The Effect of Comprehensive Use of PDCA and FMEA Management Tools on the Work Efficiency, Teamwork, and Self‐Identity of Medical Staff: A Cohort Study with Zhongda Hospital in China as an Example

Chen, Hui; Zhao, Tao; Zhou, C.; Zhao, S. J.; Xing, Yue; Min, Lu

doi:10.1155/2022/5286062

Cited by 5 publications

(3 citation statements)

References 30 publications

(27 reference statements)

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“…This will improve the effectiveness of clinical treatment. Refined management can improve the comprehensive quality of medical staff in the operating room, develop good working habits, and refine their professional abilities [27][28][29][30]. It was found in this study that under the refined management, the comprehensive quality of medical staff improved from (13.87±5.89) to (18.21±5.30), and satisfaction rate was improved.…”

Section: Discussionmentioning

confidence: 82%

Effect of refined management in operating room nursing on surgical efficiency and nursing satisfaction during laparoscopic radical resection of colon cancer

Pan

2024

Am J Transl Res

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Aim: To assess the effect of refined management in the operating room nursing on surgical efficiency and nursing satisfaction during laparoscopic radical resection of colon cancer. Methods: In this retrospective study, 100 patients with laparoscopic radical resection of colon cancer were enrolled into this study. There were 51 patients who received refined management (the observation group) and 49 patients who received routine nursing intervention (the control group). The effect of refined management in the operating room nursing was evaluated by comparing the surgical efficiency, quality of care ratings, pain scores, and the nursing satisfaction between the two groups. Results: The preoperative preparation time, surgical time, intraoperative bleeding volume, and time to first postoperative defecation in the observation group were all less than those in the control group after nursing intervention (all P<0.05). The observation group had higher scores than the control group in five categories: operating room environment and safety, drug and instrument management, hygiene and sterilization, nursing records, and nursing professionalism (all P<0.05). The numerical rating scale (NRS) pain scores of the patients in the observation group were lower than those of the control group at 12, 24, and 48 hours postoperatively (all P<0.05). The rate of satisfaction in the observation group was 96.1%. This was higher than the 91.8% in the control group (P<0.05). The multivariate regression analysis demonstrated that refined management intervention is an independent factor for patients' prognosis. Conclusion: The implementation of a refined management model in the operating room is effective in improving the quality of surgical care and surgical efficiency, and increasing patient satisfaction with nursing staff.

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Section: Discussionmentioning

confidence: 82%

Effect of refined management in operating room nursing on surgical efficiency and nursing satisfaction during laparoscopic radical resection of colon cancer

Pan

2024

Am J Transl Res

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“…There is a wealth of global evidence confirming that hospital digitalization indeed leads to improved work efficiency, enhanced healthcare quality, and strengthened collaboration and communication (16,17). As reported by Smart et al, online consultation within South West England effectively mitigated the workload and stress of healthcare professionals during the COVID-19 pandemic (18).…”

Section: Introductionmentioning

confidence: 93%

The “Trinity” smart hospital construction policy promotes the development of hospitals and health management in China

Zhang

Gong

et al. 2023

Front. Public Health

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Recently, in order to comprehensively promote the development of medical institutions and solve the nationwide problems in the healthcare fields, the government of China developed an innovative national policy of “Trinity” smart hospital construction, which includes “smart medicine,” “smart services,” and “smart management”. The prototype of the evaluation system has been established, and a large number of construction achievements have emerged in many hospitals. In this article, the summary of this field was performed to provide a reference for medical workers, managers of hospitals, and policymakers.

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“…• a study on the comprehensive application of PDCA and FMEA in healthcare centers' management, whose findings revealed significant improvements in efficiency, in teamwork, and in the moral of the medical staff (Chen, H. et al, 2022). • an investigation on the allocation of medical staff and the corresponding occupation levels carried out by personnel of the Community Child Healthcare Medical Staff (CCHCMS) of Guangzhou in China revealed understaffing problems and high risk of staff turnover.…”

Section: Introductionmentioning

confidence: 99%

Developing Models Based on Machine Learning Algorithms to Estimate Medical Staffing Needs at Intensive Care Units

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Generally, hospitals and health centers have a specialized area called Intensive Care Unit (ICU) aimed to provide treatment for people who are extremely ill. An ICU is staffed with highly trained professionals who operate advance equipment. Patients are referred to ICUs when they have a condition that cannot be treated in a regular hospital room. For example: after being in a serious car accident, after having a heart attack or stroke, when suffering a serious infection such as pneumonia, or after having major surgery. The present research focusses in the development of prediction models based on machine learning algorithms to help planning and allocating medical staff at an ICU. The research was carried out following a classic 4-phase methodology: analysis, design, development, and validation. In the analysis, independent variables such as patient's age, gender, reason for visit, and arrival mode are identified. Data are then pre-processed and prepared for the following phases. Specialize software packages for data mining and machine learning were selected for the investigation. Most of the investigation's activities were planned at this point. During the design, different subsets of the independent variables were selected to be build prediction models. The size of the training and test set as well as the size of the validation set were also defined here. The phases of development and validation were carried out entirely using the software WEKA 3.9.6. By means of executing a battery of experiment and "trial and error" adjustments the prediction models were completed. To accomplish the goals of this research, thousands of records collected from a public hospital were considered. The dataset was divided in a part for training and test (80%) and another part for validation (20%). The approach to predict the medical specialty require for the arriving patients considered the development of models based on artificial neural networks and decision trees. In all cases, instead of a simple hold-out, a 10-fold crossvalidation scheme was applied. Results showed that, in general, prediction models with fewer classes generate higher rates of correct predictions and, therefore, they can be more useful when planning and allocating medical staff at an ICU. Even though correct prediction rates fluctuate significantly from one model to another, between 80% and 83%, some of the proposed models are reliable. In conclusion, results showed that prediction models based on machine learning algorithms can help planning and managing the need for professionals and support staff at ICU. The research might help understand the benefits of using machine learning in hospitals and health centers.

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The Effect of Comprehensive Use of PDCA and FMEA Management Tools on the Work Efficiency, Teamwork, and Self‐Identity of Medical Staff: A Cohort Study with Zhongda Hospital in China as an Example

Cited by 5 publications

References 30 publications

Effect of refined management in operating room nursing on surgical efficiency and nursing satisfaction during laparoscopic radical resection of colon cancer

Effect of refined management in operating room nursing on surgical efficiency and nursing satisfaction during laparoscopic radical resection of colon cancer

The “Trinity” smart hospital construction policy promotes the development of hospitals and health management in China

Developing Models Based on Machine Learning Algorithms to Estimate Medical Staffing Needs at Intensive Care Units

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