A case study of patient journey mapping to identify gaps in healthcare: Learning from experience with cancer diagnosis and treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it