Evidence map›Paper›PMID 20233389›Full record

ArticleBMC medical informatics and decision making2010

Determinants of frequency and longevity of hospital encounters' data use.

Ricardo J Cruz-Correia, Jeremy C Wyatt, Mario Dinis-Ribeiro, Altamiro Costa-Pereira

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Dynamic prediction of hospital admission with medical claim data.BMC medical informatics and decision making · 2019
    Article
  2. Analysis of the quality of hospital information systems Audit Trails.BMC medical informatics and decision making · 2013
    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.

Ricardo J Cruz-CorreiaDepartment of Biostatistics and Medical Informatics, Faculty of Medicine of University of Porto, Al Prof Hernâni Monteiro, 4200-319 Porto, Portugal. ricardo.jc.correia@gmail.com
Jeremy C Wyatt
Mario Dinis-Ribeiro
Altamiro Costa-Pereira

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe identification of clinically relevant information enables improvement in user interfaces and in data management. However, it is difficult to identify what information is important in daily clinical care, and what is used occasionally. This study aims to determine for how long clinical documents are used in a Hospital Information System (HIS).

methodsThe access logs of 3 years of usage of a HIS were analysed concerning report departmental source, type of hospital encounter, and inpatient encounter ICD-9-CM main diagnosis. Reports median life indicates the median time elapsed between information creation and its usage. The models that better explains report views over time were explored.

resultsThe number of report views in the study period was 656,583. Fifty two percent of the reports viewed by medical doctors in emergency encounters were from previous encounters - 21% at outpatient attendance, 19% in inpatient (wards) and 12% during emergency encounters. In an inpatient setting, 20% of the reports viewed were produced in previous encounters. The median life of information in documents is 1.5 days for emergency, 4.8 days for inpatient and 37.8 days for outpatient encounters. Immune-hemotherapy reports reach their median lives faster (7 days) than clinical pathology (15 days), gastroenterology (80 days) and pathology (118 days). The median life of reports produced in inpatient encounters varied from 36 days for neoplasms as the main diagnosis to 0.7 days for injury and poisoning. The model with the best fit (R2 > 0.9) was the exponential.

conclusionsThe usage of past patient information varied significantly according to patient age, type of information, type of hospital encounter and medical cause (main diagnosis) for the encounter. The exponential model is a good fit to model how the reports are seen over time, so the design of user interfaces and repository management algorithms should take it in consideration.

Indexed as

AdolescentAdultAgedChildFemaleHospital Information SystemsHumansMaleMiddle AgedModels, TheoreticalTime FactorsUser-Computer InterfaceYoung Adult

Identifiers

PMID20233389
PMCPMC2846873

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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.