Understanding Barriers Along the Patient Journey in Alzheimer’s Disease Using Social Media Data
Bibliographic record
Abstract
INTRODUCTION: We speculated that social media data from Alzheimer's disease (AD) stakeholders (patients, caregivers, and clinicians) could identify barriers along the patient journey in AD, and that insights gained may help devise strategies to remove barriers, and ultimately improve the patient journey. METHODS: Our sample was drawn from a repository of social media posts extracted from 112 public sources between January 1998 and December 2021 using natural language processing text-mining algorithms. The patient journey was classified into three phases: (1) early signs/experiences (Early Signs); (2) screening/assessment/diagnosis (Screening); and (3) treatment/management (Treatment). In the Early Signs phase, issues/challenges derived from a conceptual AD identification framework (ADIF) were examined. In subsequent phases, behavioral/psychiatric challenges, access/barriers to health care, screening/diagnostic methods, and symptomatic treatments for AD were identified. Posts were classified by AD stakeholder type or disease stage, if possible. RESULTS: We identified 225,977 AD patient journey-related social media posts. Anxiety was a predominant issue/challenge in all patient journey phases. In the Screening and Treatment phases combined, access/barriers to care were described in 16% of posts; unwillingness/resistance to seeking care was a major barrier (≥ 75% of access-related posts across all stakeholders). Commonly identified structural barriers (e.g., affordability/cost, geography/transportation/distance) were more common in patient/caregiver posts than clinician posts. Among Screening-related posts, imaging/scans were commonly mentioned by all stakeholders; biomarkers were more commonly mentioned by patients than clinicians. Treatment-related concerns were identified in 17% of stakeholder-specified posts that named pharmacological agents/classes for the symptomatic management of AD. CONCLUSION: This descriptive analysis of out-of-clinic experiences reflected in AD social media posts found that unwillingness/resistance to seeking care was a key barrier, followed by structural barriers to health care, such as affordability/cost. Insights from the lived experiences of AD stakeholders are valuable and highlight the need to improve the patient journey in AD and ease patient and caregiver burden.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".