Transitions in Care: A Data-Driven Exploration of Patient Pathways in the Canadian Healthcare System
Bibliographic record
Abstract
In the complex landscape of healthcare, patients navigate through various institutions from hospitals to long-term care facilities, and each step of their journey plays a crucial role in their disease progression and treatment plan. Traditional analyses often focus on individual transitions, offering limited insight into the broader picture of patient care and disease progression. This thesis aims to explore the entire sequence of patient transitions within the Canadian healthcare system to uncover meaningful patterns and commonalities. This research employs an innovative approach to leveraging the Canadian Institute for Health Information (CIHI) dataset, consisting of around 250,000 patient records after data cleaning and including approximately 10-11 variables. Extracting a diverse category of features, such as temporal, semantic, and clinical information, constructs a detailed profile for each patient journey. These profiles then undergo an parallel mini-batch average agglomerative hierarchical clustering process, grouping together patients with similar healthcare trajectories to identify prevailing pathways and transitions within the system. By understanding these patterns, healthcare providers and policymakers can gain insights into the patient experience, potentially revealing areas for improvement, optimization, and personalization of care. Key findings include uncovering transitions in the healthcare environment, identifying the most common pathways, and studying the alternate level of care length of stay for each scenario. Looking ahead, the research anticipates incorporating additional layers of data, such as specific interventions and medications, to enrich the analysis. This expansion aims to offer a more comprehensive view of patient journeys, further enhancing the ability to tailor healthcare services to meet individual needs effectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".