The SMITH Project...• 2016 – 2017: nine month conceptual phase seven consortia participated...

The SMITH Project

Smart Medical Information Technology for Healthcare

Oliver MaaßenUniversity Hospital RWTH Aachen

15.06.2018

SMITH – Oliver Maaßen – 15.06.2018 2

Perspectives & Discussion6

ASIC - Innovating Diagnostics and Treatment on ICUs5

The Use Cases4

Data Integration Centres3

The SMITH Project2

Medical Informatics Initiative in Germany1

Agenda




The Use Cases4


The SMITH Project2


Agenda


Medical Informatics Initiative

4

Tabelle1

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IDAdmin 0FüllfarbeLinienfarbeBeschriftung

ADAndorra

ALAlbania

ATAustria

BABosnia and Herzegovina

BEBelgium

BGBulgaria

BYBelarus

CHSwitzerland

CYCyprus

CZCzech Republic

DEGermany

DKDenmark

EEEstonia

ESSpain

FIFinland

FRFrance

GBUnited Kingdom

GEGeorgia

GRGreece

HRCroatia

HUHungary

IEIreland

IQIraq

ISIceland

ITItaly

LILiechtenstein

LTLithuania

LULuxembourg

LVLatvia

MCMonaco

MDMoldova

MEMontenegro

MKMacedonia

MTMalta

NLNetherlands

NONorway

PLPoland

PTPortugal

RORomania

RSRepublic of Serbia

RURussia

SESweden

SISlovenia

SKSlovakia

SMSan Marino

SYSyria

TRTurkey

UAUkraine

VAVatican

XKKosovo



• German Federal Ministry for Education and Research (BMBF) launched its medical informatics funding scheme in 2015

• Total funding amount 150 million € (2018 – 2021)• The aim:

– make data from healthcare and research more useful and meaningful

– strengthen medical research and improve patient care

• “Bridging the gap between research and healthcare“

• www.medizininformatik-initiative.de

VorführenderPräsentationsnotizenIn the initiative’s first phase, the successful consortia are to establish and link data integration centres. These centres will allow research and healthcare data to be aggregated and integrated across multiple entities and sites.

Additionally, innovative IT solutions for concrete medical applications will be developed to demonstrate the benefits of high-tech digital healthcare services and infrastructures. The functionality of the data integration centers must be demonstrated in clinical use cases showing a benefit for patient care



• 2016 – 2017: nine month conceptual phase seven consortia participated encompassinguniversity hospitals, research institutions andbusinesses in Germany

• July 2017: four consortia were chosen for theimplementation in the subsequent developmentand networking phase

VorführenderPräsentationsnotizen*with the most attractive concepts

3 phases planed: Conception Phase: 2016 - 2017Developing and networking Phase: 2018 – 2021Consolidation and further development phase: 2022 – 2025



VorführenderPräsentationsnotizen3 or even 4 phases planned: Conception Phase: 2016 - 2017Developing and networking Phase: 2018 – 2021Consolidation and further development phase: 2022 – 2025




The Use Cases4


The SMITH Project2


Agenda


The SMITH Project

• The founder University Hospitals and Universitiesof SMITH

Leipzig Jena Aachen


The Project Partners

NLP procedures and terminology server (use case PheP)

text analysis and metadata

classification (DIC)reference data

(use case ASIC)

general cooperation

IHE-conform data integration (development

and implementation)high-performance

computingIndustrial Data Space Medical Data Space

mutual advancementof communication

standards

IHE-conform data integration (configuration

and support)DIC network and security

technologyimplementation of

marketplace

general cooperation

EPR / roll-out

10


The New Partner University Hospitals

• Four University Hospitals joined SMITH as fullmembers:– Halle– Hamburg– Bonn– Essen

• Two University Hospitals joined SMITH asnetworking partner:– Rostock– Düsseldorf


Members of the Consortium

Networking Partners of the Consortium

VorführenderPräsentationsnotizen*with the most attractive concepts


SMITH key features:

– Strong network of industrial and research partners – Two clinical and one methodological use case– Standardized structure of data integration centres– SMITH market place– Immediately launchable roll-out concept

• SMITH receives a funding of >35 Mio € + ~10 Mio. € for the new project partners




The Use Cases4


The SMITH Project2


Agenda


SMITH Data Integration Centres

University Hospital (UH) Medical Faculty (MF)

Steering Board CMO & CIO (UH) Dean & Scientists (MF)

Promoting clinical institutions (usage):

Anaesthesiology and Intensive Care (ASIC, HELP)

Microbiology (HELP) Hygiene (HELP) Clinical Chemistry …

Data Integration Centre(organisational unit of UH)

Head of DIC

Data Trustee Service Data Transfer Service Health Data Space Mgmt. IT-Systems Management

Promoting research institutions (methods):

Medical Informatics (PheP) Biometry & Epidemiology (PheP) Centre for Clinical Trials Biobank Computer Science (PheP) …

Promoting regulatory institutions

Ethics Committee Data Security Representative Legal Services

Promoting IT-infrastructure UH-IT Departments

Cooperating SMITH partners (methods & usage)

IT industry NLP industry / provider External users / consortia

Use and AccessCommittee



• Technical Standards for the Integration of Data

− PIX / PDQ− ATNA− BPPC / APPC− XDS− XCA− XUA

− CDA

− FHIR

− CQL

− SNOMED-CT

− LOINC

− ICQ/OPS

− IHE-D Value Sets

Medical terminologies



• Technical Standards for the Integration of Data

Austria:ELGA electronic health records

Switzerland:electronic

patient dossier

Germany: electronic case record

eFA 2.0

VorführenderPräsentationsnotizenDifferent architecture, but same standards in Switzerland and Austria



• In Aachen “SAP-based”Health Data Repository = based on SAP Connected Health PlatformAnalytics for Researchers = based on SAP Medical Research Insight

Affinity Domain

Aachen

Facade

HealthDataRepository

FHIR Server

MPI

IDP

XDS Repository

XDS Registry

ARR

Further Components

Analytics

SAPTiani/März

FHIR2CDA – Generation App

VorführenderPräsentationsnotizenDIC Aachen - Architecture



Data Integration center

PhenotypeClassifiers

Rules-Factory (UL, USE Case teams)

Semantic Text Extraction Methods

NLP-Factory (UJ, Averbis, ID)

MetadataRepository

Method and Items(UL, UA, März)

InteroperablityStandard, tools and Processes

(UKJ, UKA, SAP, März, Tiani, Cisco)

Use Case Decision support

systems(UKJ, UKA, UL,

Diagnostic-Industry)

Market PlaceInternal SMITH Tools

& Connectingexternal partners

(UKA, SAP, HC-IT-Solution)

VorführenderPräsentationsnotizenData integration centres have to integrate data from diverse data sources and to integrate and cover related topics




The Use Cases4


The SMITH Project2


Agenda


The Use Cases

PhePPhenotype Pipeline,

algorithms for phenotyping and NLP on EMR data

HELPHospital-wide EMR-based computerized decision support system to improve outcomes of patients with bloodstream infections

ASICAlgorithmic Surveillance of ICU patients to improve personalized management of care



HPC in the ASIC Use Case (Morris Riedel)5.1


The Use Cases4


The SMITH Project2


Agenda


Use Case ASIC

• Algorithmic Surveillance of ICU patients

• Setting: Intensive Care Units (ICUs) (ARDS, Respiration)

• SMITH App as user interface

PIs:- Gernot Marx (Clinician)- Andreas Schuppert (Modelling)HPC: - Morris Riedel


Use Case ASIC

• Application of „High-Performance Computing“ for model-based, clinical decision support issuing alerts for diagnostic and therapeutic actions

• Providing virtual patient models for clinical research and education (partners: Research Centre Jülich, Bayer AG)

• Outcomes: personalized management of ARDS, reduced organ dysfunctions, reduced mortality


ARDS Definition

• Acute Respiratory Distress Syndrome– High incidence and high mortality on ICUs– Severe accompanying diseases, e.g. organ-dysfunctions– High direct and indirect costs

• Treatment costs due to long-term treatment• Social costs

– Clear definition of diagnostic criteria (Berlin Definition, 2012)

– Good prognosis when patients if ARDS is diagnosed earlyand patients are treated conforn to guidelines

VorführenderPräsentationsnotizenARDS - a critical state with an incidence rate of nearly 9% in the ICU

Often underdiagnosed

(e.g. return to work time after sickness)


ASIC Goals 1 & 2

• Goal 1: Data connection & App Development– provide relevant patient data to doctors in standardized format – ensure that doctors have structured access to all patient data– ensure that doctors get data updates in time– ask doctors to document their diagnosis and interventions

• Goal 2: Guideline Sequencing– provide doctors with information on “similar” cases from patient data

base– Provide doctors with structured information on disease evolution/

interventions/ therapy outcome of “similar” patient cases


ASIC Goals 1 & 2: The ASIC App

VorführenderPräsentationsnotizenASIC App will be used on a tablet computer like the Apple iPad


ASIC Goals 1 & 2: The ASIC App

VorführenderPräsentationsnotizenBut also should be used on mobile phones


ASIC Goals 3 & 4

• Goal 3: Diagnostic Expert Advisor:– Establish an early warning system for critical states of patients

• Goal 4: Virtual Patient Model:– Enable doctors to simulate the result of interventions for

individual patients by training of doctors on critical situations by training simulator

VorführenderPräsentationsnotizenIdea: develop an integrated model of human physiology (patophysiology)Use patient-specific modeling to support medical decisionsValidated patophysiological simulation modelsGlobal optimization methods powerful framework to perform quantitative virtual experiments on an infinitely compliant and strickly controlled in silico virtual patient

Result: Simulations suggest possible settings for subsequent testing in clinical trials


Key performance indicators

Key performance indicatorsIncrease of:- Detection rate of ARDS- Adherence to lung protective ventilation of patients with ARDS

Reduction of:- Mortality rate

Secondary endpointsIncrease of:- Technology (mobile devices) acceptance and usage

Reduction of:- Organ dysfunctions- Length of ICU stay- Long-time ventilation

Health economic factorsReduction of:- Total costs of treatment- ICU readmission- Hospital readmissions- Return to work time after sickness absence


Data Structure

• Structured data– Time series

• Tight sampling rate (e.g. heartbeat, breathing rate)• Medium sampling rate [30min-6h]• Low sampling rate (e.g. lab parameters, 6h - 1d)

– Stationary data• Gender, BMI,… • Initial diagnosis data (including imaging)

– Therapeutic interventions• Medication• Surgical• Nutrition...


Data Structure

• Unstructured data– Time series

• Low sampling rate (diagnostic monitoring (Visite), 6h - 1d)– Stationary data

• Anamnesis• “Arztbriefe”

– Therapeutic interventions• Physiotherapy (?)• ...


Data Preparation

• Structured Time series– Data preparation

• Measurement error identification• Normalisation• Data matching• Data compression

– Therapeutic Interventions handling• Data matching• Annotations• Process simulation


ASIC Data Collection

• Definition of a list of parameters and describe these by doctors together with data specialists: ~150 parameters

• Next steps:– Start with as much parameters as possible and then

end up with the parameters which are relevant / predictive in the end after using the machinery

VorführenderPräsentationsnotizenMore information about the models applied will present Morris now



HPC in the ASIC Use Case (Morris Riedel)5.1


The Use Cases4


The SMITH Project2


Agenda


HPC in the ASIC Use Case

• Morris Riedel




The Use Cases4


The SMITH Project2


Agenda


SMITH Rollout Concept

Market Place

IHE IHE

Hospital CTiani-based

Hospital BSAP-based

DICLeipzig

DICJena

DICAachen

Hospital ACooperation

UH Halle

Hospital DHosted by

GP-Networks

IHE IHEDIC

HamburgEppendorf

DICBonn

DICEssen

IHE

VorführenderPräsentationsnotizenCentral contracting platform for internal and external data consumers and providersContract between data user and data providerProvide access to data and knowledge to all researcherFunctions for identifying relevant data setsNo data repository but access to data repository via central servicesManage data use and access rules Data Use and Access Committee reviewActivation of required policiesCross-consortia connectionSharing of services and integration of third party products


Data Analytics and HPC in Medicine

• High potential to improve patient care• Relevant for everyone (with a need for

healthcare services)• Health and genetic data belong to the category

of sensitive data*• At the same time the usage of patient data is

important to advance research, healthcare practices and patients’ rights*

*European Patient Forum (2016) The new EU Regulation on the protection of personal data: what does it mean for patients? - A guide for patients and patients’ organisations

VorführenderPräsentationsnotizenOn the other hand, the processing of health data is fundamental for the good functioning of healthcare services, for patients’ safety, and to advance research and improve public health. Patients organisations are also gathering and using patients’ data in their advocacy or research activities. So being able to use patients’ personal data is sometimes important to advance research, healthcare practices or patients’ rights.

http://www.eu-patient.eu/globalassets/policy/data-protection/data-protection-guide-for-patients-organisations.pdf


More information

• www.smith.care

• Publication will soon be available:„Smart Medical Information Technology for Healthcare (SMITH) –Data Integration based on Interoperability Standards”


Thank you for your attention!

The SMITH ProjectAgendaAgendaMedical Informatics InitiativeMedical Informatics InitiativeMedical Informatics InitiativeMedical Informatics InitiativeAgendaThe SMITH ProjectThe Project PartnersThe New Partner University HospitalsFoliennummer 12SMITH key features: AgendaSMITH Data Integration CentresSMITH Data Integration CentresSMITH Data Integration CentresSMITH Data Integration CentresSMITH Data Integration CentresAgendaThe Use CasesAgendaUse Case ASICUse Case ASICARDS DefinitionASIC Goals 1 & 2ASIC Goals 1 & 2: The ASIC AppASIC Goals 1 & 2: The ASIC AppASIC Goals 3 & 4Key performance indicatorsData StructureData StructureData PreparationASIC Data CollectionAgendaHPC in the ASIC Use CaseAgendaSMITH Rollout ConceptData Analytics and HPC in MedicineMore informationThank you for your attention!

The SMITH Project...• 2016 – 2017: nine month conceptual phase seven consortia participated...

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Transcript of The SMITH Project...• 2016 – 2017: nine month conceptual phase seven consortia participated...