Literature DB >> 23920636

Applying multiple methods to assess the readability of a large corpus of medical documents.

Danny T Y Wu1, David A Hanauer, Qiaozhu Mei, Patricia M Clark, Lawrence C An, Jianbo Lei, Joshua Proulx, Qing Zeng-Treitler, Kai Zheng.   

Abstract

Medical documents provided to patients at the end of an episode of care, such as discharge summaries and referral letters, serve as an important vehicle to convey critical information to patients and families. Increasingly, healthcare institutions are also experimenting with granting patients direct electronic access to other types of clinical narratives that are not typically shared unless explicitly requested, such as progress notes. While these efforts have great potential to improve information transparency, their value can be severely diminished if patients are unable to read and thus unable to properly interpret the medical documents shared to them. In this study, we approached the problem by contrasting the 'readability' of two types of medical documents: referral letters vs. other genres of narrative clinician notes not explicitly intended for direct viewing by patients. To establish a baseline for comparison, we also computed readability scores of MedlinePlus articles - exemplars of fine patient education materials carefully crafted for lay audiences. We quantified document readability using four different measures. Differences in the results obtained through these measures are also discussed.

Entities:  

Mesh:

Year:  2013        PMID: 23920636      PMCID: PMC5369652     

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  7 in total

1.  Voice-dictated versus typed-in clinician notes: linguistic properties and the potential implications on natural language processing.

Authors:  Kai Zheng; Qiaozhu Mei; Lei Yang; Frank J Manion; Ulysses J Balis; David A Hanauer
Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

2.  Open notes: doctors and patients signing on.

Authors:  Tom Delbanco; Jan Walker; Jonathan D Darer; Joann G Elmore; Henry J Feldman; Suzanne G Leveille; James D Ralston; Stephen E Ross; Elisabeth Vodicka; Valerie D Weber
Journal:  Ann Intern Med       Date:  2010-07-20       Impact factor: 25.391

3.  Text characteristics of clinical reports and their implications for the readability of personal health records.

Authors:  Qing Zeng-Treitler; Hyeoneui Kim; Sergey Goryachev; Alla Keselman; Laura Slaughter; Catherine Arnott Smith
Journal:  Stud Health Technol Inform       Date:  2007

4.  Beyond surface characteristics: a new health text-specific readability measurement.

Authors:  Hyeoneui Kim; Sergey Goryachev; Graciela Rosemblat; Allen Browne; Alla Keselman; Qing Zeng-Treitler
Journal:  AMIA Annu Symp Proc       Date:  2007-10-11

5.  Inviting patients to read their doctors' notes: a quasi-experimental study and a look ahead.

Authors:  Tom Delbanco; Jan Walker; Sigall K Bell; Jonathan D Darer; Joann G Elmore; Nadine Farag; Henry J Feldman; Roanne Mejilla; Long Ngo; James D Ralston; Stephen E Ross; Neha Trivedi; Elisabeth Vodicka; Suzanne G Leveille
Journal:  Ann Intern Med       Date:  2012-10-02       Impact factor: 25.391

6.  The missing link: bridging the patient-provider health information gap.

Authors:  Paul C Tang; David Lansky
Journal:  Health Aff (Millwood)       Date:  2005 Sep-Oct       Impact factor: 6.301

7.  A framework for the evaluation of patient information leaflets.

Authors:  Mark Garner; Zhenye Ning; Jill Francis
Journal:  Health Expect       Date:  2011-02-18       Impact factor: 3.377

  7 in total
  6 in total

1.  Assessing the readability of ClinicalTrials.gov.

Authors:  Danny T Y Wu; David A Hanauer; Qiaozhu Mei; Patricia M Clark; Lawrence C An; Joshua Proulx; Qing T Zeng; V G Vinod Vydiswaran; Kevyn Collins-Thompson; Kai Zheng
Journal:  J Am Med Inform Assoc       Date:  2015-08-11       Impact factor: 4.497

2.  Simplified Readability Metric Drives Improvement of Radiology Reports: an Experiment on Ultrasound Reports at a Pediatric Hospital.

Authors:  Wei Chen; Claire Durkin; Yungui Huang; Brent Adler; Steve Rust; Simon Lin
Journal:  J Digit Imaging       Date:  2017-12       Impact factor: 4.056

3.  Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

Authors:  Renu Balyan; Scott A Crossley; William Brown; Andrew J Karter; Danielle S McNamara; Jennifer Y Liu; Courtney R Lyles; Dean Schillinger
Journal:  PLoS One       Date:  2019-02-22       Impact factor: 3.240

4.  Predicting the readability of physicians' secure messages to improve health communication using novel linguistic features: Findings from the ECLIPPSE study.

Authors:  Scott A Crossley; Renu Balyan; Jennifer Liu; Andrew J Karter; Danielle McNamara; Dean Schillinger
Journal:  J Commun Healthc       Date:  2020-09-24

5.  Readability of written medicine information materials in Arabic language: expert and consumer evaluation.

Authors:  Sinaa Al Aqeel; Norah Abanmy; Abeer Aldayel; Hend Al-Khalifa; Maha Al-Yahya; Mona Diab
Journal:  BMC Health Serv Res       Date:  2018-02-27       Impact factor: 2.655

6.  Developing and Testing Automatic Models of Patient Communicative Health Literacy Using Linguistic Features: Findings from the ECLIPPSE study.

Authors:  Scott A Crossley; Renu Balyan; Jennifer Liu; Andrew J Karter; Danielle McNamara; Dean Schillinger
Journal:  Health Commun       Date:  2020-03-02
  6 in total

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