Evidence map›Paper›PMID 20178586›Full record

ArticleBMC medical informatics and decision making2010

Assessing the accuracy of an inter-institutional automated patient-specific health problem list.

Lise Poissant, Laurel Taylor, Allen Huang, Robyn Tamblyn

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 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. A method for systematic discovery of adverse drug events from clinical notes.Journal of the American Medical Informatics Association : JAMIA · 2015
    Article
  7. Functional evaluation of out-of-the-box text-mining tools for data-mining tasks.Journal of the American Medical Informatics Association : JAMIA · 2015
    Article
  8. Article
  9. Extracting drug indication information from structured product labels using natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2013
    Article
  10. Automated Detection of Systematic Off-label Drug Use in Free Text of Electronic Medical Records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2013
    Article
  11. Analyzing patterns of drug use in clinical notes for patient safety.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2012
    Article
  12. Article
  13. Detecting pregnancy use of non-hormonal category X medications in electronic medical records.Journal of the American Medical Informatics Association : JAMIA · 2011
    Article
  14. 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.

Lise PoissantCentre for Interdisciplinary Research in Rehabilitation of Greater Montreal, Montreal, Qc, Canada. lise.poissant@umontreal.ca
Laurel Taylor
Allen Huang
Robyn Tamblyn

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth problem lists are a key component of electronic health records and are instrumental in the development of decision-support systems that encourage best practices and optimal patient safety. Most health problem lists require initial clinical information to be entered manually and few integrate information across care providers and institutions. This study assesses the accuracy of a novel approach to create an inter-institutional automated health problem list in a computerized medical record (MOXXI) that integrates three sources of information for an individual patient: diagnostic codes from medical services claims from all treating physicians, therapeutic indications from electronic prescriptions, and single-indication drugs.

methodsData for this study were obtained from 121 general practitioners and all medical services provided for 22,248 of their patients. At the opening of a patient's file, all health problems detected through medical service utilization or single-indication drug use were flagged to the physician in the MOXXI system. Each new arising health problem were presented as 'potential' and physicians were prompted to specify if the health problem was valid (Y) or not (N) or if they preferred to reassess its validity at a later time.

resultsA total of 263,527 health problems, representing 891 unique problems, were identified for the group of 22,248 patients. Medical services claims contributed to the majority of problems identified (77%), followed by therapeutic indications from electronic prescriptions (14%), and single-indication drugs (9%). Physicians actively chose to assess 41.7% (n = 106,950) of health problems. Overall, 73% of the problems assessed were considered valid; 42% originated from medical service diagnostic codes, 11% from single indication drugs, and 47% from prescription indications. Twelve percent of problems identified through other treating physicians were considered valid compared to 28% identified through study physician claims.

conclusionAutomation of an inter-institutional problem list added over half of all validated problems to the health problem list of which 12% were generated by conditions treated by other physicians. Automating the integration of existing information sources provides timely access to accurate and relevant health problem information. It may also accelerate the uptake and use of electronic medical record systems.

Indexed as

Medical Records, Problem-OrientedMedical Records Systems, ComputerizedElectronic Health RecordsForms and Records ControlHumansInterinstitutional RelationsQuebec

Identifiers

PMID20178586
PMCPMC2837859

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LicenceCC BY
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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.