Use of Cognitive Assessments for Alzheimer’s Disease

NEWDIGS at Tufts Medical CenterIssue Briefs

KEY TAKEWAYS

  • Structured cognitive assessments are an important tool in the detection and diagnosis of early Alzheimer’s disease.
  • Most older adults are aware of cognitive assessment screenings and most report being willing to take a cognitive assessment or early detection test for Alzheimer's disease.
  • Despite their usefulness, cognitive assessments have several inherent limitations and biases.  Pairing cognitive assessments with other tools such as the use of clinical algorithms and biomarker testing for higher-risk individuals can improve detection and diagnostic accuracy.

How are Cognitive Assessments used for Alzheimer's Disease?

Structured cognitive assessments for Alzheimer's disease (AD) screen memory, thinking, and problem-solving, using brief tools like the Mini-Mental State Exam (MMSE) and Montreal Cognitive Assessment (MoCA) for general function, alongside more detailed neuropsychological tests such as the Alzheimer’s Disease Assessment Scale (ADAS-Cog) for deeper evaluation, focusing on domains like memory, language, executive function, and visuospatial skills to detect impairment and track changes over time.

Most older adults are aware of cognitive screening, but fewer than half report having ever had a cognitive assessment. There are a number of reasons for this including stigma and fear of receiving a dementia diagnosis, and normalization of memory loss as part of normal aging. However, a recent RAND study reported that 80% of 50 to 70 year olds would take a cognitive assessment if the out-of-pocket costs were zero.

Structured cognitive assessments are an important tool for the detection and diagnosis of early AD they  have several inherent limitations that need to be addressed.

Test Limitations and Biases

  • Sensitivity and Specificity: Brief standardized assessment tools may produce false positives, causing unnecessary anxiety, or false negatives, failing to detect subtle early changes. They are often not sensitive enough to distinguish between normal aging, mild cognitive impairment (MCI), and early Alzheimer's.
  • Cultural and Educational Bias:  Many standard tests are sensitive to an individual's educational level and cultural background. Higher education can mask cognitive decline (false negatives), and  individuals with lower education may score poorly due to unfamiliarity with tasks rather than cognitive decline (false positives). Similarly, tests developed in one population (e.g., white, English-speaking) may perform poorly in diverse groups. For example, Black and Hispanic individuals are more likely to receive false positive results.
  • Gender and Age Bias:  Women are more likely to have early AD undetected as verbal memory skills can mask symptoms of cognitive decline in standardized tests.20 For individuals under age 65, cognitive symptoms are more likely to receive a psychiatric or no diagnosis.
  • Practice Effects: Repeated administration of the same cognitive tests can lead to "practice effects," where patients improve their scores simply by becoming familiar with the questions, potentially masking early decline.

Clinical and Systemic Challenges

  • Underutilization: Despite coverage by Medicare, only about one-third of beneficiaries receive formal cognitive assessments during their annual wellness visits. 
  • Time and Resource Constraints: Primary care providers often lack the time during a typical 10 to15-minute visit to conduct thorough cognitive assessments. In-depth neuropsychological evaluations can take several hours.
  • Provider Training: Many physicians report feeling unsure about which cognitive assessment tools to use or how to interpret and communicate complex results to families. Interventions such as the Cognition in Primary Care intervention at the University of Washington health system which provided integrated education and in-exam room cognitive assessment tools, led to significant increases in both cognitive assessments and new AD diagnoses by PCPs.

Addressing Cognitive Assessment Challenges through System Change Design

The validity and use of structured cognitive assessments can be improved through the use of technological advances such as AI-driven risk stratification and pairing with other tools.

Increased use of AI-driven Risk Stratification tools that integrate clinical, genetic, laboratory, imaging, and proteomic data, as well as information from passive digital markers (such as wearable sensors and device cameras), and run in the background can detect subtle changes to predict which individuals are at higher risk of AD. Such methods have demonstrated the ability to detect AD pathology or predict dementia diagnosis years before symptoms appear. When integrated into Electronic Health Record (EHR) systems, they can guide PCPs to which patients to target for cognitive assessments and/or biomarker testing. This optimizes scarce PCP resources by focusing on those at higher risk of developing AD. One large study found that this improved uptake of the use of structured cognitive assessments over baseline in primary care practices.

Pairing cognitive assessments with other diagnostic testing such as PET scans, CSF analysis, or FDA-cleared blood biomarker tests (BBMs) help avoid the false positives seen with the sole use of cognitive assessments and have demonstrated similarly robust clinical validity across diverse populations. Pairing also reduces false negatives from practice effects, preventing delayed diagnosis of early AD and early AD progression. Studies of pairing BBMs with cognitive assessments have found that it improves the number of patients flagged by PCPs for MCI or dementia.

Learn more:

Alzheimer’s Association Cognitive Screening and Assessment. https://www.alz.org/professionals/health-systems-medical-professionals/cognitive-assessment

Alzheimer's Association Clinical Practice Guideline recommendations for primary care

 

NEWDIGS Issue Briefs

This Issue Brief is part of a series of reports from the NEWDIGS Consortium on strategy to expand patient access to the new generation of disease modifying therapies (DMTs) for early Alzheimer’s disease.

The NEWDIGS project on Alzheimer’s Disease (AD) is organized around a hypothesis that ensuring safe, effective, and equitable patient access to DMTs for AD will require a shift toward a more primary care-centered model of care including detection, diagnosis, treatment, and monitoring.

AD is the first case study in the Biomedical Health Efficiency (BHE) Project of NEWDIGS, launched in 2026. BHE is focused on re-engineering life science innovation to streamline access for all patients to biomedical products in ways that optimize outcomes while minimizing the use of resources.


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About the Center for Biomedical System Design

The NEWDIGS Consortium is dedicated to improving health by accelerating appropriate, timely, and equita­ble patient access to biomedical products in ways that work for all stakeholders.

Based at the Center for Biomedical System Design at Tufts Medical Center in Boston, NEWDIGS aims to help the health care system catch up with the science of biomedical innovation by removing barriers and designing methods to ensure that cutting-edge treatment is made available to patients. The consortium’s collaborators include patients, clinicians, payers, bio­pharmaceutical companies, regulators, and investors, among others.

Launched at MIT in 2009, the organization moved to Tufts Medical Center in 2022 to be closer to patient care and to longstanding collaborators. Among its successes are payment innovations for durable cell and gene therapies, and regulatory innovations that inspired a European-wide pilot led by the European Medicines Agency focused on Adaptive (Licensing) Pathways.

Its current work integrates insights from all prior projects to advance “Biomedical Health Efficiency” - a new system innovation methodology focused on optimizing outcomes with fewer resources for all patients through improved alignment of stakeholder goals, strategies, incentives, and metrics.