A concept for a visual computer interface to make error taxonomies useful at the point of primary care

Evidence suggests that the quality of care delivered by the healthcare industry currently falls far short of its capabilities. Whilst most patient safety and quality improvement work to date has focused on inpatient settings, some estimates suggest that outpatient settings are equally important, wit...

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Bibliographic Details
Main Authors: Ranjit Singh, Wilson Pace, Sonjoy Singh, Ashok Singh, Gurdev Singh
Format: Article
Language:English
Published: BCS, The Chartered Institute for IT 2008-01-01
Series:Journal of Innovation in Health Informatics
Subjects:
Online Access:https://hijournal.bcs.org/index.php/jhi/article/view/662
Description
Summary:Evidence suggests that the quality of care delivered by the healthcare industry currently falls far short of its capabilities. Whilst most patient safety and quality improvement work to date has focused on inpatient settings, some estimates suggest that outpatient settings are equally important, with up to 200 000 avoidable deaths annually in the United States of America (USA) alone. There is currently a need for improved error reporting and taxonomy systems that are useful at the point of care. This provides an opportunity to harness the benefits of computer visualisation to help structure and illustrate the 'stories' behind errors. In this paper we present a concept for a visual taxonomy of errors, based on visual models of the healthcare system at both macrosystem and microsystem levels (previously published in this journal), and describe how this could be used to create a visual database of errors. In an alphatest in a US context, we were able to code a sample of 20 errors from an existing error database using the visual taxonomy. The approach is designed to capture and disseminate patient safety information in an unambiguous format that is useful to all members of the healthcare team (including the patient) at the point of care as well as at the policy-making level.
ISSN:2058-4555
2058-4563