Where to Find Neurology Real-World Data for Research

Recently, there has been an influx of evidence-based research in neurology. Historically, neurological research was very dependent on randomized controlled trials, observational studies, registries, and academic cohorts. While all these methods are important, they do not always reflect how patients are clinically diagnosed, treated, and followed up. Both of these are useful, but they do not describe how patients are clinically diagnosed, treated, and followed up. This is precisely what neurology real-world data can provide, which is why there is an urgent need for quality longitudinal neurology real-world data amongst pharmaceutical sponsors, contract research organizations (CROs), academic researchers, and health systems.

The guide on neurology real-world data the importance of neurology real-world data in current neuroscientific research, and, most importantly, where this information can be found.

What Is Neurology Real-World Data?

Real-world data (RWD) are data pertaining to patient health status and/or health care delivery that are routinely generated from a number of sources outside the conventional clinical trial setting. Some examples of RWD in neurology would be:

• Neurology EHR data - structured and unstructured electronic health record content, including visit notes, diagnoses (ICD-9/ICD-10 codes), problem lists, and clinical assessments
• Neuroimaging - MRI, CT, PET, and other imaging data used to characterize structural, metabolic, or functional changes in the brain
• Laboratory and biomarker results - spinal fluid analyses, genetic testing, and blood-based biomarkers
• Medication and treatment history - prescribing patterns, dosing, and treatment switches
• Claims and billing data - insurance claims that reveal cost, utilization, and care pathways
• Cognitive and functional scores - standardized assessments used in dementia, MS, and movement disorder care
• Patient-reported outcomes - symptom burden and quality-of-life measures reported directly by patients

Together, these components represent what researchers have called neurological research data: a longitudinal real-world depiction of actual disease progression and management in the absence of the artificial environment of a clinical trial. Neurology real-world data is derived from routine clinical encounters and thus represents a wide variety of patients, comorbidities, and management practices that would be excluded by clinical trials using strict eligibility criteria, offering a more complete picture of how neurological conditions actually present and progress across the general patient population.

Why Researchers Need Neurology Real-World Data

There are several reasons neurology real-world data has become essential to modern research and drug development:

1. Prospective research on neurodegenerative disorders is difficult. Diseases such as Alzheimer's and Parkinson's span many years or even decades. Real-world data from neurology has the necessary longitudinal information that enables prospective analysis of disease progression without conducting a long-term prospective study.

2. While validated biomarkers have come a long way, there is still room for improvement in terms of availability, implementation, longitudinal data, and standardization of biomarkers within neurological disorders.

3. It may be difficult to conduct clinical trials in neurology due to the complexity of the enrollment criteria, heterogeneity of the patient population, and lack of access to neurological facilities. Neurology patient data may assist sponsors in identifying potentially eligible groups more efficiently than manual chart reviews.

4. Regulators increasingly accept RWE. The FDA uses real-world data and real-world evidence across the medical-product lifecycle, including postmarket safety monitoring and, in appropriate circumstances, regulatory decisions involving effectiveness, labeling changes, and new indications. RWE may also support certain external control-arm approaches when the underlying data and study methodology are fit for purpose.

5. Health economics and outcomes research (HEOR) depends on it. Payers and health systems want to understand real-world treatment patterns, adherence, and outcomes - something only neurology real-world data can reveal.

Categories of Neurology Real-World Data Sources

Neurology real-world data isn't housed in one single repository. It's spread across several categories of sources, each with different strengths, access requirements, and scale.

1. Neurology-Specific Real-World Data Platforms

A newer category of platforms has emerged that focuses exclusively on neurology, rather than treating it as one therapeutic area among many. NeuroDiscovery AI is a leading example of this model. As part of NeuroDiscovery AI, our technology platform – comprising NeuroTerminal, NeuroDiscovery AI Patient App, and NeuroLLM™ – is premised precisely on such convergence, connecting 1,000+ neurologists and APPs across 100+ U.S. sites, giving us a foundation of 6 million+ longitudinal patient records (including 3 million+ active patients).

NeuroDiscovery's core data asset, described on its homepage as "the largest U.S. neurology dataset," unifies several data modalities into a single queryable model, including:

• Neurology EHR data (structured clinical documentation and ICD-9/ICD-10 diagnoses)
• Labs and biomarkers
• Neuroimaging
• Electrophysiology (e.g., EEG/EMG-derived data)
• Cognitive scores
• Treatment history
• Claims and billing data

This dataset spans conditions across the neurological spectrum - including Alzheimer's disease, Parkinson's disease, epilepsy, migraine, and multiple sclerosis - and is described as HIPAA-compliant and continuously refreshed by an active provider network. NeuroDiscovery makes this neurology real-world data accessible through NeuroTerminal, a web portal and API built for pharmaceutical companies, biotech firms, and CROs that combines trial recruitment, real-world data access, and FDA-aligned real-world evidence generation in a single workspace.

A 2025 preprint article describing the NeuroDiscovery AI database explains the database as "a de-identified repository of real-world data consisting of electronic health records (EHRs) derived from outpatient neurology practices in the United States, encompassing sociodemographics, clinical assessment, ICD-9 and ICD-10 diagnosis, medications, neuroimaging, and lab results." The same source also adds that the demographics found in the dataset "are consistent with well-established epidemiological trends, making it a trustworthy source of neurological research data through observational study designs."

Because NeuroDiscovery is purpose-built for neurology rather than being a general-purpose, all-specialty database, it can offer clinical depth - electrophysiology, detailed cognitive assessments, disease-specific staging - that broader platforms may not capture in the same detail.

2. Multi-Specialty EHR Research Networks

Beyond neurology-specific platforms, several large, multi-specialty EHR networks are widely used to source neurology patient data as part of broader research efforts:

• TriNetX is a federated global network that aggregates de-identified EHR, claims, and mortality data from healthcare organizations across the U.S., Europe, Asia, and South America. Researchers use it to build patient cohorts, generate real-world evidence, and support feasibility assessments - including for neurological conditions - using a self-service query interface built on standardized data models.
• Epic Cosmos combines de-identified data from participating organizations using Epic. The current Cosmos network reports more than 300 million patients across more than 2,200 hospitals. Cosmos supports longitudinal research across diagnoses, medications, encounters, and other clinical data. Cosmos is particularly useful for longitudinal, EHR-only research questions, though access typically requires affiliation with a participating institution.
• Oracle Health Real-World Data: Oracle provides de-identified longitudinal clinical data for approved research and life-science use cases. Its RWD offering includes EHR-derived clinical information and can support linked EHR-claims analyses

These networks are not neurology-specific, so extracting neurology real-world data from them requires careful cohort definitions using diagnosis codes, medication histories, and clinical criteria. Still, their scale makes them valuable, especially for studies that need to compare neurological populations against the broader patient population.

3. Claims and Administrative Databases

Claims-based sources capture what happens across the healthcare system from a billing and utilization perspective, which is useful for health economics and outcomes research:

• Centers for Medicare & Medicaid Services (CMS) claims data cover a large share of the U.S. population aged 65 and older, making it valuable for late-onset neurological conditions like Alzheimer's disease and Parkinson's disease.
• The commercial claims databases (based on employer-sponsored insurance data) provide access to working-age populations, and that helps in case of such diseases as multiple sclerosis, migraines, or epilepsy that have their onset in younger ages.

Claims data generally lack the clinical granularity of neurology EHR data - you won't find detailed neuroimaging findings or cognitive test scores - but they excel at capturing cost, resource utilization, and care pathways over time.

4. Disease-Specific Registries and Consortia

There are also several well-established registries that collect extensive and highly specialized data in neurology research:

• Alzheimer's Disease Neuroimaging Initiative (ADNI) is a registry providing longitudinal imaging and biomarker as well as cognitive data specifically for Alzheimer's research.
• Parkinson's Progress Markers Initiative (PPMI) is a similar registry that collects longitudinal data for Parkinson's disease patients.
• There are also national and regional MS registries tracking treatment trends and disability progression in multiple sclerosis patients.
• Academic consortia and patient advocacy groups may maintain registries for epilepsy and ALS patients providing condition-specific longitudinal data.

They are known to have smaller sample sizes in comparison with large EHR systems, but the quality of data is much higher.

5. Government and Public Research Infrastructure

Neurology real-world data from public infrastructure is still highly valuable but underutilized:

• National Institute of Neurological Disorders and Stroke (NINDS) holds Common Data Elements and supports data sharing activities within federally funded neurology research.
• CDC National Neurological Conditions Surveillance System (NNCSS): NNCSS of CDC uses various data sources to track epidemiology of neurological disorders and provide population level surveillance data. CDC also supplies condition-specific data and statistics, including those on stroke and epilepsy
• PCORnet, A national clinical research network that standardizes EHR, claims, and other health data through its Common Data Model to support multicenter patient-centered research, including observational studies

6. Academic Medical Center Data Warehouses

Many academic medical centers maintain their own enterprise data warehouses built from internal EHR systems, such as the Northwestern Medicine Enterprise Data Warehouse. These offer deep, institution-specific neurology patient data and are typically accessed through institutional review board (IRB) approval and a home institution's research informatics office.

Choosing the Right Source of Neurology Real-World Data

All research questions don’t necessarily warrant the use of the same sort of neurology data. There are a few considerations to be taken into account:

• Sample size and scale: Multi-disciplinary organizations such as TriNetX and Epic Cosmos have large-scale data while specialty neurology and disease-specific registries sacrifice scale for quality.
• Granularity of data: If your research needs include neuroimaging, electrophysiology, or cognitive staging, then you would definitely need a specialty neurology platform with neurology data from EHRs rather than general claims data.
• Depth of data: For neurodegenerative diseases, you need a dataset with longitudinal continuity instead of snapshot data due to a long follow-up period required by studies on these diseases.
• Access model: Some sources (TriNetX, Cosmos) require institutional affiliation, while commercial neurology real-world data platforms like NeuroDiscovery AI are typically accessed through a licensing agreement, web portal, or API, often used by pharmaceutical sponsors and CROs rather than individual academic researchers.
• Regulatory intent: If the resulting evidence needs to support a regulatory submission, look for platforms that are designed to support real-world evidence generation using data and methodologies intended to align with applicable regulatory expectations.
• Data provenance and quality: Evaluate how data are collected, normalized, linked, de-identified, validated, and updated. Important considerations include missingness, coding variability, duplicate records, changes in clinical practice, and the consistency of longitudinal follow-up.
• Population representativeness: Assess whether the dataset's demographics, geography, insurance mix, care settings, and disease severity reflect the population relevant to the research question.

Final Thoughts

The scope of neurology-relevant real world data has grown immensely, enabling researchers to have more ways to study neurological diseases other than clinical trials. With specialized neurology platforms such as NeuroDiscovery AI, general EHR networks such as TriNetX and Epic Cosmos, claims databases, disease-specific registries, and research infrastructures, scientists can have several ways to access neurology data through their choice of reliable platforms – whether it is to facilitate patient recruitment into clinical trials, provide real world evidence to regulatory agencies, or simply learn about how neurological diseases manifest in clinical practice.

In the continued growth of neurology real world data ecosystems, those organizations that offer extensive longitudinal data sets with comprehensive clinical details – including neurology EHR data, imaging, biomarker, and outcome data – will be well-placed to address issues that clinical trials cannot.