Circle of care modelling: an approach to assist in reasoning about healthcare change using a patient-centric system
Bibliographic record
Abstract
BACKGROUND: Many health system and health Information and Communication Technology (ICT) projects do not achieve their expected benefits. This paper presents an approach to exploring changes in the healthcare system to better understand the expected improvements and other changes by using a patient-centric modelling approach. Circle of care modeling (CCM) was designed to assist stakeholders in considering healthcare system changes using a patient centric approach. METHODS: The CCM approach is described. It includes four steps, based on soft systems methodology: finding out, conceptual modelling, structured discussion, and describing potential improvements. There are four visualizations that are used though this process: patient-persona based rich pictures of care flows (as part of finding out), and three models: provider view, communication view, and information repository view (as part of conceptual modelling). RESULTS: Three case studies are presented where CCM was applied to different real-world healthcare problems: 1. Seeking improvements in continuity of care for end of life patients. 2. Exploring current practices for medication communication for ambulatory patients prior to an update of a jurisdictional drug information system. 3. Deciding how to improve attachment of patients to primary care. The cases illustrate how CCM helped stakeholders reason from a patient centered approach about gaps and improvements in care such as: data fragmentation (in 1), coordination efforts of medication management (in 2), and deciding to support a community health centre for unattached patients (in 3). DISCUSSION: The circle of care modelling approach has proved to be a useful tool in assisting stakeholders explore health system change in a patient centric approach. It is one way to instantiate the important principle of being patient centered into practice when considering health system changes.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".