Skip to main content
SpringerLink
Log in

Making Population Health Knowledge

  • Chapter
  • First Online:
Causation in Population Health Informatics and Data Science

Abstract

This chapter revolves around the idea that knowledge is generated from data. We briefly describe Ackoff’s hierarchy, which starts with data and proceeds via information to knowledge, understanding and wisdom. In contrast, we propose to de-emphasize understanding and wisdom, and to insert evidence between information and knowledge. We outline a framework that takes data as raw symbols, which morph into information when contextualized. Information becomes evidence when compared to relevant standards. Evidence is used to test hypotheses and is transformed into knowledge by consensus. As quality checkpoints for the transition between levels we offer relevance, robustness, repeatability, and reproducibility.

We borrow the title of this chapter from Miriam Solomon’s book, Making Medical Knowledge, [186].

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 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
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

Notes

  1. 1.

    https://www.informs.org/Explore/History-and-Traditions/Biographical-Profiles/Ackoff-Russell-L; accessed 4/7/2017.

  2. 2.

    https://www.cdc.gov/healthywater/surveillance/index.html.

  3. 3.

    We do not have the space here to elaborate on how that consensus is reached. As a point of departure for such discussion, one may want to start with reading Ludwig Fleck’s work on the concept of the scientific collective [202] and Miriam Solomon’s discussion of the medical consensus conference process [186].

  4. 4.

    See https://nepis.epa.gov/Exe/ZyNET.exe/P100999U.TXT?ZyActionD=ZyDocument&Client=EPA&Index=2000+Thru+2005&Docs=&Query=&Time=&EndTime=&SearchMethod=1&TocRestrict=n&Toc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QFieldDay=&IntQFieldOp=0&ExtQFieldOp=0&XmlQuery=&File=D%3A%5Czyfiles%5CIndex%20Data%5C00thru05%5CTxt%5C00000025%5CP100999U.txt&User=ANONYMOUS&Password=anonymous&SortMethod=h%7C-&MaximumDocuments=1&FuzzyDegree=0&ImageQuality=r75g8/r75g8/x150y150g16/i425&Display=hpfr&DefSeekPage=x&SearchBack=ZyActionL&Back=ZyActionS&BackDesc=Results%20page&MaximumPages=1&ZyEntry=1&SeekPage=x&ZyPURL; page 9.

References

  1. Solomon M. Making medical knowledge. 1st ed. Oxford: Oxford University Press; 2015. xiii, 261 p.

    Book  Google Scholar 

  2. Yasnoff WA, O’Carroll PW, Friede A. Public health informatics and the health information infrastructure. In: Shortliffe EH, Cimino JJ, editors. Biomedical informatics. New York: Springer; 2006. p. 537–63.

    Chapter  Google Scholar 

  3. Revere D, et al. Understanding the information needs of public health practitioners: a literature review to inform design of an interactive digital knowledge management system. J Biomed Inform. 2007;40(4):410–21.

    Article  Google Scholar 

  4. Ackoff RL. From data to wisdom. J Appl Syst Anal. 1989;16:3–9.

    Google Scholar 

  5. Rowley J. The wisdom hierarchy: representations of the DIKW hierarchy. J Inf Sci. 2007;33(2):163–80.

    Article  Google Scholar 

  6. Chaffey D, Wood SJ. Business information management: improving perfomance using information systems. Upper Saddle River: Prentice Hall; 2005.

    Google Scholar 

  7. Choo CW. The knowing organization: how organizations use information to construct meaning, create knowledge and make decisions. Int J Inf Manag. 1996;16(5):329–40.

    Article  Google Scholar 

  8. Heller RF. Evidence for population health. Oxford/New York: Oxford University Press; 2005. xii, 126 p.

    Book  Google Scholar 

  9. Heller RF, Page J. A population perspective to evidence based medicine: evidence for population health. J Epidemiol Community Health. 2002;56(1):45–7.

    Article  CAS  Google Scholar 

  10. Woodward J. Making things happen – a theory of causal explanation. 2003.

    Google Scholar 

  11. Holland PW. Statistics and causal inference. J Am Stat Assoc. 1986;81(396):945–60.

    Article  Google Scholar 

  12. Mensah E, Goderre JL. Data sources and data tools. In: Magnuson JA, Fu PC, editors. Public health informatics and information systems. London: Springer; 2014. p. 107–31.

    Chapter  Google Scholar 

  13. Audi R. The Cambridge dictionary of philosophy. 2nd ed. Cambridge/New York: Cambridge University Press; 1999. xxxv, 1001 p.

    Google Scholar 

  14. Brownson RC, Fielding JE, Maylahn CM. Evidence-based public health: a fundamental concept for public health practice. Annu Rev Public Health. 2009;30:175–201.

    Article  Google Scholar 

  15. Viehbeck SM, Petticrew M, Cummins S. Old myths, new myths: challenging myths in public health. Am J Public Health. 2015;105(4):665–9.

    Article  Google Scholar 

  16. Beer KD, et al. Surveillance for waterborne disease outbreaks associated with drinking water – United States, 2011-2012. MMWR Morb Mortal Wkly Rep. 2015;64(31):842–8.

    Article  Google Scholar 

  17. Ichikawa JJ, Steup M. The analysis of knowledge. The stanford Encyclopedia of philosophy 2013 03/07/2014. Available from: http://plato.stanford.edu/archives/fall2013/entries/knowledge-analysis/.

  18. Gettier J. Is justified true belief knowledge? Analysis. 1963;23(6):121–3.

    Article  Google Scholar 

  19. Lucey KG. On knowing and the known : introductory readings in epistemology. Amherst: Prometheus Books; 1996. 437 p.

    Google Scholar 

  20. Johns ML. Information management for health professions, The health information management series. 2nd ed. Albany: Delmar Thomson Learning; 2002. xx, 390 p.

    Google Scholar 

  21. Fleck L. Genesis and development of a scientific fact. Chicago: University of Chicago Press; 1979. xxviii, 203 p.

    Google Scholar 

  22. Bellinger DC. Lead contamination in flint – an abject failure to protect public health. N Engl J Med. 2016;374(12):1101–3.

    Article  Google Scholar 

  23. Dean K. Creating a new knowledge base for the new public health. J Epidemiol Community Health. 1994;48(3):217–9.

    Article  CAS  Google Scholar 

  24. Eriksson C. Learning and knowledge-production for public health: a review of approaches to evidence-based public health. Scand J Public Health. 2000;28(4):298–308.

    CAS  PubMed  Google Scholar 

  25. Russell SJ, Norvig P. Artificial intelligence: a modern approach. Upper Saddle River: Prentice Hall; 2010.

    Google Scholar 

  26. Best A, et al. Knowledge integration: conceptualizing communications in cancer control systems. Patient Educ Couns. 2008;71(3):319–27.

    Article  Google Scholar 

  27. Greenhalgh T, Wieringa S. Is it time to drop the knowledge translation metaphor? A critical literature review. J R Soc Med. 2011;104(12):501–9.

    Article  Google Scholar 

  28. Green LW, et al. Diffusion theory and knowledge dissemination, utilization, and integration in public health. Annu Rev Public Health. 2009;30:151–74.

    Article  Google Scholar 

  29. Khoury MJ, et al. Knowledge integration at the center of genomic medicine. Genet Med. 2012;14(7):643–7.

    Article  Google Scholar 

  30. Murray KS, Porter BW. Developing a tool for knowledge integration – initial results. Int J Man Mach Stud. 1990;33(4):373–83.

    Article  Google Scholar 

  31. Kamper-Jorgensen F. Knowledge-base, evidence and evaluation in public health. Scand J Public Health. 2000;28(4):241–3.

    Article  CAS  Google Scholar 

  32. Worrall J. Why there’s no cause to randomize. Br J Philos Sci. 2007;58(3):451–88.

    Article  Google Scholar 

  33. Cartwright N. Are RCTs the gold standard? BioSocieties. 2007;2(1):11–20.

    Article  Google Scholar 

  34. Woodward J. Agency and interventionist theories. In: Beebee H, Hitchcock C, Menzies P, editors. The Oxford handbook of causation. Oxford: OUP; 2009. p. 234–62.

    Google Scholar 

  35. Sujansky W. Heterogeneous database integration in biomedicine. J Biomed Inform. 2001;34(4):285–98.

    Article  CAS  Google Scholar 

  36. Louie B, et al. Data integration and genomic medicine. J Biomed Inform. 2007;40(1):5–16.

    Article  CAS  Google Scholar 

  37. Galperin MY, Rigden DJ, Fernandez-Suarez XM. The 2015 nucleic acids research database issue and molecular biology database collection. Nucleic Acids Res. 2015;43(Database issue):D1–5.

    Article  CAS  Google Scholar 

  38. Nsoesie EO, Brownstein JS. Computational approaches to influenza surveillance: beyond timeliness. Cell Host Microbe. 2015;17(3):275–8.

    Article  CAS  Google Scholar 

  39. Wolkenhauer O. Why model? Front Physiol. 2014;5:21.

    Article  Google Scholar 

  40. Dammann O, Follett P. Toward multi-scale computational modeling in developmental disability research. Neuropediatrics. 2011;42(3):90–6.

    Article  CAS  Google Scholar 

  41. Wolkenhauer O, et al. Enabling multiscale modeling in systems medicine. Genome Med. 2014;6(3):21.

    Article  Google Scholar 

  42. Wolkenhauer O, et al. The road from systems biology to systems medicine. Pediatr Res. 2013;73(4 Pt 2):502–7.

    Article  Google Scholar 

  43. Broadbent A. Philosophy of epidemiology. New directions in the philosophy of science. Houndmills, UK: Palgrave Macmillan; 2013.

    Book  Google Scholar 

  44. Feitelson DG. From repeatability to reproducibility and corroboration. ACM SIGOPS Oper Syst Rev. 2015;49(1):3–11.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, USA

    Olaf Dammann

  2. The African Centre for Epistemology and Philosophy of Science, University of Johannesburg, Johannesburg, South Africa

    Benjamin Smart

Authors
  1. Olaf Dammann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Benjamin Smart
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Dammann, O., Smart, B. (2019). Making Population Health Knowledge. In: Causation in Population Health Informatics and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-319-96307-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-96307-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96306-8

  • Online ISBN: 978-3-319-96307-5

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation