An Evaluation of Understandability of Patient Journey Models in Mental Health
Bibliographic record
Abstract
BACKGROUND: There is a significant trend toward implementing health information technology to reduce administrative costs and improve patient care. Unfortunately, little awareness exists of the challenges of integrating information systems with existing clinical practice. The systematic integration of clinical processes with information system and health information technology can benefit the patients, staff, and the delivery of care. OBJECTIVES: This paper presents a comparison of the degree of understandability of patient journey models. In particular, the authors demonstrate the value of a relatively new patient journey modeling technique called the Patient Journey Modeling Architecture (PaJMa) when compared with traditional manufacturing based process modeling tools. The paper also presents results from a small pilot case study that compared the usability of 5 modeling approaches in a mental health care environment. METHOD: Five business process modeling techniques were used to represent a selected patient journey. A mix of both qualitative and quantitative methods was used to evaluate these models. Techniques included a focus group and survey to measure usability of the various models. RESULTS: The preliminary evaluation of the usability of the 5 modeling techniques has shown increased staff understanding of the representation of their processes and activities when presented with the models. Improved individual role identification throughout the models was also observed. The extended version of the PaJMa methodology provided the most clarity of information flows for clinicians. CONCLUSIONS: The extended version of PaJMa provided a significant improvement in the ease of interpretation for clinicians and increased the engagement with the modeling process. The use of color and its effectiveness in distinguishing the representation of roles was a key feature of the framework not present in other modeling approaches. Future research should focus on extending the pilot case study to a more diversified group of clinicians and health care support workers.
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How this classification was reachedexpand
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 | 0.004 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".