Sustained improvement in clinical preventive service delivery among independent primary care practices after implementing electronic health record systems.

INTRODUCTION: Studies showing sustained improvements in the delivery of clinical preventive services are limited. Fewer studies demonstrate sustained improvements among independent practices that are not affiliated with hospitals or integrated health systems. This study examines the continued improvement in clinical quality measures for a group of independent primary care practices using electronic health records (EHRs) and receiving technical support from a local public health agency. METHODS: We analyzed clinical quality measure performance data from a cohort of primary care practices that implemented an EHR at least 3 months before October 2009, the study baseline. We assessed trends for 4 key quality measures: antithrombotic therapy, blood pressure control, smoking cessation intervention, and hemoglobin A1c (HbA1c) testing based on monthly summary data transmitted by the practices. RESULTS: Of the 151 practices, 140 were small practices and 11 were community health centers; average time using an EHR was 13.7 months at baseline. From October 2009 through October 2011, average rates increased for antithrombotic therapy (from 58.4% to 74.8%), blood pressure control (from 55.3% to 64.1%), HbA1c testing (from 46.4% to 57.7%), and smoking cessation intervention (from 29.3% to 46.2%). All improvements were significant. CONCLUSION: During 2 years, practices showed significant improvement in the delivery of several key clinical preventive services after implementing EHRs and receiving support services from a public health agency.

Social Risks Among Primary Care Patients in a Large Urban Health System.

INTRODUCTION: Health systems are increasingly interested in addressing the social determinants of health via social risk screening. The objective of this study is to understand the variability in the number and types of social risks overall and in population subgroups among primary care patients routinely screened in a large urban health system. METHODS: Between April and December 2018, a total of 24,633 primary care patients completed a 10-item screener across 19 ambulatory sites within a health system in the Bronx, NY. The prevalence of any social risk and specific social risks was estimated overall and for population subgroups. Wald tests were used to determine statistically significant differences by subgroup. Data were analyzed in winter/spring 2019. RESULTS: Twenty percent of patients presented with at least 1 social risk. The most frequently reported risks included housing quality (6.5%) and food insecurity (6.1%). Middle-aged (30-59 years) respondents (24.7%, 95% CI=23.6%, 25.7%) compared with those aged 18-29 years (17.7%, 95% CI=16.4%, 19.2%, p<0.001), and Medicaid patients (24.8%, 95% CI=24.0%, 25.5%) compared with commercially insured patients (11.8%, 95% CI=11.1%, 12.5%, p<0.001), were more likely to report social risks. The strongest predictor of housing quality risk was residing in public housing (15.1%, 95% CI=13.8%, 16.6%) compared with those not in public housing (5.6%, 95% CI=5.3%, 5.9%, p<0.001). Housing quality was the most frequently reported risk for children (aged <18 years) and older adults (aged ≥70 years), whereas, for middle-aged respondents (30-69 years), it was food insecurity. CONCLUSIONS: There are important differences in the prevalence of overall and individual social risks by subgroup. These findings should be considered to inform clinical care and social risk screening and interventions.

Medicare underpayment for Diabetes Prevention Program: implications for DPP suppliers.

OBJECTIVES: To examine if Medicare reimbursements for the Diabetes Prevention Program (DPP) cover program costs. STUDY DESIGN: A retrospective modeling study. METHODS: A microcosting approach was used to calculate the costs of delivering DPP in 2016 to more than 300 patients from Montefiore Health System (MHS), a large healthcare system headquartered in Bronx, New York. Attendance and weight loss outcomes were used to estimate Medicare reimbursement. We also modeled revenue assuming that our program outcomes had been similar to those observed in national data. RESULTS: The 1-year cost of delivering DPP to 322 participants in 2016 was $177,976, or $553 per participant. The costliest components of delivery were direct instruction (28% of total cost) and patient outreach, enrollment, and eligibility confirmation (24%). Based on our program outcomes (14.3% lost ≥5% of their initial weight and 50% attended ≥4 sessions), MHS would be reimbursed $34,625 ($108/patient). If outcomes were in line with national CDC reports (eg, better attendance and weight loss outcomes), MHS would have been reimbursed $61,270 ($190/patient). CONCLUSIONS: In a large urban health system serving a diverse population, the costs of delivering DPP far outweighed Medicare reimbursement amounts. Analyzing and documenting the costs associated with delivering the evidence-based DPP may inform prospective suppliers and payers and aid in advocacy for adequate reimbursement.

Lessons from Launching the Diabetes Prevention Program in a Large Integrated Health Care Delivery System: A Case Study.

There is urgent need for health systems to prevent diabetes. To date, few health systems have implemented the evidence-based Diabetes Prevention Program (DPP), and the few that have mostly partnered with community-based organizations to implement the program. Given the recent decision by the Centers for Medicare & Medicaid Services to reimburse for diabetes prevention, there is likely much interest in how such programs can be implemented within large health systems or how community partnerships can be expanded to support DPP implementation. Beginning in 2010, Montefiore Health System (MHS), a large health care system in the Bronx, NY, partnered with the Young Men's Christian Association (YMCA) of Greater New York to deliver the YMCA's DPP. Over 4 years, 1390 referrals to YMCA's DPP were made; 287 participants attended ≥3 classes, and average weight loss was 3.4%. Because of increased patient demand and internal capacity, MHS assumed responsibility for DPP implementation in May 2015. Fully integrating the program within the health system took 5-6 months, including configuring electronic health record templates/reports, hiring a coordinator, and creating clinical referral workflows/training guides. Billing workflows were designed for risk-based contracts. In the first 11 months of implementation, 1277 referrals were made, and referrals increased over time. Twenty-four class cycles were initiated, and 282 patients began attending classes. Average weight loss among 61 graduates from the Summer/Fall 2015 wave of MDPP classes was 3.8%. Additional opportunities for expansion include training allied health staff, providing patient incentives, increasing master trainer capacity, offering DPP to employees, and securing reimbursement.

Quality measure performance in small practices before and after electronic health record adoption.

INTRODUCTION: To date, little research has been published on the impact that the transition from paper-based record keeping to the use of electronic health records (EHR) has on performance on clinical quality measures. This study examines whether small, independent medical practices improved in their performance on nine clinical quality measures soon after adopting EHRs. METHODS: Data abstracted by manual review of paper and electronic charts for 6,007 patients across 35 small, primary care practices were used to calculate rates of nine clinical quality measures two years before and up to two years after EHR adoption. RESULTS: For seven measures, population-level performance rates did not change before EHR adoption. Rates of antithrombotic therapy and smoking status recorded increased soon after EHR adoption; increases in blood pressure control occurred later. Rates of hemoglobin A1c testing, BMI recorded, and cholesterol testing decreased before rebounding; smoking cessation intervention, hemoglobin A1c control and cholesterol control did not significantly change. DISCUSSION: The effect of EHR adoption on performance on clinical quality measures is mixed. To improve performance, practices may need to develop new workflows and adapt to different documentation methods after EHR adoption. CONCLUSIONS: In the short term, EHRs may facilitate documentation of information needed for improving the delivery of clinical preventive services. Policies and incentive programs intended to drive improvement should include in their timelines consideration of the complexity of clinical tasks and documentation needed to capture performance on measures when developing timelines, and should also include assistance with workflow redesign to fully integrate EHRs into medical practice.

Factors related to clinical quality improvement for small practices using an EHR.

OBJECTIVE: To analyze the impact of three primary care practice transformation program models on performance: Meaningful Use (MU), Patient-Centered Medical Home (PCMH), and a pay-for-performance program (eHearts). DATA SOURCES/STUDY SETTING: Data for seven quality measures (QM) were retrospectively collected from 192 small primary care practices between October 2009 and October 2012; practice demographics and program participation status were extracted from in-house data. STUDY DESIGN: Bivariate analyses were conducted to measure the impact of individual programs, and a Generalized Estimating Equation model was built to test the impact of each program alongside the others. DATA COLLECTION/EXTRACTION METHODS: Monthly data were extracted via a structured query data network and were compared to program participation status, adjusting for variables including practice size and patient volume. Seven QMs were analyzed related to smoking prevention, blood pressure control, BMI, diabetes, and antithrombotic therapy. PRINCIPAL FINDINGS: In bivariate analysis, MU practices tended to perform better on process measures, PCMH practices on more complex process measures, and eHearts practices on measures for which they were incentivized; in multivariate analysis, PCMH recognition was associated with better performance on more QMs than any other program. CONCLUSIONS: Results suggest each of the programs can positively impact performance. In our data, PCMH appears to have the most positive impact.

Patient-centered medical home and quality measurement in small practices.

OBJECTIVES: To assess performance on quality measures among small primary care practices that recently adopted an electronic health record (EHR), and how performance differs between practices that have achieved patient-centered medical home (PCMH) recognition and those that have not. STUDY DESIGN: Retrospective cohort study. METHODS: Comparison of practice characteristics and performance on quality measures across 150 independent practices from 2009 to 2011 by recognition status for Physician Practice Connections-PCMH. RESULTS: PCMH-recognized practices performed significantly better than nonrecognized practices on 5 out of 7 clinical quality measures at baseline, and the differences were maintained over the 2-year study period. Both groups improved on all clinical quality measures. Though the magnitude of differences was small, PCMHrecognized practices had a higher number of patients diagnosed with hypertension and proportionally more black patients. A significant difference in PCMH-recognized practices is that they received, on average, 4 additional quality improvement visits compared with nonrecognized practices. CONCLUSIONS: Among small practices that have adopted EHRs, practices with PCMH recognition consistently outperformed practices without recognition on most clinical quality measures. With adequate assistance, small, resource-strapped practices can continue to have higher performance on clinical quality measures.

Health information systems in small practices. Improving the delivery of clinical preventive services.

BACKGROUND: Despite strong evidence that clinical preventive services (CPS) reduce morbidity and mortality, CPS performance has not improved in adult primary care. In addition to implementing electronic health records (EHRs), key factors for improving CPS include providing actionable information at the point of care, technical support staff, and quality-improvement assistance. These resources are not typically available in small practices. PURPOSE: Estimate the impact on CPS delivery after a software upgrade to embed a clinical decision support system and practice-level quality-improvement support services. METHODS: Practices were recruited from the Primary Care Information Project, a citywide initiative assisting practices adopt health information technology. Data were collected in 2009 and 2010, and analyses were conducted in 2010 and 2011. Across two time periods, receipt of CPS was calculated for 56 practices. Period 1 measured CPS delivery 2-37 months following implementation of an EHR. Period 2 measured CPS delivery within the first 6 months after an EHR software upgrade. RESULTS: Substantial increases in the delivery of selected CPS were observed after the EHR software upgrades. Blood pressure control for patients with hypertension increased from 46.0% to 54.8%. Breast cancer screening, recorded BMI, and HbA1c testing for patients with diabetes also increased. More than half of the practices increased their patients' blood pressure control, recorded BMI, breast cancer screening, and HbA1c screening by ≥5 percentage points. CONCLUSIONS: Delivery of CPS can increase in small primary care practices that implement an EHR that includes comprehensive quality-improvement support.