Contextualizing Online Laboratory (lab) Results and Mapping the Patient Journey
Bibliographic record
Abstract
The 21st century has brought forth unprecedented technological advances, such as the advent of portable digital devices [1]. This trend has also permeated the health care sector, with the introduction of digital health services, like providing citizens with access to their online laboratory (lab) results. This qualitative study will illustrate the patient journey, namely participant 16 (P16), to address the research question: what phases does a person go through when accessing their lab results online? The findings revealed that lab results were accessed from two types of devices a tablet (e.g., portable computer) when at home and a mobile phone when away from home. We also found that interpretation of results can be a challenge and it was unclear if P16 was able to understand her lab results. To illustrate the complexity of interpreting and accessing online lab results, the authors created a Customer Journey Map to contextualize the experiences of P16. The journey map depicts a combination of factors such as: eHealth literacy, limited access to providers, difficulty interpreting lab test results. Additionally, recommendations for online lab portal functionality enhancements were discovered through the mapping exercise. This study demonstrated that along with providing citizens with access to digital health technologies and services, considerations to eHealth literacy, the digital divide and health equity are paramount. As evidenced by the visualization, journey maps hold promise to serve as efficient tools to build empathy and identify the unique needs and perspectives of citizens.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 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, 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".