Implementation of lean practices to reduce healthcare associated infections

Ferraro, Anna; Centobelli, Piera; Cerchione, Roberto; Cicco, Maria Vincenza Di; Montella, Emma; Raiola, Eliana; Triassi, Maria; Improta, Giovanni

doi:10.1504/ijhtm.2020.116783

Cited by 23 publications

(11 citation statements)

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“…Finally, processes to identify causes and solutions of the most critical cases were not analyzed. Approaches such as Lean Six Sigma [ 47 , 48 , 49 ] or Fuzzy Logic [ 50 ] have been shown to be valid supports for reducing the risk of infection and will, therefore, be the subject of future studies.…”

Section: Discussionmentioning

confidence: 99%

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

Scala

Loperto

Triassi

et al. 2022

IJERPH

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Background: Surgical site infections (SSIs) have a major role in the evolution of medical care. Despite centuries of medical progress, the management of surgical infection remains a pressing concern. Nowadays, the SSIs continue to be an important factor able to increase the hospitalization duration, cost, and risk of death, in fact, the SSIs are a leading cause of morbidity and mortality in modern health care. Methods: A study based on statistical test and logistic regression for unveiling the association between SSIs and different risk factors was carried out. Successively, a predictive analysis of SSIs on the basis of risk factors was performed. Results: The obtained data demonstrated that the level of surgery contamination impacts significantly on the infection rate. In addition, data also reveals that the length of postoperative hospital stay increases the rate of surgical infections. Finally, the postoperative length of stay, surgery department and the antibiotic prophylaxis with 2 or more antibiotics are a significant predictor for the development of infection. Conclusions: The data report that the type of surgery department and antibiotic prophylaxis there are a statistically significant predictor of SSIs. Moreover, KNN model better handle the imbalanced dataset (48 infected and 3983 healthy), observing highest accuracy value.

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Section: Discussionmentioning

confidence: 99%

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

Scala

Loperto

Triassi

et al. 2022

IJERPH

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“…Indeed, different methodologies have been used to define, measure, analyze, improve, and control the onset of healthcare-associated infections that compromise patient safety [ 7 ]. Nowadays, the monitoring and prevention of healthcare-associated infections (HAIs) are a priority for the healthcare sector [ 8 , 9 ]. The SSI are the most common type of HAI, contributing to significant morbidity, costs, and deaths [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Inference System for the Assessment of Indoor Air Quality in an Operating Room to Prevent Surgical Site Infection

Colella

Valente

Rossano

et al. 2022

IJERPH

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Indoor air quality in hospital operating rooms is of great concern for the prevention of surgical site infections (SSI). A wide range of relevant medical and engineering literature has shown that the reduction in air contamination can be achieved by introducing a more efficient set of controls of HVAC systems and exploiting alarms and monitoring systems that allow having a clear report of the internal air status level. In this paper, an operating room air quality monitoring system based on a fuzzy decision support system has been proposed in order to help hospital staff responsible to guarantee a safe environment. The goal of the work is to reduce the airborne contamination in order to optimize the surgical environment, thus preventing the occurrence of SSI and reducing the related mortality rate. The advantage of FIS is that the evaluation of the air quality is based on easy-to-find input data established on the best combination of parameters and level of alert. Compared to other literature works, the proposed approach based on the FIS has been designed to take into account also the movement of clinicians in the operating room in order to monitor unauthorized paths. The test of the proposed strategy has been executed by exploiting data collected by ad-hoc sensors placed inside a real operating block during the experimental activities of the “Bacterial Infections Post Surgery” Project (BIPS). Results show that the system is capable to return risk values with extreme precision.

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“…Moreover, some studies also shown that implementing lean methodologies reduce patient waiting times, improve patient flow time, increase efficiency of services, improve patient outcome, and even decrease mortality [ 8 – 10 ]. A recent study also showed that implementation of lean six sigma approach could significantly decrease the risk of healthcare associated infections [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Lean Management Improves the Process Efficiency of Controlled Ovarian Stimulation Monitoring in IVF Treatment

Natadisastra

Firman

2022

Journal of Healthcare Engineering

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Background. Evaluation of patients’ experiences and satisfaction is vital for assessing the quality of healthcare service, including in fertility clinics. One promising concept that has recently been widely used to increase efficiency and service quality in hospitals is the lean concept. Lean is a form of philosophy that focuses on reducing waste of a process and continuous improvement so that consumers receive greater value. This study aims to identify waste and improve efficiency using lean management methods in the controlled ovarian stimulation (COS) monitoring process during in vitro fertilization (IVF) treatment in a fertility clinic. Methods. This study used an action research approach by observing the total service time of monitoring ovarian stimulation in IVF patients (n = 40). The identified waste and solutions were then compiled for use in a focus group discussion (FGD). From the FGD, a priority plan was obtained for the implementation of lean management. This study uses the PDCA cycle for improvement. Results. Three priority solutions were chosen, which are as follows: (1) evaluating ovarian stimulation via USG only; (2) allocating more time during doctor’s counselling; and (3) increasing counselling time by nurses in the injection room. The total patient wait time was reduced to 6 hours 32 minutes over the three visits, 13 hours 35 minutes decrease from before the intervention. In addition, the value-added ratio (VAR) was increased from 9% to 22% after the intervention. Conclusion. This research provides theoretical and practical contributions for the lean management principles in IVF treatment. The findings of this study will contribute to the pursuit of knowledge and dissemination of lean principles in the management of healthcare, including IVF clinics.

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Implementation of lean practices to reduce healthcare associated infections

Cited by 23 publications

References 0 publications

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

A Fuzzy Inference System for the Assessment of Indoor Air Quality in an Operating Room to Prevent Surgical Site Infection

Lean Management Improves the Process Efficiency of Controlled Ovarian Stimulation Monitoring in IVF Treatment

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