Failure mode and effect analysis (FMEA) to identify and mitigate failures in a hospital rapid response system (RRS)

Application of failure mode and effect analysis in ICU admission of potentially COVID-19 infected patients

BACKGROUND

To analyze the admission and treatment process of potentially COVID-19-infected patients in the intensive care unit under normalization, prevention, and control of the pandemic.

METHODS

A multidisciplinary team was assembled to develop a flowchart of potentially COVID-19-infected patients admitted to the intensive care unit and identify potential failure steps and modes throughout the process using the failure mode and effect analysis method. Through risk priority number (RPN) analysis of each failure mode, those with the highest impact on nosocomial infection were identified, and the priority of implementation was determined. Related corrective measures have been developed to continuously improve clinical practice and management.

RESULTS

Eighty potential failure modes were identified, and 8 potential failure modes were identified with RPNs greater than 100. These high RPNs of the failure modes were associated with careless inquiries of epidemiological histories by nurses, inadequate implementation of management standards by nursing assistants, and exposure of attending physicians to potentially risky environments. Finally, 18 general corrective measures are proposed.

CONCLUSIONS

Application of the failure mode and effect analysis method for quality improvement is a powerful tool for predicting potential failures in the process and can suggest corrective measures that could help avoid nosocomial infection during a pandemic.

Evaluation of the failure effects of a screwing station using a new approached FMEA

Recognizing the importance of risk assessment and the large-scale industrial spread of network research, we developed a new approach to risk assessment.•The risk assessment takes into account the chains of impact between each level and the frequency of effects and their causes.•In contrast to the traditional FMEA methodology, we evaluate the frequency of occurrence and detectability not only at the level of causes but also at the level of effects.•All this is complemented by a toolkit of network research methodology. The new methodology is validated through a real industry example, which is a risk assessment of a screwdriver station.

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.

Vital signs and early warning score monitoring: perceptions of clinical staff about current practices and introducing an electronic rapid response system

Aims and objectives

This study investigated clinical staff perceptions of learning about current monitoring practices and the planned introduction of an electronic system for patient monitoring. The aim of this research was to evaluate the perceptions of clinical staff (nurses and doctors) about the perceived strengths and weaknesses of the current state of the rapid response system (RRS) and how those strengths and weakness would be affected by introducing an electronic RRS.

Methods

This research applied a descriptive study methodology. Two detailed sessions on demonstration on the electronic RRS for measuring and recording vital signs and the electronic Early Warning System (EWS) were followed by two structured surveys administered through an online portal (SurveyMonkey) for nurses and doctors working at Taranaki District Health Board. The study was planned and conducted between October 2020 and May 2021 at Taranaki Base Hospital, New Plymouth, New Zealand.

Results

We found that the perceptions of clinical staff were a combination of key practice issues with current manual monitoring, expectations of improved visibility of vital sign charts, better communication between staff and thus improved patient care with the introduction of an electronic system. A majority (24, 60%) of nurses reported that, when called to assess deteriorating patients, the responders arrive at bedside within 5–30 min and an additional 11 (27%) said the responders arrive within 5 min. That is a collective 87% responder arrival within 30 min

Conclusion

Staff believe that an electronic RRS could improve communication, speed up decision making and have a positive impact on patient outcomes.