Requirements and Use Cases for eHealth Solutions in Flexible Assertive Community Treatment Teams: Design Science Study
Bibliographic record
Abstract
Background: Health care delivery is often fragmented, with different services being delivered by different organizations. Various forms of teamwork are often used in health care, aiming to mitigate the challenges related to this fragmentation. One example of teamwork in mental health is Flexible Assertive Community Treatment (FACT). FACT is a model for comprehensive and integrated care for patients with long-term, serious mental illness. FACT teams deliver services using assertive outreach to treat patients who can be hard to reach by health care services. However, Norwegian FACT teams have issues with the current eHealth solutions related to the fragmentation of health care. Objective: This study aimed to identify requirements and develop use cases and use case diagrams for eHealth solutions that support effective teamwork within FACT teams, using them in a case study for collaborative health care delivery. Methods: A design science framework was used to explicate the problems of eHealth solutions in FACT teams. This included performing the subactivities of defining the problem precisely, positioning and justifying the problem, and finding root causes. Based on this explication, we derived a set of requirements, use cases, and use case diagrams for FACT teams. Results: We present the explication of the problems of eHealth in Norwegian FACT teams. Building on the results, we present functional and nonfunctional requirements for electronic health records, electronic whiteboards, video conference solutions, and digital questionnaires. Improved integration across these systems was identified as a recurring need. We also provide use cases and diagrams illustrating system use in practice. Conclusions: FACT teams in Norway require more integrated and tailored eHealth solutions. The requirements and use cases presented in this study offer a foundation for developing tools that better support the collaborative and mobile nature of FACT team operations.
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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 | 0.060 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".