Increasing the Involvement of Specialist Physicians in Chronic Disease Management
Bibliographic record
Abstract
BACKGROUND: The Capital Health (CH) region in Alberta serves the population of the Edmonton area as well as a large referral population in western Canada. CH is responsible for the delivery of the spectrum of patient care, from inpatient to outpatient services. Growth in outpatient care, in particular, has led to the development of several ambulatory care facilities from which the delivery of care to several populations with a chronic disease will be coordinated. ASSESSMENT OF PROBLEM: The traditional model of care delivery is unsuited to the management of chronic diseases. Physicians must be part of the planning and implementation of new models if they are to be successful and sustainable. The concept of integration into a delivery team is not well understood or practised. This is not conducive to the integration of specialist physicians into multidisciplinary teams in ambulatory care that serves the needs of patients from a large geographic area. RESULTS: Chronic disease management using the Chronic Care Model has proven to be an effective method of delivering care to this wide population. Specialist physicians have not always taken advantage of opportunities to be involved in the planning and development of such new health care projects. In CH, physician integration in the planning, development and implementation of this new model has proven vital to its success. STRATEGIES FOR CHANGE: We based our strategy for change on Wagner's Chronic Care Model. This involved eight steps, the first four of which have been completed and the fifth and sixth are underway. LESSONS AND MESSAGES: Five factors contributed to the successful integration of specialist physicians in chronic disease management: collaboration between disciplines and organizations; creating patient-centred services; organizational commitments; strong clinical leadership; and early involvement of clinicians.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".