Data integration between clinical research and patient care: a framework for context-depending data sharing and in silico predictions

Abstract

AbstractUsually, it takes quite some time until new insights from basic or clinical research are ultimately transferred into clinical routine. On the other hand, there are still many hurdles to directly provide and use routine data in the context of basic and clinical research. Specifically, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings.Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, we emerge from a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: aclinical (i.e., treatment context) perspectivefocusing on patient-specific data visualization, analysis and outcome prediction, and aresearch perspectivefocusing on the exploration of aggregated, but pseudonymized data.We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes.Author summaryPatient-oriented research is based on comprehensive, quality-assured medical data that is visualized and analysed to gain knowledge. Based hereon, computer models can be developed, which e.g., calculate risk scores or predict treatment success. Such approaches can be used for risk staging or for selecting the optimal therapy for a specific patient. In recent years, a lot of efforts have been made to develop generic concepts for data processing and for providing the data in the research context. What has been missing so far is a suitable software infrastructure to facilitate the direct backward propagation of scientific results into everyday clinical practice to support the treating clinicians in their decision-making processes. To close this gap, we designed a generic software framework into which, in principle, any computational model or algorithm can be integrated. For demonstration purposes, we developed a web application that integrates two mathematical models from the field of haematology, specifically relating to chronic myeloid leukaemia (CML). Both models calculate the leukaemia recurrence probability of a specific patient, after the intended stopping of the applied therapy. The particular prediction is based on patient-specific molecular diagnostic data and can be used for personalized treatment adaptation.