Patient journey mapping: Current practices, challenges and future opportunities in healthcare
Bibliographic record
Abstract
Patient Journey Maps are an emerging concept that visually map each interactive touchpoint that the patient experiences as they navigate the care continuum. The purpose of this article is to: 1) Identify the ways that patient journey mapping has been used to identify efficiencies and inefficiencies in the healthcare process from a patient perspective, 2) Identify the type of approaches that have been documented to visually identify the patient journey, 3) Identify how information tools can be taken into account to improve gaps identified by patient journey mapping; and 4) Detail what patient journey visualization and mapping tools currently exist (and are used) in research and healthcare practice. A scoping review literature exploration, following the Arksey and O’Malley Framework (2005) was conducted in the PubMed database, with a focus on English publications only, using the search terms “patient journey map.” Two researchers iteratively assessed the articles based on inclusion and exclusion criteria; 30 articles were included in the study. A thematic analysis was conducted and the findings were tabulated in the data extraction table. The patient journey map has considerable promise but continues to be an underutilized resource in industry - further research and standardization is required to increase adoption in the healthcare setting.
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 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 | 0.090 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.022 | 0.033 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".