Nursing Research on Patients with Chronic Obstructive Pulmonary Disease and Respiratory Failure Based on Big Data

Area of Science:

• Nursing
• Health Informatics
• Pulmonology

Background:

• Chronic obstructive pulmonary disease (COPD) and respiratory failure (RF) require continuous, effective management.
• Traditional follow-up methods may not fully address patient needs for chronic respiratory conditions.
• Integrating technology into nursing care offers potential for improved patient outcomes.

Purpose of the Study:

• To explore a continuous care model for COPD and RF patients using big data and individualized nursing plans.
• To assess the impact of this integrated approach on patient symptoms, acute episodes, hospitalizations, and quality of life.
• To evaluate the effectiveness of big data-driven continuous nursing care compared to traditional methods.

Main Methods:

• A study involving 100 hospitalized COPD and RF patients, divided into control and experimental groups.
• The control group received standard telephone follow-up care.
• The experimental group received individualized continuous nursing plans utilizing Internet and big data techniques, including pulmonary rehabilitation assessments, exercise guidance, and health education.

Main Results:

• Individualized continuous care combined with big data significantly improved lung function in stable COPD and RF patients.
• The experimental group experienced fewer acute COPD and RF attacks and reduced hospital readmissions compared to the control group.
• Enhanced self-management abilities and improved quality of life were observed in patients receiving the big data-integrated care.

Conclusions:

• Individualized continuous nursing care, augmented by big data technology, is an effective model for managing COPD and RF.
• This approach demonstrates significant benefits in improving patient outcomes and quality of life.
• The model is applicable and implementable for continuous nursing care in respiratory medicine.