Shaping a data-driven era in dementia care pathway through computational neurology approaches

KongFatt Wong-Lin, Paula McClean, Niamh McCombe, Daman Kaur, Jose Sanchez-Bornot, Paddy Gillespie, Stephen Todd, David Finn, Alok Joshi, Joseph Kane, Bernadette McGuinness

Research output: Contribution to journalReview article

Abstract

Background
Dementia is caused by a variety of neurodegenerative disease(s) and is associated with a decline in memory and other cognitive abilities, while inflicting enormous socioeconomic burden. The complexity of dementia and its associated comorbidities, present immense challenges for dementia research and care, particularly in clinical decision-making.

Main Body
Despite lack of disease modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven Computational Neurology approaches, and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations.

Conclusion
The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.
Original languageEnglish
JournalBMC Medicine
DOIs
Publication statusAccepted/In press - 4 Nov 2020

Keywords

  • Dementia
  • Alzheimer’s disease
  • dementia care pathway
  • data science
  • computational neurology
  • computational neuroscience
  • healthcare economics
  • clinical decision support systems

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