Evidence map›Paper›PMID 24179266›Full record

ArticlePublic health reports (Washington, D.C. : 1974)

Electronic health information quality challenges and interventions to improve public health surveillance data and practice.

Brian E Dixon, Jason A Siegel, Tanya V Oemig, Shaun J Grannis

Abstract readComparative Study
In one paragraph

Article in Public health reports (Washington, D.C. : 1974). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 4 pooled it
–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

36 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  3. Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023
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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

4 authors.

Brian E DixonIndiana University, School of Informatics and Computing, Indianapolis, IN ; Regenstrief Institute, Indianapolis, IN ; Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service, Center of Excellence on Implementing Evidence-Based Practice, Indianapolis, IN.
Jason A Siegel
Tanya V Oemig
Shaun J Grannis

Funding

PHITPO CDC HHS #5P01HK000077PHITPO CDC HHS P01 HK000077
6 · The paper itself

Abstract

objectiveWe examined completeness, an attribute of data quality, in the context of electronic laboratory reporting (ELR) of notifiable disease information to public health agencies.

methodsWe extracted more than seven million ELR messages from multiple clinical information systems in two states. We calculated and compared the completeness of various data fields within the messages that were identified to be important to public health reporting processes. We compared unaltered, original messages from source systems with similar messages from another state as well as messages enriched by a health information exchange (HIE). Our analysis focused on calculating completeness (i.e., the number of nonmissing values) for fields deemed important for inclusion in notifiable disease case reports.

resultsThe completeness of data fields for laboratory transactions varied across clinical information systems and jurisdictions. Fields identifying the patient and test results were usually complete (97%-100%). Fields containing patient demographics, patient contact information, and provider contact information were suboptimal (6%-89%). Transactions enhanced by the HIE were found to be more complete (increases ranged from 2% to 25%) than the original messages.

conclusionELR data from clinical information systems can be of suboptimal quality. Public health monitoring of data sources and augmentation of ELR message content using HIE services can improve data quality.

Indexed as

Public Health SurveillanceClinical Laboratory Information SystemsDisease NotificationElectronic Health RecordsHumansIndianaResearch DesignWisconsin

Identifiers

PMID24179266
PMCPMC3804098

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.