A Methodological Review of Patient Healthcare-Seeking Journeys from Symptom Onset to Receipt of Care
Bibliographic record
Abstract
Abstract Background For many diseases, early diagnosis and treatment are more cost-effective, reduce community spread of infectious diseases, and result in better patient outcomes. However, healthcare-seeking and diagnoses for several diseases are unnecessarily delayed. For example, in 2022, 3 million and 5.6 million people living with TB and HIV respectively were undiagnosed. Many patients never access appropriate testing, remain undiagnosed after testing or drop out shortly after treatment initiation. This underscores challenges in accessing healthcare for many individuals. Understanding healthcare-seeking obstacles can expose bottlenecks in healthcare delivery and promote equity of access. We aimed to synthesize methodologies used to portray healthcare-seeking trajectories and provide a conceptual framework for patient journey analyses. Design/Methods We conducted a literature search using keywords related to “patient/care healthcare-seeking/journey/pathway analysis” AND “TB” OR “infectious/pulmonary diseases” in PubMED, CINAHL, Web of Science and Global Health (OVID). From a preliminary scoping search and expert consultation, we developed a conceptual framework and honed the key data points necessary to understand patients’ healthcare-seeking journeys, which then served as our inclusion criteria for the subsequent expanded review. Retained papers included at least three of these data points. Results Our conceptual framework included 5 data points and 7 related indicators that contribute to understanding patients’ experiences during healthcare-seeking. We retained 66 studies that met our eligibility criteria. Most studies (56.3%) were in Central and Southeast Asia, explored TB healthcare-seeking experiences (76.6%), were quantitative (67.2%), used in-depth, semi-structured, or structured questionnaires for data collection (73.4%). Healthcare-seeking journeys were explored, measured and portrayed in different ways, with no consistency in included information. Conclusions We synthesized various methodologies in exploring patient healthcare-seeking journeys and found crucial data points necessary to understand challenges patients encounter when interacting with health systems. and offer insights to researchers and healthcare practitioners. Our framework proposes a standardized approach to patient journey research. Key Questions What is already known about this subject? Accessing healthcare is challenging for half of the world’s population. Understanding healthcare-seeking obstacles can help to expose bottlenecks in healthcare delivery and improve access. What does this study add? We synthesized the different methodologies used by researchers to portray healthcare- seeking trajectories. We also provide a conceptual framework and recommendations for patient journey analyses. How do the new findings imply? Our analysis revealed a lack of consistency in how patient journeys to care are represented and a notable complexity in generating insightful depictions of journeys to care. The use of our conceptual framework, namely the data points and indicators, could increase the reliability and generalisability patient journey analyses.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.131 | 0.356 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.039 | 0.048 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".