Acceptance Measure of Quality Improvement Information System among Long-term Care Workers: A Psychometric Assessment

Taehoon Lee; Young-il Jung; Hongsoo Kim

doi:10.12799/jkachn.2017.28.4.513

Journal List > J Korean Acad Community Health Nurs > v.28(4) > 1058494

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Lee, Jung, and Kim: Acceptance Measure of Quality Improvement Information System among Long-term Care Workers: A Psychometric Assessment

Original Article

J Korean Acad Community Health Nurs 2017;28(4):513-523.

Published online: 12 December 2017

DOI: https://doi.org/10.12799/jkachn.2017.28.4.513

Acceptance Measure of Quality Improvement Information System among Long-term Care Workers: A Psychometric Assessment

Taehoon Lee¹, Young-il Jung², Hongsoo Kim³

¹Department of Health Science, Seoul National University Graduate School of Public Health, Seoul, Korea

²Seoul National University Institute of Health and Environment, Seoul, Korea

³Department of Health Science, Seoul National University Graduate School of Public Health · Seoul National University Institute of Health and Environment · Seoul National University Institute of Aging, Seoul, Korea

Corresponding author: Kim, Hongsoo Department of Health Science, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea. Tel: +82-2-880-2723, Fax: +82-2-762-9105, E-mail: hk65@snu.ac.kr

Received 4 August 2017 Revised 29 November 2017 Accepted 29 November 2017

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose

We evaluated the psychometric properties of a questionnaire on the acceptance of the quality improvement information system (QIIS) among long-term care workers (mostly nurses).

Methods

The questionnaire composes of 21 preliminary questions with 5 domains based on the Technology Acceptance Model and related literature reviews. We developed a prototype web-based comprehensive resident assessment system, and collected data from 126 subjects at 75 long-term care facilities and hospitals, who used the system and responded to the questionnaire. A priori factor structure was developed using an exploratory factor analysis and validated by a confirmatory factor analysis; its reliability was also evaluated.

Results

A total of 16 items were yielded, and 5 factors were extracted from the explanatory factor analysis: Usage Intention, Perceived Usefulness, Perceived Ease of Use, Social Influence, and Innovative Characteristics. The five-factor structure model had a good fit (Tucker-Lewis index [TLI]=.976; comparative fit index [CFI]=.969; standardized root mean squared residual [SRMR]=.052; root mean square error of approximation [RMSEA]=.048), and the items were internally consistent(Cronbach’s ⍺=.91).

Conclusion

The questionnaire was valid and reliable to measure the technology acceptance of QIIS among long-term care workers, using the prototype.

Keywords: Quality improvement, Health information systems, Psychometrics, Long-term care

References

1. Organization for Economic Cooperation and Development. A good life in old age?: Monitoring and improving quality in long-term care. Paris: OECD Publishing;2013. p. 268. https://doi.org/10.1787/9789264194564-en.

2. Bezboruah KC, Paulson D, Smith J. Management attitudes and technology adoption in long-term care facilities. Journal of Health Organization and Management. 2014; 28(3):344–365. https://doi.org/10.1108/JHOM-11-2011-0118.

3. Ahn JH, Yi SH. Factors associated with usage intention of smart technology in long-term care facilities: Based on the technology acceptance model(TAM & TAM2). Korean Journal of Gerontological Social Welfare. 2015; 68:357–387.

4. Berg M. Implementing information systems in health care organizations: Myths and challenges. International Journal of Medical Informatics. 2001; 64(2-3):143–156. https://doi.org/10.1016/S1386-5056(01)00200-3.

5. Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science. 2000; 46(2):186–204. https://doi.org/10.1287/mnsc.46.2.186.11926.

6. Hudak S, Sharkey S. Health information technology: Are long term care providers ready? [Internet]. Oakland (CA): California Health Care Foundation (US);2007. [cited 2017 August 15]. Available from:. http://www.chcf.org/publications/2007/04/health-information-technology-are-long-term-care-providers-ready.

7. Yu P, Li H, Gagnon MP. Health IT acceptance factors in longterm care facilities: A cross-sectional survey. International Journal of Medical Informatics. 2009; 78(4):219–229. https://doi.org/10.1016/j.ijmedinf.2008.07.006.

8. Chang P, Hsu CL, Liou Y, Kuo YY, Lan CF. Design and development of interface design principles for complex documentation using PDAs. Computers Informatics Nursing. 2011; 29(3):174–183. https://doi.org/10.1097/NCN.0b013e3181f9db8c.

9. Vanneste D, Vermeulen B, Declercq A. Healthcare professionals' acceptance of BelRAI, a web-based system enabling person-centred recording and data sharing across care settings with interRAI instruments: A UTAUT analysis. BioMedCentral Medical Informatics and Decision Making. 2013; 13(1):129–142. https://doi.org/10.1186/1472-6947-13-129.

10. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly. 1989; 13(3):319–340. https://doi.org/10.2307/249008.

11. Lee EJ, Seo YJ, Kim YH, Oh JY. Determinants of the intent to use a wireless technology of a university hospital nurses. Korean Journal of Health Policy & Administration. 2010; 20(3):58–72. https://doi.org/10.4332/KJHPA.2010.20.3.058.

12. Ko IS, Chang HJ. Development of extended technology acceptance model on the intention of using PHR. Journal of The Korean Society of Health Informatics and Statistics. 2013; 38(1):26–38.

13. Holden RJ, Karsh BT. The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics. 2010; 43(1):159–172. https://doi.org/10.1016/j.jbi.2009.07.002.

14. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: A comparison of two theoretical models. Management Science. 1989; 35(8):982–1003.

15. You JH, Park C. A comprehensive review of technology acceptance model researches. Entrue Journal of Information Technology. 2010; 9(2):31–50.

16. Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decision Sciences. 2008; 39(2):273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x.

17. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly. 2003; 27(3):425–478. https://doi.org/10.2307/30036540.

18. Schnall R, Bakken S. Testing the technology acceptance model: HIV case managers' intention to use a continuity of care record with context-specific links. Informatics for Health and Social Care. 2011; 36(3):161–172. https://doi.org/10.3109/17538157.2011.584998.

19. Lee CW, Jang SH. U-healthcare service adaptation in long-term care hospitals-focused on TAM. Productivity Review. 2010; 24(4):305–332.

20. Yen PY, Sousa KH, Bakken S. Examining construct and predictive validity of the Health-IT usability evaluation scale: Confirmatory factor analysis and structural equation modeling results. Journal of the American Medical Informatics Association. 2014; 21(e2):e241–e248. https://doi.org/10.1136/amiajnl-2013-001811.

21. June KJ, Kim EY. Development of a database system for home care service based on RAI (Resident Assessment Instrument). The Journal of Korean Community Nursing. 2003; 14(1):75–82.

22. Gray LC, Berg K, Fries BE, Henrard JC, Hirdes JP, Steel K, et al. Sharing clinical information across care settings: The birth of an integrated assessment system. BioMedCentral Health Services Research. 2009; 9(1):71–80. https://doi.org/10.1186/1472-6963-9-71.

23. Kim HS, Jung YI. A review of studies of comprehensive geriatric assessment using RAI-FC or RAI-HC in South Korea. Journal of Korean Gerontological Nursing. 2012; 14(1):58–68.

24. Hatcher L. A step-by-step approach to using the SAS® system for factor analysis and structural equation modeling. Cary, NC: SAS Institute;1994. p. 608.

25. Agarwal R, Prasad J. The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences. 1997; 28(3):557–582. https://doi.org/10.1111/j.1540-5915.1997.tb01322.x.

26. Agarwal R, Prasad J. Are individual differences germane to the acceptance of new information technologies? Decision Sciences. 1999; 30(2):361–391. https://doi.org/10.1111/j.1540-5915.1999.tb01614.x.

27. Chau PY, Hu PJ. Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of Management Information Systems. 2002; 18(4):191–229. https://doi.org/10.1080/07421222.2002.11045699.

28. Suh CK, Seong SJ. Individual characteristics affecting user's intention to use internet shopping mall. Asia Pacific Journal of Information Systems. 2004; 14(3):1–22.

29. Hong SH. The criteria for selecting appropriate fit indices in structural equation modeling and their rationales. Korean Journal of Clinical Psychology. 2000; 19(1):161–177.

30. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling. 1999; 6(1):1–55. https://doi.org/10.1080/10705519909540118.

Figure 1.

A snapshot of the prototype web-based comprehensive resident assessment system used in this study.

Figure 2.

Measurement model.

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