Developing an integrated dementia care model using service blueprinting: process visualization and failure point analysis.

Alzheimer Disease (AD) is a neurodegenerative progressive disease and the most common cause of dementia and is characterised by a deterioration in memory, cognitive ability, and capacity to carry out everyday tasks []. It is pathologically characterised by the presence of extracellular amyloid-β plaques and intracellular neurofibrillary tangles consisting of hyperphosphorylated tau protein, resulting in the dysfunction of the synapses and neuronal loss []. AD is one of the significant health issues in the world, especially among older generations. World Health Organization [] states that the number of individuals living with dementia worldwide is more than 55 million with Alzheimer disease comprising about 60–70% of these cases. It is estimated that the global burden is likely to increase to 139 million by 2050, and most of it is due to demographic ageing but with low- and middle-income countries showing the greatest increases []. This increasing prevalence highlights the necessity to develop better approaches to early detection and treatment. Clinical assessment of the patient (patient history, cognitive testing, and functional evaluation) has long been used as a tool to diagnose Alzheimer disease and in many cases, neuroimaging and cerebrospinal fluid (CSF) biomarkers have been used to supplement the diagnosis []. Developed diagnostic models, including those put forward by the National Institute on Aging-Alzheimer’s Association (NIA-AA), include biomarkers of amyloid deposition, tau pathology, and neurodegeneration [, ]. Nevertheless, the existing methods of diagnosis have a number of limitations. The neuroimaging methods like positron emission tomography (PET) are costly and not widely accessible especially in resource constrained environments. On the same note, CSF biomarkers analysis necessitates lumbar puncture which is invasive but might not be acceptable by some patients and is not feasible in large scale screening []. As a result, there are cases where the diagnosis is usually made during relatively late phases of the disease, which limits the effectiveness of possible treatment methods. To respond to these challenges, blood-based biomarkers have become a promising, less invasive alternative for the early detection of AD. Blood-based biomarkers are measurable biological molecules present in plasma or serum that reflect pathological processes associated with AD, including amyloid pathology (Aβ42/Aβ40), tau pathology (p-tau181 and p-tau217), neurodegeneration (NfL), and astrocytic activation (GFAP [, ]. The development of ultra-sensitive techniques of assays, including single molecule array (Simoa) and mass spectrometry, has greatly facilitated the accuracy and reliability of these biomarkers in blood detection []. Notably, these markers have demonstrated high levels of correlations with known CSF and imaging biomarkers, and capacity to predict disease onset at preclinical stages []. In this way, blood-based biomarkers have significant potential in large-scale screening, early diagnosis, and disease progression monitoring both in the clinical and community environment [].