Development and preliminary findings of a scoping review on the use of real‐world data in health services research for Alzheimer’s disease and related dementias
Bibliographic record
Abstract
BACKGROUND: The Alzheimer's Association has prioritized advancing health services research (HSR) to improve care and outcomes for people living with Alzheimer's disease and related dementias (AD/ADRD). HSR plays a critical role in investigating how care and treatment for AD/ADRD are accessed, delivered, and experienced. Real-world data (RWD), including electronic health records (EHRs), claims, administrative, and registry data, offer opportunities to assess healthcare utilization, quality, and population-level outcomes. This scoping review aims to map the use of RWD in AD/ADRD-focused HSR in the United States and identify gaps and priorities to advance HSR and inform high-value care. This review addresses the research question: How is RWD being used in recent HSR focused on AD/ADRD in the United States? METHOD: This review follows the Arksey and O'Malley framework and PRISMA-ScR guidelines. A protocol was developed with input from experts across healthcare delivery, policy, public health, and psychosocial research, shaping the research question, database selection, keywords, and coding framework. A systematic search (2020-present) was conducted across PubMed, Embase, CINAHL, Web of Science, and EconLit. Seven reviewers independently screened articles using Covidence. Thematic analysis will be conducted using MAXQDA, followed by a consensus-building workshop with healthcare ecosystem representatives to refine findings and inform future research. RESULT: A total of 2,354 articles were identified; 599 full-text studies were assessed for eligibility, and 520 were included. Data extraction is ongoing. Preliminary findings show variation in RWD (e.g., claims, EHRs, registries), study designs, and populations. Common topics include healthcare utilization, treatment outcomes, and health and social characteristics of people with AD/ADRD. Studies span from diagnosis to end-of-life care. Some studies used quasi-experimental or machine-learning methods and novel sources, such as clinical notes, to examine outcomes. Gaps include limited data integration, underrepresented populations, and inconsistent evaluation of care models. CONCLUSION: This review provides an overview of how RWD is used in AD/ADRD-focused HSR and highlights where future work is needed. Findings will support stronger use of RWD in scientific studies, improve treatment and support for people living with dementia, and inform healthcare planning and decision-making across settings and populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.324 | 0.453 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.054 | 0.040 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".