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Paper text:
patients seen in the ed between january 1 , 2014 and may 31 , 2015 ( either discharged from the ed or admitted to general internal medicine or critical care medicine ) were identified .
variables of interest were extracted from the emr of a simple random sample of 3,000 of these patients .
variables of interests included demographics ( age , gender , and ethnicity ) , mean of arrival to the ed ( ambulance , other , missing ) , date and time of clinical events ( ed admission , admission order placed , and transfer to inpatient bed ) , ed mews , and defining variables ( ed mews was defined as mews from the time the patient was admitted to the ed up until the time the patient was transferred to an inpatient bed ) . in addition , outcomes of interest including admission to the hospital , mortality , and date of discharge were extracted .
analysis was performed by a member of the biostatistics core . the number and percentage of ed patients discharged and admitted to the hospital are reported .
demographics and ed mews ( mean , maximum , and median ) of patients not admitted to the hospital are compared with those admitted to the hospital by using pearson 's chi - squared or wilcoxon rank - sum tests .
those patients admitted to the hospital are categorized by admission disposition ( icu , high dependency unit ( hdu ) , or general ward ) , and variables are compared using pearson 's chi - squared , fisher 's exact , or kruskal  wallis tests .
logistic regression was performed to determine whether the mean and maximum ed mews is associated with admission disposition after adjusting for variables . to determine the association of the mews with inpatient mortality , the aforementioned analysis was repeated .
2,422 ( 80.7% ) patients were seen in the ed and not admitted to the hospital ; 578 ( 19.3% ) patients were admitted to general internal medicine or critical care medicine . of these ,
2,147 ( 1,574 not admitted to the hospital and 573 admitted to the hospital ) had auto - calculated mews in their emr and were included for analysis .
patients who were admitted to the hospital were older , got to the ed by ambulance , and were more likely to be male than female ( table 2 ) .
they had a higher mean , maximum , and median ed mews than patients not admitted to the hospital ( 1.1 vs. 0.2 , 2 vs. 1 , and 1 vs. 0 , respectively ; p<0.0001 ) even after adjusting for age , gender , ethnicity , and mode of arrival .
patients admitted to the icu had a higher mews than those admitted to hdu and general ward after adjusting for other variables ( table 3 ) .
demographics and characteristics of the sample ed mews association with admission to the hospital patients excluded from model if they had missing values for mews score or ethnicity .
patients excluded from model if they had missing values for mews . patients who died ( n=21 ) during the encounter had a higher mean , maximum , and median ed mews than patients who did not die ( medians of 2.6 vs. 0.3 , 4 vs. 1 , 3 vs. 1 , respectively ; p<0.0001 ) ( table 4 ) .
there was a mild and statistically significant relationship between los and mean , maximum , and median ed mews , as shown in table 5 .
ed mews association with mortality patients excluded from model if they had missing values for mews , ethnicity , or mode of arrival .
similar results were found in a study done in south africa ; the proportion of admitted patients increased as the mews increased .
however , the mean mews among admitted patients ( 4 ) and non - admitted ( 2.7 ) patients in their study were much higher than the mean mews of our patients ( 2 ) .
a more recent study based on a us national survey found that for every 1 point increase in the mews , patients were 33% more likely to be admitted to the hospital ( aor=1.33 ) ( 8) .
in - hospital mortality has been associated with higher mews in multiple studies . in the validation study ,
a maximum mews of 5 was associated with an increased risk of death ( or 5.4 ) , icu admission ( or 10.9 ) , and hdu admission ( or 3.3 ) ( 1 ) . in another study , the mean mews was higher among those patients who died ( 4.5 ) than those who lived ( 3.8 ) ( 2 ) .
although the association of mean , maximum , and median mews with los was statistically significant , it is weak and probably not clinically relevant .
although there is no protocol guiding patient disposition based on mews , admitting physicians were not blinded to the ed mews and may have used these scores in their decisions about patient disposition . because of the retrospective nature of this study , there were missing individual mews .
we therefore had to exclude patients from analysis and could have introduced selection bias ; however , most of the patients without mews were not admitted to the hospital . despite our limitations
we analyzed many individual ed mews , our sample size was large , and we had very few exclusion criteria , making our results more generalizable . to have the impact on quality of care and mortality that has been described in the past ( 4 , 9 , 10 )
, the mews has to be implemented and used in a systematic and protocolized way .
health - care personnel must remember that this , as any other triage system , should just support clinical decision making .
as one study suggested ( 10 ) , we believe that the use of emr is helpful when implementing and using the mews and might have eased its implementation at our institution .
physicians have real - time access to auto - calculated mews and graph trends , which could be beneficial when providing care .
we also provide evidence that this association is maintained when using an auto - calculated mews , based on vital signs documented in the emr .
there is convincing evidence that ed mews is associated with higher odds of admission to the hospital , admission to icu and hdus , and mortality .
further studies might explore inpatient mews association with patient 's flow through the hospital including transfer to different level of care , discharge , and effects of its implementation on quality and mortality at our institution .
backgroundgeisinger health system implemented the modified early warning score ( mews ) in 2011 and is fully integrated to the electronic medical record ( emr ) . 
 our objective was to assess whether the emergency department ( ed ) mews ( auto - calculated by emr ) is associated with admission to the hospital , admission disposition , inpatient mortality , and length of stay ( los ) 4 years after its implementation.methodsa random sample of 3,000 patients first encounter in the ed was extracted in the study period ( between january 1 , 2014 and may 31 , 2015 ) . 
 logistic regression was done to analyze whether mean , maximum , and median ed mews is associated with admission disposition , mortality , and los.resultsmean , maximum , and median ed mews is associated with admission to the hospital , admission disposition , and mortality . 
 it correlates weakly with los.conclusionmews can be integrated to the emr , and the score automatically generated still helps predict catastrophic events . 
 mews can be used as a triage tool when deciding whether and where patients should be admitted .