An Adaptive, Contextual, Technology‐Aided Support (ACTS) System for Chronic Illness Self‐Management
Bibliographic record
Abstract
Policy Points Fundamental changes are needed in how complex chronic illness conditions are conceptualized and managed. Health management plans for chronic illness need to be integrated, adaptive, contextual, technology aided, patient driven, and designed to address the multilevel social environment of patients' lives. Such primary care-based health management plans are feasible today but will be even more effective and sustainable if supported by systems thinking, technological advances, and policies that create and reinforce home, work, and health care collaborations. CONTEXT: The current health care system is failing patients with chronic illness, especially those with complex comorbid conditions and social determinants of health challenges. The current system combined with unsustainable health care costs, lack of support for primary care in the United States, and aging demographics create a frightening probable future. METHODS: Recent developments, including integrated behavioral health, community resources to address social determinants, population health infrastructure, patient-centered digital-health self-management support, and complexity science have the potential to help address these alarming trends. This article describes, first, the opportunity to integrate these trends and, second, a proposal for an integrated, patient-directed, adaptive, contextual, and technology-aided support (ACTS) system, based on a patient's life context and home/primary care/work-setting "support triangle." FINDINGS: None of these encouraging trends is a panacea, and although most have been described previously, they have not been integrated. Here we discuss an example of integration using these components and how our proposed model (termed My Own Health Report) can be applied, along with its strengths, limitations, implications, and opportunities for practice, policy, and research. CONCLUSIONS: This ACTS system builds on and extends the current chronic illness management approaches. It is feasible today and can produce even more dramatic improvements in the future.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".