Relationship of Staff Information Sharing and Advice Networks to Patient Safety Outcomes

Brewer, Barbara B.; Carley, Kathleen M.; Benham‐Hutchins, Marge; Effken, Judith A.; Reminga, Jeffrey; Kowalchuck, Michael

doi:10.1097/nna.0000000000000646

Cited by 9 publications

(12 citation statements)

References 29 publications

(47 reference statements)

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“…There remains significant room to advance SNA-informed public health interventions by promoting standardized SNA approaches which conform to best practices for modeling social network exposures and health outcomes as outlined here (Shelton et al, 2019). For example, SNA can be used to optimize the design of health organizations or health facilities to improve health outcomes and patient safety (Benham-Hutchins et al, 2018; Brewer et al, 2018; Effken et al, 2011). Many of the concepts and theories in this field of work are still evolving; we hope this review is a timely introduction to the application of SNA in health research, particularly related to the health environment.…”

Section: Discussionmentioning

confidence: 99%

“…In SNA, health behaviors and outcomes may be modeled as emergent properties of the larger network system to gain a unique understanding of social influences on health and disease. SNA approaches have been applied to several domains of health research including epidemiology and public health (K. Smith & Christakis, 2008), health organizational behavior (Merrill et al, 2007(Merrill et al, , 2008(Merrill et al, , 2010(Merrill et al, , 2012, healthcare quality and safety (Brewer et al, 2018;Effken et al, 2011), and health services delivery (Provan et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…SNA approaches have been applied to several domains of health research including epidemiology and public health (K. Smith & Christakis, 2008), health organizational behavior (Merrill et al, 2007, 2008, 2010, 2012), healthcare quality and safety (Brewer et al, 2018; Effken et al, 2011), and health services delivery (Provan et al, 2010).…”

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confidence: 99%

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Best Practices for Modeling Egocentric Social Network Data and Health Outcomes

Burgette

Rankine

Culyba

et al. 2021

HERD

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Objective/Aim: We describe best practices for modeling egocentric networks and health outcomes using a five-step guide. Background: Social network analysis (SNA) is common in social science fields and has more recently been used to study health-related topics including obesity, violence, substance use, health organizational behavior, and healthcare utilization. SNA, alone or in conjunction with spatial analysis, can be used to uniquely evaluate the impact of the physical or built environment on health. The environment can shape the presence, quality, and function of social relationships with spatial and network processes interacting to affect health outcomes. While there are some common measures frequently used in modeling the impact of social networks on health outcomes, there is no standard approach to social network modeling in health research, which impacts rigor and reproducibility. Methods: We provide an overview of social network concepts and terminology focused on egocentric network data. Egocentric, or personal networks, take the perspective of an individual who identifies their own connections (alters) and also the relationships between alters. Results: We describe best practices for modeling egocentric networks and health outcomes according to the following five-step guide: (1) model selection, (2) social network exposure variable and selection considerations, (3) covariate selection related to sociodemographic and health characteristics, (4) covariate selection related to social network characteristics, and (5) analytic considerations. We also present an example of SNA. Conclusions: SNA provides a powerful repertoire of techniques to examine how relationships impact attitudes, experiences, and behaviors—and subsequently health.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Best Practices for Modeling Egocentric Social Network Data and Health Outcomes

Burgette

Rankine

Culyba

et al. 2021

HERD

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“…A patient’s safety and the hospital unit’s safety culture also depend on clear communication from one clinician to another. This points to the importance of handoffs and information exchange as important influences in perceptions of safety culture 13–15 . To preserve the quality of care for patients during their hospital stay, it is important that during each shift change, clinical staff provide up-to-date information and health status about the patient and handoff the case to the next clinical care team.…”

Section: Ease Of Handoffs and Information Exchangementioning

confidence: 99%

Patient Safety Perception Within Hospitals: An Examination of Job Type, Handoffs and Information Exchange, and Hospital Management Support

Yue¹,

Meehan

2023

J Patient Saf

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Objectives The study has 3 aims: (1) to assess to what extent a person’s job role relates to their evaluation of patient safety in the hospital setting; 2) to identify the relationship from hospital management aspects, including level of organizational learning–continuous improvement, level of management support, and level of leader support to patient safety perception in the hospital setting; and 3) to examine the relationship between the rating of the ease of information exchange and clinical handoffs and perceived patient safety in the hospital setting. Methods This study used a publicly available, deidentified cross-sectional data set from the 2021 Agency for Healthcare Research and Quality’s Survey on Patient Safety Culture Hospital Survey 2.0. Welch’s analysis of variance and multiple linear regression were used to examine each factor’s impact on patient safety rating. Results Supervisors had a higher (P < 0.001) patient safety perception than people in other job types, whereas nurses had a lower (P < 0.001) patient safety perception than other job types. Level of organizational learning–continuous improvement (P < 0.001), level of hospital management (P < 0.001), level of leader support (P < 0.001), and ease of handoffs and information exchange (P < 0.001) were positively related to perceived patient safety. Conclusions This study highlights the importance of identifying the unique issues affecting nurses and supervisors, different from other job types, that may explain their lower patient safety ratings. Findings from this study suggest that it is critical for organizations to focus on initiatives and policies that promote leadership, management, ease in information exchange and handoffs, and continuous learning.

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“…These include patterns of communication on hospital units [10][11][12][13] and during patient handoff (ie, transfer of responsibility) between providers during shift change and transfer between units [14,15]. Previous reports from the NetFIT study include the use of SNA to examine the relationship between nursing unit design, staff communication, and patient falls [16] with advice networks, staff information sharing, and patient-safety outcomes [17].…”

Section: Introductionmentioning

confidence: 99%

Nursing Unit Communication During a US Public Health Emergency: Natural Experiment

Benham‐Hutchins¹,

Carley²,

Brewer³

et al. 2018

JMIR Nursing

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Background In the second half of 2014, the first case of Ebola virus disease (EVD) was diagnosed in the United States. During this time period, we were collecting data for the Measuring Network Stability and Fit (NetFIT) longitudinal study, which used social network analysis (SNA) to study relationships between nursing staff communication patterns and patient outcomes. One of the data collection sites was a few blocks away from where the initial EVD diagnosis was made. The EVD public health emergency during the NetFIT data collection time period resulted in the occurrence of a natural experiment. Objective The objectives of the NetFIT study were to examine the structure of nursing unit decision-making and information-sharing networks, identify a parsimonious set of network metrics that can be used to measure the longitudinal stability of these networks, examine the relationship between the contextual features of a unit and network metrics, and identify relationships between key network measures and nursing-sensitive patient-safety and quality outcomes. This paper reports on unit communication and outcome changes that occurred during the EVD natural disaster time period on the 10 hospital units that had data collected before, during, and after the crisis period. Methods For the NetFIT study, data were collected from nursing staff working on 25 patient care units, in three hospitals, and at four data collection points over a 7-month period: Baseline, Month 1, Month 4, and Month 7. Data collection was staggered by hospital and unit. To evaluate the influence of this public health emergency on nursing unit outcomes and communication characteristics, this paper focuses on a subsample of 10 units from two hospitals where data were collected before, during, and after the EVD crisis period. No data were collected from Hospital B during the crisis period. Network data from individual staff were aggregated to the nursing unit level to create 24-hour networks and three unit-level safety outcome measures—fall rate, medication errors, and hospital-acquired pressure ulcers—were collected. Results This analysis includes 40 data collection points and 608 staff members who completed questionnaires. Participants (N=608) included registered nurses (431, 70.9%), licensed vocational nurses (3, 0.5%), patient care technicians (133, 21.9%), unit clerks (28, 4.6%), and monitor watchers (13, 2.1%). Changes in SNA metrics associated with communication (ie, average distance, diffusion, and density) were noted in units that had changes in patient safety outcome measures. Conclusions Units in the hospital site in the same city as the EVD case exhibited multiple changes in patient outcomes, network communication metrics, and response rates. Future research using SNA to examine the influence of public health emergencies on hospital communication networks and relationships to patient outcomes is warranted.

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Relationship of Staff Information Sharing and Advice Networks to Patient Safety Outcomes

Cited by 9 publications

References 29 publications

Best Practices for Modeling Egocentric Social Network Data and Health Outcomes

Best Practices for Modeling Egocentric Social Network Data and Health Outcomes

Patient Safety Perception Within Hospitals: An Examination of Job Type, Handoffs and Information Exchange, and Hospital Management Support

Nursing Unit Communication During a US Public Health Emergency: Natural Experiment

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