Personal Health Train The benefits explained with use cases

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1 Andre Dekker Maastricht UMC+, MAASTRO Clinic Personal Health Train The benefits explained with use cases

2 Learning Health Care System Need for internal and external data Need for dissemination of findings Need for predictions, evidence & data driven medical and management decisions Research influences practice and practice influences Circulation. 2017;135:e826 research

3 Data landscape Clinical research 3% of patients 100% of features 5% missing 285 data points Clinical exchange & registries 100% of patients 3% of features 20% missing 240 data points Data elements Patients Clinical sources 100% of patients 100% of features 80% missing 2000 data points

4 Academic Viewpoint -> PHT True personalized medicine and a learning health care system are only possible if all data on all patients are available for care AND science Centralizing data is not possible in any kind of context (locally, regionally and (inter)nationally) for administrative, political, ethical and technical reasons A scalable system is needed where questions can be asked while the data stays where they are under full control of the data holder and data subject This system can be realized by making data FAIR at the source

5 Examples / use cases of the PHT PHT Distributed learning Benchmarking with other institutes Practice differences Decision support systems PHT Personal Locker & Consent PHT Distributed registry

6 Andre Dekker Maastricht UMC+, MAASTRO Clinic PHT - Distributed Learning Benchmarking, Practice Differences & Decision Support

7 CORAL Community in Oncology for RApid Learning meercat Lung - Dyspnea U Michigan MAASTRO The Christie Interest to join Erasmus (Breast) BCCA (Breast) Bloemfontein (Cervix) Odense (HN, Lung) Aalst (Lung) McGill (Brain) Map Copyright Showeet.com cancat Lung SBRT - Control Princess Margaret MAASTRO 7 ducat Lung - Dysphagia MAASTRO Radboud NKI BIONIC Radiomics MAASTRO Tata Memorial 4 eurocat Lung - Survival UK Aachen LOC Hasselt Catharina MAASTRO CHU Liege worldcat Rectum - Local Control Fudan Rome/EU RTOG/NRG

8 Benchmarking Model prognosis based case mix corrected survival 1 Good prognosis (n=67, 30%) Medium prognosis (n=132, 59%) Poor prognosis (n=26, 12%) % Survival % p< % Years from the start of radiotherapy

9 Practice in NL 100%

10 Practice Difference 50%+ of stage I-IIIB treated with a low dose Histology St age Radical RT 2yr survival Tumorload? NSCLC SCLC? Stage I-IIIB Stage IV? 4500 cgy <4500 cgy? Known? Known n= Patients

11 Practice difference NL & Australia 50% 100% 50%

12 Decision Support System Manual entry now > with PHT automated entry

13 Decision Support Systems 100% 1 in 4 patients have a poor prognosis (6 months) DSS prediction No survival benefit between low and high dose High dose gives only side effects 50% 50% 20% alive at two year with low dose DSS prediction 1 in 6 patients have a good prognosis 60% alive at two years if high dose was given

14 Volgende stappen Health-RI ELIXIR-NL NWO Roadmap CZ My Best Treatment

15 Andre Dekker Maastricht UMC+, MAASTRO Clinic PHT - Distributed Learning Benchmarking, Practice Differences & Decision Support

16 Andre Dekker Maastricht UMC+, MAASTRO Clinic PHT Personal Locker & Consent

17 PHT Limburg Ben ik Bekend? BSN FAIR Data Dock HuisartsenOZL Bekend FAIR Data Dock HuisartsenOZL BSN FAIR Broker BSN FAIR Data Dock Zuyderland U bent bekend bij HuisartsenOZL Zuyderlan d MAASTRO Clin ic FAIR Broker Bekend FAIR Data Dock Zuyderland BSN Onbekend FAIR Data Dock MAASTRO Clinic FAIR Data Dock MAASTRO Clinic

18 Philips - Consent

19 PHT Limburg Volgende Stappen Uitwisseling voor de zorg? Care OncoZON Proeftuinen Mijn Zorg PGO MedMij Regionaal Patiënten Portaal VIPP CBS Nederlandse WetenschapsAgenda Waardecreatie door verantwoorde toegang tot en gebruik van big data

20 Andre Dekker Maastricht UMC+, MAASTRO Clinic PHT Personal Locker & Consent

21 Andre Dekker Maastricht UMC+, MAASTRO Clinic PHT - Distributed Registry

22 Huidige Situatie Curated Data Clinical Systems DICOM Registries Dashboards 1. Klinische informatie systemen met geïmplementeerde ZIBs 2. Standaard data extractie pipelines om de data uit de klinische systemen te halen 3. Een data opslag met de juiste ontologieën (FAIR) als basis voor alle externe koppelingen 4. Beveiligde data koppelingen met externe data bewerkers (b.v. IKNL) 5. Geanonimiseerde koppeling met ecrf systemen (b.v OpenClinica, CASTOR)

23 Volgende stappen Kunnen we deze vragen lokaal stellen? DICA/DHD / Zorginstituut / NZA etc. indicatoren IKNL/NKR incidentie Voordelen Tijdwinst Compliant met standaarden ZIBs, FHIR, XDS, DICOM Lokale controle en verantwoordelijkheid voor data kwaliteit Indicator is niet meer een verassing NFU Data4LifeSciences & Registratie aan de Bron

24 Andre Dekker Maastricht UMC+, MAASTRO Clinic PHT - Distributed Registry

25 Andre Dekker Maastricht UMC+, MAASTRO Clinic Thank you for your attention