Factors Influencing Patients Using Long-Term Care Service of Discharge Planning by Andersen Behavioral Model: A Hospital-Based Cross-Sectional Study in Eastern Taiwan.
Yi-Chien ChenWei-Ting ChangChin-Yu HuangPeng-Lin TsengChao-Hsien LeePublished in: International journal of environmental research and public health (2021)
Taiwan has been an aged society since March 2018, and the elderly population suffer from multiple comorbidities and long duration of disability. Therefore, the service of discharge planning of long-term care 2.0 is an important stage before patients go back to the community. Strengthening the sensitivity when identifying predisabled patients is a principal development of discharge planning. In the current study, we analyzed the characteristics and predictive factors of patients who used the service of long-term care 2.0 from the perspective of discharge planning. In this retrospective study, we included patients who received the discharge planning service in a hospital located in southern Hualien during November 2017 to October 2018. The data were collected and classified as predisposing factors, enabling factors, and need factors according to the analysis architecture of the Andersen Behavioral Model. There were 280 valid patients included in this current study; age, medical accessibility, possession of a disability card, and cerebrovascular diseases, cardiovascular diseases, and diabetes mellitus were the vital factors which influenced the coherence and cohesion between discharge planning and the service of long-term care 2.0. Among them, the most influencing factor was age. We hope that the current study will make policymakers in hospitals pay attention to the usage of the discharge planning service to link long-term care 2.0 and effectively promote the usage of long-term care 2.0.
Keyphrases
- long term care
- end stage renal disease
- healthcare
- mental health
- ejection fraction
- chronic kidney disease
- newly diagnosed
- peritoneal dialysis
- prognostic factors
- cardiovascular disease
- multiple sclerosis
- metabolic syndrome
- coronary artery disease
- emergency department
- adipose tissue
- big data
- artificial intelligence
- health insurance
- patient reported