Patient-Centred Multidisciplinary Inpatient Care-Have Diagnosis-Related Groups an Effect on the Doctor-Patient Relationship and Patients’ Motivation for Behavioural Change?
Bibliographic record
Abstract
INTRODUCTION: The aim of this, the largest survey of patients performed to date, is to analyse the effects of diagnosis related groups (DRGs) on the doctor-patient relationship in the context of interdisciplinary patient-centered care. In addition, it is intended to investigate the possibility of motivating patients to change their behavioural patterns and lifestyle in the context of holistic therapy. METHODS: Over a period of five years, a continuous survey was performed of hospitalised patients who were exercising their entitlement to interdisciplinary therapy in an acute, inpatient setting. RESULTS: The therapy was evaluated as good to very good both with and without the conditions of the case tariff fee system. Effects of the diagnosis related groups on the quality of the doctor-patient relationship could not be demonstrated (Mann-Whitney U test, p>0,05). A clear trend was evident in the influence on motivation to change behavioural patterns and lifestyle (Fisher's exact test, p=0,000). CONCLUSIONS: Studies of the effects of reimbursement systems in the context of interdisciplinary care are still in their infancy, despite the widespread use of diagnosis related groups. The mandatory character implicit in the case tariff fee system, which requires minimum qualitative standards for structural and procedural parameters in the context of providing interdisciplinary patient-centered care, can influence patients' behavioural patterns and lifestyle.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".