Manual Handling in the Healthcare Sector: The International Approach to Risk Assessment

2014 ◽  
pp. 30-39
Keyword(s):  
Risk Assessment ◽  
Healthcare Sector ◽  
Manual Handling

Patient manual handling risk assessment among hospital nurses

Work ◽  
2015 ◽  
Vol 50 (4) ◽  
pp. 669-675 ◽  
Author(s):  
R. Abedini ◽  
A.R. Choobineh ◽  
J. Hasanzadeh
Keyword(s):  
Risk Assessment ◽  
Hospital Nurses ◽  
Manual Handling

scholarly journals Automated fall risk assessment of elderly using wearable devices

2020 ◽  
Vol 7 ◽  
pp. 205566832094620
Author(s):  
Marian Haescher ◽  
Wencke Chodan ◽  
Florian Höpfner ◽  
Gerald Bieber ◽  
Mario Aehnelt ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Human Error ◽  
Visual Analysis ◽  
Ground Truth ◽  
Wearable Devices ◽  
Healthcare Sector ◽  
Observational Assessment ◽  
Timed Up And Go ◽  
Fall Risk Assessment

Introduction Falls cause major expenses in the healthcare sector. We investigate the ability of supporting a fall risk assessment by introducing algorithms for automated assessments of standardized fall risk-related tests via wearable devices. Methods In a study, 13 participants conducted the standardized 6-Minutes Walk Test, the Timed-Up-and-Go Test, the 30-Second Sit-to-Stand Test, and the 4-Stage Balance Test repeatedly, producing 226 tests in total. Automated algorithms computed by wearable devices, as well as a visual analysis of the recorded data streams, were compared to the observational results conducted by physiotherapists. Results There was a high congruence between automated assessments and the ground truth for all four test types (ranging from 78.15% to 96.55%), with deviations ranging all well within one standard deviation of the ground truth. Fall risk (assessed by questionnaire) correlated with the individual tests. Conclusions The automated fall risk assessment using wearable devices and algorithms matches the validity of the ground truth, thus providing a resourceful alternative to the effortful observational assessment, while minimizing the risk of human error. No single test can predict overall fall risk; instead, a much more complex model with additional input parameters (e.g., fall history, medication etc.) is needed.

scholarly journals The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5750
Author(s):  
Alberto Ranavolo ◽  
Arash Ajoudani ◽  
Andrea Cherubini ◽  
Matteo Bianchi ◽  
Lars Fritzsche ◽  
...  
Keyword(s):  
Risk Assessment ◽  
International Standards ◽  
Motor Tasks ◽  
Traditional Methods ◽  
Work Related ◽  
Work Related Musculoskeletal Disorders ◽  
Manual Handling ◽  
Heavy Loads ◽  
Human Augmentation ◽  
Human Robot Collaboration

Due to the epochal changes introduced by “Industry 4.0”, it is getting harder to apply the varying approaches for biomechanical risk assessment of manual handling tasks used to prevent work-related musculoskeletal disorders (WMDs) considered within the International Standards for ergonomics. In fact, the innovative human–robot collaboration (HRC) systems are widening the number of work motor tasks that cannot be assessed. On the other hand, new sensor-based tools for biomechanical risk assessment could be used for both quantitative “direct instrumental evaluations” and “rating of standard methods”, allowing certain improvements over traditional methods. In this light, this Letter aims at detecting the need for revising the standards for human ergonomics and biomechanical risk assessment by analyzing the WMDs prevalence and incidence; additionally, the strengths and weaknesses of traditional methods listed within the International Standards for manual handling activities and the next challenges needed for their revision are considered. As a representative example, the discussion is referred to the lifting of heavy loads where the revision should include the use of sensor-based tools for biomechanical risk assessment during lifting performed with the use of exoskeletons, by more than one person (team lifting) and when the traditional methods cannot be applied. The wearability of sensing and feedback sensors in addition to human augmentation technologies allows for increasing workers’ awareness about possible risks and enhance the effectiveness and safety during the execution of in many manual handling activities.

International consensus on manual handling of people in the healthcare sector: Technical report ISO/TR 12296

2014 ◽  
Vol 44 (1) ◽  
pp. 191-195 ◽  
Author(s):  
S. Hignett ◽  
M. Fray ◽  
N. Battevi ◽  
E. Occhipinti ◽  
O. Menoni ◽  
...  
Keyword(s):  
Healthcare Sector ◽  
International Consensus ◽  
Technical Report ◽  
Manual Handling

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.

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