Literature DB >> 31061565

NOTIFIABLE VIRAL INFECTIOUS DISEASES: IDENTIFYING PATTERNS OF LEARNING IN CLINICAL DECISION SUPPORT.

Kieran Walsh1, Lilah Basil1, Lalitha Bhagavatheeswaran1.   

Abstract

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Year:  2019        PMID: 31061565      PMCID: PMC6500402     

Source DB:  PubMed          Journal:  Ulster Med J        ISSN: 0041-6193


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Editor, Notifiable viral infectious diseases are a significant public health risk and it is important for frontline healthcare professionals to correctly detect and diagnose patients with these diseases. Healthcare professionals can use online clinical decision support resources to ensure that their knowledge of these diseases is evidence-based, practical and current.1 However, there are few analyses on how doctors use clinical decision support tools at the point-of-care or how they use them in specific specialties - such as the field of infectious diseases.2,3 The purpose of this report is to attempt to fill this gap in the literature by analysing the usage of a point-of-care decision support tool - BMJ Best Practice - in the field of viral infectious diseases. In December 2018, we conducted an analysis of patterns of use on BMJ Best Practice related to notifiable viral infectious diseases over the previous 12 months.4 We looked to see which of the notifiable viral infectious diseases generated the most usage on the clinical decision support tool and also which sections of the content were most used. We found that the most common notifiable viral infectious diseases are the most used. The most viewed diseases include measles, hepatitis C, Ebola virus infection, hepatitis B, and mumps. With the exception of Ebola, these are amongst the most common notifiable viral infectious diseases worldwide.5 Thus, it is not surprising that these are well-used. However, this also suggests that the content is being used to guide practical and common decisions that doctors and healthcare professionals take every day. The exception is Ebola – this is still a rare disease. However, it has received a great deal of public attention and this may account for some of its popularity. We also looked at what sections of the topics received most views. The sections of the topics with the most page views suggest a clear pattern of usage. The top two sections include the topic homepage and the “highlights-summary” page. However, this is to be expected as these are the first pages that users land on when they go to a topic. Where they go next is of more interest; and here there are clear messages from the data. Six of the next ten most popular sections relate to diagnosis – these include the sections on “approach to diagnosis”, “history and examination”, “differential diagnosis”, “investigations”, “diagnosis: step- by-step” and “case history”.4 Of the remaining, three relate to issues in management. These include the sections on “treatment options”, “treatment details”, and “approach to management”. The data suggests that users are utilising the clinical decision support tool to aid their decisions in diagnosis and management of notifiable viral infectious diseases and that they need help in the basics of taking a history, conducting an examination, ordering tests and ruling in or out differential diagnoses.5 Equally it may be that they want to confirm what they are doing is correct. The usage behaviour is largely related to the clinical workflow and suggests that users are using the tool at the point-of-care and not as a referential source that they might look at after the clinical event.
  2 in total

1.  Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design.

Authors:  Roosan Islam; Charlene R Weir; Makoto Jones; Guilherme Del Fiol; Matthew H Samore
Journal:  BMC Med Inform Decis Mak       Date:  2015-11-30       Impact factor: 2.796

Review 2.  Providing Doctors With High-Quality Information: An Updated Evaluation of Web-Based Point-of-Care Information Summaries.

Authors:  Koren Hyogene Kwag; Marien González-Lorenzo; Rita Banzi; Stefanos Bonovas; Lorenzo Moja
Journal:  J Med Internet Res       Date:  2016-01-19       Impact factor: 5.428

  2 in total

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