Open Access
Review
Table 2
Literature survery in Surgery department
| Author and Year | Details of work done | Types of chart | Variables | Types of study | Country |
|---|---|---|---|---|---|
| Levett and Carey [30] | Presented various examples of SPC to monitor the coronary artery bypass grafting mortality, length of stay and admission rate in intensive care unit. The authors ended up with conclusion that control charts allow making the process stable by detecting and eliminating the special or assignable causes of variations. |  Chart |
Mortality rate, length of stay and admission time | Longitudinal study | US |
| Mohammed [31] | Recommended the use of statistical process control to improve the quality of healthcare. The examples surgeon specific mortality rate following colorectal cancer surgery was presented with the help of  chart. In addition, background feature of control chart and how it transformed to healthcare were also provided. |
 Chart |
Mortality rate | Retrospective study | UK |
| Duclos and Voirin [32] | Provided essential points on how to develop and interpret SPC for healthcare practice. The implementation of p chart into healthcare practice required the thorough investigation of the root causes of the variations identified. The every quality initiative was must supported by the top management to be successful. Implementation of p chart also encouraged the healthcare practitioner to do self-examination of the care delivered. | p chart | Number of complication | Retrospective Study | France |
| Jones and Steiner [33] | Determined the performance of risk adjusted CUSUM chart in the presence of estimation error. Estimation error effect on risk adjusted CUSUM chart performance was evaluated and found to have significant effect in terms of variation of true average run length (ARL). It is estimated that main source of estimation error was the overall adverse event. | CUSUM Chart | Mortality rate of 30 days | Longitudinal study | Australia |
| Smith et al. [34] | The authors checked the daily morbidity and mortality review of surgical process at a single site. The variables like length of stay or readmission less than 28 days, bypass duration, acute pre and post-procedural complications etc. were found to be suitable for monitoring healthcare outcomes. The special causes of variation were evaluated deeply and assigned to variations in healthcare practice as well as individual performance. | CUSUM and EWMA | Length of stay, complications. | Longitudinal study | Australia |
| Keller et al. [35] | Initiated the SPC in order to identify the length of stay (LOS) outliers and evaluate the effect of process improvement. The out of control points were identified and these points indicated longer operating times and temporary nursing at discharge. Higher readmission rate was identified for outliers as emergent open, emergent lap and elective lap. | Run Chart | Length of Stay | Retrospective study | US |
| Oguntunde et al. [36] | Identified the importance of SPC to healthcare with application of real data set. The XMR control chart from the previous study showed that only one sample point was outside the upper control limit (i.e Surgeon H) while the p-chart in this article reveals that three sample points (i.e., Surgeons E, H and J) were outside the upper control limit. Surgeons corresponding to sample points outside the control limits were hazardous to the patients as well as to the health institution. | p chart | Proportions of death | Retrospective study | Nigeria |
| Woodall et al. [37] | The authors made use of SPC to reduce surgical site infections and mortality. The positive results were obtained as the reduction in mortality rate from 3.7% to 1.8% which shows approximately 50% decline in mortality rate. According to the hospital administrative department, approximately 20,000 surgical procedure were carried out in a year and decrease in mortality rate caused approximately saving of 300 lives. | Run Chart | Rate of surgical site infections and 30 day mortality | Longitudinal study | US |
| Schrem et al. [38] | Determined the utility of G chart for healthcare process control. G chart examined the changes in detection rate of cancer during aftercare and no significant relation was found with identified risk factor for cancer free survival i.e., showing p value less than 0.05. There was increment in SIR for renal cell carcinoma (SIR = 22.46) and post-transplant lymph proliferative disorder (SIR = 8.36). | G chart | Site Specific standardized incidence ratio (SIR) | Retrospective study | Germany |
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