Electronic Medical Record (EMR) Utilization for Public Health Surveillance

INTRODUCTION Public health surveillance systems need to be refined. We intend to use a generic approach for early identification of patients with severe influenza-like illness (ILI) by calculating a score that estimates a patients disease-severity. Accordingly, we built the Intelligent Severity Score Estimation Model (ISSEM), structured so that the inference process would reflect experts decision-making logic. Each patients disease-severity score is calculated from numbers of respiratory ICD9 encounters, and laboratory, radiologic, and prescription-therapeutic orders in the EMR. Other ISSEM components include chronic disease evidence, probability of immunodeficiency, and the providers general practice-behavior patterns. RESULTS Sensitivity was determined from 200 randomly selected patients with upper- and lower-respiratory tract ILI; specificity, from 300 randomly selected patients with URI only. For different age groups, ISSEM sensitivity ranged between 90% and 95%; specificity was 72% to 84%. CONCLUSION Our preliminary assessment of ISSEM performance demonstrated 93.5% sensitivity and 77.3% specificity across all age groups.

[1]  M. Fine,et al.  Guidelines for the management of adults with community-acquired pneumonia. Diagnosis, assessment of severity, antimicrobial therapy, and prevention. , 2001, American journal of respiratory and critical care medicine.

[2]  Reducing radiation dose to pediatric patients. , 2005, AJR. American journal of roentgenology.

[3]  Andrew W. Moore,et al.  Rule-based anomaly pattern detection for detecting disease outbreaks , 2002, AAAI/IAAI.

[4]  Michael M. Wagner,et al.  Technical Description of RODS: A Real-time Public Health Surveillance System , 2003, Journal of the American Medical Informatics Association.

[5]  William B. Lober,et al.  Review Paper: Implementing Syndromic Surveillance: A Practical Guide Informed by the Early Experience , 2003, J. Am. Medical Informatics Assoc..

[6]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[7]  Colleen A Bradley,et al.  BioSense: implementation of a National Early Event Detection and Situational Awareness System. , 2005, MMWR supplements.

[8]  Dean F. Sittig,et al.  The emerging science of very early detection of disease outbreaks. , 2001, Journal of public health management and practice : JPHMP.

[9]  Marcello Pagano,et al.  Using temporal context to improve biosurveillance , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Urania G Dafni,et al.  Algorithm for statistical detection of peaks--syndromic surveillance system for the Athens 2004 Olympic Games. , 2004, MMWR supplements.

[11]  Torsten Staab,et al.  The Rapid Syndrome Validation Project (RSVP) , 2001, AMIA.

[12]  Andrew W. Moore,et al.  Algorithms for rapid outbreak detection: a research synthesis , 2005, J. Biomed. Informatics.

[13]  J. Maselli,et al.  Physician Practice Patterns: Chest X-Ray Ordering for the Evaluation of Acute Cough Illness in Adults , 2006, Medical decision making : an international journal of the Society for Medical Decision Making.

[14]  D. Buckeridge,et al.  Systematic Review: Surveillance Systems for Early Detection of Bioterrorism-Related Diseases , 2004, Annals of Internal Medicine.

[15]  William B. Lober,et al.  Roundtable on bioterrorism detection: information system-based surveillance. , 2002, Journal of the American Medical Informatics Association : JAMIA.

[16]  M. Kulldorff,et al.  A Space–Time Permutation Scan Statistic for Disease Outbreak Detection , 2005, PLoS medicine.

[17]  W. Knaus,et al.  APACHE II: a severity of disease classification system. , 1985 .

[18]  Russ P Lopez Disease Surveillance: A Public Health Informatics Approach , 2007 .

[19]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..