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Vital signs

Biosurveillance systems commonly use emergency department (ED) patient chief complaint data (CC) for surveillance of influenza-like illness (ILI). Daily volumes are tracked using a computerized patient CC classifier for fever (CC Fever) to identify febrile patients. Limitations in this method have led to efforts to identify other sources of ED data. At many EDs the triage nurse measures the patient’s temperature on arrival and records it in the electronic medical record. This makes it possible to directly identify patients who meet the CDC temperature criteria for ILI: temperature greater than 100 degrees F (T>100F).

Objective

To evaluate whether a classifier based on temperature >100F would perform similarly to CC Fever and might identify additional patients.

Referenced File
Submitted by hparton on

Current influenza-like illness (ILI) monitoring in Idaho is based on syndromic surveillance using laboratory data, combined with periodic person-to-person reports collected by Idaho state workers. This system relies on voluntary reporting.

Electronic medical records offer a method of obtaining data in an automated fashion. The Computerized Patient Record System (CPRS) captures real-time visit information, vital signs, ICD-9, pharmacy, and lab data. The electronic medical record surveillance has been utilized for syndromic surveillance on a regional level. Funds supporting expansion of electronic medical records offer increased ability for use in biosurveillance. The addition of temporo-spatial modeling may improve identification of clusters of cases. This abstract reviews our efforts to develop a real-time system of identifying ILI in Idaho using Veterans Administration data and temporo-spatial techniques.

 

Objective

The objective of this study is to describe initial efforts to establish a real-time syndromic surveillance of ILI in Idaho, using data from the Veterans Administration electronic medical record (CPRS).

Referenced File
Submitted by hparton on

The status of each Intensive Care Unit (ICU) patient is routinely monitored and a number of vital signs are recorded at sub-second frequencies which results in large amounts of data. We propose an approach to transform this stream of raw vital measurements into a sparse sequence of discrete events. Each such event represents significant departure of an observed vital sequence from the null distribution learned from reference data. Any substantial departure may be indicative of an upcoming adverse health episode. Our method searches the space of such events for correlations with near-future changes in health status. Automatically extracted events with significant correlations can be used to predict impending undesirable changes in the patient's health. The ultimate goal is to equip ICU physicians with a surveillance tool that will issue probabilistic alerts of upcoming patient status escalations in sufficient advance to take preventative actions before undesirable conditions actually set in.

 

Objective

To present a statistical data mining approach designed to: 1. Identify change points in vital signs which may be indicative of impending critical health events in ICU patients and 2. Identify utility of these change points in predicting the critical events.

Referenced File
Submitted by elamb on

Clinician reporting of notifiable diseases has historically been slow, labor intensive, and incomplete. Manual and electronic laboratory reporting (ELR) systems have increased the timeliness, efficiency, and completeness of notifiable disease reporting but cannot provide full demographic information about patients, integrate an array of pertinent lab tests to yield a diagnosis, describe patient signs and symptoms, pregnancy status, treatment rendered, or differentiate a new diagnosis or from follow-up of a known old diagnosis. Electronic medical record (EMR) systems are a promising resource to combine the timeliness and completeness of ELR systems with the clinical perspective of clinician initiated reporting. We describe an operational system that detects and reports patients with notifiable diseases to the state health department using EMR data.

 

Objective

To leverage EMR systems to improve the timeliness, completeness, and clinical detail of notifiable disease reporting.

Referenced File
Submitted by elamb on

Traditionally Emergency Department syndromic surveillance methods have relied on ICD-9 codes and chief complaints. The implementation of electronic medical record keeping has made much more information available than can potentially be used for surveillance. For example, information such as vital signs, review of systems and physical exam data are being stored discreetly. These data have the potential to detect specific diseases or outbreaks in a community earlier that the traditionally used ICD-9 and chief complaint.

 

Objective

This paper describes the integration of novel data sets from an Emergency Department Electronic Medical Record into a syndromic surveillance application.

Referenced File
Submitted by elamb on
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