AI Article Synopsis

  • Chronic Kidney Disease (CKD) affects around 850 million people globally, but current care models may not effectively improve patient outcomes.
  • There are gaps in understanding CKD's causes, prevention strategies, and care resources, highlighting the need for better multidisciplinary approaches rather than relying solely on nephrologists.
  • A proposed novel care structure utilizing modern technologies, such as machine learning and mobile care, aims to enhance CKD management while minimizing human contact and exposure to infections like COVID-19.

Article Abstract

The concept of chronic kidney disease (CKD) originated in the 2000s, and an estimated 850 million patients are currently suffering from health threats from different degrees of CKD. However, it is unclear whether the existing CKD care systems are optimal for improving patient prognosis and outcomes, so this review summarizes the burden, existing care models, effectiveness, challenges, and developments of CKD care. Even under the general care principles, there are still significant gaps in our understanding of the causes of CKD, prevention or care resources, and care burdens between countries worldwide. Receiving care from multidisciplinary teams rather than only a nephrologist shows potential profits in comprehensive and preferable outcomes. In addition, we propose a novel CKD care structure that combines modern technologies, biosensors, longitudinal data visualization, machine learning algorithms, and mobile care. The novel care structure could simultaneously change the care process, significantly reduce human contact, and make the vulnerable population less likely to be exposed to infectious diseases such as COVID-19. The information offered should be beneficial, allowing us to rethink future CKD care models and applications to reach the goals of health equality and sustainability.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219474PMC
http://dx.doi.org/10.3390/jpm13050702DOI Listing

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