Sustaining Improvements of Surgical Site Infections by Six Sigma DMAIC Approach.

BACKGROUND: SSIs (surgical site infections) are associated with increased rates of morbidity and mortality. The traditional quality improvement strategies focusing on individual performance did not achieve sustainable improvement. This study aimed to implement the Six Sigma DMAIC method to reduce SSIs and to sustain improvements in surgical quality. The surgical procedures, clinical data, and surgical site infections were collected among 42,233 hospitalized surgical patients from 1 January 2019 to 31 December 2020. Following strengthening leadership and empowering a multidisciplinary SSI prevention team, DMAIC (Define, Measure, Analyze, Improve, and Control) was used as the performance improvement model. An evidence-based prevention bundle for reduction of SSI was adopted as performance measures. Environmental monitoring and antimicrobial stewardship programs were strengthened to prevent the transmission of multi-drug resistant microorganisms. Process change was integrated into a clinical pathway information system. Improvement cycles by corrective actions for the risk events of SSIs were implemented to ensure sustaining improvements. We have reached the targets of the prevention bundle elements in the post-intervention period in 2020. The carbapenem resistance rates of Enterobacteriaceae and P. aeruginosa were lower than 10%. A significant 22.2% decline in SSI rates has been achieved, from 0.9% for the pre-intervention period in 2019 to 0.7% for the post-intervention period in 2020 (p = 0.004). Application of the Six Sigma DMAIC approach could significantly reduce the SSI rates. It also could help hospital administrators and quality management personnel to create a culture of patient safety.

Traditional versus Microsphere Embolization for Hepatocellular Carcinoma: An Effectiveness Evaluation Using Data Mining.

Background: For hepatocellular carcinoma ("HCC"), the current standard of treatment is hepatic artery embolization, generally through trans-catheter arterial chemoembolization ("TACE"). There are two types: traditional ("conventional" or "cTACE") and microsphere ("DC bead TACE"). Unfortunately, the literature comparing the relative effectiveness of cTACE versus DC bead TACE is inconclusive, partially due to the complexity of HCC and its response to treatment. Data mining is an excellent method to extract meaning from complex data sets. Purpose: Through the application of data mining techniques, to compare the relative effectiveness of cTACE and DC bead TACE using a large patient database and to use said comparison to establish usable guidelines for developing treatment plans for HCC patients. Materials and Methods: The data of 372 HCC patients who underwent TACE in Taichung Veterans General Hospital were analyzed. The chi-square test was used to compare the difference in the effectiveness of the two therapies was compared. Logistic regression was used to calculate the odds ratios. Furthermore, using the C4.5 decision tree, the two therapies were classified into applicable fields. Chi-square test, the t-test, and logistic regression were used to verify the classification results. Results: In Barcelona Clinic Stages A and B cancers, cTACE was found to be 22.7% more effective than DC bead TACE. By using the decision tree C4.5 as a classifier, the effectiveness of either treatment for small tumors was 8.475 times than that for large tumors. DC bead TACE was 3.39 times more successful in treating patients with a single tumor than with multiple tumors. For patients with a single tumor, the chi-square test showed that 100-300 µm microspheres were significantly more effective than 300-500 µm. While these findings provide a reference for the selection of an appropriate TACE approach, we noted that overall accuracy was somewhat low, possibly due to the limited population. Conclusions: We found that data mining could be applied to develop clear guidelines for physician and researcher use in the case of complex pathologies such as HCC. However, some of our results contradicted those elsewhere in the literature, possibly due to a relatively small sample size. Significantly larger data sets with appropriate levels of granularity could produce more accurate results.

Applying Data Mining to Investigate Cancer Risk in Patients with Pyogenic Liver Abscess.

Pyogenic liver abscess is usually a complication of biliary tract disease. Taiwan features among the countries with the highest incidence of colorectal cancer (CRC) and hepatocellular carcinoma (HCC). Few studies have investigated whether patients with pyogenic liver abscess (PLA) have higher incidence rates of CRC and HCC. However, these findings have been inconclusive. The risks of CRC and HCC in patients with PLA and the factors contributing to cancer development were assessed in these patients. The clinical tests significantly associated with cancers in these patients with PLA were determined to assist in the early diagnosis of these cancers. Odds ratios (ORs) and 95% confidence intervals (CIs) were determined using binary logistic regression Cancer classification models were constructed using the decision tree algorithm C5.0 to compare the accuracy among different models with those risk factors of cancers and then determine the optimal model. Thereafter, the rules were summarized using the decisi8on tree model to assist in the diagnosis. The results indicated that CRC and HCC (OR, 3.751; 95% CI, 1.149-12.253) and CRC (OR, 6.838; 95% CI, 2.679-17.455) risks were higher in patients with PLA than those without PLA. The decision tree analysis demonstrated that the model with the PLA variable had the highest accuracy, and that classification could be conducted using fewer factors, indicating that PLA is critical in HCC and CRC. Two rules were determined for assisting in the diagnosis of CRC and HCC using the decision tree model.