Skip to main content
SpringerLink
Log in

Just What the Doctor Ordered – Towards Design Principles for NLP-Based Systems in Healthcare

  • Conference paper
  • First Online:
The Transdisciplinary Reach of Design Science Research (DESRIST 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13229))

  • 1501 Accesses

Abstract

Patient data is mainly transmitted in the form of unstructured free texts in medical documentation. Natural language processing (NLP)-based systems can help to structure and extract information from these free texts to support the work of healthcare professionals. However, the healthcare sector must meet certain information quality requirements to comply with regulations and provide optimal patient care. Therefore, we argue that a design guideline is needed to tailor NLP-based systems to the unique requirements of clinical processes and to catalyze the practical application of such systems. In this paper, we report the results of a design science research study, focusing on the requirements of NLP-based systems used by healthcare professionals. In doing so, we shed light on the needs of practitioners when working with sophisticated NLP-based systems that extract and analyze text-based information from medical documentation. By providing evaluated, testable propositions and detailed design principles, we support the practical endeavor of such systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mihailescu, M.I., Mihailescu, D., Carlsson, S.A.: Understanding healthcare digitalization: a critical realist approach. In: ICIS (2017)

    Google Scholar 

  2. Safdar, S., Zafar, S., Zafar, N., Khan, N.F.: Machine learning based decision support systems (DSS) for heart disease diagnosis: a review. Artif. Intell. Rev. 50(4), 597–623 (2017). https://doi.org/10.1007/s10462-017-9552-8

    Article  Google Scholar 

  3. Wang, Y., et al.: Clinical information extraction applications: A literature review. J. Biomed. Inform. 77, 34–49 (2018)

    Article  Google Scholar 

  4. Davenport, T., Kalakota, R.: The potential for artificial intelligence in healthcare. Futur. Healthc. J. 6, 94–98 (2019). https://doi.org/10.7861/futurehosp.6-2-94

    Article  Google Scholar 

  5. Jiang, F., et al.: Artificial intelligence in healthcare: Past, present and future. Stroke Vasc. Neurol. 2, 230–243 (2017). https://doi.org/10.1136/svn-2017-000101

    Article  Google Scholar 

  6. Panch, T., Mattie, H., Celi, L.A.: The “inconvenient truth” about AI in healthcare. npj Digit. Med. 2, 77 (2019). https://doi.org/10.1038/s41746-019-0155-4

  7. Ford, E., Carroll, J.A., Smith, H.E., Scott, D., Cassell, J.A.: Extracting information from the text of electronic medical records to improve case detection: a systematic review. J. Am. Med. Informatics Assoc. 23, 1007–1015 (2016)

    Article  Google Scholar 

  8. Holzinger, A., Geierhofer, R., Mödritscher, F., Tatzl, R.: Semantic information in medical information systems: utilization of text mining techniques to analyze medical diagnoses. J. Univers. Comput. Sci. 14, 3781–3795 (2008)

    Google Scholar 

  9. Jensen, K., et al.: Analysis of free text in electronic health records for identification of cancer patient trajectories. Sci. Rep. 7, 46226 (2017). https://doi.org/10.1038/srep46226

    Article  Google Scholar 

  10. Hevner, A.: A three cycle view of design science research. Scand. J. Inf. Syst. 19 (2007)

    Google Scholar 

  11. Hevner, A., et al.: Design science in information systems research. Manag. Inf. Syst. Q. 28, 75 (2004)

    Google Scholar 

  12. Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research. J. Manag. Inf. Syst. 24, 45–77 (2007). https://doi.org/10.2753/MIS0742-1222240302

    Article  Google Scholar 

  13. Dieleman, J.L., et al.: Factors associated with increases in us health care spending, 1996–2013. JAMA 318, 1668 (2017). https://doi.org/10.1001/jama.2017.15927

    Article  Google Scholar 

  14. Fernández, E.: Innovation in healthcare: harnessing new technologies. J. Midwest Assoc. Inf. Syst. 107–120 (2017). https://doi.org/10.17705/3jmwa.00034

  15. Johansen, F., van den Bosch, S.: The scaling-up of neighbourhood care: from experiment towards a transformative movement in healthcare. Futures 89, 60–73 (2017). https://doi.org/10.1016/j.futures.2017.04.004

    Article  Google Scholar 

  16. World health statistics 2019: monitoring health for the SDGs, sustainable development goals. https://apps.who.int/iris/handle/10665/324835. Accessed 28 Jan 2022

  17. Shreffler, J., Huecker, M., Petrey, J.: The impact of COVID-19 on healthcare worker wellness: a scoping review. West. J. Emerg. Med. 21 (2020)

    Google Scholar 

  18. Gjestsen, M.T., Wiig, S., Testad, I.: What are the key contextual factors when preparing for successful implementation of assistive living technology in primary elderly care? A case study from Norway. BMJ Open 7, e015455 (2017)

    Article  Google Scholar 

  19. Sunarti, S., Fadzlul Rahman, F., Naufal, M., Risky, M., Febriyanto, K., Masnina, R.: Artificial intelligence in healthcare: opportunities and risk for future. Gac. Sanit. 35, S67–S70 (2021). https://doi.org/10.1016/j.gaceta.2020.12.019

    Article  Google Scholar 

  20. Koleck, T.A., Dreisbach, C., Bourne, P.E., Bakken, S.: Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review. J. Am. Med. Informatics Assoc. 26, 364–379 (2019)

    Article  Google Scholar 

  21. Khurana, D., Koli, A., Khatter, K., Singh, S.: Natural Language Processing: State of The Art, Current Trends and Challenges (2017)

    Google Scholar 

  22. Brendel, A.B., Brennecke, J.T., Hillmann, B.M., Kolbe, L.M.: The design of a decision support system for computation of carsharing pricing areas and its influence on vehicle distribution. IEEE Trans. Eng. Manag. 1–15 (2020)

    Google Scholar 

  23. Gregor, S., Hevner, A.R.: Positioning and presenting design science research for maximum impact. MIS Q. 37, 337–355 (2013)

    Article  Google Scholar 

  24. Fu, S., et al.: Clinical concept extraction: a methodology review. J. Biomed. Inform. 109, 103526 (2020)

    Article  Google Scholar 

  25. Houssein, E.H., Mohamed, R.E., Ali, A.A.: Machine learning techniques for biomedical natural language processing: a comprehensive review. IEEE Access. 9, 140628–140653 (2021). https://doi.org/10.1109/ACCESS.2021.3119621

    Article  Google Scholar 

  26. Jones, D., Gregor, S.: The anatomy of a design theory. J. Assoc. Inf. Syst. 8, 312–335 (2007). https://doi.org/10.17705/1jais.00129

  27. Velupillai, S., et al.: Using clinical natural language processing for health outcomes research: overview and actionable suggestions for future advances. J. Biomed. Inform. 88, 11–19 (2018)

    Article  Google Scholar 

  28. Sterckx, L., et al.: Clinical information extraction for preterm birth risk prediction. J. Biomed. Inform. 110, 103544 (2020). https://doi.org/10.1016/j.jbi.2020.103544

    Article  Google Scholar 

  29. Viani, N., et al.: A natural language processing approach for identifying temporal disease onset information from mental healthcare text. Sci. Rep. 11, 757 (2021)

    Article  Google Scholar 

  30. Fu, J.T., Sholle, E., Krichevsky, S., Scandura, J., Campion, T.R.: Extracting and classifying diagnosis dates from clinical notes: a case study. J. Biomed. Inform. 110, 103569 (2020). https://doi.org/10.1016/j.jbi.2020.103569

    Article  Google Scholar 

  31. Zheng, K., et al.: Ease of adoption of clinical natural language processing software: an evaluation of five systems. J. Biomed. Inform. 58, S189–S196 (2015)

    Article  Google Scholar 

  32. Nehme, F., Feldman, K.: Evolving role and future directions of natural language processing in gastroenterology. Dig. Dis. Sci. 66(1), 29–40 (2020). https://doi.org/10.1007/s10620-020-06156-y

    Article  Google Scholar 

  33. Petitgand, C., Motulsky, A., Denis, J.L., Régis, C.: Investigating the barriers to physician adoption of an artificial intelligence-based decision support system in emergency care: an interpretative qualitative study. Stud. Health Technol. Inform. 270, 1001–1005 (2020). https://doi.org/10.3233/SHTI200312

    Article  Google Scholar 

  34. Wen, A., et al.: Desiderata for delivering NLP to accelerate healthcare AI advancement and a mayo clinic NLP-as-a-service implementation. npj Digit. Med. 2, 130 (2019). https://doi.org/10.1038/s41746-019-0208-8

  35. Liberati, E.G., et al.: What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implement. Sci. 12, 113 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Georg-August-Universität Göttingen, 37073, Göttingen, Germany

    Marvin Braun, Aycan Aslan, Till Ole Diesterhöft, Maike Greve & Lutz M. Kolbe

  2. Technische Universität Dresden, 01069, Dresden, Germany

    Alfred Benedikt Brendel

Authors
  1. Marvin Braun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aycan Aslan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Till Ole Diesterhöft
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maike Greve
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alfred Benedikt Brendel
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lutz M. Kolbe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marvin Braun .

Editor information

Editors and Affiliations

  1. Victoria University of Wellington, Wellington, New Zealand

    Andreas Drechsler

  2. University of Pretoria, Pretoria, South Africa

    Aurona Gerber

  3. University of South Florida, Tampa, FL, USA

    Alan Hevner

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Braun, M., Aslan, A., Ole Diesterhöft, T., Greve, M., Benedikt Brendel, A., Kolbe, L.M. (2022). Just What the Doctor Ordered – Towards Design Principles for NLP-Based Systems in Healthcare. In: Drechsler, A., Gerber, A., Hevner, A. (eds) The Transdisciplinary Reach of Design Science Research. DESRIST 2022. Lecture Notes in Computer Science, vol 13229. Springer, Cham. https://doi.org/10.1007/978-3-031-06516-3_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06516-3_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06515-6

  • Online ISBN: 978-3-031-06516-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation