From patient generated data to precision medicine: An example of a patient-centered application in oncology.

e13559 Background: To practice precision medicine, the inclusion of patients in therapy is becoming increasingly important. Terms such as ‘patient empowerment’ (PE), ‘patient reported outcome’, and ‘patient generated data’ (PGD) are becoming quite present in current research. In order to implement PE consistently and integrate PGD in therapy, it is necessary to provide patients with specific tools. Methods: In order to develop such a corresponding system, challenges for implementation were identified as well as the current state of PGD in oncology. The aim was to design a system suitable for everyday use that allows the measurement of individual effects like quality of life (QoL) directly at the patient. Based on the positive experience of a previous developed questionnaire tool (EQU) to support physician-patient communication and the findings of the methodology a new patient-centered system was conceptualized with an automated data collection using sensor systems. A decisive factor was to support patients in self-management between treatment periods. This solution was then discussed extensively with stakeholders in order to identify and prioritize functionality and therefore incorporate specific user needs of oncological patients. Results: A great acceptance and need for tools to support patients especially between appointments was identified. In order to enable the recognition of individual characteristics, a continuous survey is essential. Based thereon an application called Lion-App for longitudinal QoL assessment in oncology was developed. A user-centered development aligns the app with the needs of the patients while HL7 FHIR provides the framework for an interoperable exchange. To reduce the burden of continuous interaction, sensors are used for data collection. With an interpretation of this data in the context of QoL, patients are able to visualize their personal course to gain an even better understanding of their disease. With the possibility to perform self-management between appointments, patients are lifted to another level of empowerment. In addition, the gained knowledge may then be used to focus on the effects of the disease in the next appointment and thus take a self-determined role in treatment. First evaluations of the app have been started. Conclusions: There are many ways to use PGD in the manner of precision medicine. The main factor is to create awareness among patients, collect data in an exchangeable way and involve end users early into development. A great opportunity to reduce user interactions and therefore enable long-term usage is the integration of sensor technologies. As a secondary use, algorithms can be extended via the expanded data base to become even more individualized to the patient. Thus, in the long run, such a system may help to further strengthen the cooperation between patient and physician and thus to enable a personalized, QoL-based treatment in oncology.