Accurate survival or outcome information is important for many clinical studies, clinical routine and epidemiology. The standard method for estimating survival is the Kaplan-Meier plot (KM-plot)  with high relevance in medical research. Particularly in oncology the Kaplan-Meier technique is used to compare survival information between different therapy strategies or stages of the disease . Survival time and follow-up status are used to compute an estimate of a survival curve for censored data.
In the German healthcare system a distinction can be made between the routine documentation which is commonly performed in hospital information systems or still paper based and the research documentation mainly performed in electronic data capture systems (EDC) or on paper based case report forms (CRF). There are several systems in healthcare resulting in separate documentation of medical routine, research and quality management data. In the context of clinical studies survival information is currently captured on paper based CRF and occasionally on electronic CRF (eCRF), but generally separated from HIS. However, some patients already have at least a basic electronic medical record . Currently survival analysis is not possible within the HIS as the required data is not available. A problem of these external databases consists in the resulting difficult multiple usage of information. Data from a single patient can be relevant for several studies (e.g. therapy study, biomarker discovery study, epidemiological studies) in addition to clinical routine. Redundant documentation is common but inefficient regarding a resource limited setting and carries the danger of inconsistent information. In this setting it would be attractive to use health data outside of direct care delivery. A secondary use of documented data for research and quality management  may be of potential benefit for those physicians who already use electronic documentation . For instance, the idea of the REUSE project implies that clinical data should be based in the electronic health record (EHR) independent from the context, in which data is captured . The secondary use of clinical data has enormous potential for improving quality of care [7, 8].
Furthermore, there is heterogeneity in the required follow-up documentation depending on the disease and the department in which the data is obtained. Physicians need further information to interpret the status in combination with clinical data. Also, the parameter values for the follow-up status may be different between oncological diseases. Therefore, an efficient implementation should be based on a generic data model which is suitable for several diseases.
Studies of health services research, epidemiological studies and phase III/IV studies often consist of a high number of cases resulting in laborious documentation and high data management costs. Using routine documentation may reduce the documentation workload and reduce costs. To reach these goals the respective source data have to be "accurate, legible, contemporaneous, original, attributable, complete and consistent" .
However, data quality of follow-up documentation is often unsatisfactory  and requires adaption before it can be used for research. The follow-up documentation needs to be complete to obtain meaningful KM-plots as incomplete information can bias the analysis of the results . One strategy to increase completeness and achieve higher data quality is to use an electronic documentation tool [12, 13]. This approach could be extended to use the HIS for clinical and research documentation which would also result in high data quality . Commercial HIS usually cover only events during hospitalisation so there is a need for a special follow-up module to allow the survival documentation for inpatients and outpatients.
With respect to clinical quality management it is important to obtain timely KM-plots of all patients and not just from those in clinical studies. Thus, it would be desirable to integrate the follow-up documentation into clinical routine and document it in the EHR within the HIS. Using this method, it will be made available for all treating physicians and clinical research projects can be combined with routine documentation by reusing the EHR .
Because of the high relevance, especially in oncology, we analyse whether an integrated follow-up documentation is feasible and focus on the following objectives:
Is it feasible to design a follow-up documentation system in the HIS which is suitable for several oncological diseases and provides a secondary use of data?
Is it possible to extract survival information from routine HIS documentation so that physicians can obtain KM-plots in a timely manner?
What level of data quality can be achieved with respect to completeness of forms and completeness of items per form?