Perspectives on Lower Extremity Peripheral Artery Disease: A Qualitative Study of Early Diagnosis and Treatment and the Impact of Health Disparities.

Lower-extremity peripheral artery disease (PAD), the accumulation of atherosclerotic plaque in the arteries of the legs, causes substantial morbidity and mortality. Frequent under- and delayed diagnosis result in poor outcomes, disproportionately affecting individuals from racial and ethnic minority groups. To understand barriers to early detection and treatment and factors contributing to disparities, American Medical Group Association (AMGA) conducted roundtable discussions and semistructured interviews in 2021. Eighteen participants discussed PAD evaluation, diagnosis, early medical management, and disparities in care. A qualitative case study approach and data reduction methods were used to generate themes, draw conclusions, and make actionable recommendations. Identified themes included lack of (1) prioritization of PAD for population health; (2) engagement of primary care providers in early evaluation and referral; (3) "ownership" of lower-extremity PAD within health systems; and (4) focus on disparities in care. Participant solutions included (1) financial impact of early PAD management, in the context of value-based payment; (2) embedding an advanced practice provider into a vascular surgery practice to facilitate evaluation and provide medical therapy; and (3) leveraging care coordination, multidisciplinary clinics, and telehealth technology to provide comprehensive care for patients with PAD and address disparities. A deliberate focused effort is necessary to close gaps and the accompanying disparities in early evaluation, diagnosis, and treatment for people with lower-extremity PAD. The authors describe 3 models that can be emulated to improve care for this high-risk population. With improved reimbursement and better medical therapies, now is the time to focus on early diagnosis and management of PAD.

Health systems and telemedicine adoption for diabetes and hypertension care.

OBJECTIVES: The COVID-19 pandemic accelerated telemedicine use nationally, but differences across health systems are understudied. We examine telemedicine use for adults with diabetes and/or hypertension across 10 health systems and analyze practice and patient characteristics associated with greater use. STUDY DESIGN: Encounter-level data from the AMGA Optum Data Warehouse for March 13, 2020, to December 31, 2020, were analyzed, which included 3,016,761 clinical encounters from 764,521 adults with diabetes and/or hypertension attributed to 1 of 1207 practice sites with at least 50 system-attributed patients. METHODS: Linear spline regression estimated whether practice size and ownership were associated with telemedicine during the adoption (weeks 0-4), de-adoption (weeks 5-12), and maintenance (weeks 13-42) periods, controlling for patient socioeconomic and clinical characteristics. RESULTS: Telemedicine use peaked at 11% to 42% of weekly encounters after 4 weeks. In adjusted analyses, small practices had lower telemedicine use for adults with diabetes during the maintenance period compared with larger practices. Practice ownership was not associated with telemedicine use. Practices with higher proportions of Black patients continued to expand telemedicine use during the de-adoption and maintenance periods. CONCLUSIONS: Practice ownership was not associated with telemedicine use during first months of the pandemic. Small practices de-adopted telemedicine to a greater degree than medium and large practices. Technical support for small practices, irrespective of their ownership, could enable telemedicine use for adults with diabetes and/or hypertension.

Predicting Chronic Opioid Use Among Patients With Osteoarthritis Using Electronic Health Record Data.

OBJECTIVE: To estimate the risk of a patient with osteoarthritis (OA) developing chronic opioid use (COU) within 1 year of a new opioid prescription by using electronic health record (EHR) data and predictive models. METHODS: We used EHR data from 13 health care organizations to identify patients with OA with an opioid prescription between March 1, 2017 and February 28, 2019 and no record of opioid use in the prior 6 months. We evaluated 4 machine learning models to estimate patients' risk of COU (≥3 prescriptions ≥84 days, maximum gap ≤60 days). We also estimated the transportability of models to organizations outside the training set. RESULTS: The cohort consisted of 33,894 patients with OA, of whom 2,925 (8.6%) developed COU within 1 year. All models demonstrated good discrimination, with the best-performing model (random forest) achieving an area under the receiver operating characteristic curve (AUC) of 0.728 (95% CI 0.711-0.745), but the simplest regression model performed nearly as well (AUC 0.717 [95% CI 0.699-0.734]). Predicted risk deciles spanned a range of 2% risk for the 10th percentile to 18% risk for the 90th percentile for well-calibrated models. Models showed highly variable discrimination across organizations (AUC 0.571-0.842). CONCLUSIONS: We found that EHR-based predictive models could estimate the risk of future COU among patients with OA to help inform care decisions. Black-box methods did not have significant advantages over more interpretable models. Care should be taken when extending all models into organizations not included in model training because of a high variability in performance across held-out organizations.

Assessing opportunities to advance quality measures in adult obesity.

OBJECTIVES: To evaluate the methodological soundness and performance of 3 obesity quality measures aimed at promoting improvements in obesity care. STUDY DESIGN: Retrospective, clinical, and administrative data-based observational research study to evaluate scientific soundness, feasibility, and performance of obesity quality measures. METHODS: Four test sites (clinicians/clinician groups) submitted clinical and administrative health data including patient demographics, diagnoses, and encounter information for patient panels encompassing individuals aged 18 to 79 years with at least 1 ambulatory visit between July 1, 2017, and June 30, 2018 (measurement period). Clinician/clinician group data were supplemented by an Optum data set contributing patient information from 21 health care organizations with approximately 6 million qualifying patients to assess the impact of using a larger data set for measure testing. Patients were excluded if they met any of the following criteria: were pregnant during the measurement period or in the 6 months prior to the measurement period, had died during the measurement year, or had evidence of palliative or hospice care during the measurement period. RESULTS: This study resulted in the identification of a clinician/clinician group-level measure, Documentation of Obesity Diagnosis, as being feasible and reliable; however, the measure requires additional evaluation and potential adjustments to determine validity. Other measures included in our evaluation had feasibility and methodological challenges due to data capture and coding limitations. CONCLUSIONS: Findings of our current study suggest that there are emerging opportunities to capture data and advance obesity measurement incrementally. A process measure focused on obesity diagnosis has the most potential for immediate implementation by clinicians, and additional measures focused on change in body mass index over time and use of evidence-based obesity treatment remain challenging to implement due to data capture and benefit coverage.

Chronic Kidney Disease Testing Among Primary Care Patients With Type 2 Diabetes Across 24 U.S. Health Care Organizations.

OBJECTIVE: Clinical guidelines for people with diabetes recommend chronic kidney disease (CKD) testing at least annually using estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (uACR). We aimed to understand CKD testing among people with type 2 diabetes in the U.S. RESEARCH DESIGN AND METHODS: Electronic health record data were analyzed from 513,165 adults with type 2 diabetes receiving primary care from 24 health care organizations and 1,164 clinical practice sites. We assessed the percentage of patients with both one or more eGFRs and one or more uACRs and each test individually in the 1, 2, and 3 years ending September 2019 by health care organization and clinical practice site. Elevated albuminuria was defined as uACR ≥30 mg/g. RESULTS: The 1-year median testing rate across organizations was 51.6% for both uACR and eGFR, 89.5% for eGFR, and 52.9% for uACR. uACR testing varied (10th-90th percentile) from 44.7 to 63.3% across organizations and from 13.3 to 75.4% across sites. Over 3 years, the median testing rate for uACR across organizations was 73.7%. Overall, the prevalence of detected elevated albuminuria was 15%. The average prevalence of detected elevated albuminuria increased linearly with uACR testing rates at sites, with estimated prevalence of 6%, 15%, and 30% at uACR testing rates of 20%, 50%, and 100%, respectively. CONCLUSIONS: While eGFR testing rates are uniformly high among people with type 2 diabetes, testing rates for uACR are suboptimal and highly variable across and within the organizations examined. Guideline-recommended uACR testing should increase detection of CKD.

A Retrospective Analysis of Therapeutic Inertia in Type 2 Diabetes Management Across a Diverse Population of Health Care Organizations in the USA.

INTRODUCTION: If their target glycated hemoglobin (HbA1c) is not achieved after 3 months, timely treatment intensification is recommended in people with type 2 diabetes to maintain glycemic control and minimize vascular complications. We retrospectively investigated potential therapeutic inertia in the management of type 2 diabetes in multiple health care organizations across the USA. METHODS: Electronic health records were analyzed from 22 American Medical Group Association (AMGA) health care organizations. Bolus insulin-naïve patients with type 2 diabetes and HbA1c ≥ 8.0% (≥ 64 mmol/mol) at baseline were followed for 24 months to identify the frequency and average duration of therapeutic inertia (no new class of glucose-lowering medication prescribed, or not achieving their target HbA1c [< 8.0%; < 64 mmol/mol]). RESULTS: The study cohort comprised almost 28,000 patients. Therapeutic inertia was observed in ≈ 50% of patients after 6 months, and in > 10% after 24 months. Less therapeutic inertia was observed in patients receiving one or no oral antidiabetic drugs (OADs) (36% or 28%, respectively, at 6 months), while more inertia was seen following multiple OADs or basal insulin (54% of those on baseline basal insulin at 6 months). Although an observable action was recorded for 90% of patients, many (44%) had still not achieved their target HbA1c after 24 months. CONCLUSION: The results corroborate the presence of therapeutic inertia in people with type 2 diabetes, suggesting that treatment intensification guidelines are not being followed. Extensive variability in the presence of therapeutic inertia was observed both across and within organizations; investigating this further and sharing best practices across providers might help improve the quality of patient care at organizational and national levels.

People with type 2 diabetes have their glycated hemoglobin (HbA_1c) level measured regularly by their care provider to check their blood sugar levels over the previous 2­3 months and the diabetes control achieved with their current treatment. To keep HbA_1c within an individually recommended range, changes to therapies or doses may be needed, which is known as 'treatment intensification.' Despite guidelines describing this best-practice approach, 'therapeutic inertia' (not intensifying treatment when needed) is common. This therapeutic inertia may be a result of complicated or confusing guidelines, a lack of time or awareness/understanding on the part of the health care provider, or patient-specific barriers such as treatment cost or fear of side effects. Due to therapeutic inertia, patients can have poorly controlled diabetes for a long time, increasing their risk of other diabetes-related health problems or complications. This study describes widespread therapeutic inertia in the management of type 2 diabetes across the USA, suggesting that treatment intensification in patients with poor diabetes control is not taking place when needed. Diabetes-related health complications caused by poorly controlled disease over a period of time can significantly reduce quality of life. Diabetes and its complications also increase costs for the health care system due to the resulting medical costs and diabetes-related reductions in productivity. It is important to encourage early diagnosis of diabetes and appropriate and timely treatment. Investigating the variations in therapeutic inertia seen within and between health care organizations and sharing the lessons learned by the top-performing organizations may help spread best practices and improve the quality of patient care.

Diagnosing Obesity as a First Step to Weight Loss: An Observational Study.

OBJECTIVE: This study aimed to explore the relationship between an obesity diagnosis and weight loss as a percentage of total body weight loss over 9 to 15 months, using electronic health record data. METHODS: An observational study of 688,878 adult patients at 15 health systems with BMI ≥ 30 kg/m2 examined the relationship between weight loss and documentation of obesity diagnosis. Multivariable logistic regression models were created using a stepwise backwards elimination procedure to identify potential predictors of weight loss. RESULTS: Of patients with BMI ≥ 30, 44.9% had an obesity diagnosis on a claim or electronic health record problem list; 16.9% and 5.9% lost ≥ 5% and ≥ 10% of their body weight, respectively. Multivariable logistic regression models revealed a diagnosis of obesity on the same day as the initial weight (odds ratio [OR] = 1.3; CI: 1.2-1.3; P < 0.001) as a predictor of ≥ 5% total body weight loss in 9 to 15 months. Other significant predictors included an antiobesity medication prescription, female sex, diagnosis of type 2 diabetes, Medicare/Medicaid insurance, and number of ambulatory visits. CONCLUSIONS: While controlling for potentially confounding factors, documentation of an obesity diagnosis remained independently predictive of at least 5% weight loss. This suggests that documenting a diagnosis of obesity may be an important step toward engaging patients to lose weight.

Application of a Tool to Identify Undiagnosed Hypertension - United States, 2016.

Approximately 11 million U.S. adults with a usual source of health care have undiagnosed hypertension, placing them at increased risk for cardiovascular events (1-3). Using data from the National Health and Nutrition Examination Survey (NHANES), CDC developed the Million Hearts Hypertension Prevalence Estimator Tool, which allows health care delivery organizations (organizations) to predict their patient population's hypertension prevalence based on demographic and comorbidity characteristics (2). Organizations can use this tool to compare predicted prevalence with their observed prevalence to identify potential underdiagnosed hypertension. This study applied the tool using medical billing data alone and in combination with clinical data collected among 8.92 million patients from 25 organizations participating in American Medical Group Association (AMGA) national learning collaborative* to calculate and compare predicted and observed adult hypertension prevalence. Using billing data alone revealed that up to one in eight cases of hypertension might be undiagnosed. However, estimates varied when clinical data were included to identify comorbidities used to predict hypertension prevalence or describe observed hypertension prevalence. These findings demonstrate the tool's potential use in improving identification of hypertension and the likely importance of using both billing and clinical data to establish hypertension and comorbidity prevalence estimates and to support clinical quality improvement efforts.

Agreement and validity of electronic health record prescribing data relative to pharmacy claims data: A validation study from a US electronic health record database.

BACKGROUND: Granular clinical and laboratory data available in electronic health record (EHR) databases provide researchers the opportunity to conduct investigations that would not be possible in insurance claims databases; however, for pharmacoepidemiology studies, accurate classification of medication exposure is critical. OBJECTIVE: The aim of this study was to evaluate the validity of classifying medication exposure using EHR prescribing (EHR-Rx) data. METHODS: We conducted a retrospective cohort study among patients with linked claims and EHR data in OptumLabs™ Data Warehouse. The agreement between EHR-Rx data and pharmacy claims (PC-Rx) data (for 40 medications) was determined using the positive predictive value (PPV) and medication possession ratio (MPR)-calculated in 1- and 12-month medication exposure periods (MEPs). Secondary analyses were restricted to incident vs prevalent EHR-Rxs, age ≥65 vs <65, white vs black race, males vs females, and number of EHR-Rxs. RESULTS: The validity metrics varied substantially among the 40 medications assessed. Across all medications, the period PPV and MPR were 62% and 63% in the 1-month MEP. They were 78% and 43% in the 12-month MEP. Overall, PPV and MPR were higher for patients with a prevalent EHR-Rx and age <65. CONCLUSIONS: Despite substantial variability among different medications, there was very good agreement between EHR-Rx data and PC-Rx data. To maximize the validity of classifying medication exposure with EHR prescribing data, researchers may consider using longer MEPs (eg, 12 months) and potentially require multiple EHR-Rxs to classify baseline medication exposure.