Literature survery in Emergecny department
|Authors and Year||Details of work done||Types of chart||Variables||Types of study||Country|
|Kaminsky et al. ||Recommended the use of SPC to analyse the quality indicators in healthcare organizations. The use and interpretation of p chart was preferred. The examples of use of SPC for number of patients leaving Emergency departments and number of birth with caesarean were shown.||p chart||Number of patients leaving ED, Number of birth with caesarean||Retrospective Study||US|
|Callahan and Griffen ||Recommended the use of SPC to reduce door to reperfusion time of patients. After the successful intervention, it was found that there was run of 14 data points below the centre line which showed huge improvement in the process. The efforts were in right direction and helpful in reducing the door to reperfusion times. Initially the upper control limit of data was 206 minutes which was reduced to 140 minutes.||X bar chart||Door to reperfusion time||Longitudinal Study||US|
|Gilligan and Walters ||Reduced the hospital mortality rate by reducing medical errors and timely interventions to facilitate flow of patient. After intervention, there was punctuality in daily senior medical review and in planning of discharge. The early warning training allowed reducing the mortality rate in Emergency department which is reflected in hospital standardised mortality rate.||Run chart and CUSUM||Mortality rate, Medical outliers||Longitudinal Study||UK|
|Minne et al. ||The authors made use of SPC in order to validate a classification tree model for estimating mortality rate. It was investigated that proposed tree model did not provide the reliable results and hence could not be used for purpose of benchmarking. It should be noted that in order to identify the high risk subgroup, at least two out of three pre-identified subgroups could be used.||EWMA||Mortality rate||Retrospective Study||Netherlands|
|Moran et al. ||Implemented Statistical Process Control (SPC) to control the mortality in the intensive care society of Australia and New Zealand. Initially the average raw mortality was 14.07%. The raw average and standard deviation for mortalities ranged from 0.012 and 0.113 to 0.296 and 0.457 respectively whereas the expected mortality average and standard deviation ranged from 0.013 and 0.045 to 0.278 and 0.247 respectively. Out of control signalling was shown for raw mortality series in risk adjusted exponential weighted moving average chart. It was recommended to hospital to investigate the assignable causes of variations.||EWMA||Mortality rate||Retrospective Study||Australia|
|Harrou et al. ||Decreased the time for detection of abnormal daily patient arrivals at the Paediatric Emergency department. Auto regressive moving average model was used to analyse the data collected during January–December 2011. The used model provided positive results in terms of detection of abnormal patient arrivals. The benefit of early detection of abnormal situations was to encourage the control of these conditions.||EWMA||Daily attendance||Retrospective Study||France|
|Pagel et al. ||The authors found the initial and final point of winter surge in intensive care unit. Initially the optimal Bollinger band thresholds were found to be 1.2 and 1 standard deviation above and below of 41 day demand moving average respectively. In the end, positive results were obtained as surge was identified from 18th November 2013 to 4th January 2014.||Run chart||Demand for retrieval||Retrospective Study||UK|
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