Evaluation of Participants Characteristics and Health Economic Aspects of Outpatient Disease Management Programs (DMP) for Chronic Heart Disease Patients in Germany
Bibliographic record
Abstract
Background: In Germany, the first DMP for patients with chronic heart disease was introduced in 2005. Sickness funds are obligated by the Social Security Code to evaluate DMP. However, due to conditions of DMP in Germany high quality evaluations are hardly practicable. For example: the linkage of the DMP with the risk adjustment scheme forces the sickness funds to enrol any potential candidate for the DMP as fast as possible. Thus randomised comparisons of DMP vs. non-DMP are virtually impossible. Objective: The objective of this study is to investigate quality of care between DMP vs. non-DMP patients systematically. Furthermore, the size and sociodemographic characteristics of the inscription rate of people with chronic heart disease in DMP will be analysed. Methods: The database is the population-based KORA (Kooperative Gesundheitsforschung in der Region Augsburg) Myocardial Infarction Register, which registered all patients with an acute myocardial infarction (AMI) treated in hospital between 1985 and 2004. All 24-hours surviving AMI-patiens were interviewed while they were in hospital and after discharge in a concluding chart review treatment data were gathered. A follow up survey of all 4392 registered alive persons was performed by autumn 2006. The postal questionnaire included in particular information on sociodemographic characteristics (age, sex, education), medical condition (diabetes, state of health), process parameters (physician counselling for smoking, nutrition, physical activity and medication) and quality of life (EQ-5D). The data were analysed using a logistic regression model. Results: The response rate of the study was over 65%, which resulting in a sample size of 2950 individuals. At the time of the investigation about one third of the study population were inscribed in DMP. Our first findings suggest that there is a difference in quality of care between patients inscribed in DMP vs. standard treatment (non-DMP). The pysician counselling for smoking, nutrition and physical activity of DMP patients are significantly higher than for non-DMP patients. Furthermore, the investigation did show scarcely significant differences between DMP and non DMP-group with regard to age, sex and quality of life (EQ-5D). Conclusions: Based on this regional cross sectional survey, DMP seems to have positive effects according to quality of care. The inscription decision seems to be less related to basic socio-demographic characteristics, which points to a broad acceptance within the target population.
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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 | 0.011 | 0.000 |
| 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 | 0.001 |
| 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".