Priorities to improve the care for chronic conditions and multimorbidity: a survey of patients and stakeholders nested within the ComPaRe e-cohort
Bibliographic record
Abstract
OBJECTIVE: To set priorities to improve consultations, care structures and the healthcare system from the perspective of patients with chronic conditions, care professionals, hospital administrators and health policy makers. METHODS: Adult patients with chronic conditions recruited from the ComPaRe e-cohort in France ranked their 15 most important areas of improvement among 147 previously defined by patients. Priorities at a population level were obtained by using logit models for sets of ranked items in a data set calibrated to represent the French population of patients with chronic conditions. Care professionals, hospital managers and health policy makers rated the complexity involved in improving the areas identified. We calculated the number of patients who considered as a priority at least one of the areas considered easy to implement. RESULTS: Between September 2018 and May 2019, 3002 patients (84% women, 47% with multimorbidity) and 149 professionals (including 50 care professionals, 79 hospital directors, 11 health policy decision makers) were recruited. Patients' top priorities were (1) Transforming care to be holistic and personalised, at a consultation level; (2) Smoothing patients' journey in the care system, increasing their knowledge of their own health and improving care coordination, at a care structure level (3) Training clinicians in better interpersonal skills and knowledge of specific conditions/treatments, reducing stigma and making care more affordable, at a healthcare system level. In total, 48%, 71% and 57% patients ranked in their top priorities one area considered easy to improve by professionals at consultation, care structure and health system levels, respectively. CONCLUSION: This is the first comprehensive map of patients' priorities to improve the management of chronic conditions. Implementing simple actions could benefit a large number of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".