While the U.S. has more nursing-home residents than any other country, families have become reluctant to send their elders to long term care facilities (LTCFs) due to fear of rampant infection. These facilities need to raise the confidence of their clients by revamping their approaches to cleanliness and providing reassuring proof to families that their elders will be safe. Effective cleaning in LTCFs involves monitoring the efficacy of the cleaning methods used. Cleaning procedures in LTCFs are increasingly moving towards more standardized methods, while monitoring of cleaning efficacy is generally based on visual assessment. Some facilities are beginning to adopt measures beyond visual assessment such as swab-based Adenosine Triphosphate (ATP) bioluminescence, but these costly and time-consuming swab tests are not widely adopted. Studies show most LTCFs would benefit from standardized tools such as checklists, more frequent staff education including specific product use training. The main objective of this project is to bring a new auditing system to long-term care facilities that provides instant contamination detection, rapid surface disinfection of invisible contamination (e.g. body fluids, saliva and respiratory droplets that can host pathogens) and documents proof of cleanliness. We believe our Contamination, Sanitization Inspection and Disinfection (CSI-D) technology will change cleanliness monitoring standards by allowing users to see contamination, in real time, for immediate remediation. The CSI-D system is not intended to be a primary disinfection or cleaning tool; instead, it acts as a post-cleaning audit solution complementary to other post-cleaning auditing tools (ATP, FT-IR, etc.), as well as providing documentation of cleanliness. The system's disinfection capability is intended to provide spot disinfection only during audits or incident response and not employed as a large area disinfection method (e.g. fogging). This Phase I SBIR will validate the usability of the CSI-D as a post cleaning auditing system to detect, disinfect and document residual contamination, and apply risk-mapping algorithms to improve the current management of cleanliness in LTCFs. This project is a collaboration with AI and Machine learning researchers at the University of North Dakota and Valley Senior Living in Grand Forks, ND, and Edgewood Healthcare in Fargo, ND who will provide important input to the design process and access to long term care facilities for pilot studies. Aim 1 will include building the CSI-D software for long term care facilities, including developing the software specification, software for data management, and the contamination detection algorithm. Aim 2 will include the pilot test at LTCFs, including the development of the audit inspection procedure (task list), comparing efficacy of CSI-D with visual assessment and ATP bioluminescence detection, and development of the dynamic risk analysis algorithm. Future directions: Phase II will include more extensive longitudinal studies, with up to 20 of 64 Edgewood Healthcare facilities using the CSI-D technology as an auditing tool for one year, and thereafter the facilities with SafetySpect cleanliness monitoring program will be compared with remaining sites on infection control outcomes.
Public Health Relevance Statement: NARRATIVE While the U.S. has more nursing-home residents than any other country, families have become reluctant to send their elders to long term care facilities (LCTFs) due to fear of rampant infection. Facilities need to raise the confidence of their clients by revamping their approaches to cleanliness and providing reassuring proof to families that their elders will be safe. The main objective of this project is to bring a new auditing system to LTCFs that provides instant contamination detection, rapid surface disinfection of invisible contamination (e.g. body fluids, saliva and respiratory droplets that can host pathogens) and documents proof of cleanliness.
Project Terms: Adenosine Triphosphate; Adenylpyrophosphate; Elderly; advanced age; elders; geriatric; late life; later life; older adult; older person; senior citizen; Algorithms; Artificial Intelligence; AI system; Computer Reasoning; Machine Intelligence; Back; Dorsum; Body Fluids; Client; Cessation of life; Death; Disease; Disorder; Disinfection; Education; Educational aspects; Family; Fright; Fear; Future; Goals; Health; Health care facility; Health Facilities; Healthcare Facility; care facilities; Recording of previous events; History; Infection; Light; Photoradiation; Long-Term Care; extended care; longterm care; Longitudinal Studies; long-term study; longitudinal outcome studies; longterm study; Methods; North Dakota; nursing home; Nursing Homes; optical; Optics; pilot study; Pilot Projects; Scientific Publication; Publications; Research; Investigators; Researchers; Research Personnel; Risk; Safety; Saliva; Sanitation; Semantics; Software; Computer software; Designing computer software; Software Design; Standardization; Technology; Testing; Time; Universities; Measures; Infection Control; Administrator; Healthcare; health care; Risk Assessment; Bioluminescence; base; improved; Procedures; Site; Area; Surface; Residual state; Residual; Phase; Link; Training; Visual; Data Bases; data base; Databases; Human Figure; Human body; WWW; web; world wide web; Internet; Collaborations; Spottings; tool; machine learned; Machine Learning; programs; Adopted; Scanning; System; Fogs; Location; Country; respiratory; data management; Reporting; Modeling; response; develop software; developing computer software; software development; Far Go; FarGo; Documentation; model development; Bacterial Antibiotic Resistance; antibiotic resistant bacteria; bacterial antibiotic resistant; bacterial resistance to antibiotic; Norovirus; Norwalk-like Viruses; Swab; Institution; Effectiveness; Detection; Small Business Innovation Research Grant; SBIR; Small Business Innovation Research; Update; Monitor; Process; Development; developmental; pandemic disease; pandemic; Image; imaging; cost; foodborne illness; food-born illness; food-borne disease; food-borne illness; foodborn illness; foodborne disease; software systems; design; designing; remediation; Outcome; pathogen; Prevalence; Consumption; usability; comparative efficacy; compare efficacy; Algorithmic Analysis; Algorithmic Analyses; Analyses of Algorithms; Analysis of Algorithms; dashboard; COVID-19; COVID19; CV-19; CV19; corona virus disease 2019; coronavirus disease 2019; coronavirus disease-19; coronavirus infectious disease-19; pilot test
