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Danish research in quality registers

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Presentation on theme: "Danish research in quality registers"— Presentation transcript:

1 Danish research in quality registers
Mette Nørgaard, MD, PhD, Department of Clinical Epidemiology, Aarhus University Hospital, Denmark

2 Research in clinical quality databases

3 Developments in the health care system
Shorter admissions  increased risk of readmissions More patients treated in an outpatient setting Risk of adverse reactions and complications increases with more advanced treatments and changing indications A number of readmissions and adverse events are preventable Exponentially increasing amounts of data – only used sporadically for monitoring purposes (large data and big data) Patient safety and clinical quality should be monitored locally

4 You can’t fatten the pig by weighing it; But…

5 Strengths of Danish data sources
Public health care system Record linkage at the individual level Time and money saving Data-collection independent of research question Large populations and long-term follow up Liberal data law enabling data access Relatively inexpensive to get data


7 Clinical Quality Databases (approx. 60 databases – 30MIO DKK)
The Danish Registry of Biologic Treatments in Rheumatology The Danish Colorectal Cancer Group Database The Danish Stroke Registry The Danish Breast Cancer Cooperative Group Register The Danish Transfusion Data Base The Danish Registry on Regular Dialysis and Transplantation The Danish Quality Database for Breast Cancer Screening The Danish Cervix Cancer Screening Register The Danish Hysterectomy Database The Danish Hip Arthroplasty Registry The Danish Knee Arthroplasty Registry The Danish Cruciate Ligament Register The Danish Shoulder Alloplasty Registry The Danish Urological Cancer Group Database

8 DK Quality of care registries - Mission
Improvement: Improving prevention, diagnostics, treatment and rehabilitation Management/Accountability: Documentation for clinical governance and organisational priority setting Transparency: Information for citizens and patients Innovation: Research infrastructure

9 Danish Breast Cancer Cooperative Group (DBCG) as an example
Established in 1977 To standardize treatment and improve breast cancer prognosis Data were collected on paper forms until online reporting since Included originally patients with invasive breast cancer, but now expanded to patients with in situ breast cancer and hereditary breast and ovarian cancer families Information on diagnosis, operation, radiation and medical oncological treatment and follow-up When compared with the National Registry of Patients it is estimated that the DBCG lacks about 5% of the total number of women with breast cancer in Denmark – those not included are primarily elderly women

10 The rolls royce model: The National Indicator Project

11 The National Indicator Project (NIP)
Established in 1999 in the Danish Healthcare System. A concerted action between a number of Danish institutions, including the Ministry of Health, the National Board of Health, the Centre for Evaluation and Assessment of Medical Technology, the Association of Danish Regions, the five regions, the Danish Medical Association, the Scientific Societies, the Danish Nursing Association, the Danish Physiotherapists Association the Occupational Therapists Association.

12 National clinical audit
Data transmission via Internet Capture of relevant data or direct reporting by responsible clinicians Data analyses by clinical epidemiologists Clinical Registry Real or virtual Clinical activities and data registration Monthly/quarterly feedback to all clinical departments and MIS Quality improvement Feedback of risk adjusted data once a year Public release National clinical audit Regional clinical audit

13 The Danish Registry for COPD (DrKOL


15 CONCLUSIONS: The positive predictive value of acute COPD discharge diagnoses in the Danish National Patient Registry is high (PPV=92%). At the same time, there is a substantial underrecording of COPD during hospitalizations with other acute respiratory disorders like pneumonia and respiratory failure (NPV=81%).




19 What did the clinical quality databases show?
Stroke Time of admission 30-day mortality OR (95% CI) adj. OR* (95% CI) adj. OR** (95% CI) Weekday 0-7, 15-24 1079/11462 (9.41%) 0.99 ( ) 0.99 ( ) Weekend or holiday 1004/9044 (11.10%) 1.15 ( ) 1.12 ( ) 1.00 ( ) Weekday 7-15 1207/12695 (9.51%) 1.00 (reference) * Adjusted for age, sex, Charlson comorbidity score. ** Adjusted for age, sex, Charlson comorbidity score, marital status, previous stroke, diabetes, atrial fibrillation, smoking, alcohol use, Scandinavian Stroke Scale score, hypertension and type of stroke


21 Strengths of clinical quality databases as research ressource
More detailed clinical data than the central health registries Defined trajectory for the specific disease Easy linkage to national registries via the CPR-number

22 Conclusion: A high pre-diagnostic alcohol seem to have an effect on the course of the disease. This could not be explained by differences in tumor presentation

23 Conclusion: Data do not support the hypothesis that β-blockers attenuate breast cancer recurrence risk


25 Clinical care research
Medical Care Variation Research (Comparative) effectiveness research Evaluation of Quality Improvement Stategies

26 Routine clinical practice
Conclusion: approximately 3% experienced VTE, MI, stroke or bleeding. These risks did not decline during the 15-year study period, while the risk of dying fell substantially

27 Routine clinical practice
Conclusion: Radiotherapy for breast cancer increased the subsequent rate of ischemic heart disease proportional to the mean radiation dose to the heart. The increase continued for at least 20 years.

28 Routine clinical practice


30 Quality of early stroke care and hospital costs
Evaluation Quality of early stroke care and hospital costs Svendsen ML1,2, Ehlers LH3, Hundborg HH1, Ingeman A1, Johnsen SP1 1 Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark 2 Health Technology Assessment & Health Services Research, Public Health and Quality Improvement, Aarhus, Denmark 3 Danish Center for Health Care Improvements, Faculty of Social Science and Faculty of Health Science, Aalborg University, Aalborg, Denmark

Processes received Mean LOS (SD) Days Mean cost USD Adjusted cost difference (95% CI)* 0%–24% 31.4 (33.4) Reference 25%-49% 28.8 (33.9) ) 50%-74% 24.5 (31.2) 75%-100% 16.3 (26.1)

32 CONCLUSION Early stroke care in agreement with key recommendations for the early management of patients with stroke may be associated with potentially large hospital cost savings

33 Medical care variation

34 Effectiveness research

35 Trials


37 PROCRIN – programme for clinical research infrastructure
AIM: to strengthen integration among all participating databases, registries, and biobanks in the health care sector to facilitate data analysis to integrate research findings into daily clinical work, building bridges between research and clinical practice


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