Medical computer science

mHealth & Apps

In the last decade, apps for mobile phones and tablets changed our life completely. Since the introduction of iOS (Apple Inc., USA) in 2008, apps are ubiquitous, and more than 5 million apps are available in the leading app stores. Many of these support us in our everyday lives and ensure time savings or entertainment: the possibilities are huge and range from simple weather apps to complex three-dimensional games. Also, the healthcare sector has been enriched by numerous innovations such as apps for weight reduction, depression, or diabetes. Wearables and devices such as fitness trackers, blood pressure monitors, blood glucose meters, and personal scale gears are popular and convey the impression of high acceptance for collecting medical data. The World Health Organization (WHO) defines all these tools under the labels electronic heath (eHealth) and mobile health (mHealth).
It is apparent the willingness to use mHealth apps or devices is high and the need is growing. mHealth is always closely associated with telemedicine, which the WHO defines as: “The delivery of health care services, where distance is a critical factor, by all health care professionals using information and communication technologies for the exchange of valid information for diagnosis, treatment, and prevention of disease and injuries, research and evaluation...”.
Currently, the medical field shows an evolving trend in developing apps used as tools. Only a few researchers like Volkova et al. use mobile apps as supporting tools in trials. In their research, they launched the app-based Food Label Trial investigating the impact of labels on consumer behavior. They created the very apt term “smartRCTs” (app-accompanied randomized controlled trials). In our research group, we also work on different approaches regarding mHealth.

Kessel KA et al. Usability study: Mobile App assessing Health-related Quality of Life in Oncological Patients with the EORTC QLQ-C30 questionnaire JMIR Mhealth Uhealth. 2018; 6:e45
Kessel KA et al. Mobile Health in Oncology: A Patient Survey About App-Assisted Cancer Care. JMIR Mhealth Uhealth 2017; 5: e81-7. doi.org/10.2196/mhealth.7689
Vogel MME et al. mHealth and Application Technology Supporting Clinical Trials: Today's Limitations and Future Perspective of smartRCTs. Front Oncol 2017; 7: 262-6. doi.org/10.3389/fonc.2017.00037
Kessel KA et al. Mobile Apps in Oncology: A Survey on Health Care Professionals' Attitude Toward Telemedicine, mHealth, and Oncological Apps. J Med Internet Res 2016; 18: e312. doi.org/10.3389/fonc.2017.00037

Patient-reported Outcome (PRO)

With Patient-reported Outcome (PRO), we can improve the understanding of health during and after therapy, and be more responsive to patient preferences and needs. An essential part of PRO assessment is Quality of Life (QoL). The WHO defined QoL 1946 as "individual's perception of their position in life in the context of culture and value systems in which they live and relate to their goals, expectations, standards and concerns" (WHO, 1946). This definition is very extensive and difficult to grasp in medicine and health research. Therefore, the concept of health-related quality of life (HRQoL) became established in the 1980s. The collection of HRQoL has advantages for physicians and patients. Especially in oncology, where therapy is often life-prolonging but not curative, it is important to consider whether the treatment affects the patient's QoL. Therefore, it is necessary to evaluate and compare existing and novel therapeutic approaches to HRQoL. Continuous HRQoL recording in everyday clinical practice enables rapid supportive intervention by the medical or social services.
In our clinic, we have been dealing with the acquisition of PRO and HRQoL for a long time. The data is a relevant and useful source of information. Especially in cases where long-term side effects of therapy are interesting, PRO is an efficient method. Our research group focuses on the electronic recording (via web-interface, app or digitally on site). This offers the possibility to reduce costs and time as well as to increase patient compliance. Another step towards digital medicine - Medicine 4.0.

Kessel KA et al. High-precision radiotherapy for meningiomas: Long-term results and patient-reported outcome (PRO). Strahlenther Onkol 2017; 29: 197.
Kessel KA et al. Fractionated vs. single fraction stereotactic radiotherapy in patients with vestibular schwannoma: Hearing preservation and patients' self-reported outcome based on an established. Strahlenther Onkol 2017; 193: 192-9.

Prognostic scores

Prognostic scores are a powerful tool for therapy decision in modern medicine. We often ask ourselves: Should a patient be operated or is a conservative therapy sufficient? In oncology, we can use scientifically obtained data to develop empirically verified score systems.
The therapy decision can be determined by several patient and cancer parameters such as age, tumor staging, Karnofsky index. But also others can influence a treatment option, for example: if a resection has been performed, or the time since the first diagnosis. In general, an interdisciplinary team of specialists decides which therapy is most suitable for the patient. A scoring system can be a helpful and objective medium. Fast and IT-based computation of significant and reliable predictors can be used in patient classification and therapy recommendation.
In our department, we work on the empirical development of scores for stratification of groups, assessment of disease severity, evaluation of outcome and quality of life, and cost-benefit analyses. These scores are validated in different test series.  Further, our research group investigates the possibility of automated generation of scores using "Big Data" and radiomics strategies.

Combs SE et al. A. Re-irradiation of recurrent gliomas: Pooled analysis and validation of an established prognostic score - Report of the Radiation Oncology Group (ROG) of the German Cancer Consortium (DKTK). Cancer Med 2018; 18: 549.
Kessel KA et al. Modification and optimization of an established prognostic score after re-irradiation of recurrent glioma. PLoS One 2017; 12: e0180457-10.

Kessel KA et al. Validation of an established prognostic score after re-irradiation of recurrent glioma. Acta Oncologica 2017; :1-5.