Literature DB >> 30866000

Common Laboratory Results Frequently Misunderstood by a Sample of Mechanical Turk Users.

Nabeel Qureshi1, Ateev Mehrotra2,3, Robert S Rudin2, Shira H Fischer2.   

Abstract

OBJECTIVES: More patients are receiving their test results via patient portals. Given test results are written using medical jargon, there has been concern that patients may misinterpret these results. Using sample colonoscopy and Pap smear results, our objective was to assess how frequently people can identify the correct diagnosis and when a patient should follow up with a provider.
METHODS: We used Mechanical Turk-a crowdsourcing tool run by Amazon that enables easy and fast gathering of users to perform tasks like answering questions or identifying objects-to survey individuals who were shown six sample test results (three colonoscopy, three Pap smear) ranging in complexity. For each case, respondents answered multiple choice questions on the correct diagnosis and recommended return time.
RESULTS: Among the three colonoscopy cases (n = 642) and three Pap smear cases (n = 642), 63% (95% confidence interval [CI]: 60-67%) and 53% (95% CI: 49-57%) of the respondents chose the correct diagnosis, respectively. For the most complex colonoscopy and Pap smear cases, only 29% (95% CI: 23-35%) and 9% (95% CI: 5-13%) chose the correct diagnosis.
CONCLUSION: People frequently misinterpret colonoscopy and Pap smear test results. Greater emphasis needs to be placed on assisting patients in interpretation. Georg Thieme Verlag KG Stuttgart · New York.

Entities:  

Mesh:

Year:  2019        PMID: 30866000      PMCID: PMC6415984          DOI: 10.1055/s-0039-1679960

Source DB:  PubMed          Journal:  Appl Clin Inform        ISSN: 1869-0327            Impact factor:   2.342


  15 in total

Review 1.  Numeracy skill and the communication, comprehension, and use of risk-benefit information.

Authors:  Ellen Peters; Judith Hibbard; Paul Slovic; Nathan Dieckmann
Journal:  Health Aff (Millwood)       Date:  2007 May-Jun       Impact factor: 6.301

2.  Concealment and fabrication by experienced research subjects.

Authors:  Eric G Devine; Megan E Waters; Megan Putnam; Caitlin Surprise; Katie O'Malley; Courtney Richambault; Rachel L Fishman; Clifford M Knapp; Elissa H Patterson; Ofra Sarid-Segal; Chris Streeter; Laurie Colanari; Domenic A Ciraulo
Journal:  Clin Trials       Date:  2013-07-18       Impact factor: 2.486

Review 3.  Crowdsourcing Samples in Cognitive Science.

Authors:  Neil Stewart; Jesse Chandler; Gabriele Paolacci
Journal:  Trends Cogn Sci       Date:  2017-08-10       Impact factor: 20.229

4.  Amazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data?

Authors:  Michael Buhrmester; Tracy Kwang; Samuel D Gosling
Journal:  Perspect Psychol Sci       Date:  2011-02-03

5.  The frequency of Pap smear screening in the United States.

Authors:  Brenda E Sirovich; H Gilbert Welch
Journal:  J Gen Intern Med       Date:  2004-03       Impact factor: 5.128

6.  Patient-Centered Radiology Reporting: Using Online Crowdsourcing to Assess the Effectiveness of a Web-Based Interactive Radiology Report.

Authors:  Ryan G Short; Dana Middleton; Nicholas T Befera; Raj Gondalia; Tina D Tailor
Journal:  J Am Coll Radiol       Date:  2017-11       Impact factor: 5.532

7.  Numeracy and literacy independently predict patients' ability to identify out-of-range test results.

Authors:  Brian J Zikmund-Fisher; Nicole L Exe; Holly O Witteman
Journal:  J Med Internet Res       Date:  2014-08-08       Impact factor: 5.428

8.  Presentation of laboratory test results in patient portals: influence of interface design on risk interpretation and visual search behaviour.

Authors:  Paolo Fraccaro; Markel Vigo; Panagiotis Balatsoukas; Sabine N van der Veer; Lamiece Hassan; Richard Williams; Grahame Wood; Smeeta Sinha; Iain Buchan; Niels Peek
Journal:  BMC Med Inform Decis Mak       Date:  2018-02-12       Impact factor: 2.796

9.  Direct Release of Test Results to Patients Increases Patient Engagement and Utilization of Care.

Authors:  Francesca Pillemer; Rebecca Anhang Price; Suzanne Paone; G Daniel Martich; Steve Albert; Leila Haidari; Glenn Updike; Robert Rudin; Darren Liu; Ateev Mehrotra
Journal:  PLoS One       Date:  2016-06-23       Impact factor: 3.240

10.  ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation.

Authors:  John P Lalor; Hao Wu; Li Chen; Kathleen M Mazor; Hong Yu
Journal:  J Med Internet Res       Date:  2018-04-25       Impact factor: 5.428

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