Concordance, Compliance and Adherence in Healthcare: Closing Gaps and Improving Outcomes
Bibliographic record
Abstract
The gap between best care and usual care is large for many important diseases. In particular, poor adherence remains a significant, inadequately addressed, cause of the care gap. About half of all patients with chronic diseases stop refilling prescriptions by one year. Several effective interventions are available and adaptations of clinical trials practices offer promise for further improvement. Poor adherence is a remedial problem in healthcare quality and its improvement and accountability offer shared opportunities for providers and patients. There is a large gap between best care, defined as the optimal use of proven efficacious therapies in whole populations at risk from any disease, and usual care, the actual level of efficacious care being provided (Montague et al. 1997). This gap in patient care has four main causes: diseases may not be diagnosed, efficacious therapies may not be prescribed, access to therapy may be restricted or patients may not adhere to prescriptions. Irrespective of causation, the ultimate result of care gaps is the same--less than optimal clinical outcomes and associated lost opportunities for improved quality of life and productivity. Systematic approaches to improving prescribing practices are increasing, and there is much debate around improving patients' access to care. Poor diagnosis is judged to be relatively uncommon, leaving decayed adherence as the major under-addressed cause of care gaps and a major opportunity for improvement. This paper reviews the scope and causation of sub-optimal adherence, evaluates improvement strategies and explores a best-practice benchmark.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".