Using The Electronic Health Record Data In Real Time And Predictive Analytics To Prevent Hospital-Acquired Postoperative/Surgical Site Infections

Keywords

electronic health records; hospital acquired infection; predictive analytics; surgical site infection

Abstract

Of critical concern to hospitals today is the prevention of postoperative (surgical site) infections that often result in increased lengths of stays for patients, increased resource demands and costs, loss of public trust and lawsuits, and needless pain and suffering for patients and their families. While all surgical patients have the potential to develop a postoperative infection, the main challenge is to identify key risk factors (both patient centered and operational) through an electronic early-warning system to reduce the likelihood of a postoperative infection from occurring. Currently, most postoperative infection risk prevention practices encompass limited use of informatics technologies or do not maximize the potential benefits. In addition, from a research perspective, there has been more focus on extrapolating electronically housed data (eg, from progress notes, operative notes, laboratory, pharmacy, radiology) retrospectively to describe poor patient outcomes for benchmarking purposes (revealing poor results and opportunities for improvement) rather than using similar sources of real-time data to prevent poor patient outcomes from occurring. This article proposes that standardized indicators, both patient centered and operational, linked to the patient's electronic health record could allow for implementation of 24/7, "real-time" monitoring/surveillance to implement well-timed preventive interventions scaled to each patient and facility to assist caregivers in reducing the numbers of postoperative infections and improve the overall quality and costs of patient care.

Publication Date

1-1-2018

Publication Title

Health Care Manager

Volume

37

Issue

1

Number of Pages

58-63

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1097/HCM.0000000000000196

Socpus ID

85041492643 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85041492643

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