, overtreatment, and inconsistent screening guidelines. In 2012, the United States Preventive Services Task Force (USPSTF) issued a grade D recommendation against PSA screening, leading to a national decline in PSA testing and prostate biopsy rates. This was followed by an increase in the incidence of metastatic prostate cancer at diagnosis. The USPSTF has since revised its recommendation to grade C, emphasizing shared decision-making for men aged 55 to 69 years. However, variability in screening recommendations across professional societies has resulted in clinical uncertainty, particularly for primary care physicians (PCPs), who frequently initiate PSA testing and urology referrals. This review provides an evidence-based overview of PSA screening tailored for the PCP. We performed a comprehensive literature review using Ovid MEDLINE, PubMed, Embase, and the Cochrane Library, including systematic reviews published between January 2020 and April 2025, and current guidelines from the American Urological Association (AUA), National Comprehensive Cancer Network (NCCN), European Association of Urology (EAU), and American Cancer Society (ACS). We summarize the evolving landscape of PSA screening and its integration with contemporary tools such as shared decision-making, multiparametric magnetic resonance imaging (mpMRI), and transperineally targeted biopsy. We advocate for initiating PSA screening between the ages of 45 and 49 in men with average or elevated risk, based on long-term evidence demonstrating the value of an early baseline PSA level and risk-stratified follow-up. This approach, coupled with improved diagnostic tools, can help PCPs navigate guideline variability and implement a rational, common-sense strategy to maximize early detection while minimizing harm.

Introduction/Objectives: Little is known about the prevalence of patient-reported social risk factors and the use of the HOUSES Index, a simple, reliable method of assessing socioeconomic status (SES) based on publicly available housing data, in a predominantly rural, primary care population. Methods: We conducted a cross-sectional analysis of adult patients paneled to family medicine clinicians in a US Midwest health system as of December 31, 2022. Patients’ listed address determined HOUSES Index as quartile rank (Q1 lowest SES) and rural/urban status. Social risk data including housing, food, transportation, finances, and violence were collected from health record questionnaires. A mixed effect model was used to assess associations between social risk, HOUSES Index, and rurality. Results: Of the 352 355 patients included, rural patients were more likely than urban patients to report all social risk factors and had lower SES as measured by HOUSES quartiles. In the mixed effects analysis, HOUSES quartile was independently predictive of reporting an at-risk social risk factor (Q1 vs Q4 OR = 2.27, 95% CI = 2.19-2.37), but rurality was not (OR = 1.02, 95% CI = 0.97-1.07) after adjusting for HOUSES. Conclusions: The increased prevalence of social risk factors among rural residents is largely explained by individual SES measured by HOUSES Index.

Objectives Guidelines advise repeat screening within four months if Papanicolaou (Pap) results are unsatisfactory for evaluation. This utilizes healthcare system resources and may decrease patient satisfaction due to needing a second clinic appointment. We assessed unsatisfactory Pap test (UPT) rates and associations with clinician and patient characteristics to inform future interventions to decrease UPTs. Setting Multisite midwestern United States primary care practice. Methods Retrospective analysis of women aged 21–65 with a Pap between 7/1/2021 and 6/30/2023. Bivariate and multivariable logistic regression analyses were conducted to assess for associations between UPTs and clinician gender, degree, residency status, and experience. Results Of 51,195 Paps completed, 2.3% were unsatisfactory. Female clinicians performed the most Pap tests (83.2%) with slightly less likelihood of UPTs compared with male clinicians ( p = 0.015). There was no significant difference comparing physicians to advanced practice providers in UPTs ( p = 0.18). Residency training level did not affect UPT rates ( p = 0.95). Clinician experience was associated with higher UPT rates in first and fourth quartiles (least and most Paps performed) compared to middle two quartiles ( p = 0.004). UPTs were more likely among women aged > 50 years old ( p < 0.001), married ( p < 0.001), and Asian ( p < 0.001). Conclusions Clinician characteristics played a small role in predicting UPTs but patient age may be the factor most amenable to intervention to lower UPTs. Transitioning to primary human papillomavirus (HPV) screening in peri/post-menopausal women could decrease UPTs given Paps are performed on the clinician-collected cervical specimen only if HPV testing is positive.

Low back pain is the most common musculoskeletal complaint accounting for over 30 million visits to primary care physicians annually. Serious pathology is found in less than 1% of these visits. Therefore it is often a challenge to distinguish worrisome findings requiring further workup and treatment from common complaints of pain. Gout is an inflammatory arthritis that most commonly affects the appendicular skeleton. It is characterized by the saturation of uric acid and deposition of monosodium urate crystals in joints and tissues. Spinal involvement is rare and is not typically considered on the differential diagnosis for a patient presenting with acute low back pain. We present such a case of a 35-year-old male who presented with intractable back pain, highlighting the need to recognize signs and symptoms that raise suspicion for spinal gout.

Background Self-scheduling of medical visits is becoming more common but the complexity of applying multiple requirements for self-scheduling has hampered implementation. Mayo Clinic implemented self-scheduling in 2019 and has been increasing its portfolio of self-schedulable visits since then. Our aim was to show measures quantifying the complexity associated with medical visit scheduling and to describe how opportunities and challenges of scheduling complexity apply in self-scheduling. Methods We examined scheduled visits from January 1, 2022, through August 24, 2023. For seven visit categories, we counted all unique visit types that were scheduled, for both staff-scheduled and self-scheduled. We examined counts of self-scheduled visit types to identify those with highest uptake during the study period. Results There were 9555 unique visit types associated with 20.8 M (million) completed visits. Self-scheduled visit types accounted for 4.0% (838,592/20,769,699) of the completed total visits. Of seven visit categories, self-scheduled established patient visits, testing visits, and procedure visits accounted for 93.5% (784,375/838,592) of all self-scheduled visits. Established patient visits in primary care (10 visit types) accounted for 273,007 (32.6%) of all self-scheduled visits. Testing visits (blood and urine testing, 2 visit types) accounted for 183,870 (21.9%) of all self-scheduled visits. Procedure visits for screening mammograms, bone mineral density, and immunizations (8 visit types) accounted for 147,358 (17.6%) of all self-scheduled visits. Conclusion Large numbers of unique visit types comprise a major challenge for self-scheduling. Some visit types are more suitable for self-scheduling. Guideline-based procedure visits such as screening mammograms, bone mineral density exams, and immunizations are examples of visits that have high volumes and can be standardized for self-scheduling. Established patient visits and laboratory testing visits also can be standardized for self-scheduling. Despite the successes, there remain thousands of specific visit types that may need some staff-scheduler intervention to properly schedule.

Background Self-scheduling of medical visits is becoming available at many medical institutions. We aimed to examine the self-scheduled visit counts and rate of growth of self-scheduled visits in a multispecialty practice. Methods For 85 weeks extending from January 1, 2022 through August 24, 2023, we examined self-scheduled visit counts for over 1500 self-scheduled visit types. We compared completed self-scheduled visit counts to all scheduled completed visit counts for the same visit types. We collected counts of the most frequently self-scheduled visit types for each week and examined the change over time. We also determined the proportion that each visit type was self-scheduled. Results There were 20,769 699 completed visits during the course of the study that met the criteria for inclusion. Self-scheduled visits accounted for 4.0% of all completed visits (838 592/20,769 699). Over the 85-week span, self-scheduled visits rose from 3.0% to 5.3% of the total. There were 1887 unique visit types that were associated with completed visits. There were just 6 appointment visit types of the total 1887 self-scheduled visit types that accounted for 50.7% of the total 838 592 self-scheduled visits. Those 6 visit types were a lab blood test visit (19.5%, 163 K visits), two Family Medicine office visit types (13.0%, 109 K visits), a screening mammogram visit type (6.6%, 55 K visits), a scheduled express care visit type (6%, 50 K visits) and a COVID immunization visit type (5.7%, 48 K visits). Twenty-one visit types that were self-scheduled accounted for 75% of the total self-scheduled visits. Four seasonal visits, accounting for 10.6% of the total self-scheduled visits, were responsible for almost all the non-linear change in self-scheduling. Conclusion Self-scheduling accounted for a small but growing percent of all outpatient scheduled visits in a multispecialty, multisite practice. A wide range of visit types can be successfully self-scheduled.

Background Patients often present to emergency departments (EDs) with concerns that do not require emergency care. Self-triage and other interventions may help some patients decide whether they should be seen in the ED. Symptoms associated with low risk of hospitalization can be identified in national ED data and can inform the design of interventions to reduce avoidable ED visits. Methods We used the National Hospital Ambulatory Medical Care Survey (NHAMCS) data from the United States National Health Care Statistics (NHCS) division of the Centers for Disease Control and Prevention (CDC). The ED datasets from 2011 through 2020 were combined. Primary reasons for ED visit and the binary field for hospital admission from the ED were used to estimate the proportion of ED patients admitted to the hospital for each reason for visit and age category. Results There were 221,027 surveyed ED visits during the 10-year data collection with 736 different primary reasons for visit and 23,228 hospitalizations. There were 145 million estimated hospitalizations from 1.37 billion estimated ED visits (10.6%). Inclusion criteria for this study were reasons for visit which had at least 30 ED visits in the sample; there were 396 separate reasons for visit which met this criteria. Of these 396 reasons for visit, 97 had admission percentages less than 2% and another 52 had hospital admissions estimated between 2% and 4%. However, there was a significant increase in hospitalizations within many of the ED reasons for visit in older adults. Conclusion Reasons for visit from national ED data can be ranked by hospitalization risk. Low-risk symptoms may help healthcare institutions identify potentially avoidable ED visits. Healthcare systems can use this information to help manage potentially avoidable ED visits with interventions designed to apply to their patient population and healthcare access.

Continuity of care has been an identifying characteristic of family medicine since its inception and is an essential ingredient for high-functioning health care teams. Many benefits, including the quadruple aim of enhancing patient experience, improving population health, reducing costs, and improving care team well-being, are ascribed to continuity of care. In 2023, the Accreditation Council for Graduate Medical Education (ACGME) added two new continuity requirements—annual patient-sided continuity and annual resident-sided continuity—in family medicine training programs. This article reviews continuity of care as it applies to family medicine training programs. We discuss the various types of continuity and issues surrounding the measurement of continuity. A generally agreed upon definition of patient-sided and resident-sided continuity is presented to allow programs to begin to collect the necessary data. Especially within resident training programs, intricacies associated with maintaining continuity of care, such as empanelment, resident turnover, and scheduling, are discussed. The importance of right-sizing resident panels is highlighted, and a mechanism for accomplishing this is presented. The recent ACGME requirements represent a cultural shift from measuring resident experience based on volume to measuring resident continuity. This cultural shift forces family medicine training programs to adapt their various systems, policies, and procedures to emphasize continuity. We hope this manuscript’s review of several facets of contuinuity, some unique to training programs, helps programs ensure compliance with the ACGME requirements.