Application of Data Mining Technology-Based Nursing Risk Management in Emergency Department Care

Weiwei Han, Songqin Wang, Jianhong Gao and Naeem Jan

Mathematical Problems in Engineering, 2022, vol. 2022, 1-12

Abstract: Nursing risk refers to all unsafe events that may occur in clinical nursing work. Common risk events include bed fall, fall, scald, line dislodgement, drug extravasation, and drug administration error, which easily lead to nursing-patient disputes and seriously affect the prognosis of patients. In order to effectively avoid nursing risks, strengthening nursing risk management (NRM), improving nursing management mechanism, and improving nursing operation process have become effective ways to manage risks. The emergency department is an important window for rescuing critically ill patients in the hospital, and it is also the main department where diagnosis, nursing risk events, and medical disputes occur. The traditional risk care model has failed to meet the current demand for emergency patient management, and a more scientific and standardized management scheme is urgently needed. In order to improve the quality of NRM in emergency departments and combine the advantages and characteristics of big data-related technologies, this paper proposes an algorithm based on data mining for application in emergency care. The application of data mining in medical care is summarized and combined with the work content and requirements of hospital emergency care, and the application of big data in patient condition monitoring and early warning, medical and nursing staff scheduling, and patient emotional reassurance is discussed, and then, a solution for hospitals to optimize emergency care using data mining is proposed for the special characteristics of emergency care. Initially, the optimized solution is proposed to improve the efficiency and accuracy of patient condition monitoring and early warning, to improve the real-time scheduling of medical and nursing staff, and to solve medical care problems such as patient emotional calming. The analysis shows that the application of big data in emergency care can improve the efficiency of emergency ambulance, improve the doctor-patient relationship, and promote the development of emergency care.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2561072

DOI: 10.1155/2022/2561072

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