Patient Journey Mapping: How Attached and Unattached Community Members Access Primary Care
Bibliographic record
Abstract
Context: This study aims to understand the current primary care access experience of both attached and unattached community members, using the established qualitative technique of journey mapping. Objective: Journey mapping is a visual user engagement tool employed in service design and increasingly in a medical context, focusing on identifying opportunities for improvement. This study will be used by the Frontenac, Lennox & Addington Ontario Health Team (FLA-OHT) to guide design a person-centered medical home. Study Design & Analysis: A two phase qualitative methodology employed one-on-one interviews and focus groups wherein participants responded to an iterated patient journey map, contributing their comments and experiences. This data was then coded into a final patient journey map model including phases, touchpoints, feelings, thoughts, actions, and opportunities for improvement. Framework analysis was employed to examine the patient experience in-depth. Dataset: Qualitative data derived from one-on-one interviews (n=12) and focus groups (n=4) (attached individuals: n=16; unattached individuals: n=19). Population Studied: Both attached and unattached community members within the FLA-OHT region. Instrument: Qualitative interview guide and draft journey map(s). Outcome Measures: Iterated, finalized journey maps of the experience of attached and unattached patients; in-depth quotations to support data analysis. Results: We identified pain points and barriers throughout the patient primary care journey across functional, emotional, cognitive and social dimensions. These included: significant anxiety about securing a provider; a desire not to “bother” a provider in case they dismissed patient concern(s); feeling the need to negotiate with staff and providers to be taken seriously; the perception that care plans became fragmented following specialist; and, for unattached patients in particular, a lack of opportunity for preventive or continuous care. Opportunities for improvement at a system level were also identified. Conclusions: Primary care service transformation and co-design require community participation and feedback. Patient journey mapping is a key input in patient-centered quality improvement processes to not only generate change ideas that improve the individuals’ primary care experience, but also to demonstrate the receptiveness of the system to patient feedback on the structure and quality of their own healthcare journeys.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".