Hazard identification in newly developed antimicrobials

Abstract Background Preventing medical errors is crucial, especially during crises like the COVID-19 pandemic. Failure Modes and Effects Analysis (FMEA) is the most widely used prospective hazard analysis in healthcare. FMEA relies on brainstorming by multi-disciplinary teams to identify hazards. This approach has two major weaknesses: significant time and human resource investments, and lack of complete and error-free results. Objectives To introduce the algorithmic prediction of failure modes in healthcare (APFMH) and to examine whether APFMH is leaner in resource allocation in comparison to the traditional FMEA and whether it ensures the complete identification of hazards. Methods The patient identification during imaging process at the emergency department of Sheba Medical Center was analyzed by FMEA and APFMH, independently and separately. We compared between the hazards predicted by APFMH method and the hazards predicted by FMEA method; the total participants’ working hours invested in each process and the adverse events, categorized as ‘patient identification’, before and after the recommendations resulted from the above processes were implemented. Results APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA: the former used 21 h whereas the latter required 63 h. Following the implementation of the recommendations, the adverse events decreased by 44% annually (P = 0.0026). Most adverse events were preventable, had all recommendations been fully implemented. Conclusion In light of our initial and limited-size study, APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA. APFMH is suggested as an alternative to FMEA since it is leaner in time and human resources, ensures more complete hazard identification and is especially valuable during crisis time, when new protocols are often adopted, such as in the current days of the COVID-19 pandemic.

Download Full-text

A case study on hazard identification and risk assessment in sewage treatment plant

Materials Today Proceedings ◽

10.1016/j.matpr.2021.03.540 ◽

2021 ◽

Author(s):

M. Ramaganesh ◽

T. Raj Pradeesh ◽

S. Naresh ◽

P. Venkumar ◽

R. Manikandan

Keyword(s):

Risk Assessment ◽

Sewage Treatment ◽

Sewage Treatment Plant ◽

Treatment Plant ◽

Hazard Identification

Download Full-text

Catalogue of hazards: a fundamental part for the safe design of surgical robots

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-0009 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Lukas Theisgen ◽

Florian Strauch ◽

Matías de la Fuente ◽

Klaus Radermacher

Keyword(s):

Development Process ◽

New Technologies ◽

State Of The Art ◽

Hazard Identification ◽

Surgical Robots ◽

Safety Measures ◽

Fundamental Part ◽

Phases Of Development ◽

Early Phases ◽

Intended Use

AbstractRisk classes defined by MDR and FDA for state-of-the-art surgical robots based on their intended use are not suitable as indicators for their hazard potential. While there is a lack of safety regulation for an increasing degree of automation as well as the degree of invasiveness into the patient’s body, adverse events have increased in the last decade. Thus, an outright identification of hazards as part of the risk analysis over the complete development process and life cycle of a surgical robot is crucial, especially when introducing new technologies. For this reason, we present a comprehensive approach for hazard identification in early phases of development. With this multi-perspective approach, the number of hazards identified can be increased. Furthermore, a generic catalogue of hazards for surgical robots has been established by categorising the results. The catalogue serves as a data pool for risk analyses and holds the potential to reduce hazards through safety measures already in the design process before becoming risks for the patient.

Download Full-text