Evidence map›Paper›PMID 22195093›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2011

Practical challenges in the secondary use of real-world data: the notifiable condition detector.

Mustafa Fidahussein, Jeff Friedlin, Shaun Grannis

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Trial
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mustafa FidahusseinRegenstrief Institute, Inc, and Indiana University School of Medicine, Indianapolis, IN, USA.
Jeff Friedlin
Shaun Grannis

Funding

REGENSTRIEF MEDICAL INFORMATICS RESEARCH FELLOWSHIPST15LM007117 · NLM · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI FINNELL, JOHN T · 1997 to 2011
$5.0M
NLM NIH HHS 5T 15 LM007117-14PHITPO CDC HHS 1P01HK000077-01PHITPO CDC HHS P01 HK000077
6 · The paper itself

Abstract

The interoperability specifications for electronic laboratory reporting specify the use of HL7, LOINC, SNOMED CT and UCUM. We explored the degree to which health care transactions comply with these standards by evaluating laboratory data captured in a health information exchange to support automated detection of public health notifiable diseases. We studied the NCD's ability to detect and report Lead, Influenza and MRSA. We found that due to incomplete LOINC mapping, alternate approaches such as keyword searches within local test names and codes could identify additional potentially reportable messages. We also found that non-adherence to HL7 messaging standards and inconsistently recorded laboratory results require the use of complex systems with complementary NLP techniques to accurately report notifiable conditions. We conclude that the incomplete adoption of and adherence to specified standards poses challenges to deploying processes that utilize real-world data for secondary purposes.

Indexed as

Health Level SevenLogical Observation Identifiers Names and CodesHumansInfluenza, HumanInformation Storage and RetrievalLead PoisoningMedical Records Systems, ComputerizedMethicillin-Resistant Staphylococcus aureusSystematized Nomenclature of MedicineSystems Integration

Identifiers

PMID22195093
PMCPMC3243144

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.