Brain Health Risks: Digital Quality Measures and the Future of Neurological Care

As the American Academy of Neurology’s (AAN) Brain Health Summit convenes in September, it’s an opportune moment to explore the future of neurology and the role that digital quality measures (dQMs) will play in the evolution of neurological care.

Neurological diseases, a leading cause of disability worldwide, often result in cognitive decline, mobility issues, and behavioral changes, burdening patients, caregivers, and healthcare systems. More than one in three people globally live with a neurological condition. In the U.S., more than 6 million people have Alzheimer’s disease and related dementias, a number expected to double by 2050. Millions more are affected by stroke, epilepsy, and Parkinson’s disease.

Neurology has traditionally focused on disease-centered metrics, but there is a growing need to shift towards preventive care and lifelong brain health. Measures like the AAN-endorsed frequency of seizures in epilepsy or regular quality-of-life assessments in neurodegenerative diseases have been crucial but are increasingly insufficient.

This shift demands a broader understanding of brain health and a re-evaluation of which data are both possible and valuable to measure and track. By integrating innovative dQMs — which utilize electronic health information from diverse sources, including wearables and medical devices — it is possible to reflect the complexities of brain health more accurately.

The Power of Coupling dQMs With AI

The future of dQMs lies in capturing a broader range of data points that reflect both risk factors for neurological diseases and factors that promote brain health. Artificial intelligence (AI) can capture real-time patient data, enhancing care comprehensiveness.

As healthcare transitions to preventive or value-based models of care, it becomes essential to measure factors that promote brain health long before disease onset. These might include physical activity, sleep quality, dietary habits, and social interactions — all of which influence cognitive function and brain health.

Whether physicians gather data during visits or from smart devices, dQMs combined with AI ensure that critical information is captured and utilized effectively. However, adoption of AI is not widespread, with gaps in knowledge and access among neurology providers, and limited understanding of its full potential in care quality assessment.

Traditional disease-focused AI models are becoming outdated as broader health initiatives — including preventive care and patient well-being — take precedence.

AI can capture and store critical conversations and details often missed in traditional records. For example, consider information about supplements like folic acid for women of childbearing age on anticonvulsants. This AI-generated data allows for systematic tracking of pre-natal counseling, and if a gap in care is detected, automatic alerts prompt action on folic acid supplementation, reducing the risk of neural tube defects in babies.

However, interoperability remains a critical challenge. Integrating large language models with Fast Healthcare Interoperability Resources can enhance data interoperability, enabling more comprehensive and accurate digital quality measures. The ability to integrate data from various sources is crucial for capturing a complete picture of patient health and facilitating the use of these measures across different healthcare settings.

Standardized Data Formatting

Standardized data formats and protocols are essential for leveraging advanced technologies to improve patient outcomes. While this is widely accepted, the challenge lies in implementation.

Different electronic health record (EHR) systems are slow to change for better data management, and the fact that numerous different tech companies are attempting to address these issues adds complexity. This has turned data into a battleground of money and power, creating conflicts over who sets new standards, who must adapt, and who bears the cost of compliance.

Aligning clinical settings and EHRs with the ASTP/ONC HTI-1 rule for Certified Health Technology is essential for ensuring interoperability across the healthcare continuum. While baseline health IT standards — such as the U.S. Core Data for Interoperability — provide a strong foundation, they are insufficient to meet neurology’s unique needs.

Lack of standardization hinders clinicians’ ability to access and share critical information, compromising care and performance assessment for complex neurological conditions. This can prevent providers from identifying care gaps and avoiding unnecessary polypharmacy in older adults with dementia, leading to poorer health outcomes.

Building Accessible Infrastructure

As medical experts expand the scope of what we can measure, supporting infrastructure for new dQMs is vital. Challenges with clinical registries, such as inconsistent data formats and lack of interoperability, have hindered their effectiveness, scalability, and impact on patient outcomes.

Companies like Dell and IBM are developing healthcare data solutions, while EHR providers like Epic and Oracle use application programming interfaces and advanced tools to enhance data interoperability. Major institutions are using integrated data ecosystems to improve care and operations.

However, there is a risk that access to these digital tools will be unevenly distributed, benefiting well-resourced practices while leaving underprivileged areas behind. This digital divide could result in a two-tiered system of care, in which patients in affluent areas receive more comprehensive and personalized care, while those in resource-limited settings are left behind. As dQMs rely on these technologies, patients and practices in resource-limited settings may lack the rich data needed to measure progress and work towards improvement.

Limited access in rural areas could worsen health outcomes and create disparities. Addressing these disparities as well as potential biases in data collection and algorithmic analysis is essential to avoid perpetuating health disparities. That is crucial to ensuring that all patients, regardless of their location or socioeconomic status, receive the high-quality care they deserve.

To prevent and address disparities, it is urgent that neurologists, administrators, health advocates, and policymakers advocate for policies that ensure equitable access to these technologies. This might include private capital and federal funding for digital infrastructure in underserved areas, and subsidies for patients to access digital health tools.

Neurology must prioritize lifelong brain health to reduce disease risk and improve quality of life. This approach has the potential to positively impact billions of people, enabling longer and healthier lives.

Lidia Moura, MD, PhD, MPH, is director of Neurology Population Health, and director of the Center for Value-Based Healthcare and Sciences at Massachusetts General Hospital (MGH). She also serves as chair of the Quality Informatics Subcommittee of AAN, a clinical neurologist at MGH, and an associate professor of Neurology at Harvard Medical School. She is an OpEd Project Public Voices fellow. Esther Ndemo, MHI, is a clinical informatics manager and staff liaison for the AAN Quality Informatics Subcommittee. Allison Weathers, MD, is a neurohospitalist and an associate chief medical information officer at the Cleveland Clinic, and chair of the Epic Adult Neurology Specialty Steering Board.

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