Evidence map›Paper›PMID 22138127›Full record

ArticleJournal of medical Internet research2011

Beyond readability: investigating coherence of clinical text for consumers.

Catherine Arnott Smith, Scott Hetzel, Prudence Dalrymple, Alla Keselman

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Assessing the readability of ClinicalTrials.gov.Journal of the American Medical Informatics Association : JAMIA · 2016
    Article
  12. Article
  13. 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.

Catherine Arnott SmithSchool of Library and Information Studies, University of Wisconsin-Madison, Madison, WI 53706, United States. casmith24@wisc.edu
Scott Hetzel
Prudence Dalrymple
Alla Keselman

Funding

Improving Health Outcomes through Computer Generation of Reader-Appropriate TextsR01DK075837 · NIDDK · UNIVERSITY OF UTAH · PI ZENG, QING · 2007 to 2009
$1.4M
Intramural NIH HHSNIDDK NIH HHS 7R01DK075837NIDDK NIH HHS R01 DK075837
6 · The paper itself

Abstract

backgroundA basic tenet of consumer health informatics is that understandable health resources empower the public. Text comprehension holds great promise for helping to characterize consumer problems in understanding health texts. The need for efficient ways to assess consumer-oriented health texts and the availability of computationally supported tools led us to explore the effect of various text characteristics on readers' understanding of health texts, as well as to develop novel approaches to assessing these characteristics.

objectiveThe goal of this study was to compare the impact of two different approaches to enhancing readability, and three interventions, on individuals' comprehension of short, complex passages of health text.

methodsParticipants were 80 university staff, faculty, or students. Each participant was asked to "retell" the content of two health texts: one a clinical trial in the domain of diabetes mellitus, and the other typical Visit Notes. These texts were transformed for the intervention arms of the study. Two interventions provided terminology support via (1) standard dictionary or (2) contextualized vocabulary definitions. The third intervention provided coherence improvement. We assessed participants' comprehension of the clinical texts through propositional analysis, an open-ended questionnaire, and analysis of the number of errors made.

resultsFor the clinical trial text, the effect of text condition was not significant in any of the comparisons, suggesting no differences in recall, despite the varying levels of support (P=.84). For the Visit Note, however, the difference in the median total propositions recalled between the Coherent and the (Original+Dictionary) conditions was significant (P=.04). This suggests that participants in the Coherent condition recalled more of the original Visit Notes content than did participants in the Original and the Dictionary conditions combined. However, no difference was seen between (Original+Dictionary) and Vocabulary (P=.36) nor Coherent and Vocabulary (P=.62). No statistically significant effect of any document transformation was found either in the open-ended questionnaire (clinical trial: P=.86, Visit Note: P=.20) or in the error rate (clinical trial: P=.47, Visit Note: P=.25). However, post hoc power analysis suggested that increasing the sample size by approximately 6 participants per condition would result in a significant difference for the Visit Note, but not for the clinical trial text.

conclusionsStatistically, the results of this study attest that improving coherence has a small effect on consumer comprehension of clinical text, but the task is extremely labor intensive and not scalable. Further research is needed using texts from more diverse clinical domains and more heterogeneous participants, including actual patients. Since comprehensibility of clinical text appears difficult to automate, informatics support tools may most productively support the health care professionals tasked with making clinical information understandable to patients.

Indexed as

Consumer Health InformationAdultAgedClinical Trials as TopicCohort StudiesComprehensionDiabetes MellitusFemaleHumansMaleMiddle AgedOffice VisitsSense of CoherenceSurveys and QuestionnairesTerminology as Topic

Identifiers

PMID22138127
PMCPMC3278090

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.