In the October 2006 issue of Resuscitation, Smith etal.  published an article on the benefits of early warning associated with the monitoring and physiologic surveillance of patients in the hospital (ICU, principally). From the abstract of that publication:
“Hospitalised patients, who suffer cardiac arrest and require unanticipated intensive care unit (ICU) admission or die, often exhibit premonitory abnormalities in vital signs…”
“…It is possible for raw physiology data, early warning scores (EWS), vital signs charts and oxygen therapy records to be made instantaneously available to any member of the hospital healthcare team via the W-LAN or hospital intranet…”
From another source :
“Physiologic monitoring systems measure [pulse], blood pressure, …other vitals…Data about adverse events in hospitalized patients indicate…a majority of physiologic abnormalities are not detected early enough to prevent the event, even when some…abnormalities are present for hours before…[occurrence].”
Early warning and physiologic surveillance are not new concepts, whether in the ICU or elsewhere. What, perhaps, has evolved over the past 10 years or so since the formal introduction of the electronic medical record (EMR) is that the automated and complete collection of data normally charted within the EMR is necessary to support such early warning protocols (particularly outside of the ICU environment) so long as the data available are part of an integrated delivery system . That is, complete and contextual data are necessary to promote accurate early warning notifications that can be developed from multiple sources of data, inclusive of physiologic, laboratory, and demographic.
Early warning score predictors can include:
a. vital signs
b. laboratory test results
c. severity of illness scores
d. longitudinal chronic illness burden scores
e. transpired length of hospital stay, and
f. care directives
Escobar  reported that of 4,036 events from a cohort of 102,422 patients, modified early warning score c-statistics of 0.709 at 95% confidence. In comparison to EMR-based models, which had a c-statistic of 0.845. Best early warning performance was detected amongst those patients with gastrointestinal (GI) diagnoses (0,841) and worst amongst those with congestive heart failure (CHF) (0.683).
While performance was less robust among the modified early warning models compared with EMR-based models, the performance correlation between the two is encouraging. Perhaps a place exists for the use of early warning protocols and methods which can be based less on the availability of sophisticated information and more on the availability of data readily collected from the bedside.
 Smith GB, etal., “Hospital-wide physiological surveillance–a new approach to the early identification and management of the sick patient.” Resuscitation. 2006 Oct;71(1):19-28. Epub 2006 Aug 30.
 Yoder-Wise, Patricia S., Leading and Managing in Nursing: fifth Edition. Elsevier-Mosby. 2014. ISBN: 978-0-323-24183-0. Page 201
 Escobar etal., Early Detection of Impending Physiologic Deterioration Among Patients Who are Not in Intensive Care: Development of Predictive Models Using Data From an Automated Electronic Medical Record.” Journal of Hospital Medicine. 2012 Society of Hospital Medicine. DOI 10.1002/jhm.1929. Wileyonlinelibrary.com