Virtual versus usual in-office care for multiple sclerosis: The VIRTUAL-MS multi-site randomized clinical trial study protocol.

BACKGROUND: Multiple sclerosis (MS) affects nearly 1 million people and is estimated to cost $85.4 billion in the United States annually. People with MS have significant barriers to receiving care and telemedicine could substantially improve access to specialized, comprehensive care. In cross-sectional analyses, telemedicine has been shown to be feasible, have high patient and clinician satisfaction, reduce patient costs and burden, and enable a reasonable assessment of disability. However, no studies exist evaluating the longitudinal impact of telemedicine care for MS. Here we describe the study protocol for VIRtual versus UsuAL In-office care for Multiple Sclerosis (VIRTUAL-MS). The main objective of the study is to evaluate the impact of telemedicine for MS care on: patient clinical outcomes, economic costs, patient, and clinician experience. METHODS: This two-site randomized clinical trial will enroll 120 adults with a recent diagnosis of MS and randomize 1:1 to receive in-clinic vs. telemedicine care for 24 months. The primary outcome of the study is worsening in any one of the four Multiple Sclerosis Functional Composite 4 (MSFC4) measures at 24 months. Other study outcomes include patient and clinician satisfaction, major healthcare costs, Expanded Disability Status Scale, treatment adherence, and digital outcomes. CONCLUSION: The results of this study will directly address the key gaps in knowledge about longitudinal telemedicine-enabled care in an MS population. It will inform clinical care implementation as well as design of trials in MS and other chronic conditions. TRIAL REGISTRATION: NCT05660187.

Racial and ethnic differences in uncontrolled diabetes mellitus among adults taking antidiabetic medication.

AIM: To examine whether racial and ethnic disparities in uncontrolled type 2 diabetes mellitus (T2DM) persist among those taking medication and after accounting for other demographic, socioeconomic, and health indicators. METHODS: Adults aged ≥20 years with T2DM using prescription diabetes medication were among participants assessed in a retrospective cohort study of the National Health and Nutrition Examination Survey 2007-2018. We estimated weighted sequential multivariable logistic regression models to predict odds of uncontrolled T2DM (HbA1c ≥ 8%) from racial and ethnic identity, adjusting for demographic, socioeconomic, and health indicators. RESULTS: Of 3649 individuals with T2DM who reported taking medication, 27.4% had uncontrolled T2DM (mean HgA1c 9.6%). Those with uncontrolled diabetes had a mean BMI of 33.8, age of 57.3, and most were non-Hispanic white (54%), followed by 17% non-Hispanic Black, and 20% Hispanic identity. In multivariable analyses, odds of uncontrolled T2DM among those with Black or Hispanic identities lessened, but persisted, after accounting for other indicators (Black OR 1.38, 97.5% CI: 1.04, 1.83; Hispanic OR 1.79, 97.5% CI 1.25, 2.57). CONCLUSIONS: Racial and ethnic disparities in T2DM control persisted among individuals taking medication. Future research might focus on developmental and epigenetic pathways of disparate T2DM control across racially and ethnically minoritized populations.

Erratum: Addressing Therapeutic Inertia: Development and Implementation of an Electronic Health Record-Based Diabetes Intensification Tool. Diabetes Spectrum 2023;36:161-170 (https://doi.org/10.2337/ds22-0031).

[This corrects the article DOI: 10.2337/ds22-0031.].

Addressing Therapeutic Inertia: Development and Implementation of an Electronic Health Record-Based Diabetes Intensification Tool.

Objective: To assess whether an electronic health record (EHR)-based diabetes intensification tool can improve the rate of A1C goal attainment among patients with type 2 diabetes and an A1C ≥8%. Methods: An EHR-based tool was developed and sequentially implemented in a large, integrated health system using a four-phase, stepped-wedge design (single pilot site [phase 1] and then three practice site clusters [phases 2-4]; 3 months/phase), with full implementation during phase 4. A1C outcomes, tool usage, and treatment intensification metrics were compared retrospectively at implementation (IMP) sites versus nonimplementation (non-IMP) sites with sites matched on patient population characteristics using overlap propensity score weighting. Results: Overall, tool utilization was low among patient encounters at IMP sites (1,122 of 11,549 [9.7%]). During phases 1-3, the proportions of patients achieving the A1C goal (<8%) were not significantly improved between IMP and non-IMP sites at 6 months (range 42.9-46.5%) or 12 months (range 46.5-53.1%). In phase 3, fewer patients at IMP sites versus non-IMP sites achieved the goal at 12 months (46.7 vs. 52.3%, P = 0.02). In phases 1-3, mean changes in A1C from baseline to 6 and 12 months (range -0.88 to -1.08%) were not significantly different between IMP and non-IMP sites. Times to intensification were similar between IMP and non-IMP sites. Conclusion: Utilization of a diabetes intensification tool was low and did not influence rates of A1C goal attainment or time to treatment intensification. The low level of tool adoption is itself an important finding highlighting the problem of therapeutic inertia in clinical practice. Testing additional strategies to better incorporate, increase acceptance of, and improve proficiency with EHR-based intensification tools is warranted.

Unmet needs and opportunities for optimal management of patients with type 2 diabetes and chronic kidney disease.

This brief report utilizes EHR data from a large US health system to summarize unmet needs in patients with type 2 diabetes and chronic kidney disease and identifies areas of opportunity to optimize management within this patient population from treatment, screening and monitoring, and health care resource use perspectives.

Trends in Use of High-Cost Antihyperglycemic Drugs Among US Adults with Type 2 Diabetes.

BACKGROUND: Some antihyperglycemic drugs can reduce cardiovascular events, slow the progression of kidney disease, and prevent death, but they are more expensive than older drugs. OBJECTIVES: (1) To estimate trends in use of antihyperglycemic drugs by cost; (2) to examine use of high-cost drugs by race/ethnicity, income, and insurance status DESIGN: Cross-sectional analysis of the 2003-2018 National Health and Nutrition Examination Survey PARTICIPANTS: US adults ≥18 years with type 2 diabetes EXPOSURES: Race/ethnicity, income, and insurance status MAIN MEASURES: Low-cost noninsulin medications included any drugs that had at least one generic version approved by the Food and Drug Administration. Human regular, NPH, and premixed NPH/regular 70/30 insulins were classified as low-cost. All other noninsulin medications and insulins were considered high-cost KEY RESULTS: The sample included 7,394 patients. Prevalence of use of low-cost noninsulin drugs increased from 37% in 2003-2004 to 52% in 2017-2018. Use of high-cost noninsulin drugs decreased from 2003-2004 to 2013-2014 and then slowly increased. Use of low-cost insulin decreased from 7 to 2% while high-cost insulin rose from 4 to 16%. In multivariable analysis, non-White patients had 25-35% lower odds of receiving high-cost drugs than non-Hispanic Whites. Health insurance was associated with more than twice the odds of having high-cost drugs compared to no insurance. Patients with higher HbA1c or moderate obesity were also more likely to use high-cost drugs. Sex, income, and insurance type were not associated with receipt of high-cost drugs. CONCLUSIONS: There was a shift in utilization from high- to low-cost noninsulin drugs, but since 2013-2014 the trend has slowly reversed with increased use of newer, more expensive drug classes. High-cost insulin analogs have almost completely replaced lower cost human insulins. Disparities in receipt of diabetes drugs by race/ethnicity and insurance must be addressed to ensure that cost is not a barrier for disadvantaged populations.

Impacts of COVID-19 on Glycemia and Risk of Diabetic Ketoacidosis.

Reports indicate that coronavirus disease 2019 (COVID-19) may impact pancreatic function and increase type 2 diabetes (T2D) risk, although real-world COVID-19 impacts on HbA1c and T2D are unknown. We tested whether COVID-19 increased HbA1c, risk of T2D, or diabetic ketoacidosis (DKA). We compared pre- and post-COVID-19 HbA1c and T2D risk in a large real-world clinical cohort of 8,755 COVID-19(+) patients and 11,998 COVID-19(-) matched control subjects. We investigated whether DKA risk was modified in COVID-19(+) patients with type 1 diabetes (T1D) (N = 701) or T2D (N = 21,830), or by race and sex. We observed a statistically significant, albeit clinically insignificant, HbA1c increase post-COVID-19 (all patients ΔHbA1c = 0.06%; with T2D ΔHbA1c = 0.1%) and no increase among COVID-19(-) patients. COVID-19(+) patients were 40% more likely to be diagnosed with T2D compared with COVID-19(-) patients and 28% more likely for the same HbA1c change as COVID-19(-) patients, indicating that COVID-19-attributed T2D risk may be due to increased recognition during COVID-19 management. DKA in COVID-19(+) patients with T1D was not increased. COVID-19(+) Black patients with T2D displayed disproportionately increased DKA risk (hazard ratio 2.46 [95% CI 1.48-6.09], P = 0.004) compared with White patients, suggesting a need for further clinical awareness and investigation.

Impact of Telehealth on the Delivery of Prenatal Care During the COVID-19 Pandemic: Mixed Methods Study of the Barriers and Opportunities to Improve Health Care Communication in Discussions About Pregnancy and Prenatal Genetic Testing.

BACKGROUND: The COVID-19 pandemic brought significant changes in health care, specifically the accelerated use of telehealth. Given the unique aspects of prenatal care, it is important to understand the impact of telehealth on health care communication and quality, and patient satisfaction. This mixed methods study examined the challenges associated with the rapid and broad implementation of telehealth for prenatal care delivery during the pandemic. OBJECTIVE: In this study, we examined patients' perspectives, preferences, and experiences during the COVID-19 pandemic, with the aim of supporting the development of successful models to serve the needs of pregnant patients, obstetric providers, and health care systems during this time. METHODS: Pregnant patients who received outpatient prenatal care in Cleveland, Ohio participated in in-depth interviews and completed the Coronavirus Perinatal Experiences-Impact Survey (COPE-IS) between January and December 2021. Transcripts were coded using NVivo 12, and qualitative analysis was used, an approach consistent with the grounded theory. Quantitative data were summarized and integrated during analysis. RESULTS: Thematic saturation was achieved with 60 interviews. We learned that 58% (35/60) of women had telehealth experience prior to their current pregnancy. However, only 8% (5/60) of women had used both in-person and virtual visits during this pregnancy, while the majority (54/60, 90%) of women participated in only in-person visits. Among 59 women who responded to the COPE-IS, 59 (100%) felt very well supported by their provider, 31 (53%) were moderately to highly concerned about their child's health, and 17 (29%) reported that the single greatest stress of COVID-19 was its impact on their child. Lead themes focused on establishing patient-provider relationships that supported shared decision-making, accessing the information needed for shared decision-making, and using technology effectively to foster discussions during the COVID-19 pandemic. Key findings indicated that participants felt in-person visits were more personal, established greater rapport, and built better trust in the patient-provider relationship as compared to telehealth visits. Further, participants felt they could achieve a greater dialogue and ask more questions regarding time-sensitive information, including prenatal genetic testing information, through an in-person visit. Finally, privacy concerns arose if prenatal genetic testing or general pregnancy conversations were to take place outside of the health care facility. CONCLUSIONS: While telehealth was recognized as an option to ensure timely access to prenatal care during the COVID-19 pandemic, it also came with multiple challenges for the patient-provider relationship. These findings highlighted the barriers and opportunities to achieve effective and patient-centered communication with the continued integration of telehealth in prenatal care delivery. It is important to address the unique needs of this population during the pandemic and as health care increasingly adopts a telehealth model.

Estimating the costs of physician turnover in hospital medicine.

BACKGROUND AND OBJECTIVE: Costs of physician turnover are lacking for specialties organized around a site of care. We sought to estimate the cost of physician turnover in adult hospital medicine (HM). DESIGN, SETTING, PARTICIPANTS: A retrospective cohort study within a large integrated health system between July 2017 and June 2020. To understand likely variation across the country, we also simulated costs using national wage data and a range of assumptions. MAIN OUTCOME AND MEASURES: Direct costs of turnover borne by our department and institution and indirect costs from reduced hospital billing. In our simulation, we measured costs per hired hospitalist. RESULTS: Between July 2017 and June 2020, 34 hospitalists left the practice, 97 hospitalists were hired, and a total of 234 hospitalists provided adult care at six hospitals. Direct costs of turnover totaled $6166 per incoming physician. Additional clinical coverage required at times of transition was the largest expense, followed by physician time recruiting and interviewing prospective candidates. The salary difference between outgoing and incoming hospitalists was cost-saving, while reduced billing would add to indirect costs per hire. In our simulation using national wage data, programs hiring one hospitalist would spend a mean of $56,943 (95% CI: $27,228-$86,659), programs hiring five hospitalists would spend a mean of $33,333 per hospitalist (95% CI: $9375-$57,292), and programs hiring 10 hospitalists would spend a mean of $30,382 per hospitalist (95% CI: $6877-$53,887). CONCLUSIONS: The financial cost of turnover in HM appears to be substantially lower than earlier estimates of the cost of turnover from non-hospitalist specialties.

Implementation Experience with a 30-Day Hospital Readmission Risk Score in a Large, Integrated Health System: A Retrospective Study.

BACKGROUND: Driven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR). OBJECTIVE: We evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity. DESIGN: Retrospective cohort study. PARTICIPANTS: Adult patients discharged from a CCHS hospital April 2017-September 2020. MAIN MEASURES: Data was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic. RESULTS: The final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories. CONCLUSIONS: The CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use.

Impact of Metabolic Syndrome on Severity of COVID-19 Illness.

Background: Hypertension, diabetes, and obesity are common comorbidities that portend worse outcomes due to coronavirus disease 2019 (COVID-19). Metabolic syndrome is the common denominator of these conditions. This study aims to characterize the association of metabolic syndrome and its surrogate biomarkers with severity of COVID-19 illness. Methods: This retrospective study included adult patients who tested for COVID-19 at an academic tertiary care institution between March 8, 2020, and May 17, 2020. Metabolic syndrome was defined by the modified World Health Organization criteria. Outcomes of hospitalization, intensive care unit (ICU) admission, and death were analyzed. Results: There were 23,282 patients who tested for COVID-19 and 3679 (15.8%) had a positive result. Of these, metabolic syndrome was present in 834 (39%) of 2139 patients with available data. Patients with metabolic syndrome tended to be older, male, African American, heavier, and with more comorbidities. Metabolic syndrome was associated with higher rates of hospital admission and death (P < 0.001). On multivariable analysis, patients with metabolic syndrome had an increased risk of 77% for hospitalization, 56% for ICU admission, and 81% for death (P < 0.001). High AST:ALT and TG:HDL-C ratios were associated with hospitalization and ICU admission, but not mortality. Conclusions: Patients with metabolic syndrome had significantly worse hospitalization and mortality rates due to COVID-19, even after adjusting for covariates. Targeting obesity, hyperglycemia, dyslipidemia, and hypertension could address modifiable risk factors to reduce mortality due to COVID-19.

Reduced Cognitive Burden and Increased Focus: A Mixed-methods Study Exploring How Implementing Scribes Impacted Physicians.

BACKGROUND: Understanding how medical scribes impact care delivery can inform decision-makers who must balance the cost of hiring scribes with their contribution to alleviating clinician burden. OBJECTIVE: The objective of this study was to understand how scribes impacted provider efficiency and satisfaction. DESIGN: This was mixed-methods study. PARTICIPANTS: Internal and family medicine clinicians were included. MEASURES: We administered structured surveys and conducted unstructured interviews with clinicians who adopted scribes. We collected average days to close charts and quantity of after-hours clinical work in the 6 months before and after implementation using electronic health record data. We conducted a difference in difference (DID) analysis using a multilevel Poisson regression. RESULTS: Three themes emerged from the interviews: (1) charting time is less after training; (2) clinicians wanted to continue working with scribes; and (3) scribes did not reduce the overall inbox burden. In the 6-month survey, 76% of clinicians endorsed that working with a scribe improved work satisfaction versus 50% at 1 month. After implementation, days to chart closure decreased [DID=0.38 fewer days; 95% confidence interval (CI): -0.61, -0.15] the average minutes worked after hours on clinic days decreased (DID=-11.5 min/d; 95% CI: -13.1, -9.9) as did minutes worked on nonclinical days (DID=-24.9 min/d; 95% CI: -28.1, -21.7). CONCLUSIONS: Working with scribes was associated with reduced time to close charts and reduced time using the electronic health record, markers of efficiency. Increased satisfaction accrued once scribes had experience.

Adherence to the American Diabetes Association's Glycemic Goals in the Treatment of Diabetes Among Older Americans, 2001-2018.

OBJECTIVE: To assess trends in HbA1c and appropriateness of diabetes medication use by patient health status. RESEARCH DESIGN AND METHODS: We conducted cross-sectional analysis of 2001-2018 National Health and Nutrition Examination Survey (NHANES). We included older adults age ≥65 years who had ever been told they had diabetes, had HbA1c >6.4%, or had fasting plasma glucose >125 mg/dL. Health status was categorized as good, intermediate, or poor. Being below goal was defined as taking medication despite having HbA1c ≥1% below the glycemic goals of the American Diabetes Association (ADA), which varied by patient health status and time period. Drugs associated with hypoglycemia included sulfonylureas, insulin, and meglitinides. RESULTS: We included 3,539 patients. Mean HbA1c increased over time and did not differ by health status. Medication use increased from 59% to 74% with metformin being the most common drug in patients with good or intermediate health and sulfonylureas and insulin most often prescribed to patients with poor health. Among patients taking medications, prevalence of patients below goal increased while prevalence of those above goal decreased from 2001 to 2018. One-half of patients with poor health and taking medications had below-goal HbA1c; two-thirds received drugs associated with hypoglycemia. Patients with poor health who were below goal had 4.9 (95% CI 2.3-10.4) times the adjusted odds of receiving drugs associated with hypoglycemia than healthy patients. CONCLUSIONS: In accordance with ADA's newer Standards of Medical Care in Diabetes, HbA1c goals were relaxed but did not differ by health status. Below-goal HbA1c was common among patients with poor health; many were prescribed medications associated with a higher risk of hypoglycemia.

Patient Perspectives on Self-Monitoring of Blood Glucose When not Using Insulin: a Cross-sectional Survey.

BACKGROUND: Professional societies have recommended against use of self-monitoring blood glucose (SMBG) in non-insulin-treated type 2 diabetes (NITT2D) to control blood sugar levels, but patients are still monitoring. OBJECTIVE: To understand patients' motivation to monitor their blood sugar, and whether they would stop if their physician suggested it. DESIGN: Cross-sectional in-person and electronic survey conducted between 2018 and 2020. PARTICIPANTS: Adults with type 2 diabetes not using insulin who self-monitor their blood sugar. MAIN MEASURES: The survey included questions about frequency and reason for using SMBG, and the impact of SMBG on quality of life and worry. It also asked, "If your doctor said you could stop checking your blood sugar, would you?" We categorized patients based on whether they would stop. To identify the characteristics independently associated with desire to stop SMBG, we performed a logistic regression using backward stepwise selection. KEY RESULTS: We received 458 responses. The common reasons for using SMBG included the doctor wanted the patient to check (67%), desire to see the number (65%), and desire to see if their medications were working (61%). Forty-eight percent of respondents stated that using SMBG reduced their worry about their diabetes and 61% said it increased their quality of life. Fifty percent would stop using SMBG if given permission. In the regression model, respondents who said that they check their blood sugar levels because "I was told to" were more likely to want to stop (AOR: 1.69, 95%CI: 1.11, 2.58). Those that used SMBG due to habit and to understand their diabetes better had lower odds of wanting to stop (AOR: 0.33, 95%CI: 0.18-0.62; AOR: 0.60, 95%CI: 0.39-0.93, respectively). CONCLUSIONS: Primary care physicians should discuss patients' reasons for using SMBG and offer them the option of discontinuing.

COVID-19 Is Associated With an Increase in Visits for Anxiety but Not Depression.

Background The coronavirus disease 2019 (COVID-19) pandemic has increased concerns about mental health. We conducted a time-series analysis to determine whether the percentage of primary care visits for anxiety and depression changed after COVID-19. Methodology We assessed the adjusted weekly change in the percentage of primary care visits for anxiety and depression between August 2019 and October 2020 at a large integrated health system. To account for changes in overall visit behavior during the pandemic, we created three periods: pre-period (August 1, 2019 to March 8, 2020), initial period (March 9, 2020 to June 31, 2020), and return period (July 1, 2020 to October 31, 2020). We used hierarchical linear regression models (clustered by month) to identify the association between the time period and the adjusted mean weekly percentage of visits for depression or anxiety. We conducted the analysis in 2020 and 2021. Results There were 1,691,071 encounters among 605,105 unique adults. The median age was 55 years (interquartile range = 39-68), 57% were female, 78% were white, and 59% had private insurance. Most visits were office-based (versus virtual), of which 99% were in the pre-COVID-19 period and 75% in the return period. There was a significant increase in the percentage of visits associated with anxiety after July compared to before COVID-19 (10.4% versus 9.2%; p = 0.006), and there was no difference in the percentage of visits for depression (p > 0.05). Conclusions Outreach to individuals with depression who have not sought care may be necessary.

Associations of weight loss with obesity-related comorbidities in a large integrated health system.

AIMS: To determine the health outcomes associated with weight loss in individuals with obesity, and to better understand the relationship between disease burden (disease burden; ie, prior comorbidities, healthcare utilization) and weight loss in individuals with obesity by analysing electronic health records (EHRs). MATERIALS AND METHODS: We conducted a case-control study using deidentified EHR-derived information from 204 921 patients seen at the Cleveland Clinic between 2000 and 2018. Patients were aged ≥20 years with body mass index ≥30 kg/m2 and had ≥7 weight measurements, over ≥3 years. Thirty outcomes were investigated, including chronic and acute diseases, as well as psychological and metabolic disorders. Weight change was investigated 3, 5 and 10 years prior to an event. RESULTS: Weight loss was associated with reduced incidence of many outcomes (eg, type 2 diabetes, nonalcoholic steatohepatitis/nonalcoholic fatty liver disease, obstructive sleep apnoea, hypertension; P < 0.05). Weight loss >10% was associated with increased incidence of certain outcomes including stroke and substance abuse. However, many outcomes that increased with weight loss were attenuated by disease burden adjustments. CONCLUSIONS: This study provides the most comprehensive real-world evaluation of the health impacts of weight change to date. After comorbidity burden and healthcare utilization adjustments, weight loss was associated with an overall reduction in risk of many adverse outcomes.

Shared Medical Appointments and Prediabetes: The Power of the Group.

Shared medical appointments, which allow greater access to care and provide peer support, may be an effective treatment modality for prediabetes. We used a retrospective propensity-matched cohort analysis to compare patients attending a prediabetes shared medical appointment to usual care. Primary outcome was patient's weight change over 24 months. Secondary outcomes included change in hemoglobin A1c, low density lipoprotein, and systolic blood pressure. The shared medical appointments group lost more weight (2.88 kg vs 1.29 kg, P = .003), and achieved greater reduction in hemoglobin A1c (-0.87% vs +0.87%, P = .001) and systolic blood pressure (-4.35 mmHg vs +0.52 mmHg, P = .044). The shared medical appointment model can be effective in treating prediabetes.

Treatment of Patients with Prediabetes in a Primary Care Setting 2011-2018: an Observational Study.

BACKGROUND: Over one third of American adults are at high risk for developing diabetes, which can be delayed or prevented using interventions such as medical nutrition therapy (MNT) or metformin. Physicians' self-reported rates of prediabetes treatment are improving, but patterns of actual referral, prescription, and MNT visits are unknown. OBJECTIVE: To characterize treatment of prediabetes in primary care. DESIGN: We conducted a retrospective cohort study using electronic health record data. We described patterns of treatment and used multivariable logistic regression to evaluate the association of patient factors and PCP-specific treatment rate with patient treatment. PATIENTS: We included overweight or obese outpatients who had a first prediabetes-range hemoglobin A1c (HbA1c) during 2011-2018 and had primary care provider (PCP) follow-up within a year. MAIN MEASURES: We collected patient characteristics and the following treatments: metformin prescription; referral to MNT, diabetes education, endocrinology, or bariatric medicine; and MNT visit. We did not capture within-visit physician counseling. KEY RESULTS: Of 16,713 outpatients with prediabetes, 20.4% received treatment, including metformin prescriptions (7.8%) and MNT referrals (11.3%), but only 7.4% of referred patients completed a MNT visit. The strongest predictor of treatment was the patient's PCP's treatment rate. Some PCPs never treated prediabetes, but two treated more than half of their patients; 62% had no patients complete a MNT visit. Being younger or female and having higher body mass index or HbA1c were also positively associated with treatment. Compared to white patients, black patients were more likely to receive MNT referral and less likely to receive metformin. CONCLUSIONS: Almost 80% of patients with new prediabetes never received treatment, and those who did receive referrals had very poor visit completion. Treatment rates appear to reflect provider rather than patient preferences.

Healthcare utilization and patient and provider experience with a home visit program for patients discharged from the hospital at high risk for readmission.

BACKGROUND: Home visits after hospital discharge may reduce future healthcare utilization. We assessed the association of home visits by advanced practice registered nurses (APRN) and paramedics with healthcare utilization and mortality, and provider and patient experience. METHODS: We conducted a retrospective cohort study using convergent mixed methods in one health system including adult medical patients discharged to home from November 2017-September 2019. We assessed outcomes for home visit vs. matched comparison patients at 30, 90, and 180 days, including hospital admission, emergency department (ED) use, and death: Phase 1 (APRN or paramedic visits assigned by geographic location) and Phase 2 (APRN and paramedic visit teams assigned to patients). Patients declining home visits and those accepting were also compared. Semi-structured interviews were conducted with home visit patients and providers, primary care providers, and nurse care coordinators. RESULTS: In Phase 1, the 101 home visit matched to 303 comparison patients showed no differences in readmissions, ED visits, or death at 30, 90, and 180 days. In Phase 2, 157 home visit matched to 471 comparison patients had fewer 30-day readmissions (19.1% vs. 28.7%, p 0.024) and no differences in other outcomes. Compared with patients declining home visits, patients accepting had lower odds of 30-day readmission. In 44 interviews, themes of Medication Understanding, Knowledge Gap after Discharge, Patient Medical Complexity, Social Context, and Patient Engagement/Need for Reassurance emerged. CONCLUSION: Post-discharge home visits by APRNs and paramedics working together were associated with reduced 30-day readmissions. Identified themes could inform strategies to improve patient support.