Jesse Singer

The Hub Population Health System: distributed ad hoc queries and alerts

The Hub Population Health System enables the creation and distribution of queries for aggregate count information, clinical decision support alerts at the point-of-care for patients who meet specified conditions, and secure messages sent directly to provider electronic health record (EHR) inboxes. Using a metronidazole medication recall, the New York City Department of Health was able to determine the number of affected patients and message providers, and distribute an alert to participating practices. As of September 2011, the system is live in 400 practices and within a year will have over 532 practices with 2500 providers, representing over 2.5 million New Yorkers. The Hub can help public health experts to evaluate population health and quality improvement activities throughout the ambulatory care network. Multiple EHR vendors are building these features in partnership with the departments regional extension center in anticipation of new meaningful use requirements.

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.

Assessing Capacity and Disease Burden in a Virtual Network of New York City Primary Care Providers Following Hurricane Sandy

Urban contexts introduce unique challenges that must be addressed to ensure that areas of high population density can function when disasters occur. The ability to generate useful data to guide decision-making is critical in this context. Widespread adoption of electronic health record (EHR) systems in recent years has created electronic data sources and networks that may play an important role in public health surveillance efforts, including in post-disaster situations. The Primary Care Information Project (PCIP) at the New York City Department of Health and Mental Hygiene has partnered with local clinicians to establish an electronic data system, and this network provides infrastructure to support primary care surveillance activities in New York City. After Hurricane Sandy, PCIP generated several sets of data to contribute to the city’s efforts to assess the impact of the storm, including daily connectivity data to establish practice operations, data to examine patterns of primary care utilization in severely affected and less affected areas, and data on the frequency of respiratory infection diagnosis in the primary care setting. Daily patient visit data from three heavily affected neighborhoods showed the health department where primary care capacity was most affected in the weeks following Sandy. Overall transmission data showed that practices in less affected areas were quicker to return to normal reporting patterns, while those in more affected areas did not resume normal data transmissions for a few months. Rates of bronchitis increased after Sandy compared to the two prior years; while this was most likely attributable to a more severe flu season, it demonstrates the capacity of primary care networks to pick up on these types of post-emergency trends. Hurricane Sandy was the first disaster situation where PCIP was asked to assess public health impact, generating information that could contribute to aid and recovery efforts. This experience allowed us to explore the strengths and weaknesses of ambulatory EHR data in post-disaster settings. Data from ambulatory EHR networks can augment existing surveillance streams by providing sentinel population snapshots on clinically available indicators in near real time.

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.

Applied Use of Composite Quality Measures for EHR-enabled Practices

Introduction: The Primary Care Information Project (PCIP) of the New York City Department of Health and Mental Hygiene has been assisting providers to implement health information technology such as electronic health records (EHRs) since its founding in 2005. Currently, all practices affiliated with PCIP are offered technical support services in order to improve the use of the EHR. We studied the performance of clinical practices on EHR-derived Composite Quality Measures (CQMs) over time. Because specific EHR functionalities are important to calculating the quality measures, we hypothesize that performance on each of the CQMs will differ according to the EHR functionalities, and that this can inform the process of developing targeted technical assistance for the practices. Methods: We created four CQMs: (1) Screening, (2) Assessment, (3) Control-BP, and (4) Control-Other. Using data from 93 practices, we identified three tertiles of CQM performance (premier, average, and low tiers) for each measure. A scatterplot of CQMs in 2010 versus 2011 was used to examine the individual movement of practices by tier. A dependent t-test compared the change in mean CQMs, and a chi-square test examined the association between the score and performance tier changes. Results: Over a one-year period, low tier practices demonstrated the highest gains, average tier practices had modest gains, and premier tier practices had gains in some measures, but losses in others. On the Screening CQM 70 percent of practices remained within the same tier, with 60 percent on Assessment, 52 percent on Control-BP, and 38 percent on Control-Other; the Control-Other group showed the greatest improvement. Discussion: By considering EHR functionalities associated with each of the four CQMs, we suggest that technical assistance can be better targeted to low-tier performing practices. In addition, there is still the potential for improvement over time at practices more familiar with key functionalities.

Effect of Physician Participation in a Multi-element Health Information and Data Exchange Program on Chronic Illness Medication Adherence.

Background: The Primary Care Information Project (PCIP) includes a network of more than 10,000 physicians across New York City focusing on improving the quality of patient care through the use of health information technology and data exchange. Methods: We assessed adherence, defined as the percentage with a medication possession ratio (MPR) ≥80%, across 2 time periods for union members whose primary care providers participated in the PCIP compared with those whose providers did not participate. Using prescription claims data from 2008 and 2011, the MPR was calculated for disease-specific categories of drugs among patients with diabetes, hypertension, and both conditions. Results: Greater improvements in the number of adherent members were observed for the PCIP patients with diabetes who were taking diabetes-specific medications (odds ratio [OR], 2.03; 95% confidence interval [CI], 1.08–3.83 for PCIP, versus OR, 1.14; 95% CI, 0.81–1.60 for non-PCIP) and patients with diabetes who are taking lipid-controlling medications (OR, 1.64; 95% CI, 0.73–3.65 for PCIP versus OR, 0.85; 95% CI, 0.55–1.32 for non-PCIP). However, the magnitude and significance of these associations were diminished when practices providing reduced prescription co-pays were excluded from the analyses. Conclusion: Access to primary care providers participating in a public health initiative was associated with some improvement in medication adherence. However, reducing prescription co-pays may be a stronger factor for higher medication adherence among union members.

An All-Payer Risk Model for Super-Utilization in a Large Safety Net System

Identifying patients at high risk for super-utilization of inpatient and emergency services—and proactively managing their care—are key strategies for healthcare systems aiming to improve population health and control costs. Traditional claims-based risk scores are inadequate for uninsured patients and patients with insurance churn, and many safety net systems do not have an electronic health record (EHR) capable of advanced analytics. As the largest safety net system in the country, NYC Health + Hospitals serves a high-need population, including thousands of patients with multiple, interlinked medical, behavioral health, and social issues. More than half of the system’s patients had an emergency room (ER) visit in the past year. Seventeen percent had two or more visits, and 250 patients averaged at least a day a week in one of our emergency rooms. NYC Health + Hospitals also provides half of all uninsured emergency and inpatient care for New Yorkers, including more than 80% of uninsured non-emergency services. To be successful, risk prediction strategies must encompass NYC Health + Hospitals’ entire patient population.

Response to “Impact of a national collaborative care initiative for patients with depression and diabetes or cardiovascular disease” ☆

In the recent publication by Rossom et al., the authors demonstrated that collaborative care programs for depression and chronic disease can be established outside of clinical trials in a diverse set of health care systems and depression remission can be achieved [1]. However, their population was primarily white (69%) patients on Medicare (48%) or commercial insurance (28%). The literature suggests the prevalence of depression is higher amongMedicaid recipients (20%) than the general public (13%) [2,3], while minority populations are less likely to receive necessary mental health care [4,5]. Incorporating these marginalized populations would strengthen the evidence provided by Rossum et al. As the countrys largest safety-net health care system, NYC Health + Hospitals successfully implemented a collaborative care program outside of a clinical trial setting for a Medicaid (65%) and uninsured (25%) population in which English is not the primary language (~60%). Under New York State Hospital-Medical Home Demonstration Program [6], we established a collaborative care for depression program in 2014 based on the IMPACT model [7]. Our program provides care for patients with moderate to severe depression, or depression and a co-occurring chronic condition, in primary care clinics in 11 hospitals and 6 community health centers [8]. We implemented universal PHQ9 depression screening at every primary care visit as a part of the vital signs, and referred eligible patients who screened positive to our program. Using a patient registry, we produce patient lists and data tools to help our 17 sites prioritize outreach, track progress and design treatment workflows. We continuously monitor clinical improvement (PHQ9 b 10 or 50% reduction from baseline for individuals enrolled ≥70 days; target 50%), and evaluate how we care for patients that do not improve (Fig. 1). Among patients enrolled ≥70 days that do not improve, we help sites evaluate their psychiatric consultation rate (target 75%), and change in treatment rate (target 75%). We were able to exceed 2 of 3 targets by sharing data with site teams in 2015 and a 2016 system-wide depression performance improvement project. However, we found that sustaining such progress can be challenging, especially in a resource poor environment. In the face of staffing shortages and competing performance improvement priorities, our institutionwide improvement rate hovered around our target 50%. However, we do see variations across the sites. For example, in Q4 2016 our improvement rates ranged from 13% to 73%. Overall, sufficient and engaged staffing is a key to program successes at the institution and site levels.