Assessing the readability of ClinicalTrials.gov.
OBJECTIVE
ClinicalTrials.gov serves critical functions of disseminating trial information to the public and helping the trials recruit participants. This study assessed the readability of trial descriptions at ClinicalTrials.gov using multiple quantitative measures.


METHODS
The analysis included all 165,988 trials registered at ClinicalTrials.gov as of April 30, 2014. To obtain benchmarks, the authors also analyzed 2 other medical corpora: (1) all 955 Health Topics articles from MedlinePlus and (2) a random sample of 100,000 clinician notes retrieved from an electronic health records system intended for conveying internal communication among medical professionals. The authors characterized each of the corpora using 4 surface metrics, and then applied 5 different scoring algorithms to assess their readability. The authors hypothesized that clinician notes would be most difficult to read, followed by trial descriptions and MedlinePlus Health Topics articles.


RESULTS
Trial descriptions have the longest average sentence length (26.1 words) across all corpora; 65% of their words used are not covered by a basic medical English dictionary. In comparison, average sentence length of MedlinePlus Health Topics articles is 61% shorter, vocabulary size is 95% smaller, and dictionary coverage is 46% higher. All 5 scoring algorithms consistently rated CliniclTrials.gov trial descriptions the most difficult corpus to read, even harder than clinician notes. On average, it requires 18 years of education to properly understand these trial descriptions according to the results generated by the readability assessment algorithms.


CONCLUSIONS
Trial descriptions at CliniclTrials.gov are extremely difficult to read. Significant work is warranted to improve their readability in order to achieve CliniclTrials.gov's goal of facilitating information dissemination and subject recruitment.
School of Information, University of Michigan, Ann Arbor, MI, USA.
Danny T Y
DT
Wu
School of Information, University of Michigan, Ann Arbor, MI, USA Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA.
David A
DA
Hanauer
School of Information, University of Michigan, Ann Arbor, MI, USA Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
Qiaozhu
Q
Mei
School of Nursing, University of Michigan, Ann Arbor, MI, USA Center for Health Communication Research, University of Michigan, Ann Arbor, MI, USA.
Patricia M
PM
Clark
Center for Health Communication Research, University of Michigan, Ann Arbor, MI, USA Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Lawrence C
LC
An
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
Joshua
J
Proulx
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
Qing T
QT
Zeng
School of Information, University of Michigan, Ann Arbor, MI, USA.
V G Vinod
VG
Vydiswaran
School of Information, University of Michigan, Ann Arbor, MI, USA Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
Kevyn
K
Collins-Thompson
School of Information, University of Michigan, Ann Arbor, MI, USA School of Public Health Department of Health Management and Policy, University of Michigan, Ann Arbor, MI, USA kzheng@umich.edu.
Kai
K
Zheng
D000465:Algorithms; D000704:Analysis of Variance; D002986:Clinical Trials as Topic; D032882:Comprehension; D054626:Consumer Health Information; D016208:Databases, Factual; D049293:MedlinePlus; D009626:Terminology as Topic; D014825:Vocabulary
CliniclTrials.gov; clinical trial; comprehension; electronic health records; natural language processing; readability