Healthc Inform Res.  2011 Mar;17(1):38-50. 10.4258/hir.2011.17.1.38.

Development of Data Models for Nursing Assessment of Cancer Survivors Using Concept Analysis

• Lee MK
• Park HA

• KMID: 2166594
• DOI: http://doi.org/10.4258/hir.2011.17.1.38

Abstract

OBJECTIVES
Sharing of cancer-related information among healthcare professionals is crucial to ensuring the quality of long-term care for cancer survivors. Appropriate distribution of the essential facts can be achieved using data models. The purpose of this study was to develop and validate suitable data models for use in the nursing assessment of cancer survivors.
METHODS
The models developed in this study were based on a modification of concept analysis developed by Walker and Avant. Our approach involved determining the purpose of the analysis, identifying data elements, defining these elements and their uses, determining critical attributes, value sets, and cardinalities, and ultimately constructing data models which were examined externally by domain experts.
RESULTS
We developed 112 data models with 112 data elements, 29 critical attributes, 102 value sets, and 6 data types for the assessment of cancer survivors. External validation revealed that the data elements, critical attributes, and value sets proposed were comprehensive, relevant, and sufficiently useful to encompass nursing issues related to cancer survivors.
CONCLUSIONS
Data models developed in this study will contribute to ensuring the semantic consistency of data collected from cancer survivors, which will improve the quality of nursing assessments and in turn translate to improved long-term patient care.

Keyword

Standards of Care; Nursing Assessment; Quality of Healthcare; Electronic Health Record

MeSH Terms

Delivery of Health Care
Electronic Health Records
Humans
Long-Term Care
Nursing Assessment
Patient Care
Quality of Health Care
Semantics
Standard of Care
Survivors

Figure

• 

  Figure 1 The process of Walker and Avant's concept analysis and its modification for data model development.

Reference

1. American Cancer Society. Cancer facts & figures 2008. 2008. Atlanta: American Cancer Society;57.

2. Twombly R. What's in a name: who is a cancer survivor? J Natl Cancer Inst. 2004. 96:1414–1415.

3. Centers for Disease Control and Prevention (CDC). Cancer survivorship: United States, 1971-2001. MMWR Morb Mortal Wkly Rep. 2004. 53:526–529.

4. Feuerstein M, Findley P. The cancer survivor's guide: the essential handbook to life after cancer. 2006. New York: Marlowe & Co;1.

5. Whippen D, Deering MJ, Ambinder EP. Advancing high-quality cancer care: cancer biomedical informatics grid supports personalized medicine and the electronic health record. J Oncol Pract. 2007. 3:208–211.

6. Graybeal J. Achieving semantic interoperability. The MMI guides- navigating the world of marine metadata. 2009. cited at 2011 Mar 15. Marine Metadata Initiative;Available from: http://marinemetadata.org/guides/vocabs/cvchooseimplement/cvsemint.

7. Hovenga E, Garde S, Heard S. Nursing constraint models for electronic health records: a vision for domain knowledge governance. Int J Med Inform. 2005. 74:886–898.

8. Beale T. Baclawsk K, Kilov H, editors. Archetypes: constraint-based domain models for future-proof information systems. Eleventh OOPSLA workshop on behavioral semantics: serving the customer. 2002. Boston: Northeastern University;16–32.

9. Goossen WT. Using detailed clinical models to bridge the gap between clinicians and HIT. Stud Health Technol Inform. 2008. 141:3–10.

10. Huff SM. The GE-intermountain healthcare alliance: a new paradigm for EHR development. 2009. In : Proceeding of the 4th EHR Symposium; Seoul, KR. 14–28.

11. Kim Y, Huff SM, Ahn SJ, Cho KH, Koh YT. Clinical contents medol: definition, methods, and practical use. In : Proceedings of the 6th Asia Pacific Association for Medical Informatics (APAMI); 2009 Nov 22-24; Hiroshima, JP. W-03.

12. Johnson SB. Generic data modeling for clinical repositories. J Am Med Inform Assoc. 1996. 3:328–339.

13. Walker LO, Avant KC. Strategies for theory construction in nursing. 1988. Norwlk, CT: Appleton & Lange.

14. Matteson P, Hawkins JW. Concept analysis of decision making. Nurs Forum. 1990. 25:4–10.

15. Hawks JH. Power: a concept analysis. J Adv Nurs. 1991. 16:754–762.

16. Henneman EA, Lee JL, Cohen JI. Collaboration: a concept analysis. J Adv Nurs. 1995. 21:103–109.

17. Schadow G, Brion P, McKenzie L, Grieve G, Pratt D. HL7 version 3 standard. Data types - abstract specification, release 1. 2004. cited at 2010 Mar 15. Health Level Seven™ Inc.;Available from: http://www.hl7.org/v3ballot/html/infrastructure/datatypes/datatypes.html.

18. Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM, Biron PV, Shabo Shvo A. HL7 clinical document architecture: Release 2. J Am Med Inform Assoc. 2006. 13:30–39.

19. Chute CG, Cohn SP, Campbell JR. A framework for comprehensive health terminology systems in the United States: development guidelines, criteria for selection, and public policy implications. ANSI healthcare informatics standards board vocabulary working group and the computer-based patient records institute working group on codes and structures. J Am Med Inform Assoc. 1998. 5:503–510.

20. Bakhshi-Raiez F, Cornet R, de Keizer NF. Development and application of a framework for maintenance of medical terminological systems. J Am Med Inform Assoc. 2008. 15:687–700.

21. Kim TY, Coenen A, Hardiker N. A quality improvement model for healthcare terminologies. J Biomed Inform. 2010. 43:1036–1043.

22. Cimino JJ, Clayton PD, Hripcsak G, Johnson SB. Knowledge-based approaches to the maintenance of a large controlled medical terminology. J Am Med Inform Assoc. 1994. 1:35–50.

23. Campbell JR, Carpenter P, Sneiderman C, Cohn S, Chute CG, Warren J. Phase II evaluation of clinical coding schemes: completeness, taxonomy, mapping, definitions, and clarity: CPRI work group on codes and structures. J Am Med Inform Assoc. 1997. 4:238–251.

24. Kim HJ, McGuire DB, Tulman L, Barsevick AM. Symptom clusters: concept analysis and clinical implications for cancer nursing. Cancer Nurs. 2005. 28:270–282.

25. Armstrong TS. Symptoms experience: a concept analysis. Oncol Nurs Forum. 2003. 30:601–606.

26. Doyle N. Cancer survivorship: evolutionary concept analysis. J Adv Nurs. 2008. 62:499–509.

27. Ridner SH. Psychological distress: concept analysis. J Adv Nurs. 2004. 45:536–545.

28. Fochtman D. The concept of suffering in children and adolescents with cancer. J Pediatr Oncol Nurs. 2006. 23:92–102.

29. Sun Y, Knobf MT. Concept analysis of symptom disclosure in the context of cancer. ANS Adv Nurs Sci. 2008. 31:332–341.

30. Wen KY, Gustafson DH. Needs assessment for cancer patients and their families. Health Qual Life Outcomes. 2004. 2:11.