Designing interactive health care systems: Bridging the gap between patients and health care professionals
Bibliographic record
Abstract
As patients become more proactive about their health and turn to technologies such as the Internet to acquire knowledge, the patient-health care professional relationship has been changing. Traditionally, information has flowed from health care professional to patient, but change to a two-way dialogue is taking place. In this study, we examine a high level design of a perceived medical system and determine the implications of adding patients as active contributors. The main challenge of modifying existing systems to incorporate patient interaction is preserving system integrity. We propose a systematic approach to support scaling health care systems while preserving system integrity. Distributed systems such as personal health records and eHealth systems provide two ways in which patients can become more involved with their own health care with or without the involvement of health care professionals. It is important that modifications to such systems do not compromise patient record integrity regardless of whether the patient is working alone or with their health care professional. The lack of central control in distributed systems added to the complexity of health systems poses challenges for design and modification. Of particular interest is the identification of emergent behavior (behavior not explicitly specified in the specifications) in distributed systems not explicitly defined in the requirements of its individual components. Use of the new emergent behavior detection (EBD) tool offers potentially considerable cost savings by proactively identifying such behaviors during the design rather than the deployment phase of a project. Based on high level message sequence charts, the EBD tool highlighted a data synchronization issue between the main database and the patient's interface to the system. This provides valuable feedback of the early health system design which benefits future design development.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".