HiGHmed – An Open Platform Approach to Enhance Care and Research across Institutional Boundaries

Birger Haarbrandt; Björn Schreiweis; Sabine Rey; Ulrich Sax; Simone Scheithauer; Otto Rienhoff; Petra Knaup-Gregori; Udo Bavendiek; Christoph Dieterich; Benedikt Brors; Inga Kraus; Caroline Marieken Thoms; Dirk Jäger; Volker Ellenrieder; Björn Bergh; Ramin Yahyapour; Roland Eils; HiGHmed Consortium; Michael Marschollek

doi:10.3414/ME18-02-0002

Methods of Information in Medicine, Inhaltsverzeichnis

CC BY-NC-ND 4.0 · Methods Inf Med 2018; 57(S 01): e66-e81
DOI: 10.3414/ME18-02-0002

Focus Theme – Original Articles

Schattauer GmbH

HiGHmed – An Open Platform Approach to Enhance Care and Research across Institutional Boundaries

Birger Haarbrandt^*

¹Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Germany

,

Björn Schreiweis^*

²Institute for Medical Informatics and Statistics, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, Germany

,

Sabine Rey

³Department of Medical Informatics, University Medical Center Goettingen, Goettingen, Germany

,

Ulrich Sax

³Department of Medical Informatics, University Medical Center Goettingen, Goettingen, Germany

,

Simone Scheithauer

⁴Central Division of Infection Control and Infectious Diseases, University Medical Center Goettingen, Goettingen, Germany

,

Otto Rienhoff

³Department of Medical Informatics, University Medical Center Goettingen, Goettingen, Germany

,

Petra Knaup-Gregori

⁵Institute of Medical Biometry and Informatics, University Hospital Heidelberg, Heidelberg, Germany

,

Udo Bavendiek

⁶Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany

,

Christoph Dieterich

⁷Section of Bioinformatics and Systems Cardiology, Department of Internal Medicine III, Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, Heidelberg, Germany

,

Benedikt Brors

⁸Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany

,

Inga Kraus

³Department of Medical Informatics, University Medical Center Goettingen, Goettingen, Germany

,

Caroline Marieken Thoms

³Department of Medical Informatics, University Medical Center Goettingen, Goettingen, Germany

,

Dirk Jäger

⁹Department of Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany

,

Volker Ellenrieder

¹⁰Department of Gastroenterology and Gastrointestinal Oncology, University Medical Center Goettingen, Goettingen, Germany

,

Björn Bergh

¹¹Institute for Medical Informatics and Statistics, Kiel University and University Medical Center Schleswig-Holstein, Germany

,

Ramin Yahyapour

¹²Gesellschaft für wissenschaftliche Datenverarbeitung Göttingen (GWDG), University of Goettingen, Goettingen, Germany

,

Roland Eils

¹³Digital Health Center, Berlin Institute of Health (BIH) and Charité, Berlin, Germany

¹⁴Health Data Science Unit, University Hospital Heidelberg, Heidelberg, Germany

,

HiGHmed Consortium

¹Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Germany

,

Michael Marschollek

¹Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Germany

› Institutsangaben

Abstract

Summary

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. HiGHmed brings together 24 partners from academia and industry, aiming at improvements in care provision, biomedical research and epidemiology. By establishing a shared information governance framework, data integration centers and an open platform architecture in cooperation with independent healthcare providers, the meaningful reuse of data will be facilitated. Complementary, HiGHmed integrates a total of seven Medical Informatics curricula to develop collaborative structures and processes to train medical informatics professionals, physicians and researchers in new forms of data analytics.

Governance and Policies: We describe governance structures and policies that have proven effective during the conceptual phase. These were further adapted to take into account the specific needs of the development and networking phase, such as roll-out, carerelated aspects and our focus on curricula development in Medical Inform atics.

Architectural Framework and Methodology: To address the challenges of organizational, technical and semantic interoperability, a concept for a scalable platform architecture, the HiGHmed Platform, was developed. We outline the basic principles and design goals of the open platform approach as well as the roles of standards and specifications such as IHE XDS, openEHR, SNOMED CT and HL7 FHIR. A shared governance framework provides the semantic artifacts which are needed to establish semantic interoperability.

Use Cases: Three use cases in the fields of oncology, cardiology and infection control will demonstrate the capabilities of the HiGHmed approach. Each of the use cases entails diverse challenges in terms of data protection, privacy and security, including clinical use of genome sequencing data (oncology), continuous longitudinal monitoring of physical activity (cardiology) and cross-site analysis of patient movement data (infection control).

Discussion: Besides the need for a shared governance framework and a technical infrastructure, backing from clinical leaders is a crucial factor. Moreover, firm and sustainable commitment by participating organizations to collaborate in further development of their information system architectures is needed. Other challenges including topics such as data quality, privacy regulations, and patient consent will be addressed throughout the project.

Keywords

Secondary use - information systems - medical informatics - decision support systems - data security - semantic interoperability - EHR

Volltext

Referenzen

References
1 PricewaterhouseCoopers. Transforming Healthcare through Secondary Use of Health Data. 2009
2 Rasmussen LV. The electronic health record for translational research. J Cardiovasc Transl Res 2014; 07 (06) 607-614.
3 Sutherland SM, Kaelber DC, Downing NL, Goel VV, Longhurst CA. Electronic Health Record-Enabled Research in Children Using the Electronic Health Record for Clinical Discovery. Pediatr Clin North Am 2016; 63 (02) 251-268.
4 Liao KP, Cai T, Gainer V, Goryachev S, Zengtreitler Q, Raychaudhuri S. et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res (Hoboken) 2010; 62 (08) 1120-1127.
5 Hersh WR. Adding value to the electronic health record through secondary use of data for quality assurance, research, and surveillance. Am J Manag Care 2007; 13 (6 Part 1): 277-278.
6 Dentler K, ten Teije A, de Keizer N, Cornet R. Barriers to the reuse of routinely recorded clinical data: a field report. Stud Health Technol Inform 2013; 192: 313-317.
7 Chute CG, Beck SA, Fisk TB, Mohr DN. The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data. J Am Med Inform Assoc 2010; 17 (02) 131-135.
8 Lowe HJ, Ferris TA, Hernandez PM, Weber SC. STRIDE – an integrated standards-based translational research informatics platform. AMIA Annu Symp Proc 2009; 2009: 391-395.
9 Danciu I, Cowan JD, Basford M, Wang X, Saip A, Osgood S. et al. Secondary use of clinical data: the Vanderbilt approach. J Biomed Inform 2014; 52: 28-35.
10 Tian Q, Price ND, Hood L. Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine. J Intern Med 2012; 271 (02) 111-121.
11 Tanaka H. Omics-based medicine and systems pathology. A new perspective for personalized and predictive medicine. Methods Inf Med 2010; 49 (02) 173-185.
12 Mirnezami R, Nicholson J, Darzi A. Preparing for precision medicine. N Engl J Med 2012; 366 (06) 489-491.
13 Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016; 03: 160018.
14 Schreiweis B, Bronsch T, Stein KE, Nost S, Aguduri LS, Brandner A. et al. An architecture for the integration of clinical data from a PEHR in a regional research platform. Stud Health Technol Inform 2016; 228: 272-276.
15 Brandner A, Schreiweis B, Aguduri LS, Bronsch T, Kunz A, Pensold P. et al. The patient portal of the personal cross-enterprise electronic health record (PEHR) in the Rhine-Neckar-Region. Stud Health Technol Inform 2016; 228: 157-161.
16 Koh HK, Brach C, Harris LM, Parchman ML. A proposed ‘health literate care model’ would constitute a systems approach to improving patients’ engagement in care. Health Aff (Millwood) 2013; 32 (02) 357-367.
17 Beale T. Semantic scalability – the core challenge in e-health. 2014 [cited 2018 Jan 27]. Available from: https://wolandscat.net/2014/12/15/semantic-scalability-the-core-challenge-in-e-health/
18 Beale T. What is an ‘open platform’. 2014 [cited 2018 Jan 27]. Available from: https://wolandscat.net/2014/05/07/what-is-an-open-platform/
19 Stubbs A, Kotfila C, Uzuner Ö. Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1. J Biomed Inform 2015; 58 Suppl: S11-S19.
20 Baclawski K, Kilov H, Beale T. Archetypes: constraint-based domain models for future-proof information systems. In: Baclawski K, Kilov H. editors. Eleventh OOPSLA Workshop on Behavioral Semantics: Serving the Customer. Northeastern University; Evanston, IL, USA: 2002: 16-32.
21 openEHR Foundation. Archetype Definition Language ADL2. 2018 [cited 2018 May 9]. Available from: http://www.openehr.org/releases/AM/latest/docs/ADL2/ADL2.html
22 Garde S, Knaup P, Hovenga E, Heard S. Towards semantic interoperability for electronic health records. Methods Inf Med 2007; 46 (03) 332-343.
23 Kalra D, Lloyd D. ISO 13606 electronic health record communication part 1: reference model. ISO. 13606-1 Geneva: ISO; 2008
24 Archetype Modeling Language (AML). 2018 [cited 2018 May 9]. Available from: http://www.omg.org/spec/AML/
25 Clinical Information Modeling Initiative (CIMI). [cited 2018 Jan 27]. Available from: http://www.opencimi.org
26 Atalag K, Yang HY, Tempero E, Warren J. Model driven development of clinical information sytems using openEHR. Stud Health Technol Inform 2011; 169: 849-853.
27 Ljosland SBakke. National governance of archetypes in Norway. Stud Health Technol Inform 2015; 216: 1091.
28 Wulff A, Haarbrandt B, Marschollek M. Clinical Knowledge Governance Framework for Nationwide Data Infrastructure Projects. Stud Health Technol Inform 2018; 248: 196-203.
29 Leslie H, Ljosland SBakke. Peer Review of Clinical Information Models: A Web 2.0 Crowdsourced Approach. Stud Health Technol Inform 2017; 245: 905-909.
30 Grieve G. FHIR and openEHR. (video file). [cited 2018 May 9]. Available from: https://vimeo.com/191939835
31 Rector AL. The interface between information, terminology, and inference models. Stud Health Technol Inform 2001; 84 (Pt 1): 246-250.
32 Lee D, de Keizer N, Lau F, Cornet R. Literature review of SNOMED CT use. J Am Med Inform Assoc 2014; 21 e1 e11-e19.
33 openEHR Foundation. Archetype Query Language (AQL). 2017 [cited 2018 May 9]. Available from: https://www.openehr.org/releases/QUERY/latest/docs/AQL/AQL.html
34 HL7 International. FHIR Terminology Service. [cited 2018 Jan 27]. Available from: https://www.hl7.org/fhir/terminology-service.html
35 Seerainer C, Sabutsch SW. eHealth Terminology Management in Austria. Stud Health Technol Inform 2016; 228: 426-430.
36 Integrating the Healthcare Enterprise. IHE Cross-Enterprise Document Sharing. [cited 2018 Jan 27]. Available from: https://wiki.ihe.net/index.php/Cross-Enterprise_Document_Sharing
37 Integrating the Healthcare Enterprise. IHE Crossenterprise Document Reliable Interchange. [cited 2018 Jan 27]. Available from: https://wiki.ihe.net/index.php/Cross-enterprise_Document_Reliable_Interchange
38 openEHR Foundation. Guideline Definition Language (GDL). 2015 [cited 2018 Jan 27]. Available from: http://www.openehr.org/releases/CDS/latest/docs/GDL/GDL.html
39 Haarbrandt B, Tute E, Marschollek M. Automated population of an i2b2 clinical data warehouse from an openEHR-based data repository. J Biomed Inform 2016; 63: 277-294.
40 Haarbrandt B, Jack T, Marschollek M. Automated Transformation of openEHR Data Instances to OWL. Stud Health Technol Inform 2016; 223: 63-70.
41 HIMSS Analytics. Children’s Hospital Ljubliana Achieves HIMSS Analytics EMRAM Stage 6. 2017 [cited 2018 May 9]. Available from: http://www.himssinsights.eu/childrens-hospital-ljubljanaachieves-himss-analytics-emram-stage-6
42 HL7 International. Fast Healthcare Interoperability Resources (FHIR) Specification. [cited 2018 Jan 27]. Available from: http://hl7.org/implement/standards/fhir/
43 Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc 2016; 23 (05) 899-908.
44 Marquardt JU, Galle PR, Teufel A. Molecular diagnosis and therapy of hepatocellular carcinoma (HCC): an emerging field for advanced technologies. J Hepatol 2012; 56 (01) 267-275.
45 Acharya UR, Hagiwara Y, Sudarshan VK, Chan WY, Ng KH. Towards precision medicine: from quantitative imaging to radiomics. J Zhejiang Univ Sci B 2018; 19 (01) 6-24.
46 Owan TE, Hodge DO, Herges RM, Jacobsen SJ, Roger VL, Redfield MM. Trends in prevalence and outcome of heart failure with preserved ejection fraction. N Engl J Med 2006; 355 (03) 251-259.
47 Cassini A, Plachouras D, Eckmanns T, Abu MSin, Blank HP, Ducomble T. et al. Burden of Six Healthcare-Associated Infections on European Population Health: Estimating Incidence-Based Disability-Adjusted Life Years through a Population Prevalence-Based Modelling Study. PLoS Med 2016; 13 (10) e1002150.
48 Bundesministerium für Bildung und Forschung. Digitale Innovationen – Neue Dimensionen von Bildung und Wissenschaft erschließen. 2017 [cited 2018 May 7]. Available from: https://www.bmbf.de/pub/Digitale_Innovationen.pdf German
49 Hochschulforum Digitalisierung. The Digital Turn – Hochschulbildung im digitalen Zeitalter. 2016 [cited 2018 May 7]. Available from: https://hochschulforumdigitalisierung.de/sites/default/files/dateien/Abschlussbericht.pdf German
50 Scheer A-W, Wachter C. Digitale Bildungslandschaften. Saarbrücken: imc information multimedia communication AG; 2016
51 Weber GM, Murphy SN, McMurry AJ, Macfadden D, Nigrin DJ, Churchill S. et al. The Shared Health Research Information Network (SHRINE): a prototype federated query tool for clinical data repositories. J Am Med Inform Assoc 2009; 16 (05) 624-630.
52 McMurry AJ, Murphy SN, MacFadden D, Weber G, Simons WW, Orechia J. et al. SHRINE: enabling nationally scalable multi-site disease studies. PLoS One 2013; 08 (03) e55811.
53 Natter MD, Quan J, Ortiz DM, Bousvaros A, Ilowite NT, Inman CJ. et al. An i2b2-based, generalizable, open source, self-scaling chronic disease registry. J Am Med Inform Assoc 2013; 20 (01) 172-179.
54 Segagni D, Tibollo V, Dagliati A, Malovini A, Zambelli A, Napolitano C. et al. Clinical and research data integration: the i2b2-FSM experience. AMIA Jt Summits Transl Sci Proc. 2013 2013. 239-240 eCollection 2013.
55 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 2013. 6-8 eCollection 2013.
56 De Moor G, Sundgren M, Kalra D, Schmidt A, Dugas M, Claerhout B. et al. Using electronic health records for clinical research: the case of the EHR4CR project. J Biomed Inform 2015; 53: 162-173.
57 Chute CG, Pathak J, Savova GK, Bailey KR, Schor MI, Hart LA. et al. The SHARPn project on secondary use of Electronic Medical Record data: progress, plans, and possibilities. AMIA Annu Symp Proc 2011; 2011: 248-256.
58 The European Parliament, The European Council. General Data Protection Regulation. Off J Eur Union 2016; 2014 (March 2014): 20-30.