HIS-based Kaplan-Meier plots - a single source approach for documenting and reusing routine survival information

<p>Abstract</p> <p>Background</p> <p>Survival or outcome information is important for clinical routine as well as for clinical research and should be collected completely, timely and precisely. This information is relevant for multiple usages including quality control, clinical trials, observational studies and epidemiological registries. However, the local hospital information system (HIS) does not support this documentation and therefore this data has to generated by paper based or spreadsheet methods which can result in redundantly documented data. Therefore we investigated, whether integrating the follow-up documentation of different departments in the HIS and reusing it for survival analysis can enable the physician to obtain survival curves in a timely manner and to avoid redundant documentation.</p> <p>Methods</p> <p>We analysed the current follow-up process of oncological patients in two departments (urology, haematology) with respect to different documentation forms. We developed a concept for comprehensive survival documentation based on a generic data model and implemented a follow-up form within the HIS of the University Hospital Muenster which is suitable for a secondary use of these data. We designed a query to extract the relevant data from the HIS and implemented Kaplan-Meier plots based on these data. To re-use this data sufficient data quality is needed. We measured completeness of forms with respect to all tumour cases in the clinic and completeness of documented items per form as incomplete information can bias results of the survival analysis.</p> <p>Results</p> <p>Based on the form analysis we discovered differences and concordances between both departments. We identified 52 attributes from which 13 were common (e.g. procedures and diagnosis dates) and were used for the generic data model. The electronic follow-up form was integrated in the clinical workflow. Survival data was also retrospectively entered in order to perform survival and quality analyses on a comprehensive data set. Physicians are now able to generate timely Kaplan-Meier plots on current data. We analysed 1029 follow-up forms of 965 patients with survival information between 1992 and 2010. Completeness of forms was 60.2%, completeness of items ranges between 94.3% and 98.5%. Median overall survival time was 16.4 years; median event-free survival time was 7.7 years.</p> <p>Conclusion</p> <p>It is feasible to integrate survival information into routine HIS documentation such that Kaplan-Meier plots can be generated directly and in a timely manner.</p

CIS-based registration of quality of life in a single source approach

Background: Documenting quality of life (QoL) in routine medical care and using it both for treatment and for clinical research is not common, although such information is absolutely valuable for physicians and patients alike. We therefore aimed at developing an efficient method to integrate quality of life information into the clinical information system (CIS) and thus make it available for clinical care and secondary use. Methods: We piloted our method in three different medical departments, using five different QoL questionnaires. In this setting we used structured interviews and onsite observations to perform workflow and form analyses. The forms and pertinent data reports were implemented using the integrated tools of the local CIS. A web-based application for mobile devices was developed based on XML schemata to facilitate data import into the CIS. Data exports of the CIS were analysed with statistical software to perform an analysis of data quality. Results: The quality of life questionnaires are now regularly documented by patients and physicians. The resulting data is available in the Electronic Health Record (EHR) and can be used for treatment purposes and communication as well as research functionalities. The completion of questionnaires by the patients themselves using a mobile device (iPad) and the import of the respective data into the CIS forms were successfully tested in a pilot installation. The quality of data is rendered high by the use of automatic score calculations as well as the automatic creation of forms for follow-up documentation. The QoL data was exported to research databases for use in scientific analysis. Conclusion: The CIS-based QoL is technically feasible, clinically accepted and provides an excellent quality of data for medical treatment and clinical research. Our approach with a commercial CIS and the web-based application is transferable to other sites

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams