Clinical decision support for infection control in surgical care

M. Spruit, Sander van der Rijnst

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

Relatively few healthcare institutions have fully implemented information systems with decision support capabilities. There is an evident absence of systematic measurement of patients’ compliance in virtually every healthcare system. Patient compliance, such as compliance to treatments or adherence to medication, is all but unmeasured today. The inability to measure progress makes it exceptionally difficult to sustain efforts toward supporting improvement initiatives. Many healthcare institutions are still implementing electronic patient record systems. However, this study focuses on the design and development of a decision support prototype in surgical care for measuring process compliance pertaining to infection control. We introduce a reference surgical information model and a design prototype, adhering to the Applied Data Science research approach. First, a literature study is performed in two areas which are related to infection control and national patient safety programs on how we should design such a healthcare informatics innovation. Second, the prototype is instantiated and tested by performing an exploratory data analysis among a sample of 7000 pediatric patients to determine the usability and applicability of the prototype design on data quality aspects and compliance measurement.
Original languageEnglish
Title of host publicationInnovation in Health Informatics
Subtitle of host publicationa Smart Healthcare Primer
EditorsM. Lytras, A. Visvizi, A. Sarirete
Place of PublicationAmsterdam
PublisherElsevier
Chapter4
Pages101–121
Volume1
ISBN (Print)978-0-12-819043-2
DOIs
Publication statusPublished - 2020

Keywords

  • Clinical decision support
  • surgical care
  • surgical careinfection control
  • surgical site infection
  • CRISP-DM
  • Applied Data Science

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