BACKGROUND: Routine clinical biochemistry tests are crucial for clinical diagnostics and play a key role in enhancing outpatient turnover efficiency and patient satisfaction. This study aimed to implement Lean Six Sigma in the biochemistry laboratory of a hospital in China to improve efficiency and quality by reducing turnaround time.
METHODS: The study was conducted from January to December 2023, using the DMAIC (Define, Measure, Analyze, Improve, Control) framework, and employed tools such as the voice of the customer, Value Stream Mapping, \'5 whys\' technique, Nominal Group Technique, and Pareto chart.
RESULTS: The turnaround time for outpatient routine clinical biochemistry tests was reduced from 139 min to 58 min (p < 0.05), effectively increasing both patient and physician satisfaction.
CONCLUSIONS: Lean Six Sigma aimed to reduce the turnaround time for biochemical tests have significant advantages. This study confirms the effectiveness of Lean Six Sigma in a Chinese clinical laboratory setting and provides guidance for optimizing efficiency in global clinical laboratories with limited implementation experience, constrained technical and equipment resources, and high demand for medical diagnostics.

Under Taiwan\'s National Health Insurance (NHI) system, it\'s crucial for all healthcare providers to accurately submit medical expense claims to the National Health Insurance Administration (NHIA) to avoid incorrect deductions. With changes in healthcare policies and adjustments in hospital management strategies, the complexity of claiming rules has resulted in hospitals expending significant manpower and time on the medical expense claims process. Therefore, this study utilizes the Lean Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) management approach to identify wasteful and non-value-added steps in the process. Simultaneously, it introduces Robotic Process Automation (RPA) tools to replace manual operations. After implementation, the study effectively reduces the process time by 380 min and enhances Process Cycle Efficiency (PCE) from 69.07 to 95.54%. This research validates a real-world case of Lean digital transformation in healthcare institutions. It enables human resources to be allocated to more valuable and creative tasks while assisting hospitals in providing more comprehensive and patient-centric services.

To utilize lean six sigma (LSS) and failure model and effect analysis (FMEA) to prevent dispensing errors in a Chinese teaching hospital.
Medication errors (MEs) reported to the China Core Group of the international network for the rational use of drugs (INRUD) by pharmacists at the hospital were collected. Following LSS methodology, the data analysis was structured according to define, measure, analyse, improve, and control (DMAIC) phases, and typical LSS tools (Pareto diagrams, brainstorming sessions) were used to determine the risk factors leading to dispensing errors. FMEA was applied to generate the risk priority numbers (RPNs) of MEs events, and key medications targeted for error prevention strategies were identified through quantitative analysis of the impacts of failure. Finally, corrective measures to prevent MEs were implemented and monitored for efficacy.
Before the implementation of this programme, a total of 603 cases of dispensing errors were reported from the Year 1 to Year 6, reaching an average rate of incidence of 0.33 cases per 10 000 medication orders delivered, and no difference was found between these years (P = .9424). There was also no difference as location, error type, contributing factors, cause classification were considered. We then determined the real cause behind dispensing errors, and a total of 67 medications were targeted for specific error prevention strategies. One year after intervention, progress had been achieved in the following aspects: the incidence rate of dispensing errors was significantly decreased compared with the previous years (0.19, P = .007). Simultaneously, the incidence rate of dispensing errors occurred in outpatient pharmacy (0.04, P = .0008), with junior pharmacists (0.15, P = .0258), with LASA medications (0.06, P = .0319), as well as with memory-based errors were significantly decreased (0.03, P = .0191).
The combination of LSS and the FMEA tool can be an efficient approach for helping reduce MEs in pharmacy dispensing.

Unplanned surgery cancellation (USC) was an important quality management issue in the course of medical care for surgical patients, which caused inappropriate use of hospital resources and had negative impacts on quality and safety. This study used Lean Six Sigma to reduce the incidence of USC. Following the Lean Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) process, the main factors influencing the USC were identified, such as the time of informing patient admission, the time of submitting operation notice, and the management of test report follow-up. A series of measures were implemented including improving the health education content of virtual bed patients, standardizing the way of communication between the Admission Management Center and the patients, improving the timing of anesthesia evaluation, optimizing the process of operation notice with an information system, and implementing the regulations of virtual bed management. The incidence of USC reduced from 10.21% in Jan. 2016 to 3.8% in Dec. 2016, and the Z-score increased from 1.25 to 1.68, which improved patient safety and demonstrated that Lean Six Sigma was an effective method to solve cross-department issues in hospital.

OBJECTIVE: This study aims to reduce cesarean section rate and increase rate of vaginal delivery.
METHODS: By using Lean Six Sigma (LSS) methodology, the cesarean section rate was investigated and analyzed through a 5-phase roadmap consisting of Define, Measure, Analyze, Improve, and Control. The principal causes of cesarean section were identified, improvement measures were implemented, and the rate of cesarean section before and after intervention was compared.
RESULTS: After patients with a valid medical reason for cesarean were excluded, the main causes of cesarean section were maternal request, labor pain, parturient women assessment, and labor observation. A series of measures was implemented, including an improved parturient women assessment system, strengthened pregnancy nutrition guidance, implementation of painless labor techniques, enhanced midwifery team building, and promotion of childbirth-assist skills. Ten months after introduction of the improvement measures, the cesarean section rate decreased from 41.83% to 32.00%, and the Six Sigma score (ie, Z value) increased from 1.706 to 1.967 (P < .001).
CONCLUSIONS: LSS is an effective way to reduce the rate of cesarean section.