EMR-integrated minimal core dataset for routine health care and multiple research settings: A case study for neuroinflammatory demyelinating diseases

Autoři: Sophia von Martial aff001;  Tobias J. Brix aff001;  Luisa Klotz aff002;  Philipp Neuhaus aff001;  Klaus Berger aff003;  Clemens Warnke aff004;  Sven G. Meuth aff002;  Heinz Wiendl aff002;  Martin Dugas aff001
Působiště autorů: Institute of Medical Informatics, University of Münster, Münster, Germany aff001;  Department of Neurology, University of Münster, Münster, Germany aff002;  Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany aff003;  Department of Neurology, University of Köln, Köln, Germany aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0223886


Although routine health care and clinical trials usually require the documentation of similar information, data collection is performed independently from each other, resulting in redundant documentation efforts. Standardizing routine documentation can enable secondary use for medical research. Neuroinflammatory demyelinating diseases (NIDs) represent a heterogeneous group of diseases requiring further research to improve patient management. The aim of this work is to develop, implement and evaluate a minimal core dataset in routine health care with a focus on secondary use as case study for NIDs. Therefore, a draft minimal core dataset for NIDs was created by analyzing routine, clinical trial, registry, biobank documentation and existing data standards for NIDs. Data elements (DEs) were converted into the standard format Operational Data Model, semantically annotated and analyzed via frequency analysis. The analysis produced 1958 DEs based on 864 distinct medical concepts. After review and finalization by an interdisciplinary team of neurologists, epidemiologists and medical computer scientists, the minimal core dataset (NID CDEs) consists of 46 common DEs capturing disease-specific information for reuse in the discharge letter and other research settings. It covers the areas of diagnosis, laboratory results, disease progress, expanded disability status scale, therapy and magnetic resonance imaging findings. NID CDEs was implemented in two German university hospitals and a usability study in clinical routine was conducted (participants n = 16) showing a good usability (Mean SUS = 75). From May 2017 to February 2018, 755 patients were documented with the NID CDEs, which indicates the feasibility of developing a minimal core dataset for structured documentation based on previously used documentation standards and integrating the dataset into clinical routine. By sharing, translating and reusing the minimal dataset, a transnational harmonized documentation of patients with NIDs might be realized, supporting interoperability in medical research.

Klíčová slova:

Clinical trials – Electronic medical records – Magnetic resonance imaging – Medicine and health sciences – Multiple sclerosis – Outpatients – Physicians – Coding mechanisms


1. Costa SD. Dokumentation in der Medizin: Es ist ein Wahnsinn! [Documentation in medicine—it is a madness!]. Deutsches Ärzteblatt. 2009;106(12).

2. Krumm R, Semjonow A, Tio J, Duhme H, Bürkle T, Haier J, et al. The need for harmonized structured documentation and chances of secondary use—results of a systematic analysis with automated form comparison for prostate and breast cancer. Journal of biomedical informatics. 2014;51:86–99. doi: 10.1016/j.jbi.2014.04.008 24747879

3. Ford I, Norrie J. Pragmatic Trials. New England Journal of Medicine. 2016;375(5):454–463. doi: 10.1056/NEJMra1510059 27518663

4. Lange S, Sauerland S, Lauterberg J, Windeler J. The Range and Scientific Value of Randomized Trials. Deutsches Arzteblatt international. 2017;114(38):635–640. doi: 10.3238/arztebl.2017.0635 29017690

5. Bruland P, Forster C, Breil B, Ständer S, Dugas M, Fritz F. Does single-source create an added value? Evaluating the impact of introducing x4T into the clinical routine on workflow modifications, data quality and cost-benefit. International journal of medical informatics. 2014;83(12):915–928. doi: 10.1016/j.ijmedinf.2014.08.007 25220487

6. Grinnon ST, Miller K, Marler JR, Lu Y, Stout A, Odenkirchen J, et al. National Institute of Neurological Disorders and Stroke Common Data Element Project—approach and methods. Clinical trials (London, England). 2012;9(3):322–329. doi: 10.1177/1740774512438980

7. CDISC—Clinical Data Interchange Standards Consortium. Specification for the Operational Data Model (ODM): Version 1.3.2; 2013. Available from: http://www.cdisc.org/odm.

8. U S National Library of Medicine. NIH Common Data Elements (CDE) Repository; 2018. Available from: https://cde.nlm.nih.gov/home.

9. Biering-Sørensen F, Charlifue S, Devivo MJ, Grinnon ST, Kleitman N, Lu Y, et al. Incorporation of the International Spinal Cord Injury Data Set elements into the National Institute of Neurological Disorders and Stroke Common Data Elements. Spinal cord. 2011;49(1):60–64. doi: 10.1038/sc.2010.90 20733589

10. Harte-Hargrove LC, French JA, Pitkänen A, Galanopoulou AS, Whittemore V, Scharfman HE. Common data elements for preclinical epilepsy research: Standards for data collection and reporting. A TASK3 report of the AES/ILAE Translational Task Force of the ILAE. Epilepsia. 2017;58 Suppl 4:78–86. doi: 10.1111/epi.13906 29105074

11. Gaddale JR. Clinical Data Acquisition Standards Harmonization importance and benefits in clinical data management. Perspectives in clinical research. 2015;6(4):179–183. doi: 10.4103/2229-3485.167101 26623387

12. U S National Library of Medicine. Common Data Element (CDE) Resource Portal: Glossary; 2017. Available from: https://www.nlm.nih.gov/cde/glossary.html#cdedefinition.

13. Rubin DL, Kahn CE. Common Data Elements in Radiology. Radiology. 2017;283(3):837–844. doi: 10.1148/radiol.2016161553 27831831

14. Wu H, Toti G, Morley KI, Ibrahim ZM, Folarin A, Jackson R, et al. SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research*. Journal of the American Medical Informatics Association. 2018;25(5):530–537. doi: 10.1093/jamia/ocx160 29361077

15. Haverkamp C, Ganslandt T, Horki P, Boeker M, Dörfler A, Schwab S, et al. Regional Differences in Thrombectomy Rates: Secondary use of Billing Codes in the MIRACUM (Medical Informatics for Research and Care in University Medicine) Consortium. Clinical neuroradiology. 2018.

16. Nelson RE, Butler J, LaFleur J, Knippenberg K, Kamauu AWC, DuVall SL. Determining Multiple Sclerosis Phenotype from Electronic Medical Records. Journal of Managed Care & Specialty Pharmacy. 2016;22(12):1377–1382. doi: 10.18553/jmcp.2016.22.12.1377

17. Vuokko R, Mäkelä-Bengs P, Hyppönen H, Lindqvist M, Doupi P. Impacts of structuring the electronic health record: Results of a systematic literature review from the perspective of secondary use of patient data. International journal of medical informatics. 2017;97:293–303. doi: 10.1016/j.ijmedinf.2016.10.004 27919387

18. Köpcke F, Kraus S, Scholler A, Nau C, Schüttler J, Prokosch HU, et al. Secondary use of routinely collected patient data in a clinical trial: an evaluation of the effects on patient recruitment and data acquisition. International journal of medical informatics. 2013;82(3):185–192. doi: 10.1016/j.ijmedinf.2012.11.008 23266063

19. Peeters LM. Fair data for next-generation management of multiple sclerosis. Multiple sclerosis (Houndmills, Basingstoke, England). 2017; p. 1352458517748475.

20. Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data. 2016;3:160018. doi: 10.1038/sdata.2016.18 26978244

21. Trebst C, Jarius S, Berthele A, Paul F, Schippling S, Wildemann B, et al. Update on the diagnosis and treatment of neuromyelitis optica: recommendations of the Neuromyelitis Optica Study Group (NEMOS). Journal of Neurology. 2014;261(1):1–16. doi: 10.1007/s00415-013-7169-7 24272588

22. Brownlee WJ, Hardy TA, Fazekas F, Miller DH. Diagnosis of multiple sclerosis: progress and challenges. Lancet (London, England). 2017;389(10076):1336–1346. doi: 10.1016/S0140-6736(16)30959-X

23. Foris LA, Dulebohn SC. StatPearls: Disseminated Encephalomyelitis, Acute. Treasure Island (FL); 2018.

24. Mitsikostas DD, Goodin DS. Comparing the efficacy of disease-modifying therapies in multiple sclerosis. Multiple sclerosis and related disorders. 2017;18:109–116. doi: 10.1016/j.msard.2017.08.003 29141791

25. Nauta IM, Speckens AEM, Kessels RPC, Geurts JJG, de Groot V, Uitdehaag BMJ, et al. Cognitive rehabilitation and mindfulness in multiple sclerosis (REMIND-MS): a study protocol for a randomised controlled trial. BMC neurology. 2017;17(1):201. doi: 10.1186/s12883-017-0979-y 29162058

26. Thiel S, Leypoldt F, Röpke L, Wandinger K, Kümpfel T, Aktas O, et al. Neuroimmunological Registries in Germany. Neurology International Open. 2018;02(01):E25–E39. doi: 10.1055/s-0043-108830

27. Posevitz-Fejfár A, Wiendl H. The neuroinflammation biobank in the Department of Neurology, University Hospital Muenster, Germany. Biopreservation and biobanking. 2014;12(1):74–75. doi: 10.1089/bio.2014.1212 24620773

28. Dugas M, Meidt A, Neuhaus P, Storck M, Varghese J. ODMedit: uniform semantic annotation for data integration in medicine based on a public metadata repository. BMC medical research methodology. 2016;16:65. doi: 10.1186/s12874-016-0164-9 27245222

29. U S National Library of Medicine. Unified Medical Language System (UMLS); 2017. Available from: https://cde.nlm.nih.gov/research/umls/.

30. Dugas M, Neuhaus P, Meidt A, Doods J, Storck M, Bruland P, et al. Portal of medical data models: information infrastructure for medical research and healthcare. Database: the journal of biological databases and curation. 2016;2016. doi: 10.1093/database/bav121 26868052

31. Dugas M. Design of case report forms based on a public metadata registry: re-use of data elements to improve compatibility of data. Trials. 2016;17(1):566. doi: 10.1186/s13063-016-1691-8 27899162

32. Varghese J, Holz C, Neuhaus P, Bernardi M, Boehm A, Ganser A, et al. Key Data Elements in Myeloid Leukemia. Studies in health technology and informatics. 2016;228:282–286. 27577388

33. Storck M, Krumm R, Dugas M. ODMSummary: A Tool for Automatic Structured Comparison of Multiple Medical Forms Based on Semantic Annotation with the Unified Medical Language System. PloS one. 2016;11(10):e0164569. doi: 10.1371/journal.pone.0164569 27736972

34. The R Foundation. CRAN—Package VennDiagram; 2018. Available from: https://cran.r-project.org/web/packages/VennDiagram/index.html.

35. Agfa-Gevaert Group. AGFA HealthCare; 2018. Available from: https://global.agfahealthcare.com/main/.

36. Brooke J. SUS-A quick and dirty usability scale. Usability evaluation in industry. 1996;189(194):4–7.

37. Reinhardt W, Ruegenhagen E, Bernard R. System Usability Scale—jetzt auch auf Deutsch.—SAP User Experience Community; 2015. Available from: https://experience.sap.com/skillup/system-usability-scale-jetzt-auch-auf-deutsch/.

38. GmbH L. LimeSurvey: the online survey tool—open source surveys; 2003. Available from: https://www.limesurvey.org/de/.

39. von Martial S. User survey before application of basic documentation neuroinflammatory diseases; 2019. Available from: https://medical-data-models.org/37711.

40. von Martial S. User survey after application of basic documentation neuroinflammatory diseases; 2019. Available from: https://medical-data-models.org/37712.

41. The MSBase Foundation. The MSBase Registry; 2004. Available from: https://www.msbase.org.

42. Rojas JI, Patrucco L, Trojano M, Lugaresi A, Izquierdo G, Butzkueven H, et al. Multiple sclerosis in Latin America: A different disease course severity? A collaborative study from the MSBase Registry. Multiple sclerosis journal—experimental, translational and clinical. 2015;1. doi: 10.1177/2055217315600193 28607702

43. Jokubaitis VG, Spelman T, Lechner-Scott J, Barnett M, Shaw C, Vucic S, et al. The Australian Multiple Sclerosis (MS) Immunotherapy Study: A Prospective, Multicentre Study of Drug Utilisation Using the MSBase Platform. PloS one. 2013;8(3). doi: 10.1371/journal.pone.0059694 23527252

44. Klotz L, Geßner S. Multiple Sclerosis Studies (NCT02461069, NCT02419378); 2018. Available from: https://medical-data-models.org/29129.

45. Elements NCD. MS CDE Diagnosis and Disease Characteristics Multiple Sclerosis; 2018. Available from: https://medical-data-models.org/29133.

46. Echterhoff A, Wiendl H, Brix T, Geßner S. Neuroinflammatory Biobank, Department of Neurology University Hospital Münster; 2018. Available from: https://medical-data-models.org/29127.

47. Berger K, Maximov S, Bruland P, Geßner S. REGIMS Registry Baseline Examination; 2018. Available from: https://medical-data-models.org/29126.

48. Geßner S, Klotz L. Discharge Letter Items Neuroinflammatory Demyelinating Diseases; 2018. Available from: https://medical-data-models.org/29124.

49. Varghese J, Dugas M. Frequency analysis of medical concepts in clinical trials and their coverage in MeSH and SNOMED-CT. Methods of information in medicine. 2015;54(1):83–92. doi: 10.3414/ME14-01-0046 25346408

50. U S National Library of Medicine. The UMLS Metathesaurus; 2004. Available from: https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/.

51. Geßner S. Neuroinflammatory Demyelinating Diseases CNS Common Data Elements; 2018. Available from: https://medical-data-models.org/29131.

52. Bangor A, Kortum PT, Miller JT. An Empirical Evaluation of the System Usability Scale. International Journal of Human-Computer Interaction. 2008;24(6):574–594. doi: 10.1080/10447310802205776

53. Varghese J, Schulze Sünninghausen S, Dugas M. Standardized Cardiovascular Quality Assurance Forms with Multilingual Support, UMLS Coding and Medical Concept Analyses. Studies in health technology and informatics. 2015;216:837–841. 26262169

54. Varghese J. ODMToolbox—CDEGenerator: Institute of Medical Informatics Münster; 2018. Available from: https://odmtoolbox.uni-muenster.de/CDEGenerator/CDEGenerator.html.

55. National Institute of Neurological Disorders and Stroke. NINDS Common Data Elements; 2018. Available from: https://www.commondataelements.ninds.nih.gov/CDE.aspx.

56. Yalachkov Y, Foerch C, Wahl M, Gehrig J. A Proposal for a Patient-Oriented Five-Dimensional Approach for Surveillance and Therapy in Multiple Sclerosis. Frontiers in neurology. 2017;8:313. doi: 10.3389/fneur.2017.00313 28717353

57. McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD, et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Annals of neurology. 2001;50(1):121–127. doi: 10.1002/ana.1032 11456302

58. Polman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the McDonald Criteria. Annals of neurology. 2005;58(6):840–846. doi: 10.1002/ana.20703 16283615

59. Pittock SJ, Lucchinetti CF. Neuromyelitis optica and the evolving spectrum of autoimmune aquaporin-4 channelopathies: a decade later. Annals of the New York Academy of Sciences. 2016;1366(1):20–39. doi: 10.1111/nyas.12794 26096370

60. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. Journal of biomedical informatics. 2009;42(2):377–381. doi: 10.1016/j.jbi.2008.08.010 18929686

61. OpenClinica LLC and collaborators, Waltham, MA, USA. OpenClinica open source software; 2018. Available from: www.OpenClinica.com.

Článek vyšel v časopise


2019 Číslo 10
Nejčtenější tento týden