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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.
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.
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.
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.
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.
During the second half of 2014, the Ebola virus disease (EVD) epidemic in West Africa became a global public health emergency. On August 5, 2014, the World Health Organization declared EVD an international public health emergency and the US Centers for Disease Control and Prevention (CDC) elevated its Emergency Operations Center to the highest level [
EVD infection prevention and control presents unique challenges in the health care setting because the virus is present in all body fluids and viral loads increase as the illness progresses [
Safety and quality outcomes and coordination of patient care have been shown to depend on communication among providers in the health care setting [
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.
For the NetFIT study, data were collected from nursing staff working on 25 patient care units (PCUs), in three hospitals, and at four data collection points over a 7-month period: Baseline (B), Month 1 (M1), Month 4 (M4), and Month 7 (M7). Data collection was staggered by hospital and unit. One unit was dropped from the analysis due to low patient census and staffing. This resulted in 24 units, 96 data collection points, and 1561 licensed and unlicensed nursing staff members who completed questionnaires. 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. The units included in this analysis are Units 5, 6, and 8 from Hospital A and Units 15, 16, 18, 20, 22, 23, and 24 from Hospital C. No data were collected from Hospital B during the crisis period.
Institutional Review Board (IRB) approval was obtained from the University of Arizona, Texas Woman’s University, the University of Texas at Austin, and the participating hospitals. SNA data collection requires the participants to identify those with whom they have interacted. For that reason, we provided a list of possible contacts—limited to those working on their own units and shifts the day data were collected—for selection by the participants. Our team created a novel data collection system comprised of a secure website, application programming interface, and an Android tablet app. For interested readers, a detailed description of the development and implementation is available [
For this paper, the starting point for the EVD active crisis period (
Staff recruitment activities included presentations by research team members during staff meetings and flyers posted on the nursing units. A snack or coupon for a cupcake with a value of US $4.00 was provided to encourage participation. At the end of their shifts, individual attribute (ie, demographic) and SNA data were collected from participating PCU staff working on the designated data collection days. Baseline data were collected on a specific weekday—over a 24-hour period to capture all shifts—and on the same weekday 1, 4, and 7 months later. Network data from individual staff were aggregated to the nursing unit level to create 24-hour networks for network analysis. To create an information-sharing network, participating staff members were asked to identify how frequently they discussed patient care with staff members working on their unit during their just-completed shift. They were also asked how frequently they provided patient care-related information to staff on the next shift or received patient care-related information from staff on the previous shift. To create the decision-making network, staff members were asked how often they sought advice from other staff members, how often other staff sought them out for advice, and to rate their confidence in the advice they received.
Data collection time periods and individual patient care unit response rates
Hospital, Unit | Baseline (all 2014) | Month 1 (all 2014) | Month 4 | Month 7 (all 2015) | |||||||
|
Datea | RRb, n (%) | Date | RR, n (%) | Date | RR, n (%) | Date | RR, n (%) | |||
A, 5 | 6/7-7/30 | 22/28 (79) | 7/28-8/1 | 25/29 (86) | 10/27-10/31, 2014c | 25/31 (81) | 1/26-1/30 | 30/31 (97) | |||
A, 6 | 7/7-7/11 | 24/25 (96) | 8/4-8/8 | 19/21 (91) | 11/3-11/7, 2014c | 17/20 (85) | 1/26-1/30 | 26/28 (93) | |||
A, 8 | 7/7-7/11 | 8/9 (89) | 8/4-8/8 | 9/10 (90) | 11/3-11/7, 2014c | 11/11 (100) | 1/26-1/30 | 8/9 (89) | |||
C, 15 | 6/30-7/4 | 11/15 (73) | 7/28-8/1 | 12/15 (80) | 10/27-10/31, 2014c | 10/14 (71) | 1/26-1/30 | 9/11 (69) | |||
C, 16 | 6/30-7/4 | 15/17 (88) | 7/28-8/1 | 18/19 (95) | 10/27-10/31, 2014c | 13/17 (77) | 1/26-1/30 | 8/19 (42) | |||
C, 18 | 9/8-9/12 | 16/21 (76) | 10/6-10/10c | 22/24 (92) | 1/5-1/9, 2015 | 13/20 (65) | 3/30-4/3 | 18/22 (82) | |||
C, 20 | 9/15-9/19 | 18/26 (69) | 10/13-10/17c | 16/23 (70) | 1/12-1/16, 2015 | 24/30 (80) | 4/6-4/10 | 22/28 (79) | |||
C, 22 | 9/22-9/26 | 12/13 (92) | 10/20-10/24c | 11/15 (73) | 1/19-1/23, 2015 | 7/14 (50) | 4/13-4/17 | 10/15 (67) | |||
C, 23 | 7/7-7/11 | 14/20 (70) | 8/4-8/8 | 12/17 (71) | 11/3-11/7, 2014c | 12/14 (86) | 2/2-2/6 | 12/16 (75) | |||
C, 24 | 9/22-9/26 | 11/12 (92) | 10/20-10/24c | 7/12 (58) | 1/19-1/23, 2015 | 6/10 (60) | 4/13-4/17 | 10/11 (91) |
aData collection dates are reported as month/day, followed by year in the M4 column.
bRR: individual patient care unit response rate.
cTime period is during active Ebola virus disease period.
Network metric definitions
Network metric | Definition |
Node set size | Total number of nodes (ie, staff members) in the network. |
Average distance | The average shortest path between nodes. This statistical measure helps evaluate the efficiency of information transfer. |
Clustering coefficient | A higher clustering coefficient indicates a decentralized network and diffusion of information between staff. |
Diffusion | Computes the degree to which something could be easily diffused (ie, spread) throughout the network. This is based on the distance between nodes. A large diffusion value means that nodes are close to each other, and a smaller diffusion value means that nodes are farther apart. |
Density | Ratio comparing existing links to all possible links in the communication network. |
Weighted density | Strength of density connections based on frequency. |
Betweenness centralization | Network-level measure that helps identify how dependent the network is on specific providers. |
Eigenvector centralization | Network-level measure. High eigenvector centralization indicates that a small group of nodes (ie, staff members) form a group that is fairly densely connected. |
Participant characteristics (ie, attributes, also called composition variables) provided contextual information to assist the interpretation of network characteristics [
SNA is a distinct research method that supports the study of relationships among actors (ie, nursing unit staff) and analysis of relationship patterns [
On September 30, 2014, when the first patient with EVD in the United States was diagnosed, we had not yet started data collection at Hospital B, but we had completed Baseline and Month 1 data collection at Hospital A. At Hospital C, we had completed Baseline data collection on all 10 units and Month 1 data collection on six of the 10 units. Here we focus on the 10 nursing units with data available before, during, and after the EVD crisis.
The NetFIT study was designed to examine the structure of nursing unit decision-making and information-sharing networks. Here we report on the merging of these two networks, as a total interaction network, and the corresponding network metrics (ie, average distance, clustering coefficient, diffusion, density, weighted density, betweenness centralization, and eigenvector centralization).
Average distance is a measure of information transfer. Two nursing units had increases in average distance during or after the EVD time period. Unit 20 exhibited an increase in average distance during the Month 1 data collection period, which corresponded with the active EVD time period. Unit 16’s Month 1 data collection corresponded with the active EVD time period, but did not show an increase in average distance until the Month 7 data collection period.
The clustering coefficient metric provides information on network characteristics, such as how information spreads between employee groups. A higher metric indicates a decentralized infrastructure and local information diffusion. Standard deviations for each metric, by nursing unit, were calculated using the four data collection time period results (see
Density is the ratio of all possible links in the network and weighted density indicates the strength of the connections based on how often individual staff indicated they communicated with one another. On nursing Unit 16, the
Safety outcome measures
Unit | Baseline | Month 1 | Month 4 | Month 7 | ||||||||||
|
Fall ratea | ME ratea | HAPU rateb | Fall rate | ME rate | HAPU rate | Fall rate | ME rate | HAPU rate | Fall rate | ME rate | HAPU rate | ||
5 | 1.74 | 3.49 | 0 | 1.74 | 3.49 | 0 |
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|
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8.66 | 3.33 | 0.67 | ||
6 | 5.43 | 8.14 | 1.36 | 6.66 | 3.99 | 0 |
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2.94 | 5.87 | 0 | ||
8 | 0 | 0 | 0 | 0 | 0 | 0 |
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0 | 3.15 | 0 | ||
15 | 5.08 | 1.69 | 0 | 5.08 | 1.69 | 0 |
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1.79 | 1.79 | 6.67 | ||
16 | 0 | 1.61 | 0 | 0 | 1.61 | 0 |
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0 | 11.28 | 0 | ||
18 | 1.17 | 0 | 0 |
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4.12 | 1.03 | 0 | 3.38 | 1.13 | 0 | ||
20 | 6.47 | 4.31 | 2.86 |
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3.04 | 0 | 0 | 3.31 | 2.21 | 7.70 | ||
22 | 5.29 | 0 | 0 |
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0 | 0 | 0 | 1.70 | 3.40 | 0 | ||
23 | 1.77 | 5.03 | 0 | 1.55 | 0 | 0 |
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1.95 | 1.95 | 0 | ||
24 | 1.98 | 1.98 | 0 |
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2.02 | 4.03 | 0 | 0 | 2.19 | 0 |
aFall and medication error (ME) rates were defined as the number of falls or medication errors per month divided by patient days, then multiplied by 1000 to create a rate per 1000 patient days.
bHospital-acquired pressure ulcer (HAPU) rates were calculated as the number of HAPUs averaged over the number of patients hospitalized on the unit the day data were collected.
cOutcome measures during the active Ebola virus disease period are in italics.
Total interaction network measures
Unit and network measure | Baseline | Month 1 | Month 4 | Month 7 | Mean (SD) | ||||||
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Node set size | 25 | 26 |
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22 | 25.00 (2.16) | |||||
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Average distance | 0.35 | 0.29 |
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0.41 | 0.35 (0.05) | |||||
|
Clustering coefficient | 0.14 | 0.11 |
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0.16 | 0.14 (0.02) | |||||
|
Diffusion | 0.16 | 0.24 |
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0.17 | 0.17 (0.06) | |||||
|
Density | 0.33 | 0.29 |
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0.16 | 0.24 (0.08) | |||||
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Weighted density | 0.49 | 0.42 |
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0.48 | 0.46 (0.03) | |||||
|
Betweenness centralization | 3.72 | 5.06 |
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4.45 | 4.37 (0.56) | |||||
|
Eigenvector centralization | 0.74 | 0.64 |
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0.93 | 0.78 (0.12) | |||||
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Node set size | 25 | 21 |
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27 | 23.25 (3.30) | |||||
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Average distance | 4.75 | 5.00 |
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4.13 | 4.65 (0.37) | |||||
|
Clustering coefficient | 0.48 | 0.38 |
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0.51 | 0.46 (0.06) | |||||
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Diffusion | 0.93 | 0.87 |
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0.94 | 0.89 (0.06) | |||||
|
Density | 0.35 | 0.29 |
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0.41 | 0.36 (0.05) | |||||
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Weighted density | 0.13 | 0.12 |
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0.15 | 0.13 (0.01) | |||||
|
Betweenness centralization | 0.23 | 0.27 |
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0.30 | 0.25 (0.05) | |||||
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Eigenvector centralization | 0.41 | 0.30 |
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0.29 | 0.33 (0.06) | |||||
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Node set size | 8 | 10 |
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9 | 9.50 (1.29) | |||||
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Average distance | 2.58 | 3.73 |
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3.36 | 3.23 (0.48) | |||||
|
Clustering coefficient | 0.62 | 0.53 |
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0.46 | 0.57 (0.10) | |||||
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Diffusion | 0.96 | 0.86 |
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0.85 | 0.91 (0.07) | |||||
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Density | 0.64 | 0.47 |
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0.48 | 0.57 (0.10) | |||||
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Weighted density | 0.33 | 0.26 |
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0.29 | 0.29 (0.03) | |||||
|
Betweenness centralization | 0.10 | 0.31 |
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0.20 | 0.18 (0.09) | |||||
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Eigenvector centralization | 0.13 | 0.29 |
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0.22 | 0.22 (0.07) | |||||
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Node set size | 15 | 15 |
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12 | 13.75 (1.50) | |||||
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Average distance | 3.02 | 3.76 |
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3.04 | 3.15 (0.42) | |||||
|
Clustering coefficient | 0.63 | 0.51 |
|
0.58 | 0.57 (0.05) | |||||
|
Diffusion | 0.72 | 0.78 |
|
0.66 | 0.73 (0.05) | |||||
|
Density | 0.52 | 0.44 |
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0.50 | 0.50 (0.04) | |||||
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Weighted density | 0.29 | 0.26 |
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0.27 | 0.27 (0.02) | |||||
|
Betweenness centralization | 0.15 | 0.16 |
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0.07 | 0.12 (0.04) | |||||
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Eigenvector centralization | 0.19 | 0.26 |
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0.28 | 0.23 (0.06) | |||||
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Node set size | 17 |
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17 | 19 | 18.00 (1.15) | |||||
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Average distance | 3.57 |
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4.03 | 6.51 | 4.43 (1.40) | |||||
|
Clustering coefficient | 0.64 |
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0.53 | 0.41 | 0.55 (0.10) | |||||
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Diffusion | 0.81 |
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0.75 | 0.34 | 0.71 (0.25) | |||||
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Density | 0.57 |
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0.43 | 0.20 | 0.43 (0.17) | |||||
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Weighted density | 0.25 |
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0.18 | 0.06 | 0.18 (0.08) | |||||
|
Betweenness centralization | 0.12 |
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0.08 | 0.12 | 0.14 (0.08) | |||||
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Eigenvector centralization | 0.14 |
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0.36 | 0.55 | 0.33 (0.17) | |||||
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Node set size | 21 |
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19 | 21 | 21.25 (2.06) | |||||
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Average distance | 3.12 |
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2.59 | 2.61 | 2.93 (0.40) | |||||
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Clustering coefficient | 0.53 |
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0.63 | 0.65 | 0.59 (0.06) | |||||
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Diffusion | 0.84 |
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0.67 | 0.84 | 0.81 (0.10) | |||||
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Density | 0.43 |
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0.48 | 0.55 | 0.48 (0.05) | |||||
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Weighted density | 0.24 |
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0.26 | 0.27 | 0.25 (0.02) | |||||
|
Betweenness centralization | 0.20 |
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0.19 | 0.18 | 0.20 (0.03) | |||||
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Eigenvector centralization | 0.32 |
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0.28 | 0.29 | 0.31 (0.03) | |||||
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Node set size | 26 |
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30 | 28 | 26.75 (2.99) | |||||
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Average distance | 3.95 |
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4.57 | 4.02 | 4.89 (1.44) | |||||
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Clustering coefficient | 0.55 |
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0.52 | 0.61 | 0.51 (0.11) | |||||
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Diffusion | 0.68 |
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0.82 | 0.74 | 0.70 (0.10) | |||||
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Density | 0.37 |
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0.39 | 0.41 | 0.36 (0.06) | |||||
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Weighted density | 0.14 |
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0.15 | 0.15 | 0.14 (0.01) | |||||
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Betweenness centralization | 0.11 |
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0.16 | 0.10 | 0.14 (0.04) | |||||
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Eigenvector centralization | 0.29 |
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0.29 | 0.21 | 0.30 (0.07) | |||||
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Node set size | 12 |
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13 | 14 | 13.25 (0.96) | |||||
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Average distance | 2.62 |
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2.99 | 2.57 | 2.68 (0.21) | |||||
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Clustering coefficient | 0.72 |
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0.60 | 0.59 | 0.63 (0.06) | |||||
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Diffusion | 0.98 |
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0.53 | 0.70 | 0.75 (0.19) | |||||
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Density | 0.72 |
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0.42 | 0.46 | 0.54 (0.13) | |||||
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Weighted density | 0.37 |
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0.24 | 0.25 | 0.29 (0.06) | |||||
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Betweenness centralization | 0.16 |
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0.04 | 0.24 | 0.13 (0.09) | |||||
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Eigenvector centralization | 0.17 |
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0.25 | 0.30 | 0.25 (0.06) | |||||
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Node set size | 20 | 17 |
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16 | 16.75 (2.50) | |||||
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Average distance | 2.75 | 3.22 |
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3.20 | 3.14 (0.27) | |||||
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Clustering coefficient | 0.57 | 0.51 |
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0.63 | 0.59 (0.07) | |||||
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Diffusion | 0.69 | 0.69 |
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0.74 | 0.74 (0.07) | |||||
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Density | 0.41 | 0.42 |
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0.55 | 0.50 (0.11) | |||||
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Weighted density | 0.18 | 0.22 |
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0.28 | 0.25 (0.05) | |||||
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Betweenness centralization | 0.40 | 0.22 |
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0.28 | 0.29 (0.08) | |||||
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Eigenvector centralization | 0.45 | 0.36 |
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0.29 | 0.36 (0.07) | |||||
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Node set size | 12 |
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10 | 10 | 10.75 (0.96) | |||||
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Average distance | 3.73 |
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4.31 | 3.73 | 3.90 (0.28) | |||||
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Clustering coefficient | 0.69 |
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0.45 | 0.66 | 0.60 (0.11) | |||||
|
Diffusion | 0.90 |
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0.58 | 0.71 | 0.70 (0.14) | |||||
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Density | 0.64 |
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0.35 | 0.46 | 0.47 (0.12) | |||||
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Weighted density | 0.26 |
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0.15 | 0.29 | 0.23 (0.06) | |||||
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Betweenness centralization | 0.21 |
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0.31 | 0.27 | 0.23 (0.08) | |||||
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Eigenvector centralization | 0.28 |
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0.45 | 0.17 | 0.32 (0.12) |
aOutcome measures during Ebola virus disease active time period are in italics.
Individual respondent characteristics, environment, and the timing of data collection have been shown to influence response rates [
Understanding the negative changes in patient safety outcomes that occurred during the EVD public health emergency also requires examination of corresponding changes to the hospital work environment. The hospital work environment has been shown to influence patient safety outcomes, including rates of falls, medication errors, and HAPUs [
Communication among providers has been shown to influence patient safety outcomes [
Retrospective recognition during data analysis that a natural experiment had occurred may be viewed as a study limitation. Generalizability of the findings is influenced by the specific hospital characteristics and inclusion of only 10 units at two hospitals. We recognize that additional factors, such as unit culture or other local events concurrent with the EVD outbreak, may have influenced the nursing unit network communication patterns and unit outcome measures. However, we had not planned to measure unit culture in the larger study and were unable to measure culture and other possible factors retrospectively. Although additional research is needed, the viability of using social network analysis to study how external events influence communication and patient outcomes is promising.
This paper reported on a natural experiment that occurred during data collection for a longitudinal study designed to explore nursing unit communication patterns through the use of social network analysis. The natural experiment occurred when the first case of EVD in the United States was diagnosed in a hospital blocks away from one of our data collection sites. Findings presented in this paper focused on the 10 units that had data collection results available before, during, and after the EVD crisis period. Units in the hospital site in the same city as the EVD case exhibited negative changes in patient outcomes, network communication metrics, and response rates.
Unit 16, Baseline data collection. The day shift is shown in gray and the night shift is shown in black. The numbers designate the individuals. PCT: patient care technician; RN: registered nurse; RN Charge (head registered nurse); UC: unit clerk.
Unit 16, Month 1 data collection. Active Ebola virus disease period. The day shift is shown in gray and the night shift is shown in black. The numbers designate the individuals. PCA: patient care assistant; PCT: patient care technician; RN: registered nurse; UC: unit clerk.
Unit 16, Month 4 data collection. Decrease in diffusion and density. The day shift is shown in gray and the night shift is shown in black. The numbers designate the individuals. PCT: patient care technician; RN: registered nurse; UC: unit clerk.
Unit 16, Month 7 data collection. Increase in average distance; decrease in density and diffusion. The day shift is shown in gray and the night shift is shown in black. The numbers designate the individuals. PCA: patient care assistant; PCT: patient care technician; RN: registered nurse.
Baseline
US Centers for Disease Control and Prevention
Ebola virus disease
hospital-acquired pressure ulcer
Institutional Review Board
Month 1
Month 4
Month 7
medication error
Measuring Network Stability and Fit
patient care technician
patient care unit
personal protective equipment
persons under investigation
registered nurse
individual patient care unit response rate
social network analysis
unit clerk
None declared.