A case study of patient journey mapping to identify gaps in healthcare: Learning from experience with cancer diagnosis and treatment
Bibliographic record
Abstract
Journey mapping methods have a number of practical uses. One of the most promising applications in the area of healthcare is to apply patient journey mapping to identify a patient’s pathway through their healthcare journey. Nowhere is this more important than in the area of cancer care. With lengthy wait times in many countries and the complexity of care paths that cancer patients travel, there is ample opportunity to identify both gaps in care as well as opportunities to improve care processes. In this article the authors discuss a case study of a patient journey involving multiple care organizations, several health professionals and care in both Canada and the United States. By applying patient journey mapping a simplified version of such complexity can be presented in a visual and succinct way, allowing health professionals and managers of healthcare organizations to identify where inefficiencies in care and patient safety issues occur. Furthermore, this mapping can form the basis for optimizing care processes and holds considerable promise for patient-centred healthcare. Implications of using patient journey mapping for improving cancer care and healthcare more generally are discussed.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".