Medical information systems offer numerous data about patients today, which are recorded in connection with different health care interventions. Typical examples are clinical findings, drug prescriptions, diagnoses, hospitalization or specific therapies. In the future it can be expected that development of electronic health care systems will increase the amount of electronically available medical data about patients. Besides these individual data about patients, the public health system (health statistics) collects information about the overall health status in the population and about the health infrastructure of a country. Medical research produces also a lot of data, for example standard operating procedures (SOP) for specific treatments. Efficient use of all these information in connection with medical interventions and in public health raises a number of questions, which can be grouped into two different views, characterized by the terms micro level and macro level. At the micro level, the main question is how to use all the information in the treatment of the individual patient (evidence based individual decision). The macro level focuses on the development of standardized treatment patterns (SOPs) as well as issues of public health like epidemiological questions or proposals for preventive medical check-ups (evidence based health care).
However, information at the micro and the macro level should not be seen as separated entities: the interaction between the different views onto data is one of the major challenges for all kinds of evidence-based medicine, and the path from bench to bedside. The interaction between empirical treatment processes of and SOPs is quite obvious: standardized procedures influence the empirical process on the one side and on the other side we obtain insights for adaption and reformulation of SOPs from empirical processes. Accordingly, the proposed research cluster aims at an effective linkage of both micro and macro levels of medical information. At this, it has a dedicated focus on the development of a methodology exploiting empirical evidence, by quantification and aggregation to inquire the actual effectiveness of prescriptive regimes and to analyze the emanating differences (if any) to gathered field evidence in search of causal explanation and remedial measures.
The connection between empirical processes and public health data, summarized in so called health data cubes, can be best described by the term process configuration: from public health data can be learned what kinds of possible peculiarities are to be considered in a medical treatment process for a specific group of patients. Finally, we have to take into account in how far treatments condensed in SOPs influence the health status in the overall population using statistical analysis.
This to say that the development and continuous improvement of standardized medical practice capitalizes upon an adaptive cycle (i) composed of the implementation of normative treatment practices – based on both theory and past experience – (ii) followed by empirical outcome evaluation, and (iii) ensuing conclusions about actual process compliance and refinement indications. In particular, it is important to identify patient collectives facing specific risks (requiring, in turn, specific modifications to standard procedures) and to assess the eventual effectiveness of normative practices under a vast variety of treatment conditions and contingencies.