Understanding and improving care: Use of routine data

Introduction: The majority of dental care in Germany is provided by dentists contracting with national health insurance companies. Although the oral health of the German population is steadily improving a comprehensive description and assessment of the care provision is difficult. However, such analyses are fundamental for potential changes in the care delivery process. The aim of this article is to outline the method of routine data analysis as an essential tool for dental care research, thereby illustrating the potential for research using these data. Method/Results: Routine data analyses are research analyses based on data originally collected for other purposes. In the dental context, claims data can illustrate the spectrum of treatment provided. In cooperation with a large German national health insurance company, longitudinal analyses were carried out based on the essential components of dental treatments implemented. Additionally, routine data was used to evaluate system changes and to consider regional differences in treatments. Typical analyses are presented and critically considered. The nature of the database results in methodological restrictions of routine data analyses. The interpretation of the results is also limited because comparative studies and expected values are often missing. Nevertheless, despite these drawbacks, routine data analysis is an important method that leads to a better understanding of care provision. Previously unattainable insights into real care processes are now possible, providing data and results that could not otherwise be generated. Conclusions: The understanding of dental care provision under a national health insurance model is currently still rudimentary. However, this understanding is an impetus for improvement. The presented results based on routine data are the first milestones towards a comprehensive description of the reality of care. Schlagwörter: "Dental care/medical insurance model", data mining, health service research, outcomes, quality, routine data, secondary data, system changes