Remle Newton-Dame

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.

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data

Introduction: Electronic health records (EHRs) offer potential for population health surveillance but EHR-based surveillance measures require validation prior to use. We assessed the validity of obesity, smoking, depression, and influenza vaccination indicators from a new EHR surveillance system, the New York City (NYC) Macroscope. This report is the second in a 3-part series describing the development and validation of the NYC Macroscope. The first report describes in detail the infrastructure underlying the NYC Macroscope; design decisions that were made to maximize data quality; characteristics of the population sampled; completeness of data collected; and lessons learned from doing this work. This second report, which addresses concerns related to sampling bias and data quality, describes the methods used to evaluate the validity and robustness of NYC Macroscope prevalence estimates; presents validation results for estimates of obesity, smoking, depression and influenza vaccination; and discusses the implications of our findings for NYC and for other jurisdictions embarking on similar work. The third report applies the same validation methods described in this report to metabolic outcomes, including the prevalence, treatment and control of diabetes, hypertension and hyperlipidemia. Methods: NYC Macroscope prevalence estimates, overall and stratified by sex and age group, were compared to reference survey estimates for adult New Yorkers who reported visiting a doctor in the past year. Agreement was evaluated against 5 a priori criteria. Sensitivity and specificity were assessed by examining individual EHR records in a subsample of 48 survey participants. Results: Among adult New Yorkers in care, the NYC Macroscope prevalence estimate for smoking (15.2%) fell between estimates from NYC HANES (17.7 %) and CHS (14.9%) and met all 5 a priori criteria. The NYC Macroscope obesity prevalence estimate (27.8%) also fell between the NYC HANES (31.3%) and CHS (24.7%) estimates, but met only 3 a priori criteria. Sensitivity and specificity exceeded 0.90 for both the smoking and obesity indicators. The NYC Macroscope estimates of depression and influenza vaccination prevalence were more than 10 percentage points lower than the estimates from either reference survey. While specificity was > 0.90 for both of these indicators, sensitivity was < 0.70. Discussion: Through this work we have demonstrated that EHR data from a convenience sample of providers can produce acceptable estimates of smoking and obesity prevalence among adult New Yorkers in care; gained a better understanding of the challenges involved in estimating depression prevalence from EHRs; and identified areas for additional research regarding estimation of influenza vaccination prevalence. We have also shared lessons learned about how EHR indicators should be constructed and offer methodologic suggestions for validating them. Conclusions: This work adds to a rapidly emerging body of literature about how to define, collect and interpret EHR-based surveillance measures and may help guide other jurisdictions.

Design of the New York City Macroscope: Innovations in Population Health Surveillance Using Electronic Health Records

Introduction: Electronic health records (EHRs) have the potential to offer real-time, inexpensive standardized health data about chronic health conditions. Despite rapid expansion, EHR data evaluations for chronic disease surveillance have been limited. We present design and methods for the New York City (NYC) Macroscope, an EHR-based chronic disease surveillance system. This methods report is the first in a three part series describing the development and validation of the NYC Macroscope. This report describes in detail the infrastructure underlying the NYC Macroscope; indicator definitions; design decisions that were made to maximize data quality; characteristics of the population sampled; completeness of data collected; and lessons learned from doing this work. The second report describes the methods used to evaluate the validity and robustness of NYC Macroscope prevalence estimates; presents validation results for estimates of obesity, smoking, depression and influenza vaccination; and discusses the implications of our findings for NYC and for other jurisdictions embarking on similar work. The third report applies the same validation methods to metabolic outcomes, including the prevalence, treatment and control of diabetes, hypertension and hyperlipidemia. Methods: We designed the NYC Macroscope for comparison to a local “gold standard,” the 2013–14 NYC Health and Nutrition Examination Survey, and the telephonic 2013 Community Health Survey. NYC Macroscope indicators covered prevalence, treatment, and control of diabetes, hypertension, and hyperlipidemia; and prevalence of influenza vaccination, obesity, depression and smoking. Indicators were stratified by age, sex, and neighborhood poverty, and weighted to the in-care NYC population and limited to primary care patients. Indicator queries were distributed to a virtual network of primary care practices; 392 practices and 716,076 adult patients were retained in the final sample. Findings: The NYC Macroscope covered 10% of primary care providers and 15% of all adult patients in NYC in 2013 (8–47% of patients by neighborhood). Data completeness varied by domain from 98% for blood pressure among patients with hypertension to 33% for depression screening. Discussion: Design and validation efforts undertaken by NYC are described here to provide one potential blueprint for leveraging EHRs for population health monitoring. To replicate a model like NYC Macroscope, jurisdictions should establish buy-in; build informatics capacity; use standard, simple case defnitions; establish documentation quality thresholds; restrict to primary care providers; and weight the sample to a target population.

Monitoring Prevalence, Treatment, and Control of Metabolic Conditions in New York City Adults Using 2013 Primary Care Electronic Health Records: A Surveillance Validation Study

Introduction: Electronic health records (EHRs) can potentially extend chronic disease surveillance, but few EHR-based initiatives tracking population-based metrics have been validated for accuracy. We designed a new EHR-based population health surveillance system for New York City (NYC) known as NYC Macroscope. This report is the third in a 3-part series describing the development and validation of that system. The first report describes governance and technical infrastructure underlying the NYC Macroscope. The second report describes validation methods and presents validation results for estimates of obesity, smoking, depression and influenza vaccination. In this third paper we present validation findings for metabolic indicators (hypertension, hyperlipidemia, diabetes). Methods: We compared EHR-based estimates to those from a gold standard surveillance source - the 2013–2014 NYC Health and Nutrition Examination Survey (NYC HANES) - overall and stratified by sex and age group, using the two one-sided test of equivalence and other validation criteria. Results: EHR-based hypertension prevalence estimates were highly concordant with NYC HANES estimates. Diabetes prevalence estimates were highly concordant when measuring diagnosed diabetes but less so when incorporating laboratory results. Hypercholesterolemia prevalence estimates were less concordant overall. Measures to assess treatment and control of the 3 metabolic conditions performed poorly. Discussion: While indicator performance was variable, findings here confirm that a carefully constructed EHR-based surveillance system can generate prevalence estimates comparable to those from gold-standard examination surveys for certain metabolic conditions such as hypertension and diabetes. Conclusions: Standardized EHR metrics have potential utility for surveillance at lower annual costs than surveys, especially as representativeness of contributing clinical practices to EHR-based surveillance systems increases.

Evaluating the 2010 New York State HIV testing law in NYC ambulatory practices using electronic health records.

Introduction:In 2010, the New York State Legislature made it mandatory to offer an HIV test to people aged 13–64 years receiving hospital or primary care services, with limited exceptions. In this study, we used data from New York City practices to evaluate the impact of the law on HIV testing rates in ambulatory care. Methods:We collected quarterly testing data from the electronic health records of 218 practices. We calculated overall and stratified crude testing rates. Using univariate and multivariate generalized estimating equation models, we assessed the odds of testing in the year before the law (baseline) versus the first and second year after the laws implementation (year 1 and year 2). Results:During baseline, the odds of testing did not increase significantly. During year 1, the odds of testing significantly increased by 50% in the univariate model and 200% after adjusting for confounders. During year 2, the odds of testing increased 10%. This was only significant in the univariate model. The crude quarterly testing rate increased from 2.8% to 5.7% from baseline to year 2. Conclusions:Our evaluation showed that after the implementation of the HIV testing law, there was an increase in HIV testing among NYC ambulatory practices. Testing rates remained modest, but considerable improvement was seen in community health centers, in age ranges targeted by the law and in practices that were screening for HIV at baseline. This study suggests that legislation may be effective when used in a comprehensive prevention strategy.

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.

Generalizability of Indicators from the New York City Macroscope Electronic Health Record Surveillance System to Systems Based on Other EHR Platforms

Introduction: The New York City (NYC) Macroscope is an electronic health record (EHR) surveillance system based on a distributed network of primary care records from the Hub Population Health System. In a previous 3-part series published in eGEMS, we reported the validity of health indicators from the NYC Macroscope; however, questions remained regarding their generalizability to other EHR surveillance systems. Methods: We abstracted primary care chart data from more than 20 EHR software systems for 142 participants of the 2013–14 NYC Health and Nutrition Examination Survey who did not contribute data to the NYC Macroscope. We then computed the sensitivity and specificity for indicators, comparing data abstracted from EHRs with survey data. Results: Obesity and diabetes indicators had moderate to high sensitivity (0.81–0.96) and high specificity (0.94–0.98). Smoking status and hypertension indicators had moderate sensitivity (0.78–0.90) and moderate to high specificity (0.88–0.98); sensitivity improved when the sample was restricted to records from providers who attested to Stage 1 Meaningful Use. Hyperlipidemia indicators had moderate sensitivity (≥0.72) and low specificity (≤0.59), with minimal changes when restricting to Stage 1 Meaningful Use. Discussion: Indicators for obesity and diabetes used in the NYC Macroscope can be adapted to other EHR surveillance systems with minimal validation. However, additional validation of smoking status and hypertension indicators is recommended and further development of hyperlipidemia indicators is needed. Conclusion: Our findings suggest that many of the EHR-based surveillance indicators developed and validated for the NYC Macroscope are generalizable for use in other EHR surveillance systems.

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.