Skip to main content

Tools for Intervention Effectiveness Research, Quality Improvement Activities, and Program Evaluation

  • Chapter
  • First Online:
Intervention Effectiveness Research: Quality Improvement and Program Evaluation
  • 1466 Accesses

Abstract

A conceptualization of the research or evaluation as described in Chapters. 2 and 3 provides the basis for operationalizing the PIO MM for intervention effectiveness research, quality improvement activities, and program evaluation. This operationalization requires measures, data, and software. In this chapter, checklists for obtaining new or existing data for operationalizing PIO MM are provided for two situations, new data that will be collected prospectively, and reusing existing data. Nursing’s robust standardized terminologies are described, with emphasis on the Omaha System as an exemplar for all terminologies. Use of large datasets is described, and their unique contribution to the examination of interventions and outcomes is discussed.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 49.99
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. Mayer-Schönberger V, Cukier K (2013) Big data – a revolution that will transform how we live, work and think. John Murray Publishers, London

    Google Scholar 

  2. Hey T, Tansley S, Tolle K (2009) The fourth paradigm: data intensive scientific discovery. Microsoft Research, Redmond, WA

    Google Scholar 

  3. Schneeweiss S (2014) Learning from big health care data. N Engl J Med 370(23):2161–2163

    Article  CAS  PubMed  Google Scholar 

  4. Raghupathi W, Raghupathi V (2014) Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2(1):3

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kruse CS, Goswamy R, Raval Y, Marawi S (2016) Challenges and opportunities of big data in health care: a systematic review. JMIR Med Inform 4(4)

    Google Scholar 

  6. Tukey JW (1977) Exploratory data analysis. Addison-Wesley, Reading, MA

    Google Scholar 

  7. Olsen L, McGinnis JM (2010) Redesigning the clinical effectiveness research paradigm: innovation and practice-based approaches: workshop summary. National Academies Press, Washington, DC

    Google Scholar 

  8. Jensen PB, Jensen LJ, Brunak S (2012) Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 13(6):395–405

    Article  CAS  PubMed  Google Scholar 

  9. Martin KS (2005) The Omaha System: a key to practice, documentation, and information management, reprinted 2nd edn. Health Connections Press, Omaha, NE

    Google Scholar 

  10. Werley HH (1991) Nursing minimum data: abstract tool for standardized comparable, essential data. Am J Public Health 81(4):421–426. doi:10.2105/AJPH.81.4.421

  11. Sewell JP, Thede LQ. Nursing and informatics: opportunities and challenges. Nursing documentation in the age of the EHR [Internet], 2012. [cited 2017 May 12]. Available from: http://dlthede.net/informatics/Chap16Documentation/chap16.html

  12. American Nurses Association [ANA]. ANA recognized terminologies that support nursing practice [Internet], 2012. [cited 2017 May 12]. Available from: http://www.nursingworld.org/MainMenuCategories/Tools/Recognized-Nursing-Practice-Terminologies.pdf

  13. Huber D, Delaney C (1997) The American Organization of Nurse Executives (AONE) research column. The nursing management minimum data set. Appl Nurs Res 10:164–165

    Article  CAS  PubMed  Google Scholar 

  14. Marucci AR, De Caro W, Petrucci C, Lancia L, Sansoni J (2015) ICNP-International Classification of Nursing Practice: origin, structure and development. Prof Inferm 68(2):131

    PubMed  Google Scholar 

  15. Butcher HK, Bulechek GM, Dochterman JM, Wagner C (2013) Nursing interventions classification (NIC). Elsevier Health Sciences

    Google Scholar 

  16. Clinical care classification [Internet]. [cited 2017 May 12]. Available from: https://www.sabacare.com/about/

  17. Nielsen K, Randall R (2013) Opening the black box: presenting a model for evaluating organizational-level interventions. Eur J Work Organ Psy 22(5):601–617

    Article  Google Scholar 

  18. Herdman TH (ed) (2011) Nursing diagnoses 2012-14: definitions and classification. John Wiley & Sons

    Google Scholar 

  19. Moorhead S (2013) Nursing outcomes classification (NOC). Elsevier Health Sciences

    Google Scholar 

  20. Johnson M (ed) (2001) Nursing diagnoses, outcomes, and interventions: NANDA, NOC, and NIC linkages. Mosby Incorporated

    Google Scholar 

  21. Cohen J (1992) A power primer. Psychol Bull 112(1):155–159

    Article  CAS  PubMed  Google Scholar 

  22. Centers for Disease Control and Prevention [US] (1999) Framework for program evaluation in public health. MMWR Recomm Rep 48(RR11):1–40

    Google Scholar 

  23. Röhrig B, du Prel JB, Wachtlin D, Kwiecien R, Blettner M (2010) Sample size calculation in clinical trials: part 13 of a series on evaluation of scientific publications. Dtsch Arztebl Int 107(31-32):552

    PubMed  PubMed Central  Google Scholar 

  24. Mendenhall W, Beaver RJ, Beaver BM (2012) Introduction to probability and statistics. Cengage Learning

    Google Scholar 

  25. Green SB, Salkind NJ (2010) Using SPSS for windows and Macintosh: analyzing and understanding data. Prentice Hall Press

    Google Scholar 

  26. SAS University edition [Internet]. [cited 2017 May 12] Available from: http://www.sas.com/en_us/software/university-edition.html

  27. IBM® SPSS® Student GradPack [Internet]. [cited 2017 May 12]. Available from: http://www-03.ibm.com/software/products/en/spss-stats-gradpack

  28. What is R? [Internet]. [cited 2017 May 12]. Available from: https://www.r-project.org/about.html

  29. Tiwari V, Tiwari B, Thakur RS, Gupta S (2013) Pattern and data analysis in healthcare settings. Google Books

    Google Scholar 

  30. Weka Project http://www.cs.waikato.ac.nz/ml/weka/index.html

  31. Frank E, Hall MA, Witten IH (2016) The WEKA workbench. In: Online Appendix for “Data mining: practical machine learning tools and techniques”, 4th edn. Morgan Kaufmann

    Google Scholar 

  32. MATLAB [Internet]. [cited 2017 May 12]. Available from: https://www.mathworks.com/products/matlab/features.html#matlab-speaks-math

  33. D3 data driven documents [Internet]. [cited 2017 May 12]. Available from: https://d3js.org/

  34. What is Tableau? [Internet]. [cited 2017 May 12]. Available from: http://www.tableau.com/products/desktop#TGETWeLmfvacBMZt.99

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Monsen, K.A. (2018). Tools for Intervention Effectiveness Research, Quality Improvement Activities, and Program Evaluation. In: Intervention Effectiveness Research: Quality Improvement and Program Evaluation. Springer, Cham. https://doi.org/10.1007/978-3-319-61246-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61246-1_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61245-4

  • Online ISBN: 978-3-319-61246-1

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics