Literature DB >> 29241251

Association between Search Behaviors and Disease Prevalence Rates at 18 U.S. Children's Hospitals.

Dennis Daniel1,2, Traci Wolbrink1,2, Tanya Logvinenko3, Marvin Harper4,5, Jeffrey Burns1,2.   

Abstract

Background Usage of online resources by clinicians in training and practice can provide insight into knowledge gaps and inform development of decision support tools. Although online information seeking is often driven by encountered patient problems, the relationship between disease prevalence and search rate has not been previously characterized. Objective This article aimed to (1) identify topics frequently searched by pediatric clinicians using UpToDate (http://www.uptodate.com) and (2) explore the association between disease prevalence rate and search rate using data from the Pediatric Health Information System. Methods We identified the most common search queries and resources most frequently accessed on UpToDate for a cohort of 18 children's hospitals during calendar year 2012. We selected 64 of the most frequently searched diseases and matched ICD-9 data from the PHIS database during the same time period. Using linear regression, we explored the relationship between clinician query rate and disease prevalence rate. Results The hospital cohort submitted 1,228,138 search queries across 592,454 sessions. The majority of search sessions focused on a single search topic. We identified no consistent overall association between disease prevalence and search rates. Diseases where search rate was substantially higher than prevalence rate were often infectious or immune/rheumatologic conditions, involved potentially complex diagnosis or management, and carried risk of significant morbidity or mortality. None of the examined diseases showed a decrease in search rate associated with increased disease prevalence rates. Conclusion This is one of the first medical learning needs assessments to use large-scale, multisite data to identify topics of interest to pediatric clinicians, and to examine the relationship between disease prevalence and search rate for a set of pediatric diseases. Overall, disease search rate did not appear to be associated with hospital disease prevalence rates based on ICD-9 codes. However, some diseases were consistently searched at a higher rate than their prevalence rate; many of these diseases shared common features.

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Year:  2017        PMID: 29241251      PMCID: PMC5802318          DOI: 10.4338/ACI-2017-08-RA-0137

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


  23 in total

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Authors:  Janet Grant
Journal:  BMJ       Date:  2002-01-19

2.  Physicians' Internet information-seeking behaviors.

Authors:  Nancy L Bennett; Linda L Casebeer; Robert E Kristofco; Sheryl M Strasser
Journal:  J Contin Educ Health Prof       Date:  2004       Impact factor: 1.355

3.  The need for needs assessment in continuing medical education.

Authors:  Geoffrey R Norman; Susan I Shannon; Michael L Marrin
Journal:  BMJ       Date:  2004-04-24

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Journal:  BMJ       Date:  2010-12-15

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Authors:  Michael Blechner; Joshua Kish; Vivek Chadaga; Anand S Dighe
Journal:  Am J Clin Pathol       Date:  2006-08       Impact factor: 2.493

6.  Big data: the management revolution.

Authors:  Andrew McAfee; Erik Brynjolfsson
Journal:  Harv Bus Rev       Date:  2012-10

Review 7.  Clinical questions raised by clinicians at the point of care: a systematic review.

Authors:  Guilherme Del Fiol; T Elizabeth Workman; Paul N Gorman
Journal:  JAMA Intern Med       Date:  2014-05       Impact factor: 21.873

Review 8.  Accuracy of physician self-assessment compared with observed measures of competence: a systematic review.

Authors:  David A Davis; Paul E Mazmanian; Michael Fordis; R Van Harrison; Kevin E Thorpe; Laure Perrier
Journal:  JAMA       Date:  2006-09-06       Impact factor: 56.272

Review 9.  Internet-based learning in the health professions: a meta-analysis.

Authors:  David A Cook; Anthony J Levinson; Sarah Garside; Denise M Dupras; Patricia J Erwin; Victor M Montori
Journal:  JAMA       Date:  2008-09-10       Impact factor: 56.272

10.  Using clinicians' search query data to monitor influenza epidemics.

Authors:  Mauricio Santillana; Elaine O Nsoesie; Sumiko R Mekaru; David Scales; John S Brownstein
Journal:  Clin Infect Dis       Date:  2014-08-12       Impact factor: 9.079

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