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.

Improving Blood Pressure Control Using Smart Technology.

BACKGROUND: The authors sought to determine if wireless oscillometric home blood pressure monitoring (HBPM) that integrates with smartphone technology improves blood pressure (BP) control among patients with new or existing uncontrolled hypertension (HTN). METHODS: A prospective observational cohort study monitored BP control before and after an educational intervention and introduction to HBPM. Patients in the intervention group were instructed to track their BP using a smartphone device three to seven times per week. Cases were matched to controls at a 1:3 allocation ratio on several clinical characteristics over the same period and received usual care. The proportion of patients with controlled BP was compared between groups at pre- and postintervention, ∼9 months later. Results and Materials: The total study population included 484 patients with mean age 60 years (range 23-102 years), 47.7% female, and 84.6% Caucasian. Mean preintervention BP was 137.8 mm Hg systolic and 81.4 mm Hg diastolic. Mean BP control rates improved for patients who received HBPM from 42% to 67% compared with matched control patients who improved from 59% to 67% (p < 0.01). CONCLUSION: HBPM with smartphone technology has the potential to improve HTN management among patients with uncontrolled or newly diagnosed HTN. Technology needs to be easy to use and operate and would work best when integrated into local electronic health record systems. In systems without this capability, medical assistants or other personnel may be trained to facilitate the process. Nurse navigator involvement was instrumental in bridging communication between the patients and provider.

Development of a Follow-Up Measure to Ensure Complete Screening for Colorectal Cancer.

Importance: The current quality performance measure for colorectal cancer (CRC) screening is limited to initial screening. Despite low rates, there is no measure for appropriate follow-up with colonoscopy after receipt of an abnormal result of a stool-based screening test (SBT) for CRC. A quality performance measure is needed. Objective: To develop and test a quality performance measure for follow-up colonoscopy within 6 months of an abnormal result of an SBT for CRC. Design, Setting, and Participants: This retrospective quality improvement study examined data from January 1, 2016, to December 31, 2020, with 2018 plus 6 months of follow-up as the primary measurement period to verify performance rates, specify a potential measure, and test for validity, reliability, and feasibility. The Optum Labs Data Warehouse (OLDW), a deidentified database of health care claims and clinical data, was accessed. The OLDW contains longitudinal health information on enrollees and patients, representing a diverse mixture of ages and geographic regions across the US. For the database study, adults from 38 health care organizations (HCOs) aged 50 to 75 years who completed an initial CRC SBT with an abnormal result were observed to determine follow-up colonoscopy rates within 6 months. Rates were stratified by race, ethnicity, sex, insurance, and test modality. Three HCOs participated in the feasibility field testing. Data were analyzed from June 1, 2022, to May 31, 2023. Main Outcome and Measures: The primary outcome consisted of follow-up colonoscopy rates following an abnormal SBT result for CRC. Reliability statistics were also calculated across HCOs, race, ethnicity, and measurement year. Results: Among 20 581 adults (48.6% men and 51.4% women; 307 [1.5%] Asian, 492 [7.2%] Black, 644 [3.1%] Hispanic, and 17 705 [86.0%] White; mean [SD] age, 63.6 [7.1] years) in 38 health systems, 47.9% had a follow-up colonoscopy following an abnormal SBT result for CRC within 6 months. There was significant variation between HCOs. Notably, significantly fewer Black patients (37.1% [95% CI, 34.6%-39.5%]) and patients with Medicare (49.2% [95% CI, 47.7%-50.6%]) or Medicaid (39.2% [95% CI, 36.3%-42.1%]) insurance received a follow-up colonoscopy. A quality performance measure that tracks rates of follow-up within 6 months of an abnormal SBT result was observed to be feasible, valid, and reliable, with a median reliability statistic between HCOs of 94.5% (range, 74.3%-99.7%). Conclusions and Relevance: The findings of this observational study of 20 581 adults suggest that a measure of follow-up colonoscopy within defined periods after an abnormal result of an SBT test for CRC is warranted based on low current performance rates and would be feasible to collect by health systems and produce valid, reliable results.

Rates of Follow-up Colonoscopy After a Positive Stool-Based Screening Test Result for Colorectal Cancer Among Health Care Organizations in the US, 2017-2020.

Importance: Noninvasive stool-based screening tests (SBTs) are effective alternatives to colonoscopy. However, a positive SBT result requires timely follow-up colonoscopy (FU-CY) to complete the colorectal cancer screening paradigm. Objectives: To evaluate FU-CY rates after a positive SBT result and to assess the association of the early COVID-19 pandemic with FU-CY rates. Design, Setting, and Participants: This mixed-methods cohort study included retrospective analysis of deidentified administrative claims and electronic health records data between June 1, 2015, and June 30, 2021, from the Optum Labs Data Warehouse and qualitative, semistructured interviews with clinicians from 5 health care organizations (HCOs). The study population included data from average-risk primary care patients aged 50 to 75 years with a positive SBT result between January 1, 2017, and June 30, 2020, at 39 HCOs. Main Outcomes and Measures: The primary outcome was the FU-CY rate within 1 year of a positive SBT result according to patient age, sex, race, ethnicity, insurance type, Charlson Comorbidity Index (CCI), and prior SBT use. Results: This cohort study included 32 769 individuals (16 929 [51.7%] female; mean [SD] age, 63.1 [7.1] years; 2092 [6.4%] of Black and 28 832 [88.0%] of White race; and 825 [2.5%] of Hispanic ethnicity). The FU-CY rates were 43.3% within 90 days of the positive SBT result, 51.4% within 180 days, and 56.1% within 360 days (n = 32 769). In interviews, clinicians were uniformly surprised by the low FU-CY rates. Rates varied by race, ethnicity, insurance type, presence of comorbidities, and SBT used. In the Cox proportional hazards regression model, the strongest positive association was with multitarget stool DNA use (hazard ratio, 1.63 [95% CI, 1.57-1.68] relative to fecal immunochemical tests; P < .001), and the strongest negative association was with the presence of comorbidities (hazard ratio, 0.64 [95% CI, 0.59-0.71] for a CCI of >4 relative to 0; P < .001). The early COVID-19 pandemic was associated with lower FU-CY rates. Conclusions and Relevance: This study found that FU-CY rates after a positive SBT result for colorectal cancer screening were low among an average-risk population, with the median HCO achieving a 53.4% FU-CY rate within 1 year. Socioeconomic factors and the COVID-19 pandemic were associated with lower FU-CY rates, presenting opportunities for targeted intervention by clinicians and health care systems.

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.

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.

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.

Physician Practices With Robust Capabilities Spend Less On Medicare Beneficiaries Than More Limited Practices.

No research has considered a range of physician practice capabilities for managing patient care when examining practice-level influences on quality of care, utilization, and spending. Using data from the 2017 National Survey of Healthcare Organizations and Systems linked to 2017 Medicare fee-for-service claims data from attributed beneficiaries, we examined the association of practice-level capabilities with process measures of quality, utilization, and spending. In propensity score-weighted mixed-effects regression analyses, physician practice locations with "robust" capabilities had lower total spending compared to locations with "mixed" or "limited" capabilities. Quality and utilization, however, did not differ by practice-level capabilities. Physician practice locations with robust capabilities spend less on Medicare fee-for-service beneficiaries but deliver quality of care that is comparable to the quality delivered in locations with low or mixed capabilities. Reforms beyond those targeting practice capabilities, including multipayer alignment and payment reform, may be needed to support larger performance advantages for practices with robust capabilities.

Using Quality Measures to Drive Improvements in Immunization Rates: Findings from a Real-World Evaluation from 3 US Health Care Organizations.

Adult immunization rates are low and continue to fail to meet national targets. The coronavirus disease 2019 pandemic halted routine health care services for many, including vaccinations. This study explored whether the National Committee for Quality Assurance's Adult Immunization Status (AIS) measure, which had previously only been evaluated for reporting by health plans, could be leveraged by health care organizations (HCOs) as a tactic to improve immunization rates. Methods included a quantitative analysis of deidentified patient electronic health record data from 3 HCOs, supplemented by qualitative interviews to further understand opportunities and barriers. The analysis indicated that the data necessary for calculation of the AIS measure are available within HCOs and that measure performance could be enhanced with supplemental data from external sources, such as state immunization registries. Although HCOs rates were consistent with national estimates, this research further validated that adult immunization rates in the United States are low and highlighted the profound disparities that exist. For instance, the likelihood of completing all age-appropriate vaccinations was lower if patients were Black or African American, enrolled in Medicaid, or without health insurance. As a result of this study, the authors concluded that the AIS measure is feasible for use in medical groups and could potentially help drive quality improvements in immunization rates; however, there are considerations for implementation particularly if providers are being held accountable for measure performance.

Successful Implementation of an All-or-None Diabetes Measure in 10 US Health Systems.

Type 2 diabetes mellitus (T2DM) affects 31.5 million adults in the United States and is commonly treated in primary care settings. One promising approach to comprehensive care is to focus on an all-or-none diabetes bundle measure, which ensures each patient meets a set of guideline-recommended measures. This requires a practice-level coordinated strategy. The purpose of this initiative was to help health care organizations (HCOs) improve the care and outcomes of patients with T2DM using an all-or-none bundle measure. This observational study was carried out in the context of a national best practices learning Collaborative that implemented targeted interventions in primary care settings and measured success using an all-or-none bundle measure. Ten AMGA member-HCOs, across 8 states, treating nearly 300,000 adult patients with T2DM in primary care participated. The primary measure, the Together 2 Goal® Core Bundle, included hemoglobin A1c (A1c) control (<8%), blood pressure (BP) control (<140/90 mmHg), lipid management (prescribed a statin), and medical attention for nephropathy. All 10 HCOs improved the Core Bundle measure during the 12-month Collaborative. The rate for the Core Bundle improved from 40.2% to 42.8%, an absolute increase of 2.6% (P < 0.001). In addition, 9 HCOs improved BP control, 8 improved lipid management, 6 improved attention to nephropathy, and 4 improved A1c control. Implementing interventions in primary care settings was successful in achieving comprehensive care for an estimated additional 7700 people living with T2DM who met all 4 components of the bundle measure during the 12-month intervention period.

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.

Cardiovascular Disease in Patients With Type 2 Diabetes: A Qualitative Analysis of Knowledge, Attitudes, and Beliefs of Health Care Professionals.

Guidelines for the management of patients with type 2 diabetes mellitus (T2DM) recommend SGLT-2 (sodium-glucose cotransporter 2) inhibitors and GLP-1 RAs (glucagon-like peptide 1 receptor agonists) as second-line agents for patients with, or at risk for, cardiovascular disease. A better understanding of guideline implementation will further the provision of evidence-based health care to patients. Interviews and surveys of clinicians were conducted to understand providers' knowledge, attitudes, and beliefs related to the 2019 American Diabetes Association Standards of Care for T2DM. There was a lack of widespread knowledge of the guidelines and comfort with their use. Clinicians require additional training and education on the efficacy of the new medications and accompanying clinical guidelines.

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.

The Serious Illness Population: Ascertainment via Electronic Health Record or Claims Data.

CONTEXT: Palliative care can improve the lives of people with serious illness, yet clear operational definitions of this population do not exist. Prior efforts to identify this population have not focused on Medicare Advantage (MA) and commercial health plan enrollees. OBJECTIVES: We aimed to operationalize our conceptual definition of serious illness to identify those with serious medical conditions (SMC) among commercial insurance and MA enrollees, and to compare the populations identified through electronic health record (EHR) or claims data sources. METHODS: We used de-identified claims and EHR data from the OptumLabs Data Warehouse (2016-2017), to identify adults age ≥18 with SMC and examine their utilization and mortality. Within the subset found in both data sources, we compared the performance of claims and EHR data. RESULTS: Within claims, SMC was identified among 10% of those aged ≥18 (5.4% ages 18-64, 27% age ≥65). Within EHR, SMC was identified among 9% of those aged ≥18 (5.6% ages 18-64, 21% ages ≥65). Hospital, emergency department and mortality rates were similar between the EHR and claims-based groups. Only 50% of people identified as having SMC were recognized by both data sources. CONCLUSION: These results demonstrate the feasibility of identifying adults with SMC in a commercially insured population, including MA enrollees; yet separate use of EHR or claims result in populations that differ. Future research should examine methods to combine these data sources to optimize identification and support population management, quality measurement, and research to improve the care of those living with serious illness.

Building Successful Models in Primary Care to Improve the Management of Adult Patients with Obesity.

Although several obesity clinical practice guidelines are available and relevant for primary care, a practical and effective medical model for treating obesity is necessary. The aim of this study was to develop and implement a holistic population health-based framework with components to support primary care-based obesity management in US health care organizations. The Obesity Care Model Collaborative (OCMC) was conducted with guidance and expertise of an advisory committee, which selected participating health care organizations based on prespecified criteria. A committee comprising obesity and quality improvement specialists and representatives from each organization developed and refined the obesity care framework for testing and implementing guideline-based practical interventions targeting obesity. These interventions were tracked over time, from an established baseline to 18 months post implementation. Ten geographically diverse organizations, treating patients with diverse demographics, insurance coverage, and health status, participated in the collaborative. The key interventions identified for managing obesity in primary care were applicable across the 4 OCMC framework domains: community, health care organization, care team, and patient/family. Care model components were developed within each domain to guide the primary care of obesity based on each organization's structure, resources, and culture. Key interventions included development of quality monitoring systems, training of leadership and staff, identifying clinical champions, patient education, electronic health record best practice alerts, and establishment of community partnerships, including the identification of external resources. This article describes the interventions developed based on the framework, with a focus on implementation of the model and lessons learned.

Variation in Patient Smoking Cessation Rates Among Health-Care Providers: An Observational Study.

BACKGROUND: Physicians play a crucial role in providing smoking cessation counseling and medications. However, it is unknown whether individual physicians' approaches affect whether patients quit. RESEARCH QUESTION: This study assessed patient quit rates within a national quality-improvement learning collaborative to document variation in quit rates at the physician, practice, and health system levels. STUDY DESIGN AND METHODS: A retrospective cohort study was conducted of primary care patients identified from the Optum analytics database containing longitudinal ambulatory data for patients from 22 health-care organizations between January 2012 and December 2018. The study included smokers aged ≥ 18 years who attended at least three ambulatory visits, with two visits at least 1 year apart. The primary study outcome was abstinence for ≥ 1 year. A mixed effects logistic regression model was used to predict the probability of quitting as a function of patient variables. Quit rates were then adjusted by patient factors and calculated at the level of clinician, clinic/practice, and health system. RESULTS: Across all systems, 56% of patients had a documented smoking status in 2017. Among nearly 1 million smokers, 24% quit smoking. In the regression model, patient characteristics associated with quitting included older age, Hispanic ethnicity, being married, urban residence, commercial insurance, pregnancy, and a diagnosis of pneumonia, myocardial infarction, ischemic heart disease, cataract, or asthma. Medicaid insurance, low income, high BMI, peripheral vascular disease, alcohol-related diagnosis, and COPD were negatively associated with smoking cessation. Adjusted quit rates ranged from 14.3% to 34.5% across 20 health systems, 5% to 66% among 1,399 practice sites, and 4% to 87% among 3,803 health-care providers. Of smokers, 10.2% were prescribed smoking deterrents, and 3.9% were referred for counseling. INTERPRETATION: Smoking cessation rates varied substantially at the practitioner, practice site, and health system levels. It is likely that individual physician approaches to smoking cessation influence patients' likelihood of quitting.

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.

An Adult Immunization Best Practices Learning Collaborative: Impact, Scale Up, and Spread.

The research objective was to rapidly scale up and spread a proven learning collaborative approach (intervention) for adult vaccination rates for influenza and pneumococcal disease from 7 to 39 US health care organizations and to examine improvement in adult immunization rates after scale-up. Comparative analyses were conducted between intervention and nonintervention propensity score-matched providers on vaccination rates using a difference-in-differences approach. Qualitative data, collected during site visits and in-person and virtual meetings, were used to enhance understanding of quantitative results. In 2017-2018, an analysis of a subset of sites (n = 9) from 2 intervention cohorts (∼20 sites each) demonstrated greater improvement than their matched providers in pneumococcal vaccinations (PV) for patients ages ≥65 years (treatment effect range: 1.4%-3.7%, P < 0.01) and PV for high-risk patients (eg, with immunocompromising conditions) aged 19-64 years (0.8%-1.6%, P < 0.01). Significant effects were observed in one of the study cohorts for PV for at-risk patients (eg, with diabetes) aged 19-64 years (1.7%, P < 0.01), and influenza vaccination rates (2.4%, P < 0.001). Individual health systems demonstrated even greater improvements across all 4 vaccinations: 9.5% influenza; 8.7% PV ages ≥65 years; 11.8% PV high-risk; 16.3% PV at-risk (all P < 0.01). Results demonstrated that a 7-site pilot could be successfully scaled to 39 additional sites, with similar improvements in vaccination rates. Between 2014 and 2018, vaccination improvements among all 46 groups (7 pilot, 39 in subsequent cohorts) resulted in an estimated 5.5 million adult vaccinations administered or documented in 27 states.

Impact of a Learning Collaborative Approach on Influenza and Pneumococcal Immunization Rates in US Adults: A Mixed Methods Approach.

The objective of this research was to test the impact of a learning collaborative model (intervention) on adult vaccination rates for influenza and pneumococcal disease. A mixed methods approach was used to identify changes in adult vaccination rates over time and organizational factors contributing to successful programs. Provider-level propensity scores were used to match intervention to non-intervention providers to control for inherent selection bias of participating organizations. Comparative analyses were conducted between intervention and non-intervention sites on vaccination rates, using a difference-in-differences approach. Qualitative data (eg, semi-structured interviews) were analyzed using a constant comparison approach to identify themes related to successful strategies. From 2014-2016, intervention providers demonstrated greater improvement than their matched providers in pneumococcal vaccinations (PV) for patients aged 65 years and older (treatment effect: 4.3%, P < 0.05) and PV for high-risk patients (eg, with immunocompromising conditions) aged 19-64 years (2.7%, P < 0.001). Significant effects were also observed for PV for at-risk patients (eg, with diabetes) aged 19-64 years (1.7%, P < 0.05). Individual health systems demonstrated even greater improvements (eg, greater increase in PV rates for patients aged 65 years and older), with treatment effects as high as 20.4% (P < 0.05). A learning collaborative approach was demonstrated to be an effective approach to improve adult vaccination rates among participating integrated delivery systems and medical groups. Factors associated with success included organization type (ie, integrated delivery systems) and systems characterized by a positive learning climate and collaborative culture.

Telemedicine and the mini-mental state examination: assessment from a distance.

The objective of this study was to determine the reliability of the Mini-Mental State Examination (MMSE) administration via telehealth with a focus on the auditory and visual test components. Reliability was assessed through use of an in-person collaborator and by assessment of faxed test copies. The MMSE was administered via telehealth with the assistance of a face-to-face collaborator. Patient responses were recorded by both the remote and in-person nurse and compared item by item; total scores for each subject were also compared. Visual items were assessed through a blinded separate scoring of a faxed copy. Percent agreement per item and total score were calculated and correlations between scores were determined by Pearson correlation coefficients. Mean score differences and associated 95% confidence intervals were calculated. Eighty percent of individual items demonstrated remote to in-person agreement of >95% and all items were >85.5% in agreement. Pearson correlation coefficients demonstrated high correlations (>0.86) between 80% of the items examined. Mean differences in scored test items were not significantly different from zero. This study demonstrates the utility of using telehealth for cognitive assessment by MMSE. It supports the use of telehealth to improve healthcare access among patients for whom distance, cost, and mobility are potential barriers to attending face-to-face clinical visits. Continued validation and reliability testing is warranted to ensure that all healthcare provided via telehealth maintains an equal quality level to that of in-person care.