<p>A) Strong connections (darker lines) between hospitals (circles) are regionally bound, and form a strong community structure (circle colours). B) The distribution of connection weights shows that most connections between hospitals are weak (i.e. few shared patient between hospitals). C) The degree of a hospital (the number of hospitals it is connected to by shared patients) is related to the total number of patients it exchanges with other hospitals (hospital strength).</p
<p>Hospitals in the HAI-specific network (HAISN) (n = 1266), suspected-HAI network (SHAIN) (n = 1975...
We examine how spatial and social structures interact to shape the network of patient transfer relat...
(a) The largest network consisting of eight variables, out of 83 variables. Note: The thicknesses of...
<p>The distribution of centrality of hospitals in England, showing A) closeness centrality (at <i>α<...
Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-ass...
<p>A) The closest hospitals, within 200km, are also closest in the network, but hospitals further aw...
<div><p>The topology of the patient flow network in a hospital is complex, comprising hundreds of ov...
Hospital-acquired infections (HAI) are often seen as preventable incidents that result from unsafe p...
<div><p>Hospital-acquired infections (HAI) are often seen as preventable incidents that result from ...
In the present paper we adopt a relational perspective to study patient mobility flows among regions...
Hospital-acquired infections (HAI) are often seen as preventable incidents that result from unsafe p...
We applied social network analyses to determine how hospitals within Orange County, California, are ...
Effective sharing of clinical information between care providers is a critical component of a safe, ...
Network models of healthcare systems can be used to examine how providers collaborate, communicate, ...
We applied social network analyses to determine how hospitals within Orange County, California, are ...
<p>Hospitals in the HAI-specific network (HAISN) (n = 1266), suspected-HAI network (SHAIN) (n = 1975...
We examine how spatial and social structures interact to shape the network of patient transfer relat...
(a) The largest network consisting of eight variables, out of 83 variables. Note: The thicknesses of...
<p>The distribution of centrality of hospitals in England, showing A) closeness centrality (at <i>α<...
Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-ass...
<p>A) The closest hospitals, within 200km, are also closest in the network, but hospitals further aw...
<div><p>The topology of the patient flow network in a hospital is complex, comprising hundreds of ov...
Hospital-acquired infections (HAI) are often seen as preventable incidents that result from unsafe p...
<div><p>Hospital-acquired infections (HAI) are often seen as preventable incidents that result from ...
In the present paper we adopt a relational perspective to study patient mobility flows among regions...
Hospital-acquired infections (HAI) are often seen as preventable incidents that result from unsafe p...
We applied social network analyses to determine how hospitals within Orange County, California, are ...
Effective sharing of clinical information between care providers is a critical component of a safe, ...
Network models of healthcare systems can be used to examine how providers collaborate, communicate, ...
We applied social network analyses to determine how hospitals within Orange County, California, are ...
<p>Hospitals in the HAI-specific network (HAISN) (n = 1266), suspected-HAI network (SHAIN) (n = 1975...
We examine how spatial and social structures interact to shape the network of patient transfer relat...
(a) The largest network consisting of eight variables, out of 83 variables. Note: The thicknesses of...