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Over 37% of pregnant women report excessive daytime sleepiness or hypersomnia during the first trimester, and commonly throughout the remaining trimesters of pregnancy and the postpartum period. The clinical presentation and symptoms of central disorders of hypersomnolence (CDH) such as narcolepsy, idiopathic hypersomnia, or excessive daytime sleepiness secondary to shift work or obstructive sleep apnea may be confounded by physiological changes in sleep architecture and hormonal fluctuations occurring during pregnancy. These overlapping features may cause delays in diagnosis and treatment, impacting quality of life, maternal functioning, and overall health. Management of CDH during pregnancy and lactation requires careful consideration of maternal benefits, potential side effects of medications and fetal and neonatal safety. Here, we present a case of idiopathic hypersomnia and detail management and outcomes during pregnancy and lactation. We briefly review the existing literature on prevalence, pathophysiological mechanisms, diagnostic challenges, objective findings, and risks posed by CDH in pregnancy and lactation. In this narrative review for primary care clinicians, we also summarize currently available evidence regarding the efficacy, teratogenic and other risks of pharmacologic agents used in the treatment of CDH in pregnancy and lactation. Lastly, we provide a clinical algorithm for management of CDH during pregnancy and lactation.

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.

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.

Background: Arsenic is a well-known toxin which may contaminate household water. It is harmful when ingested over prolonged periods of time. As a result, public health experts recommend that water should be screened and treated to prevent arsenic ingestion. In the United States, the responsibility of testing and treatment of private wells falls on homeowners. Despite recommendations for routine screening, this is rarely done. Objectives: To assess the prevalence of well water use in a Midwestern patient population, how patients and clinicians perceive the risks of arsenic in well water, and whether additional resources on well water testing are desired. These findings will be used to influence tools for clinicians regarding symptom and examination findings of chronic arsenic exposure and potentiate the distribution of informational resources on well water testing. Methods: Surveys were sent via email to all actively practicing primary care clinicians at the Mayo Clinic in the United States Midwest, and all active adult patients at the Mayo Clinic in the same region. Our team analyzed survey data to determine whether both patients and clinicians are aware of the health effects of chronic arsenic toxicity from well water, the need for routine well water testing and whether each group wants more information on the associated risks. Results: Both patients and primary care clinicians worry about arsenic exposure. Patients with well water are concerned about their water safety yet feel uninformed about testing options. Clinicians do not know how prevalent well water use is among their patients, feel uninformed about the chronic risks of arsenic exposure and the physical examination associated with it. Both groups unanimously want more information on testing options. Conclusions: Our findings show a significant reliance on well water use in the American Midwest, and unanimous support for the need for further well water testing information and resources for patients and their clinicians.

Introduction: Asymptomatic LV systolic dysfunction is often underdiagnosed, leading to missed opportunities for treatment. In a recently completed pragmatic RCT, an AI algorithm performed on the ECGs ordered as part of routine practice increased early diagnosis of low EF. The current study aimed to understand whether the effectiveness of the AI algorithm integrated into routine care differed by the socioeconomic status of the patient. Methods: The main findings of the RCT were previously published. Clinicians in the intervention group had access to AI results (i.e., a positive or negative screening result, with positive results indicating a high likelihood of low EF). When a positive result was found, clinicians in the intervention group received an alert that encouraged them to order TTE if considered clinically appropriate. Clinicians in the control group did not have access or received alerts. Area deprivation index (ADI) was used to measure area-level social determinants of health. This study included 16,992 patients (control 8,225; intervention: 8,767) who had ECG performed for any indications between 8/5/2019 and 3/31/2020, no prior HF, and a valid address to derive ADI. The primary endpoint was a new diagnosis of EF≤50% within 3 months. Results: The mean age was 60.5±17.6 y. AI increased the diagnosis of low EF in the overall cohort (OR 1.42 [1.13, 1.78], p=0.01). The magnitude of benefit was greater in patients with higher socioeconomic status, e.g., living in an area that is less deprived (OR 1.88 [1.16, 3.05]), with higher median income, lower poverty rate, higher education level, better housing conditions, and higher proportion of white-collar workers, although the interaction was not statistically significant (Figure). Conclusions: AI enables early diagnosis of low EF and the magnitude of benefit might be numerically greater in patients with higher socioeconomic status, possibly due to variations in access to or referral for subsequent testing.

AbstractObjectiveTo evaluate health care costs as a function of assigned primary care clinician type and care team characteristics.MethodsAdministrative data were collected for 68 family medicine clinicians (40 physicians and 28 nurse practitioners [NPs]/physician assistant [PAs]), on 11 care teams (variable MD, NP and PA on teams), caring for 77,141 patients. We performed a generalized linear mixed multivariable regression model of standardized per member per month (PMPM) median cost as the outcome, with four practice sites included as random effects.ResultsIn bivariate analysis, cost was higher in physicians than NP/PAs, in more complex patients, and associated with emergency department (ED) visit rate. On multivariate analysis, patient complexity, ED visit rate and higher patient experience ratings were independently associated with greater PMPM cost. More time in practice was associated with lower PMPM cost. In the adjusted multivariate model, physicians had 8.3% lower median PMPM costs than NP/PAs (p = 0.046).ConclusionsThe primary drivers of greater PMPM cost were patient complexity, ED visits and patient satisfaction.

Research ObjectiveAn artificial intelligence (AI) algorithm based on ECG was integrated into clinical workflows to identify patients with a high likelihood of low ejection (EF), a condition that is underdiagnosed but treatable. The trial aimed to assess whether the AI‐powered clinical decision support enabled early diagnosis of low EF.Study DesignA pragmatic, cluster randomized controlled trial (RCT) was conducted in 45 primary care practices, including an academic medical center as well as community and rural clinics across Minnesota and Wisconsin. A total of 120 primary care teams, including 358 clinicians, were randomized to either intervention (access to AI results with positive results indicating a high likelihood of low EF; 181 clinicians) or control (usual care; 177 clinicians). The primary endpoint was a new diagnosis of EF≤50% within 90 days. A positive deviance qualitative study and a survey were used to assess implementation strategies.Population StudiedIn total, 22,641 adult patients (N = 11,573 intervention; N = 11,068 control) with ECG performed as part of routine care between 8/5/2019 and 3/31/2020 were included. Patients were excluded if they had known EF≤50% or a history of documented heart failure (HF) identified by clinical notes abstracted by natural language processing.Principal FindingsThe mean age of patients was 60.5 ± 17.6 years; 53.9% were women. Positive AI results were found in 6.0% of patients in both arms. Clinicians in the intervention group obtained more echocardiograms for patients with positive results (38.1% control vs. 49.6% intervention, p < 0.001), but in the overall cohort, the echocardiogram utilization was similar (18.2% vs. 19.2%, p = 0.17). The intervention increased the diagnosis of low EF in the overall cohort (1.6% vs. 2.1%, odds ratio [OR] 1.32 [1.01–1.61], p = 0.007) and among patients with positive results (14.5% vs. 19.5% OR 1.43 [1.08–1.91], p = 0.01). Results using different thresholds for low EF were consistent with the primary result. Among patients with positive results, the intervention increased echocardiography more significantly in women vs. men (p for interaction 0.02), in patients without prior echocardiogram vs. those with prior echocardiogram (p for interaction 0.04), and in patients whose ECGs were ordered in outpatient clinics (p for interaction 0.02).ConclusionsAn AI algorithm run on existing ECGs enabled the early diagnosis of low EF in a large cohort of patients managed in routine primary care practices. Because ECG is a low‐cost test frequently performed for a variety of purposes, the algorithm could improve early diagnosis and treatment of a condition that is often asymptomatic but has effective treatments, and thus, reduce the disease burden in broad populations.Implications for Policy or PracticeThe current study is one of the first RCTs that demonstrated the value of AI in routine practice. Low EF confers an increased risk of HF progression and mortality. Although there are effective therapies, low EF is undiagnosed, leading to missed opportunities for treatment. EF can be readily determined by echocardiography, but the imagining test is too resource‐intensive to screen unselected populations. The AI applied to the existing ECGs could identify high‐risk patients for further assessment.Primary Funding SourceMayo Clinic.

Introduction Latino populations, particularly those living in rural areas, experience a disproportionately high prevalence and poorer outcomes of type 2 diabetes mellitus (T2DM). The purpose of this study was to test the acceptability and perceived effectiveness of a group-based, facilitated digital storytelling intervention for T2DM self-management among rural Latino patients. Method Twenty Latino adults with T2DM participated in facilitated storytelling discussions at two primary clinics. Participants viewed a 12-minute T2DM self-management digital storytelling intervention, followed by a facilitated group discussion. Surveys, observations, and focus groups were used to assess for acceptability and perceived effectiveness of the intervention through descriptive and qualitative analysis, informed by narrative and social cognitive theory. Results All participants found the intervention interesting and useful and reported improvement in confidence, motivation, and behavioral intentions for T2DM self-management. Themes mapped closely with narrative theory models, further suggestive of the behavior change potential. Discussion Facilitated discussions may add value to viewing of digital stories and represent a scalable approach to provide culturally congruent health care for Latino patients with diabetes in rural settings. Within the paradigm of group-based diabetes educational programs, this lends itself well to critical transcultural nursing care.

The COVID-19 pandemic has presented new challenges in how Primary Care clinicians care for community patients. Our organization quickly allocated 1 of our community clinic sites into a dedicated COVID Clinic caring for the COVID positive or any patient with COVID like symptoms to minimize contact with the well patients. A prerequisite for all patients to be seen in the COVID Care Clinic was a virtual visit staffed with Advanced Practice Providers that would further determine if the patient needed to seek emergency medical care or be seen in the COVID Clinic. From March 23, 2020 through May 15, 2020, 852 patients with COVID symptoms were seen in this clinic rather than the emergency department. This article describes a collaborative effort to care for a community during the COVID-19 pandemic. This unique setting allowed us to focus an appropriate level of care to a high risk population in a safe and effective manner in the ongoing effort to flatten the epidemiological curve.