Platt, L S; Chen, X; Sabo-Attwood, T; Iovine, N; Brown, S and Pollitt, B (2024) Improving infection prevention briefing through predictive predesign: A computational approach to architectural programming by evaluating socioecological risk factors. Architectural Engineering and Design Management, 20(4), pp. 776-788. ISSN 1745-2007
Abstract
This research aimed to use a computational approach to aid infection prevention strategies during the predesign phase of healthcare environment planning. The objective of this study was to decompose and analyze health system catchment area 'outside design basis factors' such as demographic, public health, and environmental characteristics that may contribute to infection causing pathogen spread in healthcare environments using state of Florida data as a context for analysis. Supervised Machine Learning methodologies were used to determine relationships between Socioecological System (SES) factors and observed incidence rates of specific Hospital Acquired Infections (HAI). Results of the analysis suggest that relevant regional population SES factors, when included as independent variables, can forecast the potential co-occurrence of specific types of HAI in regional health system service areas. Outcomes of the analysis suggest that demographic and regional characteristics are significantly related to the predictive incidence of Antimicrobial-resistant infections in healthcare settings in Florida Agency for Healthcare Administration (ACHA) regions. The outcomes of this research suggest the benefit of using SES computational modeling for decision-making in health system Architectural Programming and predesign. This process allows for manifesting meaningful patterns by applying quantitative methods to analyze patient population characteristics that may influence the prevalence of pathogen spread within the interior environments of healthcare inpatient settings. This information contributes to a deeper understanding of context-specific priorities for operationalizing human-centered Infection Prevention through Design (PtD) infrastructure and planning.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | computational models; healthcare; infection control; predesign |
| Index terms: | briefing, agency, machine learning, modelling, prevention, programming, quantitative method, risk factor, population, pathogen, methodology, decision-making, public health, strategy, independent variable, computational model |
| Subjects: | scope management, programming, analytical methods, artificial intelligence, data analysis and analytics, modelling and simulation, financial risk, research methods, health risk and incident analysis, management, environmental hazards, environmental health, statistical analysis, sociology, decision analysis, demography |
| Topics: | Urban Studies, Digital Applications, Sustainability, Risk Management, Engineering Principles, Research Practice, Project Management, Health and Safety, Business Strategy, Cost Management |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here