RESUMEN
BACKGROUND: Cognitive disorders, such as Alzheimer's disease, are a global health problem. Digital healthcare technology is an innovative management tool for delaying the progression of dementia and mild cognitive impairment. Thanks to digital technology, the possibility of safe and effective care for patients at home and in the community is increasing, even in situations that threaten the continuity of care, such as the COVID-19 pandemic. However, it is difficult to select appropriate technology and alternatives due to the lack of comprehensive reviews on the types and characteristics of digital technology for cognitive impairment, including their effects and limitations. OBJECTIVE: This study aims to identify the types of digital healthcare technology for dementia and mild cognitive impairment and comprehensively examine how its outcome measures were constructed in line with each technology's purpose. METHODS: According to the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews guidelines, a literature search was conducted in August 2021 using Medline (Ovid), EMBASE, and Cochrane library. The search terms were constructed based on Population-Concept-Context mnemonic: 'dementia', 'cognitive impairment', and 'cognitive decline'; digital healthcare technology, such as big data, artificial intelligence, virtual reality, robots, applications, and so on; and the outcomes of digital technology, such as accuracy of diagnosis and physical, mental, and social health. After grasping overall research trends, the literature was classified and analysed in terms of the type of service users and technology. RESULTS: In total, 135 articles were selected. Since 2015, an increase in literature has been observed, and various digital healthcare technologies were identified. For people with mild cognitive impairment, technology for predicting and diagnosing the onset of dementia was studied, and for people with dementia, intervention technology to prevent the deterioration of health and induce significant improvement was considered. Regarding caregivers, many studies were conducted on monitoring and daily living assistive technologies that reduce the burden of care. However, problems such as data collection, storage, safety, and the digital divide persisted at different intensities for each technology type. CONCLUSIONS: This study revealed that appropriate technology options and considerations may differ depending on the characteristics of users. It also emphasises the role of humans in designing and managing technology to apply digital healthcare technology more effectively.
Asunto(s)
COVID-19 , Disfunción Cognitiva , Humanos , Inteligencia Artificial , Cognición , Disfunción Cognitiva/terapia , Disfunción Cognitiva/psicología , Atención a la Salud , PandemiasRESUMEN
BACKGROUND: Mid-to-long-term hospitalization (MLTH) can threaten the household economy with high medical costs and loss of income. Therefore, it could increase the catastrophic health expenditure (CHE), measured as the ratio of medical expenses to the ability to pay. This study aimed to determine the effect of MLTH on the incidence of CHE and the mediating effect of earned income reduction rate (EIRR). METHODS: We used 2015 to 2017 data from the Korean Welfare Panel Study and selected households with earned income through work. The final samples were 1671 households in the database. This study applied three-step regression analyses for estimating mediation effects. RESULTS: First, MLTH affected CHE increases; second, MLTH increased EIRR; third, both EIRR and MLTH increased CHE at the same time. Additionally, the bootstrapping results were 0.364 to 0.644 in the 95% confidence interval, which suggested that EIRR mediated the effects of MLTH on CHE. CONCLUSIONS: Previous studies have only focused on medical costs when interpreting CHE; however, it is also essential to recognize that the MLTH can have a negative effect on the EIRR. This study contributed to the literature by giving another insight into interpreting the cause of CHE, focusing on income loss factors.