عنوان

Using Resilient Systems Inference for Estimating Hospital Acquired Infection Prevention Infrastructure Performance

Number

TL51943

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Using Resilient Systems Inference for Estimating Hospital Acquired Infection Prevention Infrastructure Performance

General Material Designation

[Thesis]

First Statement of Responsibility

Platt, Lisa Sundahl

Subsequent Statement of Responsibility

Khasawneh, Mohammad

Name of Publisher, Distributor, etc.

State University of New York at Binghamton

Date of Publication, Distribution, etc.

2019

Text of Note

200 p.

Dissertation or thesis details and type of degree

Ph.D.

Body granting the degree

State University of New York at Binghamton

Text preceding or following the note

2019

Text of Note

The ability to predict and prevent the occurrence of Hospital Acquired Infections (HAI) continues to be an evolving challenge both in the U.S. and around the world. Care delivery environments meant to support curative efforts can often be a significant source of infectivity risk. This issue is often due to the inherent complexity and combined influences of healthcare settings themselves, the community of patients that they serve, and the geographical and socioeconomic region in which they must operate. The research presented in this dissertation seeks to demonstrate the value of applied resilient systems inference for estimating the safety performance of hospital-acquired infection prevention infrastructure. To achieve this, it validates the concept of exploring open source geographic and demographic data for determining Clostridioides difficile and Methicillin-resistant Staphylococcus aureus HAI risk factors by U.S. region. This study also aims to establish that targeted HAI resilience strategies can be mined from evidence-based case study literature and incorporated into nested fuzzy technical performance membership system attributes for evaluating system performance safety. It establishes that Supervised Learning techniques such as OLS could offer greater specificity to the weighting of different system risk and resilience fuzzy membership levels. The analysis presented reveals an approach for using Resilience Inference Fuzzy Membership Categories based on Fuzzy Risk Capacity, Resilience Capability, and Performance Safety outcomes as a basis for Fuzzy Inference System decision rules. Finally, the research methodology presented in this document establishes a process for inputting fuzzy HAI Risk and HAI Resilience membership function parameters into a Fuzzy Inference System that could estimate specific HAI Performance Safety outcomes. The operational safety inference process described in this document is meant as a framework for forecasting and considering the performance of certain types of HAI prevention strategies upstream of implementation. Its intended use is for estimating and evaluating the infectivity resilience potential of specific healthcare safety infrastructure. This approach could potentially be valuable for health systems serving populations and regional communities vulnerable to the effects of healthcare and community-onset infections caused by virulent pathogens such as Clostridioides difficile and Methicillin-resistant Staphylococcus aureus.

Subject Term

Health care management

Subject Term

Logic

Subject Term

Systems science

Platt, Lisa Sundahl

Khasawneh, Mohammad

State University of New York at Binghamton

Electronic name

p

[Thesis]

276903

a

Y