Digitale Medizin in der Onkologie: Clinical Decision Support, Real World Data und Patient Involvement

Was ist neu?

Stand der Dinge In der Onkologie kam es zuletzt zu einem rasanten Wissenszuwachs. Neue Entwicklungen in Diagnostik und Therapie von Tumorerkrankungen haben die Grundlagen für individualisierte Therapiekonzepte geschaffen. Aktuell werden innovative Onkologie-spezifische IT-Lösungen entwickelt mit dem Ziel, die Heilungschancen für Patienten mit Tumorerkrankungen langfristig zu verbessern.

Clinical Decision Support Die Komplexität onkologischer Therapieentscheidungen hat durch Einführung neuer Biomarker und zielgerichteter Therapeutika stark zugenommen. Erste „intelligente“ Systeme, die aktiv Therapieoptionen auf Basis von vorhandenen Daten vorschlagen, sind verfügbar, aber noch nicht weit verbreitet und unzureichend klinisch validiert.

Real-World Data und Real-World Evidence Durch die zunehmende Verbreitung von elektronischen Gesundheitsakten wird eine strukturierte Sammlung und Auswertung von Daten aus der onkologischen Routineversorgung möglich. Real World Data werden eingesetzt, um die Sicherheit und Nebenwirkungen von onkologischen Medikamenten zu überwachen und können helfen, onkologische Therapieleitlinien zu entwickeln.

Patient Involvement und Patient Reported Outcomes Die frühe Meldung von Symptomen und Nebenwirkungen (Patient Reported Outcomes) verspricht eine verbesserte Behandlung und eine gesteigerte Therapieadhärenz. Patient Reported Outcomes können auch im Rahmen von klinischen Studien und zur Qualitätssicherung eingesetzt werden. Erste Studien zeigen, dass eine IT-gestützte Erfassung von Patient Reported Outcomes Symptome und Überleben von Patienten mit Tumorerkrankungen positiv beeinflussen kann.

Abstract

Oncology has experienced a massive growth in knowledge over the last years. However, the growing amount of knowledge and data increases the complexity of clinical decisions and presents oncologists with new challenges. Currently, oncology-specific IT solutions are being developed with the aim to improve survival of cancer patients.

The complexity of treatment decisions in oncology has greatly increased due to introduction of novel biomarkers and targeted therapeutics. First “intelligent” systems that suggest therapies based on available data have been developed, however, these systems are not yet widely adopted and lack sufficient clinical validation.

Increasing implementation of electronic health records enables structured collection and analysis of data from routine patient care. These real-world data are used for post marketing surveillance and can help to develop therapy guidelines. Currently it is being discussed if real world data can provide sufficient evidence for regulatory decisions.

Broad availability of internet access and smartphones provides an opportunity to involve patients in surveillance and control of cancer treatments. Early reporting of symptoms and side effects is likely to improve treatment and compliance. In addition, patient reported outcomes can be employed for data collection in clinical trials and for quality surveillance. First randomized trials suggest that collection of patient reported outcomes can improve symptoms and survival of cancer patients.

Schlüsselwörter

Onkologie - Clinical Decision Support - Real World Data - Patient Involvement

Key words

oncology - clinical decision support - real world data - patient involvement

• Literatur

• 1 Forbes SA, Beare D, Gunasekaran P. et al. COSMIC. Exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res 2015; 43: D805-D811
• 2 Zhang J, Baran J, Cros A. et al. International Cancer Genome Consortium Data Portal–a one-stop shop for cancer genomics data. Database J Biol Databases Curation 2011; 2011: bar026
• 3 Chakravarty D, Gao J, Phillips SM. et al. OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol 2017; 2017
• 4 Griffith M, Spies NC, Krysiak K. et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet 2017; 49: 170-174
• 5 NCCN Mobile Apps for Smartphones and Tablets. Im Internet: https://www.nccn.org/apps/ ; Stand: 10.03.2019
• 6 ESMO. Cancer Guidelines available on mobile devices. Im Internet: https://www.esmo.org/Guidelines/Pocket-Guidelines-Mobile-App ; Stand: 10.03.2019
• 7 Easy Oncology: Tumormanagement für die Kitteltasche. Im Internet: https://www.aerzteblatt.de/archiv/200432 ; Stand: 10.03.2019
• 8 Chuchu N, Takwoingi Y, Dinnes J. et al. Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma. Cochrane Database Syst Rev 2018; 12: CD013192
• 9 IBM. Watson for Oncology – Overview. Im Internet: https://www.ibm.com/de-de/marketplace/ibm-watson-for-oncology ; Stand: 10.03.2019
• 10 Zhou N, Zhang CT, Lv HY. et al. Concordance Study Between IBM Watson for Oncology and Clinical Practice for Patients with Cancer in China. Oncologist 2018;
• 11 Lee WS, Ahn SM, Chung JW. et al. Assessing Concordance With Watson for Oncology, a Cognitive Computing Decision Support System for Colon Cancer Treatment in Korea. JCO Clin cancer informatics 2018; 2: 1-8
• 12 Somashekhar SP, Sepúlveda MJ, Puglielli S. et al. Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann Oncol Off J Eur Soc Med Oncol 2018; 29: 418-423
• 13 Schmidt C. M. D. Anderson Breaks With IBM Watson, Raising Questions About Artificial Intelligence in Oncology. J Natl Cancer Inst 2017; 109 (05)
• 14 Concerto HealthAI. Im Internet: https://concertohealthai.com/ ; Stand: 10.03.2019
• 15 Via Oncology. Im Internet: http://viaoncology.com/ ; Stand: 10.03.2019
• 16 Lewis JRR, Kerridge I, Lipworth W. Use of Real-World Data for the Research, Development, and Evaluation of Oncology Precision Medicines . JCO Precision Oncology 2017; 1: 1-11
• 17 Rubinstein SM, Warner JL. CancerLinQ: Origins, Implementation, and Future Directions. JCO Clin Cancer Inform 2018; 1-7 . doi: 10.1200/CCI.17.00060
• 18 CODE- Collaboration for Oncology Data in Europe. Im Internet: https://www.code-cancer.com/ ; Stand: 10.03.2019
• 19 Px HealthCare. Im Internet: http://www.pxhealthcare.com/ ; Stand: 10.03.2019
• 20 heartbeat. Im Internet: https://heartbeat-med.de/ ; Stand: 10.03.2019
• 21 Basch E, Deal AM, Dueck AC. et al. Overall survival results of a trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment. J Am Med Assoc 2017; 318: 197-198
• 22 Denis F, Lethrosne C, Pourel N. et al. Randomized Trial Comparing a Web-Mediated Follow-up With Routine Surveillance in Lung Cancer Patients. J Natl Cancer Inst 2017; 109: 1-8
• 23 Kamdar MM, Centi AJ, Fischer N. et al. A randomized controlled trial of a novel artificial-intelligence based smartphone application to optimize the management of cancer-related pain. Journal of Clinical Oncology 2018; 36: 34 _suppl, 76-76