Googling for a veterinary diagnosis: A replication study using Google as a diagnostic aid

Abstract

Background: The purpose of this study was to replicate in the veterinary context a BMJ study using Google to assist in diagnosis of complex cases.

Hypothesis/objectives: To assess percentage of diagnoses identified using Google as a diagnostic aid in veterinary medicine.

Animals: None; 13 cases in cats and 17 in dogs published in JAVMA.

Methods: Cross-sectional survey of Google results from searches using keywords generated independently by a generalist and a specialist veterinarian who reviewed the published case history and diagnostic components while blind to the diagnosis. They offered diagnoses and generated up to 5 search strategies for each case. The top 30 Google results for each search were reviewed by the generalist to inform a final Google-aided diagnosis. Both veterinarians' initial diagnoses and the Google-aided diagnoses were compared with the published diagnoses.

Results: Google searching led to 52 diagnoses out of 60 possible. Twenty-two (42%, 95% confidence interval [95% CI] 29%-55%) Google-aided diagnoses matched the JAVMA diagnosis. This accuracy rate does not differ significantly from 58% (n = 15/26, 95% CI 38%-77%) identified in the BMJ study. Google-aided results were not statistically different from those achieved unaided by each veterinarian (33%, 95% CI 16%-50%).

Conclusions and clinical importance: Published information found searching Google using keywords related to complicated or unusual cases could assist veterinarians to reinforce their initial diagnosis or consider other differential diagnoses. Search strategies using words representing either signs or the preliminary diagnoses can yield results useful to confirming a correct diagnosis.

Keywords: differential diagnoses; information resources; open access; problem lists.