Abstract
Using Data Collected from a Commercial Sensor System to Inform Mathematical Models of Healthcare-Associated Infections
Infection control and hospital epidemiology, Vol.41(S1), pp.s427-s427
10/2020
DOI: 10.1017/ice.2020.1086
Abstract
Background:
Hospital-acquired infections are commonly spread through the movement of healthcare professionals (HCPs). Computational simulations provide a powerful tool for understanding how HCP behavior contributes to these infections, but how well they reflect the real world rests on a number of critical parameters. Our goal is to provide accurate, fine-grained estimates of real HCP movement and interaction parameters suitable for simulating the potential spread of pathogens over different types of inpatient facilities.
Methods:
We obtained a commercial data set with 44 million deidentified elements compiled from >27,000 HCPs from >30 job types. The data were collected over 27 months from >20 facilities of varying size using a proprietary electronic sensor system. Each observation recorded an HCP visiting 1 of 12,000 rooms (38% being patient rooms) and consisted of the entry and exit time stamps, hand hygiene behavior, and for many rooms, their (x, y) geometric coordinates within the facility. From these data, we can reconstruct the behavior (including location and hand-hygiene adherence) of each instrumented HCP across multiple shifts.
Results:
Distributions describing various aspects of HCP behavior (eg, arrival rates and dwell times) were derived using HCP job function, department or unit assignment, type of shift (day vs night), time of day, facility size, and staffing of facility. In a similar fashion, we constructed HCP cross-table transition probabilities using job type, room type, department type, unit type, and facility type. These distributions were used to generate reasonable HCP movement and behavior patterns in a simulation environment. Distributions of dwell time were, for the most part, heavy tailed, but they varied by type of job and facility: dwell times over all facilities, job types, and room types averaged ∼339 seconds (SD, 495 seconds), with a mean of maximums by job type of ∼37,168 seconds. However, these distributions differ within job type but across facilities (ie, nurses in 1 facility averaged 397 seconds, but 277 seconds in another) and within facility but across job type. For example, physicians averaged 292 seconds, whereas nurses averaged 397 seconds and physical therapists averaged 861 seconds.
Conclusions:
Our results provide a unique resource for disease modelers who wish to build meaningful simulations of the transmission of hospital-acquired infections. The scale and diversity of the data gave us the unique capability to provide, with confidence, distinct parameter sets for different types and sizes of healthcare facilities across a wide range of situations.
Funding:
None
Disclosures:
None
Details
- Title: Subtitle
- Using Data Collected from a Commercial Sensor System to Inform Mathematical Models of Healthcare-Associated Infections
- Creators
- Jiazhao LiangHankyu JangD M Hasibul HasanPhilip PolgreenSriram PemmarajuAlberto Segre
- Resource Type
- Abstract
- Publication Details
- Infection control and hospital epidemiology, Vol.41(S1), pp.s427-s427
- DOI
- 10.1017/ice.2020.1086
- ISSN
- 0899-823X
- eISSN
- 1559-6834
- Language
- English
- Date published
- 10/2020
- Academic Unit
- Infectious Diseases; Epidemiology; Nursing; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Computer Science; Internal Medicine
- Record Identifier
- 9984410854502771
Metrics
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