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The Construction and Effect Analysis of Nursing Safety Quality Management Based on Data Mining
Yimei Yang1, YanHong Deng2, Haimei Zhang3
1Central Sterile Supply Department, Chongqing University Cancer Hospital, Chongqing 400030, China.
Computational Intelligence and Neuroscience
|June 13, 2022
Summary
Data mining enhances nursing safety by identifying key factors contributing to errors. Findings reveal that lack of medical knowledge and improper use of restraints are significant issues, guiding improvements in patient care.
Area of Science:
- Nursing Science
- Data Mining
- Knowledge Discovery
Background:
- Nursing safety is critical for patient well-being during treatment and recovery.
- Effective nursing requires adherence to protocols and accurate execution of medical orders.
- Data mining offers a method to uncover hidden patterns in healthcare data.
Purpose of the Study:
- To explore the construction of a nursing safety quality management system.
- To analyze the effects of data mining techniques on improving nursing processes.
- To enhance patient care through optimized hospital nursing procedures.
Main Methods:
- Utilized the FP algorithm for data mining to generate frequent itemsets.
- Proposed and implemented an association rule mining algorithm.
- Analyzed current nursing management status, identifying system and quality standard issues.
Main Results:
- Identified key factors contributing to poor nursing outcomes.
- Lack of basic medical knowledge accounted for 52% of poor nursing cases.
- Improper use of restraint devices (19%) and lack of communication (9%) were also significant factors.
Conclusions:
- Data mining provides valuable insights for improving nursing safety.
- Addressing deficiencies in medical knowledge and restraint device usage is crucial.
- Enhanced patient and family education, alongside improved communication, can optimize care effectiveness.
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