Literature DB >> 28339685

Information needs of generalists and specialists using online best-practice algorithms to answer clinical questions.

David A Cook1,2,3, Kristi J Sorensen1, Jane A Linderbaum1,4, Laurie J Pencille1,5, Deborah J Rhodes1,6.   

Abstract

OBJECTIVE: To better understand clinician information needs and learning opportunities by exploring the use of best-practice algorithms across different training levels and specialties.
METHODS: We developed interactive online algorithms (care process models [CPMs]) that integrate current guidelines, recent evidence, and local expertise to represent cross-disciplinary best practices for managing clinical problems. We reviewed CPM usage logs from January 2014 to June 2015 and compared usage across specialty and provider type.
RESULTS: During the study period, 4009 clinicians (2014 physicians in practice, 1117 resident physicians, and 878 nurse practitioners/physician assistants [NP/PAs]) viewed 140 CPMs a total of 81 764 times. Usage varied from 1 to 809 views per person, and from 9 to 4615 views per CPM. Residents and NP/PAs viewed CPMs more often than practicing physicians. Among 2742 users with known specialties, generalists ( N  = 1397) used CPMs more often (mean 31.8, median 7 views) than specialists ( N  = 1345; mean 6.8, median 2; P  < .0001). The topics used by specialists largely aligned with topics within their specialties. The top 20% of available CPMs (28/140) collectively accounted for 61% of uses. In all, 2106 clinicians (52%) returned to the same CPM more than once (average 7.8 views per topic; median 4, maximum 195). Generalists revisited topics more often than specialists (mean 8.8 vs 5.1 views per topic; P  < .0001).
CONCLUSIONS: CPM usage varied widely across topics, specialties, and individual clinicians. Frequently viewed and recurrently viewed topics might warrant special attention. Specialists usually view topics within their specialty and may have unique information needs.
© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Entities:  

Keywords:  clinical; critical pathways; decision support systems; point-of-care learning; practice guidelines as topic

Mesh:

Year:  2017        PMID: 28339685      PMCID: PMC7651958          DOI: 10.1093/jamia/ocx002

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


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