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Reforming Medical Informatics in Germany - Basis for Precision Medicine :: Prokosch HU :: 11.10.2016

Reforming Medical Informatics in Germany - Basis for Precision Medicine

Prof. Dr. H.U. Prokosch Chair of Medical Informatics Friedrich-Alexander-Universität Erlangen-Nürnberg and past board member of the GMDS

11. Oktober 2016


Agenda

2

• Precision Medicine

• Data Sources for Precision Medicine

• Exemplary Applications

• Challenges for Medical Informatics

Biomedical Informatics

(Medical Data Science)


Precision Medicine

3

http://healthitanalytics.com/features/how-precision-medicine-will-shift-from-research-to-clinical-care


The U.S. Precision Medicine Initiative

4

Launched by President Barack Obama,

January 20, 2015

Images as a courtesy of Jyothisman Pathak (Weill Cornell Medicine, Healthcare Policy & Research)

Reforming Medical Informatics in Germany - Basis for Precision Medicine :: Prokosch HU :: 11.10.2016 5

The U.S. Precision Medicine Initiative

A research cohort that will engage a million or more Americans who volunteer

to contribute their health data and biospecimen over many years to improve

health outcomes, fuel the development of new treatments for disease, and

catalyze a new era of data-based and more precise preventive care and medical

treatment. https://www.nih.gov/precision-medicine-initiative-cohort-program/precision-medicine-initiative-cohort-program-frequently-asked-questions

slide as a courtesy of

Jyothisman Pathak

(Weill Cornell Medicine,

Healthcare Policy & Research)


‘precision medicine’ is promising to provide

‘the right patient

with the right dose

of the right drug,

at the right time’.

Bevan E Huang, Widya Mulyasasmita & Gunaretnam Rajagopal (2016). The path from big data to precision medicine, Expert Review of

Precision Medicine and Drug Development, 1:2, 129-143, DOI: 10.1080/23808993.2016.1157686

Precision Medicine

The path from big data to precision medicine


Precision Medicine & Big Data


US FDA Definiton:

“… ‘the tailoring of medical treatment

to the individual characteristics, needs, and preferences

of a patient during all stages of care, including

• prevention,

• diagnosis,

• treatment, and

• follow-up’.


FDA. Paving the Way for Personalized Medicine: FDA’s Role in the New Era of Medical Product Development;

http://www.fda.gov/downloads/ScienceResearch/SpecialTopics/PersonalizedMedicine/UCM372421.pdf; 1b


US FDA Definiton:

“… ‘the tailoring of medical treatment to the individual characteristics, needs, and

preferences of a patient during all stages of care, including • prevention,

• diagnosis,

• treatment, and

• follow-up’.

Big data, as it applies to precision medicine, is the generation and

repository of large amounts of data from

• bio-specimens,

• health records,

• medical imaging and

• sensors,

from which disease-specific factors, patterns, and associations

can be computationally identified and used

to customize medical treatments unique to the individual


FDA. Paving the Way for Personalized Medicine: FDA’s Role in the New Era of Medical Product Development;

http://www.fda.gov/downloads/ScienceResearch/SpecialTopics/PersonalizedMedicine/UCM372421.pdf; 1b

Data Warehouse

Data Mining

Machine Learning


Data Sources for Precision Medicine

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Figure 1. In the future, precision medicine will be enabled by individuals surrounded by a cloud of data. Layers of the cloud represent data varying between

those directly affecting/quantifying the individual, and those quantifying the environment and indirectly affecting the individual, i.e. levels of the exposome.

Published in: Bevan E Huang; Widya Mulyasasmita; Gunaretnam Rajagopal; Expert Review of Precision Medicine and Drug Development 2016, 1, 129-143.

DOI: 10.1080/23808993.2016.1157686

Copyright © 2016 Informa UK Limited, trading as Taylor & Francis Group


Data Sources for Precision Medicine

11

Published in: Bevan E Huang; Widya Mulyasasmita; Gunaretnam Rajagopal; Expert Review of Precision Medicine and Drug Development 2016, 1, 129-143.

DOI: 10.1080/23808993.2016.1157686

Copyright © 2016 Informa UK Limited, trading as Taylor & Francis Group

Clinical

Informatics

Imaging

Informatics

Bioinformatics

Exposome

Informatics

Sensor-based

Wearables

Medical

Informatics ?


Precision Medicine

12

Exemplary applications

for research

and clinical care


Clinical Care and Translational Research two (or three ?) separate worlds

Co

mm

un

ica

tion

Se

rve

r

EHR

DWH

Feasibility Phase / Cohorte Identification

Study Protocol

Registration in Trial Registry

Patient Recruitment

Clinical Trial documentation

Pharmacovigilance

The

clinician

The clinical

researcher

The basic

researcher


Challenge: Reuse Clinical Care Data for Translational Research

Co

mm

un

ica

tion

Se

rve

r

EHR

DWH

Feasibility Phase / Cohort Identification

Study Protocol


Patient Recruitment


Pharmacovigilance

The

clinician

The clinical

researcher

The basic

researcher

Single Source Concept

Medical Informatics &

Clin. Research Informatics


Challenge: Reuse Clinical Care Data for Translational Research

Co

mm

un

ica

tion

Se

rve

r

EHR

DWH


Study Protocol


Patient Recruitment


Pharmacovigilance

The

clinician

The clinical

researcher

The basic

researcher


Discovery Research in the Genomic Era

Co

mm

un

ica

tion

Se

rve

r

EHR

DWH


Study Protocol


Patient Recruitment


Pharmacovigilance

Biomedical Informatics


Discovery Research in the Genomic Era


http://transmartfoundation.org/

Discovery Research in the Genomic Era An exemplary platform

Scheufele E, Aronzon D, Coopersmith R, McDuffie MT, Kapoor M, Uhrich CA,

Avitabile JE, Liu J, Housman D, Palchuk MB. tranSMART: An Open Source

Knowledge Management and High Content Data Analytics Platform. AMIA Jt

Summits Transl Sci Proc. 2014 Apr 7;2014:96-101.

Athey BD, Braxenthaler M, Haas M, Guo Y. tranSMART: An Open Source and

Community-Driven Informatics and Data Sharing Platform for Clinical and

Translational Research. AMIA Jt Summits Transl Sci Proc. 2013 Mar

18;2013:6-8.


Discovery Research at Erlangen University

from three separate worlds to an ecosystem which integrates phenotype data and omics data

Co

mm

un

ica

tion

Se

rve

r

EHR

DWH


Study Protocol


Patient Recruitment


Pharmacovigilance

Scheufele E, Aronzon D, Coopersmith R, McDuffie MT, Kapoor M, Uhrich CA,

Avitabile JE, Liu J, Housman D, Palchuk MB. tranSMART: An Open Source

Knowledge Management and High Content Data Analytics Platform. AMIA Jt Summits

Transl Sci Proc. 2014 Apr 7;2014:96-101.

Athey BD, Braxenthaler M, Haas M, Guo Y. tranSMART: An Open Source and

Community-Driven Informatics and Data Sharing Platform for Clinical and

Translational Research. AMIA Jt Summits Transl Sci Proc. 2013 Mar 18;2013:6-8.


Integrating phenotype data and omics data for discovery research

E

T

L

T3+T4 T1+T2

Analysis

Let me „play“

with my data

„This is a great tool to support

translational research…“


Precision Medicine Pharmacogenomic therapy recommendations

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SMART Genomic Apps for Clinical Decision Support

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Warner JL, Rioth MJ, Mandl KD, Mandel JC, Kreda DA, Kohane IS, Carbone D, Oreto R, Wang L, Zhu S, Yao H, Alterovitz G. SMART precision

cancer medicine: a FHIR-based app to provide genomic information at the point of care. J Am Med Inform Assoc. 2016 Jul;23(4):701-10.

https://gallery.smarthealthit.org/vanderbilt-university-medical-center/smart-precision-cancer-medicine


Interoperability Standards for Precision Medicine (HL7 FHIR)

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http://healthitanalytics.com/news/fhir-app-provides-precision-medicine-support-at-point-of-care


Medical Informatics in Germany Quo Vadis?

25

Methods Inf Med 2016; 55: –

http://dx.doi.org/10.3414/ME15-02-0005

Elenko E, Underwood L, Zohar D. Defining digital medicine. Nat Biotechnol.

2015 May;33(5):456-61.


From Medical Informatics to Biomedical Informatics

26

Martin-Sanchez eet al. Methods Inf Med 2016; 55: Elenko E, Underwood L, Zohar D. Defining digital medicine. Nat Biotechnol.

2015 May;33(5):456-61.

Reforming Medical Informatics in Germany - Basis for Precision Medicine :: Prokosch HU :: 11.10.2016 27


Martin-Sanchez eet al. Methods Inf Med 2016; 55:


Data scientist: the sexiest job of the 21st century. Davenport TH, Patil DJ. Harvard Business Review,

2012. Available at: https://hbr.org/2012/10/datascientist-the-sexiest-job-of-the-21st-century/

Biomedical Informatics & Data Science


… the dominant trait among data scientists is an intense curiosity

a desire to go beneath the surface of a problem,

find the questions at its heart, and

distill them into a very clear set of hypotheses that can be tested

Biomedical Informatics & Data Science

Data scientist: the sexiest job of the 21st century. Davenport TH, Patil DJ. Harvard Business Review,

2012. Available at: https://hbr.org/2012/10/datascientist-the-sexiest-job-of-the-21st-century/


The role of the biomedical informatician in a translational medicine team

Establishing Data Integration Centres…

… requires bringing

many people from

different disciplines

to talk with each other

Sarkar IN. Biomedical informatics and translational medicine. J Transl Med. 2010 Feb 26;8:22


Thank you very much!

[email protected]

Medical Informatics

Biomedical Informatics (Medical Data Science)

Health Informatics

Bioinformatics

The challenge ahead . . .

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