Human-Factor-Based Risk Management in the Healthcare to Improve Patient Safety

2020 ◽  
pp. 453-468
Author(s):  
Barbara Streimelweger ◽  
Katarzyna Wac ◽  
Wolfgang Seiringer
Keyword(s):  
Risk Assessment ◽  
Patient Safety ◽  
Human Factors ◽  
Human Factor ◽  
Improve Patient Safety ◽  
Assessment Method ◽  
Risk Level ◽  
Effect Analysis ◽  
Risk Assessment Method ◽  
Improve Patient

‘Patient Safety' tries to increase safety and transparency within healthcare systems for both patients and professionals. Within the healthcare sector, workflows become more and more complex, while time and money become scarce. As a consequence, the risk awareness, fault management and quality aspects become more important. One of the most well established risk assessment method is Failure Mode and Effect Analysis (FMEA) – a reliability analysis and risk assessment tool widely used in various industries. The traditional FMEA is using a Risk Priority Number (RPN) ranking system to evaluate and identify the risk level of failures, and to prioritize actions. However, there are several shortcomings in obtaining a quality estimate of the failure ratings with FMEA, especially when human factors play an important role. Thus, a new risk assessment method called HFdFMEA (Human Factor dependent FMEA) based on the dependency of used parameters and the observation of human factors, is proposed to address the drawbacks. The opportunity to improve patient safety is discussed as result of HFdFMEA.

Human-Factor-Based Risk Management in the Healthcare to Improve Patient Safety

2016 ◽  
Vol 7 (3) ◽  
pp. 16-28 ◽  
Author(s):  
Barbara Streimelweger ◽  
Katarzyna Wac ◽  
Wolfgang Seiringer
Keyword(s):  
Risk Assessment ◽  
Patient Safety ◽  
Human Factors ◽  
Human Factor ◽  
Improve Patient Safety ◽  
Assessment Method ◽  
Healthcare Sector ◽  
Risk Assessment Method ◽  
Quality Aspects ◽  
Improve Patient

‘Patient Safety' tries to increase safety and transparency within healthcare systems for both patients and professionals. Within the healthcare sector, workflows become more and more complex, while time and money become scarce. As a consequence, the risk awareness, fault management and quality aspects become more important. One of the most well established risk assessment method is Failure Mode and Effect Analysis (FMEA) – a reliability analysis and risk assessment tool widely used in various industries. The traditional FMEA is using a Risk Priority Number (RPN) ranking system to evaluate and identify the risk level of failures, and to prioritize actions. However, there are several shortcomings in obtaining a quality estimate of the failure ratings with FMEA, especially when human factors play an important role. Thus, a new risk assessment method called HFdFMEA (Human Factor dependent FMEA) based on the dependency of used parameters and the observation of human factors, is proposed to address the drawbacks. The opportunity to improve patient safety is discussed as result of HFdFMEA.

A New Fine-Kinney Method Based on Clustering Approach

2020 ◽  
Vol 28 (03) ◽  
pp. 497-512
Author(s):  
Cansu Dagsuyu ◽  
Murat Oturakci ◽  
Esra Sarac Essiz
Keyword(s):  
Risk Assessment ◽  
Conventional Method ◽  
Clustering Algorithms ◽  
Assessment Method ◽  
Distance Functions ◽  
Risk Level ◽  
New Approach ◽  
Clustering Techniques ◽  
Risk Assessment Method ◽  
Clustering Approach

In this study, a new approach to Fine-Kinney risk assessment method is developed in order to overcome the limitations of the conventional method with clustering algorithms. New risk level of classes are attempted to determine with K-Means and Hierarchical clustering algorithms with using two different distance functions which are Euclidean and Manhattan distances. According to the results, K-Means algorithms have provided accurate and sensitive cluster of classes. Classes from conventional and K-Means algorithms are applied and compared to the identified risks of a workshop of a medium sized textile company. Results of the study indicate that clustering techniques are new, original and applicable way to define new classes in order to prioritize risks by overcoming the drawbacks of conventional Fine-Kinney method.

scholarly journals A Dynamic Risk Assessment Method for Deep-Buried Tunnels Based on a Bayesian Network

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Yan Wang ◽  
Jie Su ◽  
Sulei Zhang ◽  
Siyao Guo ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Influencing Factors ◽  
Assessment Method ◽  
Assessment Model ◽  
Risk Level ◽  
Dynamic Risk ◽  
Risk Rating ◽  
Risk Assessment Method ◽  
Dynamic Risk Assessment

In view of the shortcomings in the risk assessment of deep-buried tunnels, a dynamic risk assessment method based on a Bayesian network is proposed. According to case statistics, a total of 12 specific risk rating factors are obtained and divided into three types: objective factors, subjective factors, and monitoring factors. The grading criteria of the risk rating factors are determined, and a dynamic risk rating system is established. A Bayesian network based on this system is constructed by expert knowledge and historical data. The nodes in the Bayesian network are in one-to-one correspondence with the three types of influencing factors, and the probability distribution is determined. Posterior probabilistic and sensitivity analyses are carried out, and the results show that the main influencing factors obtained by the two methods are basically the same. The constructed dynamic risk assessment model is most affected by the objective factor rating and monitoring factor rating, followed by the subjective factor rating. The dynamic risk rating is mainly affected by the surrounding rock level among the objective factors, construction management among the subjective factors, and arch crown convergence and side wall displacement among the monitoring factors. The dynamic risk assessment method based on the Bayesian network is applied to the No. 3 inclined shaft of the Humaling tunnel. According to the adjustment of the monitoring data and geological conditions, the dynamic risk rating probability of level I greatly decreased from 81.7% to 33.8%, the probability of level II significantly increased from 12.3% to 34.0%, and the probability of level III increased from 5.95% to 32.2%, which indicates that the risk level has risen sharply. The results show that this method can effectively predict the risk level during tunnel construction.

Promoting Patient Safety with Human Factors Methods

2016 ◽  
Vol 60 (1) ◽  
pp. 647-651 ◽  
Author(s):  
Carrie Reale ◽  
Jason J. Saleem ◽  
Emily S. Patterson ◽  
Aaron Zachary Hettinger ◽  
Shilo Anders ◽  
...  
Keyword(s):  
Patient Safety ◽  
Human Factors ◽  
Patient Outcomes ◽  
Care Delivery ◽  
Improve Patient Safety ◽  
Medication Management ◽  
Critical Issues ◽  
Discussion Panel ◽  
Improve Patient ◽  
Safe Care

Medication management is a complex and mentally demanding endeavor performed by multiple individuals in diverse settings and at variable points in time. Achieving the goal of safe care delivery with optimized patient outcomes requires ongoing communication, coordination, and interaction between an array of technologies and roles across the health care continuum. Human factors principles and methods have the potential to substantially improve patient safety throughout this high-risk cycle. The purpose of this interactive discussion panel is to explore the medication management process from a sociotechnical perspective to identify specific challenges and vulnerabilities, and recommend strategies that leverage human factors processes and design principles to efficiently and effectively address safety critical issues.

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