Clinician-Focused Connected Health Requirements Gathering for Attention-Deficit/Hyperactivity Disorder Through Clinical Journey Mapping: Design Science Study
Bibliographic record
Abstract
Background: Many health care systems globally face severe capacity issues, with lengthening waiting lists and stretched resources. Connected health has been proposed as a game changer for health care. However, the development of connected health apps is difficult and requires multidisciplinary development teams. Patient journey mapping presents an opportunity to streamline the requirements-gathering process for such apps by clearly showing the patient journey to team members who are not familiar with relevant clinical practices. This research project focuses on attention-deficit/hyperactivity disorder (ADHD) as a case study for using clinical journey mapping to represent the "gold standard" care pathway for ADHD treatment; the Dundee Clinical Care Pathway. This pathway was analyzed in detail and was further explored in discussions with stakeholders to produce a patient journey map. Objective: The objective of this paper is to answer three research questions: (1) visualizing the Dundee ADHD clinical care pathway using integrated patient journey mapping and exploring how its use benefits multidisciplinary development teams; (2) optimizing the integrated patient journey map arising from the Dundee Clinical Care Pathway, in line with the underlying clinical realities of Child and Adolescent Mental Health Service in Ireland; and (3) proposing areas where connected health integration can deliver efficiency and substantial gains for Child and Adolescent Mental Health Services. Methods: This study uses a design science approach where a sample artifact is presented to a relevant audience for review and feedback and is then leveraged to work iteratively toward an improved, final artifact. This paper presents the feedback collected from both information systems and clinical professionals at each iteration of the map. Results: This research delivers a comprehensive clinical patient journey map based on the Dundee clinical care pathway. Using unified modeling language concepts and color coding, multiple patient personas are mapped onto a streamlined diagram, allowing the diagram, at an abstract level, to cover the most typical clinical scenarios. Conclusions: Clinical journey mapping provides a way for team members to get up to speed on clinical practices, while also presenting a way for development teams to identify key gaps where connected health systems can be embedded in clinical pathways to optimize the use of clinical resources and ultimately deliver better patient outcomes.
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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 | 0.070 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".