Evidence map›Paper›PMID 22195150›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2011

Query log analysis of an electronic health record search engine.

Lei Yang, Qiaozhu Mei, Kai Zheng, David A Hanauer

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 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Trial
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  8. Electronic Health Record (EHR) Abstraction.Perspectives in health information managemen · 2021
    Article
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  18. The future state of clinical data capture and documentation: a report from AMIA's 2011 Policy Meeting.Journal of the American Medical Informatics Association : JAMIA · 2013
    Article
  19. Article
  20. Synonym, topic model and predicate-based query expansion for retrieving clinical documents.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012
    Article
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.

Lei YangSchool of Information, University of Michigan, Ann Arbor, MI, USA.
Qiaozhu Mei
Kai Zheng
David A Hanauer

Funding

MICHIGAN INSTITUTE FOR CLINICAL AND HEALTH RESEARCH (MICHR) (UL1): (BPCA)UL1RR024986 · NCRR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SHANLEY, THOMAS P. · 2007 to 2011
$51.2M
NCRR NIH HHS UL1 RR024986NCRR NIH HHS UL1RR024986NLM NIH HHS HHSN276201000032C
6 · The paper itself

Abstract

We analyzed a longitudinal collection of query logs of a full-text search engine designed to facilitate information retrieval in electronic health records (EHR). The collection, 202,905 queries and 35,928 user sessions recorded over a course of 4 years, represents the information-seeking behavior of 533 medical professionals, including frontline practitioners, coding personnel, patient safety officers, and biomedical researchers for patient data stored in EHR systems. In this paper, we present descriptive statistics of the queries, a categorization of information needs manifested through the queries, as well as temporal patterns of the users' information-seeking behavior. The results suggest that information needs in medical domain are substantially more sophisticated than those that general-purpose web search engines need to accommodate. Therefore, we envision there exists a significant challenge, along with significant opportunities, to provide intelligent query recommendations to facilitate information retrieval in EHR.

Indexed as

Electronic Health RecordsInformation Seeking BehaviorInformation Storage and RetrievalSearch Engine

Identifiers

PMID22195150
PMCPMC3243246

What OpenQuestion holds

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Registered trials

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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.