ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2008
Electronic medical record (EMR) utilization for public health surveillance.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Identification of Dyslipidemic Patients Attending Primary Care Clinics Using Electronic Medical Record (EMR) Data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) Database.Journal of medical systems · 2017Article
- Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.PloS one · 2014Article
- White Paper Report of the 2010 RAD-AID Conference on International Radiology for Developing Countries: identifying sustainable strategies for imaging services in the developing world.Journal of the American College of Radiology : JACR · 2011Article
- Data-driven approach for creating synthetic electronic medical records.BMC medical informatics and decision making · 2010Article
- Construction and validation of synthetic electronic medical records.Online journal of public health informatics · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPublic 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.
resultsSensitivity 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%.
conclusionOur preliminary assessment of ISSEM performance demonstrated 93.5% sensitivity and 77.3% specificity across all age groups.
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Identifiers
18998983PMC2656004What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.