Insights into the BMBF Medical Informatics Initiative SMITH Consortium

Transcription

1 Insights into the BMBF Medical Informatics Initiative SMITH Consortium HIMSS 2018, Potsdam Toralf Kirsten, Leipzig University

2 Partner Universities (new members) + 2 (new associated) Coordinator: Markus Löffler, Leipzig

3 DIC-Network- and Security Techniques IHE-conform Data Integration (Configuration and Support) Implementation of the Market Place Collaboration efa / Roll-out NLP Procedures and Terminology Server (Use Case PheP) Text Analysis and Metadata (DIC) Reference Data (Use Case ASIC) Development of Standards IHE-conform Data Integration (Development and Implementation) High Performance Computation Center Industrial Data Space Medical Data Space Further Collaborations

4 Objective Make EMR accessible Optimizing Health Care (2 Use Cases) Patient oriented Research (1 Use Case)

5 Use Cases HELP Hospital-wide EMR-based computerized decision support system to improve outcomes of patients with bloodstream infections ASIC Algorithmic Surveillance of ICU patients to improve personalized management of care PheP Phenotype Pipeline, Algorithms for phenotyping and NLP on EMR data Source: UKJ (A. Schroll, M. Szabó)

6 Use Case HELP EMR-based decision support for bloodstream Infections PIs: Matthias Pletz (Clinician) Andre Scherag (Modelling) Antibiotic Stewardship Setting: Normal wards and ICUs Objective: EMR-based decision support system to improve outcomes of patients with S. aureus bloodstream infections Design: Stepped wedge design with interventions and controls SMITH App as user interface (for data capture and alarming) Leading Development in Jena but roll-out to all university hospitals in SMITH Source: UKJ (A. Schroll, M. Szabó) 6

7 Use Case ASIC PIs: Gernot Marx (Clinician) Andreas Schuppert (Modelling) Algorithmic Surveillance of ICU patients Setting: Intensive Care Units (ICUs) (ARDS, Respiration) Objective: Decision support system to improve outcomes of patients with acute respiratory distress syndrome (ARDS) Design: Stepped wedge design with interventions and controls SMITH App as user interface Leading Development in Aachen but roll-out to all university hospitals in SMITH Source: UKJ (A. Schroll, M. Szabó) 7

8 ASIC App User Interface 8

9 Use Case Phep Phenotype Pipeline and NLP PIs: Markus Löffler (Phenotyping) Udo Hahn (NLP) Algorithms for phenotyping by using structured and unstructured data from electronic medical records (EMR) Development of a Rules engine and factory and a metadata repository Natural language processing engine and text corpus Create a technology to automatically mine EMR and generate phenotype classifications and annotations (Data Use Projects ) Source: UKJ (A. Schroll, M. Szabó) 9

10 Data Use Projects DUPs are patient oriented EMR-based research projects in whom data analyses or model driven actions are performed for a specific goal At present 14 projects are suggested from various fields Cardiology Pneumology Haematology Hereditary cancer Surgery Emergency Medicine Transplantation Medicine Infectiology Public health Health care research

11 Workflow to Generate a DUP Phase Action Result Acting Persons Planning Idea, Innovation Synopsis Clinician, Biometrician, PHEP-designers Concept Drafting Engine Development Definition, Specification Workflow, requirement Analysis, Coding, Testing COI-DUP, Feasibility check Model Test data available Software Validation on test data Deployment Transfer to DIC Productive in DIC DUP- HDS Idem Idem + DIC Rule-Designer, (PheP- NLP) PheP, DIC Analysis Data Use & Access Data analysed Clinician and Biometrician

12 Network of Data Integration Centers Market Place DIC Halle DIC Hamburg DIC Bonn DIC Essen DIC Leipzig DIC Jena DIC Aachen Later also Düsseldorf Rostock

13 DIC Technical Architecture Data Sources Integration via technical and semantic IOP standards Data Integr. Engine Transformation in standards Health Data Storage Central Storage for clinical data with IHE & HL7 CDA / FHIR interfaces Metadata Services Assuring semantic IOP Analytics Tools Access Control APP-Services 13

14 Thank You http.//