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Analysis of Human Error in Healthcare Industry: an Indian Perspective
Author(s) -
R. K. A. Bhalaji,
Bathrinath Sankaranarayanan,
S. Saravanasankar
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d1034.1284s219
Subject(s) - seriousness , human error , risk analysis (engineering) , health care , ranking (information retrieval) , risk assessment , human resources , human reliability , computer science , human health , fuzzy logic , process (computing) , risk management , scale (ratio) , actuarial science , business , medicine , artificial intelligence , geography , environmental health , economics , computer security , management , finance , cartography , political science , law , economic growth , operating system
In healthcare sector, employing human knowledge, on a larger scale, in the decision making process in identifying the risk sources is very common, and often the human error in the process results in adverse effects of unrecognized risks. Hence, the impact of human error in employing the human knowledge remains a major problem. To minimize the human error a fuzzy based risk assessment method using Ranked Risk Breakdown Structure (RRBS) model is proposed in this work. The proposed method is able to rank the risk resources on the basis of a risk score generating mechanism based on the probability of occurrence of risk and outcome of the risk. By recognizing the high ranking risk resources, that is, the risk drivers at higher levels of seriousness/severity, this fuzzy based methodology nullify the existing common human errors. The proposed methodology is validated with the actual past data of one decade period, the occurrence of risk and its effect in a healthcare industry situated in an urban city of India. The results of the experimentation reveal that the proposed methodology can be successfully implemented in all other industries in healthcare sector to minimize the human error in risk recognition. The suggested model is helpful for industrial managers/practitioners to tackle risk factors related with complex works.

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