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Walsh Andrew

Syndromic surveillance achieves timeliness by collecting prediagnostic data, such as emergency department chief complaints, from the start of healthcare interactions. The tradeoff is less precision than from diagnosis data, which takes longer to generate. As the use and sophistication of electronic health information systems increases, additional data that provide an intermediate balance of timeliness and precision are becoming available. Information about the procedures and treatments ordered for a patient can indicate what diagnoses are being considered. Procedure records can also be used to track the use of preventive measures such as vaccines that are also relevant to public health surveillance but not readily captured by typical syndromic data elements. Some procedures such as laboratory tests also provide results which can provide additional specificity about which diagnoses will be considered. If procedure and treatment orders and test results are included in existing syndromic surveillance feeds, additional specificity can be achieved with timeliness comparable to prediagnostic assessments.

Objective: To identify additional data elements in existing syndromic surveillance message feeds that can provide additional insight into public health concerns such as the influenza season.

Submitted by elamb on

Antimicrobial stewardship is crucial to the ongoing viability of existing therapies. To facilitate this stewardship, NHSN allows hospitals to submit data on their antimicrobial usage and receive feedback on how their usage compares to other facilities.1 This feedback can be used by hospital personnel to assess whether their antimicrobial policies are consistent with current best practices. Participation in this program has so far been limited. There are several barriers to participating, including the challenge of mapping local medication information to the NHSN list of antimicrobials, the burden of tabulating the necessary statistics, and the technical requirements of generating appropriate CDA documents for submission. An automated solution that obtained the necessary data from existing HL7 interfaces and generated CDA documents in the correct format could significantly lower some of the barriers to submitting antimicrobial usage information to NHSN.

Objective: To leverage existing healthcare transaction messages to automate the aggregation of antimicrobial usage statistics in a method compatible with submission to the National Healthcare Safety Network (NHSN) Antimicrobial Usage module.

Submitted by elamb on

In early May of 2013, two chemical spills occurred within high schools in Atlantic county. These incidents, occurring within a week of each other, highlighted the need to strengthen statewide syndromic surveillance of illnesses caused by such exposures. In response to these spills, a new 'chemical exposure' classifier was created in EpiCenter, New JerseyÕs syndromic surveillance system, to track future events by monitoring registration chief complaint data taken from emergency department visits. The primary objective behind creation of the new classifier is to provide local epidemiologists with prompt notification once EpiCenter detects an abnormal numbers of chemical exposure cases.

Objective

To describe the development of a new chemical exposure classifier in New Jersey's syndromic surveillance system (EpiCenter).

Submitted by elamb on

A goal of biosurveillance is to identify incidents that require a public health response. The challenge is creating specific definitions of such incidents so they can be detected. In syndromic surveillance, this is accomplished by classifying emergency department chief complaints, nurse triage calls, and other prediagnostic data into categories, and then looking for increases in visits related to those categories. This approach can only find incidents that match those predefined categories. It is well-suited to handle common diseases; data from prior years provides information not only on which symptoms correlate with the disease, but also on how patients report them and how they appear in prediagnostic data streams. For unique or rare events, it is hard to know in advance how they will be described or recorded. Another approach is to look for similarities in the time of the healthcare encounters alone. This method can detect events which are missed by syndrome-oriented surveillance, but healthcare encounters that only have time of occurrence aren't necessarily related. To address this limitation, we propose a set of similarity criteria which incorporates both timing and reason.

Objective

Develop a method for detecting groups of related healthcare encounters without having to specify details of the reasons for those encounters in advance.

Submitted by knowledge_repo… on

On July 11, 2012, New Jersey Department of Health (DOH) Communicable Disease Service (CDS) surveillance staff received email notification of a statewide anomaly in EpiCenter for Paralysis. Two additional anomalies followed within three hours. Since Paralysis Anomalies are uncommon, staff initiated an investigation to determine if there was an outbreak or other event of concern taking place. Also at question was whether receipt of multiple anomalies in such a short time span was statistically or epidemiologically significant.

Objective

To describe the investigation of a statewide anomaly detected by a newly established state syndromic surveillance system and usage of that system.

Submitted by dbedford on

The NJ syndromic surveillance system, EpiCenter, developed an algorithm to quantify HRI visits using chief complaint data. While heat advisories are released by the National Weather Service, an effective HRI algorithm could provide real-time health impact information that could be used to provide supplemental warnings to the public during a prolonged heat wave.

Objective:

The purpose of this evaluation is to characterize the relationship between a patient’s initial hospital emergency room chief complaint potentially related to a heat-related illness (HRI) with final primary and secondary ICD-9 diagnoses.

 

Submitted by Magou on

Timely and accurate syndromic surveillance depends on continuous data feeds from healthcare facilities. Typical outlier detection methodologies in syndromic surveillance compare predictions of counts for an interval to observed event counts, either to detect increases in volume associated with public health incidents or decreases in volume associated with compromised data transmission. Accurate predictions of total facility volume need to account for significant variance associated with the time of day and week; at the extreme are facilities which are only open during limited hours and on select days. Models need to account for the cross-product of all hours and days, creating a significant data burden. Timely detection of outages may require sub-hour aggregation, increasing this burden by increasing the number of intervals for which parameters need to be estimated. Nonparametric models for the probability of message arrival offer an alternative approach to generating predictions. The data requirements are reduced by assuming some time-dependent structure in the data rather than allowing each interval to be independent of all others, allowing for predictions at sub-hour intervals.

Objective:

Characterize the behavior of nonparametric regression models for message arrival probability as outage detection tools.

Submitted by elamb on

Opioid overdoses are a growing cause of mortality in the United States. Medical prescriptions for opioids are a risk factor for overdose. This observation raises concerns that patients may seek multiple opioid prescriptions, possibly increasing their overdose risk. One route for obtaining those prescriptions is visiting the emergency department (ED) for pain-related complaints. Here, two hypotheses related to prescription seeking and overdoses are tested. (1) Overdose patients have a larger number of prior ED visits than matched controls. (2) Overdose patients have distinct patterns of pain-related complaints compared to matched controls.

Objective:

Identifying text features of emergency department visits associated with risk of future drug overdose.

Submitted by elamb on

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

Submitted by teresa.hamby@d… on

The Affordable Care Act (ACA) was promoted with two goals: expanding health insurance coverage and reducing healthcare costs. Expanded coverage is expected to partially reduce costs. Emergency department (ED) visits are costlier than comparable primary care physician visits. If uninsured patients use the local ED more often than insured patients with comparable conditions, insuring them may change usage and lower costs. Some reports in the literature do not fit this model of ED usage. In one study, nonurgent ED visits were mainly the result of patient uncertainty about the severity of their condition. While trained medical personnel distinguished urgent and nonurgent cases after the fact, initial presentations were similar. In Oregon, an expansion of Medicaid increased health insurance coverage; ED usage increased rather than decreased. Thus, the motivating narrative about insurance coverage and ED usage informing the ACA may not be the complete story. Reduction of hospital readmissions is also expected to cut costs under the ACA. Hospital process improvements are expected to realize this reduction. Recently it was reported that up to 60% of hospital readmissions are predicted by patient demographics, raising questions about how much control a hospital has over its readmission rate. This research will examine whether data collected via syndromic surveillance can corroborate these findings.

Objective

To determine if data collected for syndromic surveillance can inform policy questions related to emergency department utilization and inpatient readmission.

 

Submitted by Magou on
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