Skip to main content
SpringerLink
Log in

Technologies for the Computable Representation and Sharing of Data and Knowledge in Mental Health

  • Chapter
  • First Online:
Mental Health Informatics

Part of the book series: Health Informatics ((HI))

  • 871 Accesses

Abstract

Mental health as a domain is in dire need of more efficient, cost effective methods for acquiring and disseminating new knowledge. We need new methods not only to efficiently develop the knowledge base upon which precision mental healthcare can be based, but also reliable methods to put this knowledge in the hands of consumers, front-line researchers, and providers. More than two decades of experience in general biomedical healthcare has demonstrated that computerized information systems are essential for achieving these goals. Our ability to acquire and disseminate high quality knowledge using health information systems, however, depends on our ability to both represent and exchange data in ways that computerized systems understand. In this chapter we introduce the foundational informatics technologies upon which this knowledge acquisition and dissemination depends—technologies for standardizing the representation and exchange of data, information, and knowledge. We introduce technologies for data and knowledge representation, such as terminologies, ontologies, and information models. We describe methods for specifying the kinds of information required as inputs for a specific purpose, such as minimum clinical data sets (MCDSs) and common data elements (CDEs), with an emphasis on those used in the context of mental health. Next, we introduce the concept of “standards” and describe three basics types of standards and their critical role in enabling both technical and semantic interoperability. Finally, we highlight the substantial gaps in systems for both concept and knowledge representation in mental health and outline a preliminary foundation for the systematic enhancement of technologies for the computable representation of mental health data and knowledge.

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 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Skovira RJ. Ontological grounding of a knowledge mapping methodology: defining data, information, and knowledge. Issues Inf Sci. 2007;VII(2):258–64.

    Google Scholar 

  2. Zins C. Conceptual approaches for defining data, information, and knowledge. J Am Soc Inf Sci Technol. 2007;58(4):479–93.

    Article  Google Scholar 

  3. Sanders J Defining terms: data, information and knowledge. In: 2016 SAI computing conference (SAI). 2016. IEEE.

    Google Scholar 

  4. Dervos DA, Coleman AS. A common sense approach to defining data, information, and metadata. Würzburg: Ergon-Verlag; 2006.

    Google Scholar 

  5. Boisot M, Canals A. Data, information and knowledge: have we got it right? J Evol Econ. 2004;14(1):43–67.

    Article  Google Scholar 

  6. Aamodt A, Nygård M. Different roles and mutual dependencies of data, information, and knowledge—an AI perspective on their integration. Data Knowl Eng. 1995;16(3):191–222.

    Article  Google Scholar 

  7. Kanehisa M, et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 2014;42(D1):D199–205.

    Article  CAS  Google Scholar 

  8. Chute CG. Clinical classification and terminology: some history and current observations. J Am Med Inform Assoc. 2000;7(3):298–303.

    Article  CAS  Google Scholar 

  9. States U Data standards. 2021. Available from https://resources.data.gov/standards/concepts/

  10. Cimino JJ. Desiderata for controlled medical vocabularies in the twenty-first century. Methods Inf Med. 1998;37(4–5):394.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Tuleya LGE. Thesaurus of psychological index terms. Washington, DC: American Psychological Association; 2007.

    Google Scholar 

  12. Zhu X, et al. A review of auditing methods applied to the content of controlled biomedical terminologies. J Biomed Inform. 2009;42(3):413–25.

    Article  Google Scholar 

  13. Gruber TR. A translation approach to portable ontology specifications. Knowl Acquis. 1993;5(2):199–220.

    Article  Google Scholar 

  14. Ogden CK, Richards IA. The meaning of meaning. San Diego, CA: Harcourt, Brace; 1923.

    Google Scholar 

  15. Kim TY, Coenen A, Hardiker N. A quality improvement model for healthcare terminologies. J Biomed Inform. 2010;43(6):1036–43.

    Article  Google Scholar 

  16. Elkin PL, et al. Guideline and quality indicators for development, purchase and use of controlled health vocabularies. Int J Med Inform. 2002;68(1–3):175–86.

    Article  Google Scholar 

  17. Bales ME, et al. Qualitative assessment of the international classification of functioning, disability, and health with respect to the desiderata for controlled medical vocabularies. Int J Med Inform. 2006;75(5):384–95.

    Article  Google Scholar 

  18. Association, A.P. Diagnostic and statistical manual of mental disorders (DSM-5®). Arlington, VA: American Psychiatric Pub; 2013.

    Book  Google Scholar 

  19. IHTSDO, Standard nomenclature of medicine-clinical terms. International Release January 2011.

    Google Scholar 

  20. Lee, Y.T. Information modeling: from design to implementation. In: Proceedings of the second world manufacturing congress. 1999. Canada/Switzerland: International Computer Science Conventions.

    Google Scholar 

  21. Oniki TA, et al. Lessons learned in detailed clinical modeling at Intermountain Healthcare. J Am Med Inform Assoc. 2014;21(6):1076–81.

    Article  Google Scholar 

  22. Martínez-Costa C, et al. Isosemantic rendering of clinical information using formal ontologies and RDF. Stud Health Technol Inform. 2013;192:1085.

    PubMed  Google Scholar 

  23. Schulz S, Martínez-Costa C. How ontologies can improve semantic interoperability in health care. In: Process support and knowledge representation in health care. Cham: Springer; 2013. p. 1–10.

    Google Scholar 

  24. Tenenbaum JD, Sansone S-A, Haendel M. A sea of standards for omics data: sink or swim? J Am Med Inform Assoc. 2014;21(2):200–3.

    Article  Google Scholar 

  25. Baker M. 1,500 scientists lift the lid on reproducibility. Nature. 2016;533:452–4.

    Article  CAS  Google Scholar 

  26. Society F. Minimum Information about a high-throughput SEQuencing Experiment. 2012 10/4/2020]; Available from http://fged.org/projects/minseqe/

  27. Ghitza UE, et al. Common data elements for substance use disorders in electronic health records: the NIDA clinical trials network experience. Addiction. 2013;108(1):3–8.

    Article  Google Scholar 

  28. Saver JL, et al. Standardizing the structure of stroke clinical and epidemiologic research data: the National Institute of Neurological Disorders and Stroke (NINDS) stroke common data element (CDE) project. Stroke. 2012;43(4):967–73.

    Article  Google Scholar 

  29. Health N.I.O NIH CDE repository. 2020 [cited 2020 10/09/2020].

    Google Scholar 

  30. Svensson-Ranallo PA, Adam TJ, Sainfort F. A framework and standardized methodology for developing minimum clinical datasets. AMIA Jt Summits Transl Sci Proc. 2011;2011:54.

    PubMed  PubMed Central  Google Scholar 

  31. Werley HH, Devine EC, Zorn CR. Nursing needs its own minimum data set. Am J Nurs. 1988;88(12):1651–3.

    CAS  PubMed  Google Scholar 

  32. Glover G. Adult mental health care in England. Eur Arch Psychiat Clin Neurosci. 2007;257:71–82.

    Article  Google Scholar 

  33. Morris R, et al. The Irish nursing minimum data set for mental health--a valid and reliable tool for the collection of standardised nursing data. J Clin Nurs. 2010;19(3–4):359–67.

    Article  Google Scholar 

  34. Regenstrief Institute. Logical observation identifiers names and codes (LOINC®). Indiana, IN: Regenstrief Institute, Inc.; 2021.

    Google Scholar 

  35. Benson T, Grieve G. Hl7 version 2. In: Principles of health interoperability. Cham: Springer; 2016. p. 223–42.

    Chapter  Google Scholar 

  36. Beeler GW. HL7 version 3—an object-oriented methodology for collaborative standards development. Int J Med Inform. 1998;48(1–3):151–61.

    Article  CAS  Google Scholar 

  37. (HL7), H.L., HL7 CDA R2 IG: Consolidated CDA Templates for Clinical Note (US Realm), DSTU R2.1—Vol. 1: Intro. August 2015 with 2019 June errata.

    Google Scholar 

  38. Sansone SA, et al. FAIRsharing as a community approach to standards, repositories and policies. Nat Biotechnol. 2019;37(4):358–67.

    Article  CAS  Google Scholar 

  39. Taylor CF, et al. Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project. Nat Biotechnol. 2008;26(8):889–96.

    Article  CAS  Google Scholar 

  40. Gibson F, et al. Minimum information about a neuroscience investigation (MINI): electrophysiology. Nat Prec. 2009:1–1. https://doi.org/10.1038/npre.2009.1720.2.

  41. Ranallo PA, et al. Psychological assessment instruments: a coverage analysis using SNOMED CT, LOINC and QS terminology. AMIA Annu Symp Proc. 2013;2013:1333–40.

    PubMed  PubMed Central  Google Scholar 

  42. American Psychological Association. PsycINFO®. Washington, DC: APA EBSCO; 2012.

    Google Scholar 

  43. American Psychological Association. APA Databases. Washington, DC: APA EBSCO; 2012.

    Google Scholar 

  44. American Psychological Association. APA Databases: PsycINFO® Homepage. 2012 October [cited 2012 November 9]; Available from http://www.apa.org/pubs/databases/psycinfo/index.aspx

  45. Institute of Medicine. Improving the quality of health care for mental and substance-use conditions. Quality chasm series, vol. xxiii. Washington, DC: National Academies Press; 2006. 504 p

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Six Aims for Behavioral Health, Minneapolis, MN, USA

    Piper A. Ranallo

  2. Duke University, Durham, NC, USA

    Jessica D. Tenenbaum

Authors
  1. Piper A. Ranallo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jessica D. Tenenbaum
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Piper A. Ranallo .

Editor information

Editors and Affiliations

  1. Duke University, Durham, NC, USA

    Jessica D. Tenenbaum

  2. Six Aims for Behavioral Health, Minneapolis, MN, USA

    Piper A. Ranallo

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ranallo, P.A., Tenenbaum, J.D. (2021). Technologies for the Computable Representation and Sharing of Data and Knowledge in Mental Health. In: Tenenbaum, J.D., Ranallo, P.A. (eds) Mental Health Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-70558-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-70558-9_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70557-2

  • Online ISBN: 978-3-030-70558-9

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation