[AKTIN - The German Emergency Department Data Registry - real-time data from emergency medicine : Implementation and first results from 15 emergency departments with focus on Federal Joint Committee's guidelines on acuity assessment]. / Das AKTIN-Notaufnahmeregister ­ kontinuierlich aktuelle Daten aus der Akutmedizin : Ergebnisse des Registeraufbaus und erste Datenauswertungen aus 15 Notaufnahmen unter besonderer Berücksichtigung der Vorgaben des Gemeinsamen Bundesausschusses zur Ersteinschätzung.

BACKGROUND: Emergency care in Germany is in transition. Emergency departments (EDs) treat their patients based on symptoms and acuity. However, this perspective is not reflected in claims data. The aim of the AKTIN project was to establish an Emergency Department Data Registry as a data privacy-compliant infrastructure for the use of routine medical data. METHODS: Data from the respective documentation systems are continuously transmitted to local data warehouses using a standardized interface. They are available for several applications such as internal reports but also multicentre studies, in compliance with data privacy regulations. Based on a 12-months period we evaluate the population with focus on acuity assessment (triage) and vital parameters in combination with presenting complaints. RESULTS: For the period April 2018 to March 2019, 436,149 cases from 15 EDs were available. A triage level is documented in 86.0% of cases, and 70.5% were triaged within 10 min of arrival. Ten EDs collected a presenting complaint regularly (82.3%). The respective documentation of vital signs shows plausible patterns. CONCLUSIONS: The AKTIN registry provides an almost real-time insight into German EDs, regardless of the primary documentation system and health insurance claims data. The Federal Joint Committee's requirements are largely met. Standardized presenting complaints allow for symptom-based analyses as well as health surveillance.

Predictors of hospitalizations for diabetes in Germany: an ecological study on a small-area scale.

OBJECTIVES: Our objective was to evaluate the role of potential predictors in explaining spatial variation among diabetes hospitalization rates in Germany. STUDY DESIGN: This was an ecological analysis using hospital routine data. METHODS: County-level hospitalization rates (n = 402) in 2015 were calculated based on the German Diagnosis Related Groups database. We used a funnel plot to identify counties with high hospitalization rates. To examine the impact of predictors such as socio-economic status or structure of primary care, we performed linear and logistic regression analyses. RESULTS: The crude hospitalization rate was 262 admissions per 100,000 population. In multivariable logistic models, we found the percentage of employees with academic degree (odds ratio [OR]: 0.72, 95% confidence interval [CI]: 0.56-0.91), high hospital bed rate (4th quartile vs 1st quartile; OR: 2.73, CI: 1.03-7.24), and diabetes prevalence (OR: 1.49, CI: 1.17-1.90) to be significant predictors for high hospitalization rates. In multivariable linear models, the percentage of unemployed (regression coefficient b: 4.79, CI: 0.81-8.78) and rurality (b: 0.52, CI: 0.19-0.85) explained the variation in addition to predictors from logistic regression. Primary care structure was not a significant predictor in multivariable models. CONCLUSIONS: The non-significant impact of primary care in adjusted models casts the use of diabetes hospitalizations as indicators for access and quality of primary care into doubt. Diabetes hospitalizations may rather reflect demand for care.