A social network of hospital acquired infection built from electronic medical record data

Cusumano-Towner, Marco; Li, Daniel Y.; Tuo, Shanshan; Krishnan, Gomathi; Maslove, David

doi:10.1136/amiajnl-2012-001401

Cited by 33 publications

(21 citation statements)

References 29 publications

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“…Since the SARS outbreaks in 2003, there has been an emerging recognition of the complexity of hospital-based contact structures, and that this complexity varies by occupational type [25]. While prior studies have focused on individual hospital wards [18,19,26], patient-to-patient contact [15], or simulated/hypothetical patient-to-HCW contact [27], we report on actual self-reported patterns of movement and contact of over 3000 HCW in three Canadian urban tertiary care university affiliated hospitals. The resulting facility-specific networks identify occupational categories and specific locations within each unique setting that have high and low contact rates.…”

Section: Discussionmentioning

confidence: 99%

“…These studies capture patient movement as it pertains explicitly to the more complex clinical services they receive but fail to capture HCW social or casual movement, such as visits to the cafeteria or meeting rooms, or some types of clinical contact (e.g. a second staff member assisting with patient mobilization or bathing, or cross-covering a colleague on break) [14][15][16]. Contact patterns for HCWs have been examined using radio frequency identification (RFID) tags, mote-based sensors, and direct observation [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Contact among healthcare workers in the hospital setting: developing the evidence base for innovative approaches to infection control

et al. 2018

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BackgroundNosocomial, or healthcare-associated infections (HAI), exact a high medical and financial toll on patients, healthcare workers, caretakers, and the health system. Interpersonal contact patterns play a large role in infectious disease spread, but little is known about the relationship between health care workers’ (HCW) movements and contact patterns within a heath care facility and HAI. Quantitatively capturing these patterns will aid in understanding the dynamics of HAI and may lead to more targeted and effective control strategies in the hospital setting.MethodsStaff at 3 urban university-based tertiary care hospitals in Canada completed a detailed questionnaire on demographics, interpersonal contacts, in-hospital movement, and infection prevention and control practices. Staff were divided into categories of administrative/support, nurses, physicians, and “Other HCWs” - a fourth distinct category, which excludes physicians and nurses. Using quantitative network modeling tools, we constructed the resulting HCW “co-location network” to illustrate contacts among different occupations and with locations in hospital settings.ResultsAmong 3048 respondents (response rate 38%) an average of 3.79, 3.69 and 3.88 floors were visited by each HCW each week in the 3 hospitals, with a standard deviation of 2.63, 1.74 and 2.08, respectively. Physicians reported the highest rate of direct patient contacts (> 20 patients/day) but the lowest rate of contacts with other HCWs; nurses had the most extended (> 20 min) periods of direct patient contact. “Other HCWs” had the most direct daily contact with all other HCWs. Physicians also reported significantly more locations visited per week than nurses, other HCW, or administrators; nurses visited the fewest. Public spaces such as the cafeteria had the most staff visits per week, but the least mean hours spent per visit. Inpatient settings had significantly more HCW interactions per week than outpatient settings.ConclusionsHCW contact patterns and spatial movement demonstrate significant heterogeneity by occupation. Control strategies that address this diversity among health care workers may be more effective than “one-strategy-fits-all” HAI prevention and control programs.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3093-x) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Contact among healthcare workers in the hospital setting: developing the evidence base for innovative approaches to infection control

et al. 2018

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“…The essence of an HAI surveillance program is to interpret databases generated from multiple data sources to prospectively monitor trends, identify clusters and outbreaks in a timely fashion, track the impact of quality improvement programs, and predict future trends. An HAI social network generated from electronic healthcare record (EHR) patient and caregiver contacts was used to simulate outbreaks of methicillin-resistant Staphylococcus aureus and influenza, and identify potentially mitigating interventions [9]. Machine learning applications have been used to predict the risk of nosocomial Clostridioides difficile infection (CDI) [10•,11,12].…”

Section: Surveillance Of Healthcare-associated Infectionmentioning

confidence: 99%

Using Artificial Intelligence in Infection Prevention

Fitzpatrick

Doherty

Lacey

2020

Curr Treat Options Infect Dis

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Purpose of Review Artificial intelligence (AI) offers huge potential in infection prevention and control (IPC). We explore its potential IPC benefits in epidemiology, laboratory infection diagnosis, and hand hygiene. Recent Findings AI has the potential to detect transmission events during outbreaks or predict high-risk patients, enabling development of tailored IPC interventions. AI offers opportunities to enhance diagnostics with objective pattern recognition, standardize the diagnosis of infections with IPC implications, and facilitate the dissemination of IPC expertise. AI hand hygiene applications can deliver behavior change, though it requires further evaluation in different clinical settings. However, staff can become dependent on automatic reminders, and performance returns to baseline if feedback is removed. Summary Advantages for IPC include speed, consistency, and capability of handling infinitely large datasets. However, many challenges remain; improving the availability of high-quality representative datasets and consideration of biases within preexisting databases are important challenges for future developments. AI in itself will not improve IPC; this requires culture and behavior change. Most studies to date assess performance retrospectively so there is a need for prospective evaluation in the real-life, often chaotic, clinical setting. Close collaboration with IPC experts to interpret outputs and ensure clinical relevance is essential.

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“…To examine patient mobility, we use graph theory, the mathematical analysis of networks that allows us to construct a network of movement how hospital units are connected by patient movement between them [6]. Network analysis has been previously used to examine intra-hospital transfers and ambulatory care [7,8,9], but limited studies of inter-hospital mobility [10,11]. Grouping patients by their location in a hospital gives us the opportunity to examine susceptibility and risk of CDI from a population perspective, and calculation of network centrality [6] provides us with context of how inpatient units are connected in our hospital via patient transfers.…”

Section: Introductionmentioning

confidence: 99%

Inpatient mobility to predict hospital-onset Clostridium difficile: a network approach

Bush

Barbosa

Farooq

et al. 2018

Preprint

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With hospital-onset Clostridium difficile Infection (CDI) still a common occurrence in the U.S., this paper examines the relationship between unit-wide CDI susceptibility and inpatient mobility and creates a predictive measure of CDI called "Contagion Centrality". A mobility network was constructed using two years of patient electronic health record (EHR) data within a 739-bed hospital (Jan. 2013 -Dec. 2014; n=72,636 admissions). Network centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms and patient transfers between units (edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and compared to network centrality measures to determine the relationship between unit CDI susceptibility and patient mobility. Closeness centrality was a statistically significant measure associated with unit susceptibility (p-value < 0.05), highlighting the importance of incoming patient mobility in CDI prevention at the unit-level. Contagion Centrality (CC) was calculated using incoming inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. This measure is statistically significant (p-value <0.05) with our outcome of hospital-onset CDI cases, and captures the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create an easily interpretable and informative clinical tool showing this relationship and risk of hospital-onset CDI in real-time. Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak, and thus provide clinicians and infection prevention staff with advanced warning and specific location data to concentrate prevention efforts.

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A social network of hospital acquired infection built from electronic medical record data

Abstract: Inpatient social networks represent a novel secondary use of EMR data, and can be used to simulate nosocomial infections. Future work should focus on prospective validation of the simulations, and adapting such networks to other tasks.

Cited by 33 publications

References 29 publications

Contact among healthcare workers in the hospital setting: developing the evidence base for innovative approaches to infection control

Contact among healthcare workers in the hospital setting: developing the evidence base for innovative approaches to infection control

Using Artificial Intelligence in Infection Prevention

Inpatient mobility to predict hospital-onset Clostridium difficile: a network approach

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