Chronic Disease

COPD is a prevalent chronic disease among older adults; exacerbations often result in ED visits and subsequent hospital admissions. A portion of such patients return to the ED within a few days or weeks. In this study, we investigated patterns of hospital admissions and short-term return visits resulting from COPD-related ED visits.

Objective

To investigate hospital admissions and short-term return visits re- sulting from chronic obstructive pulmonary disease (COPD)-related emergency department (ED) visits. 

Syndromic surveillance is one of the meaningful use public health menu set objectives for eligible professionals. The value of this data for syndromic surveillance as an adjunct to the more widely adopted emergency department registrations has not been studied extensively. It may be that it would improve the sensitivity or timeliness of detecting certain communicable disease events, or it may just contain signals comparable to what is available via other syndromic surveillance data streams. The value of making the effort to collect this data is considered contingent on the answer to that question.

Public health is concerned with more than just communicable diseases, however. Chronic diseases and their underlying causes are also a significant public health concern. Obesity alone is estimated to be responsible for 2.5% of the global disease burden, and represents a higher fraction in many developed nations. Since chronic diseases are not associated with singular events of brief duration, they are difficult to track with traditional surveillance methods. They are also not typically managed via emergency departments, so syndromic surveillance does not capture them well either.

Chronic diseases are often treated by physicians at ambulatory practices. Thus data from eligible professionals may provide a means for monitoring chronic diseases, or metrics associated with chronic diseases, that would not otherwise be as feasible. As a proof of concept, this study seeks to determine if body mass index (BMI), the standard measure of obesity, can be obtained from ambulatory syndromic surveillance messages.

Objective

To demonstrate the utility of ambulatory syndromic surveillance data to public health domains beyond communicable diseases

Clinical data captured in electronic health records (EHR) for patient health care could be used for chronic disease surveillance, helping to inform and prioritize interventions at a state or community level. While there has been significant progress in the collection of clinical information such as immunizations for public health purposes, greater attention could be paid to the collection of data on chronic illness. Obesity is a chronic disease that affects over a third of the US adult population1 , making it an important public health concern. Both HL7 v.2.5.12 and Clinical Document Architecture (CDA) messages3 can be used to facilitate the collection of HW EHR data. These standards include anthropometric and demographic information along with the option to transmit behavioral, continuity of care, community resource identification and care plan information. We worked with vendors participating in the Integrating the Healthcare Enterprise initiative (IHE) in developing, testing and showcasing scenarios to facilitate system development, increase the visibility of HW standards and demonstrate potential usages of obesity-related information.

Objective

To demonstrate the feasibility of using healthy weight (HW) IT standards in public health surveillance through the collection and visualization of patient height, weight and behavioral data.

In 2012, half of all adults in the US had one or more chronic health conditions; at least 25% had two or more chronic health conditions. Seven of the top ten causes of death in 2010 were chronic diseases; two of the seven chronic diseases, heart disease and cancer, account almost for over 50% of all deaths. Chronic disease is one of the most costly contributors in healthcare expenditures; once diagnosed many patients must be followed for a lifetime. In lower-income countries chronic disease is now the biggest contributor to mortality. Socioeconomic inequalities are a major driver of the chronic disease epidemic. Chronic disease in the US, such as cancer, heart disease, renal end stage disease and diabetes are tracked in national datasets but are not linked. Chronic diseases share many risk factors, major risk factors, e.g. tobacco, diet, alcohol, and physical inactivity are already known, their interactions with comorbidities are important and clinical practice indicates that the chronic disease epidemic may be addressed more effectively using a holistic approach. However, this approach has not yet been implemented in disease surveillance activities as data collection is still disease specific. Data collection is still one disease at a time, without connecting our disease surveillance efforts to get better, more complete and encompassing data. Health inequities result in lower quality of healthcare, worse healthcare outcomes for minority racial/ ethnic populations and people with low socioeconomic status, increased direct and indirect healthcare costs, and decreased productivity.

Objective

Utilize existing data sets and data sources to address health equity and improve the management of chronic disease

The widespread adoption of Electronic Health Records and the formation of Health Information Exchanges has opened up new possibilities for public health monitoring. Since 2009, The New York City (NYC) Department of Health and Mental Hygiene (DOHMH) has been developing two public health surveillance systems for chronic diseases. The first is the NYC Macroscope, which is built on a distributed query network (the Hub) of 740 New York City ambulatory practices all using proprietary software from one EHR vendor (eClinicalWorks). The second model, Query Health, still in its initial phase, accesses data collected by Healthix, the largest NYC HIE. This study compares these two models for potential disease surveillance and public health application.

Objective

To compare two clinical surveillance systems in development in New York City, one built on a distributed query network of electronic health records (EHRs) and the other accessing data from a Health Information Exchange (HIE).