Integrating Quality Tools and Methods to Analyze and Improve a Hospital Sterilization Process

Improving the quality of hospital sterilization process using failure modes and effects analysis, fuzzy logic, and machine learning: experience in tertiary dental centre

Activities practiced in the hospital generate several types of risks. Therefore, performing the risk assessment is one of the quality improvement keys in the healthcare sector. For this reason, healthcare managers need to design and perform efficient risk assessment processes. Failure modes and effects analysis (FMEA) is one of the most used risk assessment methods. The FMEA is a proactive technique consisting of the evaluation of failure modes associated with a studied process using three factors: occurrence, non-detection, and severity, in order to obtain the risk priority number using fuzzy logic approach and machine learning algorithms, namely the support vector machine and the k-nearest neighbours. The proposed model is applied in the case of the central sterilization unit of a tertiary national reference centre of dental treatment, where its efficiency is evaluated compared to the classical approach. These comparisons are based on expert advice and machine learning performance metrics. Our developed model proved high effectiveness throughout the results of the expert's vote (she agrees with 96% fuzzy-FMEA results against 6% with classical FMEA results). Furthermore, the machine learning metrics show a high level of accuracy in both training data (best rate is 96%) and testing data (90%). This study represents the first study that aims to perform artificial intelligence approach to risk management in the Moroccan healthcare sector. The perspective of this study is to promote the application of the artificial intelligence in Moroccan health management, especially in the field of quality and safety management.

Scaling up evidence-based treatments in youth behavioral healthcare: Social work licensing influences on task-shifting opportunities

Youth behavioral healthcare workforce shortages have inhibited the scale-up of evidence-based treatments to address longstanding unmet needs andinequitable service coverage. Task-shifting is a strategy that could bolster workforce shortages. Legal and regulatory barriers, such as scope of practice licensing regulations, have hampered the use of task-shifting. Social workers make up the majority of the behavioral healthcare workforce in the U.S. and most social workers provide services to children and families. As such, social workers would play a pivotal role in any scale-up effort. In this guest editorial, we discuss the importance of social work licensing and use a case example to illustrate the unintended consequences that certain licensing regulations have on scaling-up evidence-based treatments via task-shifting. We conclude with recommendations on how social workers could be involved in taskshifting efforts to scale-up evidence-based treatments.