gms | German Medical Science

66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V. (TMF)

26. - 30.09.2021, online

The evaluation of interoperability of CIRS reports

Meeting Abstract

  • Thomas Schrader - Technische Hochschule Brandenburg, Brandenburg an der Havel, Germany
  • Laura Tetzlaff - Technische Hochschule Brandenburg, Brandenburg an der Havel, Germany
  • Anne-Maria Purohit - Technische Hochschule Brandenburg, Brandenburg an der Havel, Germany
  • Celine Elsholz - Technische Hochschule Brandenburg, Brandenburg an der Havel, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF). sine loco [digital], 26.-30.09.2021. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocAbstr. 73

doi: 10.3205/21gmds024, urn:nbn:de:0183-21gmds0245

Published: September 24, 2021

© 2021 Schrader et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.


Outline

Text

Introduction: Medical errors harm about 400,000 patients in Germany every year. In the context of patient safety, Critical Incident Reporting Systems (CIRS) play an essential role in learning from errors and identifying potential risks at an early stage through systematic analysis. In Germany, different systems exist but it is not possible to easily transfer cases across systems. The problem lies in the missing interoperability and evaluability of reports. For this reason, reports generated by different reporting systems with regards to syntactic and semantic interoperability were investigated.

Methods: Syntactic interoperability was analyzed by mapping input items from six reporting and notification systems to the WHO Minimal Information Models (MIM) related to equivalent content. The semantic interoperability analysis includes retrieving nouns and bigrams (adjective and noun) to an interface of SNOMED CT, expecting a SNOMED CT code as a result.

Results: The analysis covers reports of seven publicly available reporting systems. In terms of syntactic interoperability, the CIRSmedical and the CIRS hospital have the most similarities. The semantic interoperability analysis revealed the availability of 37% (n = 9721) out of a total of 26360 terms in SNOMED CT.

Discussion: A structural overlap between the systems studied and the MIM exists only for the fields What happened? /event as free text and the Reporters Role. A REST interface for the automated query was not present. There is no syntactic interoperability. One possible solution would be to use HL7 FHIR.

Regarding semantic interoperability, only a few extracted terms are part of the SNOMED CT terminology. The availability of SNOMED CT is a good base for semantic interoperability under the assumption of adding patient safety-relevant terms.

Conclusion: The development of a Patient Safety Ontology could contribute significantly to syntactic and semantic interoperability and allows a better and more efficient analysis of critical incidents.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.


References

1.
Rodziewicz TL, Houseman B, Hipskind JE. Medical error reduction and prevention. In: StatPearls. Treasure Island (FL): StatPearls Publishing.
2.
Klauber J, Geraedts M, Friedrich J, Wasem J. Krankenhaus-Report 2014: Schwerpunkt: Patientensicherheit. Schattauer; 2014.
3.
Doupi P. National Reporting Systems for Patient Safety Incidents - A review of the situation in Europe. Report No. 13/2009. Helsinki: National Institute for Health and Welfare (THL); 2009.
4.
Macrae C. The problem with incident reporting. BMJ Qual Saf. 2016;25:71–75. DOI: 10.1136/bmjqs-2015-004732 External link
5.
Schrappe M. APS-Weißbuch Patientensicherheit - Sicherheit in der Gesundheitsversorgung neu denken, gezielt verbessern. Aktionsbündnis Patientensicherheit e.V., editor. Berlin: MWV Medizinisch Wissenschaftliche Verlagsgesellschaft; 2018. p. 618.
6.
Aktionsbündnis Patientensicherheit e.V. (Deutschland); Plattform Patientensicherheit (Österreich); Patientensicherheit Schweiz. Einrichtung und erfolgreicher Betrieb eines Berichts- und Lernsystems (CIRS) - Handlungsempfehlung für stationäre Einrichtungen im Gesundheitswesen. 2016. Available from: http://www.aps-ev.de External link
7.
CIRSmedical.de [Internet]. [cited 5 Apr 2020]. Available from: http://www.cirsmedical.de/cirsmedical External link
8.
Tetzlaff L, Schröder C, Beck E, Schrader T. Die Datenqualität des CIRSmedical – geeignet für eine systematische Analyse? GMS Med Inform Biom Epidemiol. 2018;14(2):Doc10. DOI: 10.3205/mibe000188 External link
9.
Ärztliches Zentrum für Qualität in der Medizin (ÄZQ). CIRSmedical.de. Available from: https://www.aezq.de/patientensicherheit/cirs/cirsmedical-de/ External link
10.
CIRS-AINS [Internet]. Available from: https://www.cirs-ains.de/ External link
11.
Beyer M, Blazejewski T, Güthlin C, Klemp K, Wunder A, Hoffmann B, et al. Das hausärztliche Fehlerberichts- und Lernsystem ‚jeder-fehler-zaehlt.de‘ – Berichtsbestand und Nutzungsperspektiven [jeder-fehler-zaehlt.de: Content of and prospective benefits from a critical incident reporting and learning system (CIRS) for primary care]. Z Evid Fortbild Qual Gesundhwes. 2015;109:62–68. DOI: 10.1016/j.zefq.2014.06.013 External link
12.
CIRSforte - Aktionsbündnis Patientensicherheit [Internet]. [cited 13 Mar 2021]. Available from: https://www.cirsforte.de/ External link
13.
CIRS Health Care COVID-19 [Internet]. [cited 13 Mar 2021]. Available from: https://asp4.intrafox.net/cgi-bin/external_intrafox.app?P=z6BkOH6C2M External link
14.
NRLS Reporting [Internet]. Available from: https://report.nrls.nhs.uk/nrlsreporting/ External link
15.
LüFMS Erfahrung teilen [Internet]. [cited 13 Mar 2021]. Available from: https://luefms.aps-ev.de/ External link
16.
Willkommen auf der cocos.team Website [Internet]. [cited 13 Mar 2021]. Available from: http://www.cocos.team/ External link
17.
Sass J, Bartschke A, Lehne M, Essenwanger A, Rinaldi E, Rudolph S, et al. The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond. BMC Med Inform Decis Mak. 2020;20:341. DOI: 10.1186/s12911-020-01374-w External link
18.
Bauer J, Rohner-Rojas S, Holderried M. Einrichtungsübergreifende Interoperabilität: Herausforderungen und Grundlagen für die technische Umsetzung [Cross-enterprise interoperability: Challenges and principles for technical implementation]. Radiologe. 2020;60:334–341. DOI: 10.1007/s00117-019-00626-9 External link
19.
Lehne M, Sass J, Essenwanger A, Schepers J, Thun S. Why digital medicine depends on interoperability. npj Digital Med. 2019;2:79. DOI: 10.1038/s41746-019-0158-1 External link
20.
Hammond WE. eHealth interoperability. Stud Health Technol Inform. 2008;134:245–253.
21.
Engel K, Blobel B, Pharow P. Standards for enabling health informatics interoperability. Stud Health Technol Inform. 2006;124:145–150.
22.
WHO. Minimal Information Model for Patient Safety [Internet]. [cited 19 Aug 2020]. Available from: https://www.who.int/patientsafety/implementation/information_model/en/ External link
23.
WHO. EU Validation of the Minimal Information Model for Patient Safety [Internet]. [cited 19 Aug 2020]. Available from: https://www.who.int/patientsafety/implementation/taxonomy/eu-mim-validation/en/ External link
24.
WHO. Patient Safety. The Conceptual Framework for the International Classification for Patient Safety (ICPS). 2010. Available from: https://www.who.int/patientsafety/implementation/taxonomy/ICPS-report/en/ External link
25.
BfArM - SNOMED CT National Release Center (NRC) [Internet]. Available from: https://www.bfarm.de/DE/Kodiersysteme/Terminologien/SNOMED-CT/_node.html External link
26.
SNOMED International. Systematized Nomenclature of Medicine [Internet]. [cited 5 Apr 2020]. Available from: https://www.snomed.org/ External link
27.
Bird S, Klein E, Loper E. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O'Reilly Media; 2009.
28.
spacy.io/ [Internet]. [cited 25 Mar 2021]. Available from: https://spacy.io/ External link
29.
DeepL Übersetzer - DeepL Translate [Internet]. [cited 13 Mar 2021]. Available from: https://www.deepl.com/translator External link
30.
Khvastova M, Witt M, Essenwanger A, Sass J, Thun S, Krefting D. Towards Interoperability in Clinical Research - Enabling FHIR on the Open-Source Research Platform XNAT. J Med Syst. 2020;44:137. DOI: 10.1007/s10916-020-01600-y External link
31.
Saripalle R, Runyan C, Russell M. Using HL7 FHIR to achieve interoperability in patient health record. J Biomed Inform. 2019;94:103188. DOI: 10.1016/j.jbi.2019.103188 External link
32.
Oemig F. HL7 version 2.x goes FHIR. Stud Health Technol Inform. 2019;267:93–98. DOI: 10.3233/SHTI190811 External link
33.
Tariq RA, Sharma S. Inappropriate Medical Abbreviations. In: StatPearls. Treasure Island (FL): StatPearls Publishing. Available from: https://www.ncbi.nlm.nih.gov/books/NBK519006/ External link
34.
Zhou X, Zheng A, Yin J, Chen R, Zhao X, Xu W, et al. Context-Sensitive Spelling Correction of Consumer-Generated Content on Health Care. JMIR Med Inform. 2015;3: e27. DOI: 10.2196/medinform.4211 External link
35.
Kim T, Han SW, Kang M, Lee SH, Kim J-H, Joo HJ, et al. Similarity-Based Unsupervised Spelling Correction Using BioWordVec: Development and Usability Study of Bacterial Culture and Antimicrobial Susceptibility Reports. JMIR Med Inform. 2021;9: e25530. DOI: 10.2196/25530 External link
36.
Tolentino HD, Matters MD, Walop W, Law B, Tong W, Liu F, et al. A UMLS-based spell checker for natural language processing in vaccine safety. BMC Med Inform Decis Mak. 2007;7: 3. DOI: 10.1186/1472-6947-7-3 External link
37.
Lu CJ, Aronson AR, Shooshan SE, Demner-Fushman D. Spell checker for consumer language (CSpell). J Am Med Inform Assoc. 2019;26:211–218. DOI: 10.1093/jamia/ocy171 External link
38.
Gong Y. Terminology in a voluntary medical incident reporting system: A human-centered perspective. In: Proceedings of the ACM international conference on Health informatics - IHI '10. New York: ACM Press; 2010. p. 2-7. DOI: 10.1145/1882992.1882996 External link