The Use of a Computerized Cognitive Assessment to Improve the Efficiency of Primary Care Referrals to Memory Services: Protocol for the Accelerating Dementia Pathway Technologies (ADePT) Study

A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities

This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, incorporating both electroencephalography (EEG) patterns and cognitive abnormalities for enhanced classification. Data were collected from 45 participants, including 20 with BD II and 25 with BPD. Analysis involved utilizing EEG signals and cognitive tests, specifically the Wisconsin Card Sorting Test and Integrated Cognitive Assessment. The k-nearest neighbors (KNN) algorithm achieved a balanced accuracy of 93%, with EEG features proving to be crucial, while cognitive features had a lesser impact. Despite the strengths, such as diverse model usage, it's important to note limitations, including a small sample size and reliance on DSM diagnoses. The study suggests that future research should explore multimodal data integration and employ advanced techniques to improve classification accuracy and gain a better understanding of the neurobiological distinctions between BD II and BPD.

Intelligent decision support systems for dementia care: A scoping review

In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted a comprehensive scoping review of empirical studies that utilised AI-powered clinical decision support systems in dementia care. The results indicate that AI applications in dementia care primarily focus on diagnosis, with limited attention to other aspects outlined in the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia 2017-2025 (GAPD). A trifecta of challenges, encompassing data availability, cost considerations, and AI algorithm performance, emerges as noteworthy barriers in adoption of AI applications in dementia care. To address these challenges and enhance AI reliability, we propose a novel approach: a digital twin-based patient journey model. Future research should address identified gaps in GAPD action areas, navigate data-related obstacles, and explore the implementation of digital twins. Additionally, it is imperative to emphasize that addressing trust and combating the stigma associated with AI in healthcare should be a central focus of future research directions.

Health economic analysis of the integrated cognitive assessment tool to aid dementia diagnosis in the United Kingdom

Objectives

The aim of this study was to develop a comprehensive economic evaluation of the integrated cognitive assessment (ICA) tool compared with standard cognitive tests when used for dementia screening in primary care and for initial patient triage in memory clinics.

Methods

ICA was compared with standard of care comprising a mixture of cognitive assessment tools over a lifetime horizon and employing the UK health and social care perspective. The model combined a decision tree to capture the initial outcomes of the cognitive testing with a Markov structure that estimated long-term outcomes of people with dementia. Quality of life outcomes were quantified using quality-adjusted life years (QALYs), and the economic benefits were assessed using net monetary benefit (NMB). Both costs and QALYs were discounted at 3.5% per annum and cost-effectiveness was assessed using a threshold of £20,000 per QALY gained.

Results

ICA dominated standard cognitive assessment tools in both the primary care and memory clinic settings. Introduction of the ICA tool was estimated to result in a lifetime cost saving of approximately £123 and £226 per person in primary care and memory clinics, respectively. QALY gains associated with early diagnosis were modest (0.0016 in primary care and 0.0027 in memory clinic). The net monetary benefit (NMB) of ICA introduction was estimated at £154 in primary care and £281 in the memory clinic settings.

Conclusion

Introduction of ICA as a tool to screen primary care patients for dementia and perform initial triage in memory clinics could be cost saving to the UK public health and social care payer.

The use of the integrated cognitive assessment to improve the efficiency of primary care referrals to memory services in the accelerating dementia pathway technologies study

Background

Current primary care cognitive assessment tools are either crude or time-consuming instruments that can only detect cognitive impairment when it is well established. This leads to unnecessary or late referrals to memory services, by which time the disease may have already progressed into more severe stages. Due to the COVID-19 pandemic, some memory services have adapted to the new environment by shifting to remote assessments of patients to meet service user demand. However, the use of remote cognitive assessments has been inconsistent, and there has been little evaluation of the outcome of such a change in clinical practice. Emerging research has highlighted computerized cognitive tests, such as the Integrated Cognitive Assessment (ICA), as the leading candidates for adoption in clinical practice. This is true both during the pandemic and in the post-COVID-19 era as part of healthcare innovation.

Objectives

The Accelerating Dementias Pathways Technologies (ADePT) Study was initiated in order to address this challenge and develop a real-world evidence basis to support the adoption of ICA as an inexpensive screening tool for the detection of cognitive impairment and improving the efficiency of the dementia care pathway.

Methods

Ninety-nine patients aged 55-90 who have been referred to a memory clinic by a general practitioner (GP) were recruited. Participants completed the ICA either at home or in the clinic along with medical history and usability questionnaires. The GP referral and ICA outcome were compared with the specialist diagnosis obtained at the memory clinic.Participants were given the option to carry out a retest visit where they were again given the chance to take the ICA test either remotely or face-to-face.

Results

The primary outcome of the study compared GP referral with specialist diagnosis of mild cognitive impairment (MCI) and dementia. Of those the GP referred to memory clinics, 78% were necessary referrals, with ~22% unnecessary referrals, or patients who should have been referred to other services as they had disorders other than MCI/dementia. In the same population the ICA was able to correctly identify cognitive impairment in ~90% of patients, with approximately 9% of patients being false negatives. From the subset of unnecessary GP referrals, the ICA classified ~72% of those as not having cognitive impairment, suggesting that these unnecessary referrals may not have been made if the ICA was in use. ICA demonstrated a sensitivity of 93% for dementia and 83% for MCI, with a specificity of 80% for both conditions in detecting cognitive impairment. Additionally, the test-retest prediction agreement for the ICA was 87.5%.

Conclusion

The results from this study demonstrate the potential of the ICA as a screening tool, which can be used to support accurate referrals from primary care settings, along with the work conducted in memory clinics and in secondary care. The ICA's sensitivity and specificity in detecting cognitive impairment in MCI surpassed the overall standard of care reported in existing literature.

New Opportunities for the Early Detection and Treatment of Cognitive Decline: Adherence Challenges and the Promise of Smart and Person-Centered Technologies

Early detection of age-related cognitive decline has transformative potential to advance the scientific understanding of cognitive impairments and possible treatments by identifying relevant participants for clinical trials. Furthermore, early detection is also key to early intervention once effective treatments have been developed. Novel approaches to the early detection of cognitive decline, for example through assessments administered via mobile apps, may require frequent home testing which can present adherence challenges. And, once decline has been detected, treatment might require frequent engagement with behavioral and/or lifestyle interventions (e.g., cognitive training), which present their own challenges with respect to adherence. We discuss state-of-the-art approaches to the early detection and treatment of cognitive decline, adherence challenges associated with these approaches, and the promise of smart and person-centered technologies to tackle adherence challenges. Specifically, we highlight prior and ongoing work conducted as part of the Adherence Promotion with Person-centered Technology (APPT) project, and how completed work will contribute to the design and development of a just-in-time, tailored, smart reminder system that infers participants' contexts and motivations, and how ongoing work might build toward a reminder system that incorporates dynamic machine learning algorithms capable of predicting and preventing adherence lapses before they happen. APPT activities and findings will have implications not just for cognitive assessment and training, but for technology-mediated adherence-support systems to facilitate physical exercise, nutrition, medication management, telehealth, and social connectivity, with the potential to broadly improve the engagement, health, and well-being of older adults.