A task-fit measure of health information technology use
Abstract
Rationale, aims and objectives: Introducing profession concerns into the evaluation of health information technology (HIT) use is an important and developing practice. A comprehensive evaluation should include the intellect elements of HIT use. This paper proposes a task-fit measure of HIT that integrates an information/knowledge quality scale into a validated judicious HIT use measure. It also presents some statistics that have implications for policy-making and curriculum development.
Methods: Statistical analyses were performed on a subset of survey data. A structural equation modelling technique was applied to examine the associations among intent to use HIT, professional concerns and information/knowledge quality.
Results: The statistical results show that altruism, autonomy, physician-patient relationship, (subconscious) autonomy, digestible information and medical history associate with each other to different extents. Only altruism and medical history show to be significant determinants of intent to use at P<0.001 and P<0.05 respectively. The scaled χ2 difference test shows that this model is not significantly different from the judicious HIT use model.
Conclusion: The statistical results suggest that professional concerns, digestible information and person-related information are HIT use decision factors. Perhaps physicians may prefer HITs considered to be compatible with practising the science, humanism and ethics of medicine simultaneously. This research direction will potentially contribute to identifying the task-fit HITs and the corresponding policies for re-orientating medicine to be a science-using and compassionate practice in this eHealth era, thereby promoting the development of person-centered healthcare.Keywords
Full Text:
PDFReferences
Hartzband, P. & Groopman, J. (2009). Keeping the patient in the equation - humanism and health care reform, New England Journal of Medicine 361 (6) 554-555.
Miles, A. & Loughlin, M. (2011). Models in the balance: evidence-based medicine versus evidence-informed individualised care. Journal of Evaluation in Clinical Practice 17 (4) 531-536.
Miles, A. & Mezzich, J.E. (2011). The care of the patient and the soul of the clinic: person-centered medicine as an emergent model of modern clinical practice. International Journal of Person Centered Medicine 1 (2) 207-222.
Miles, A. (2012). Person-centered medicine - at the intersection of science, ethics and humanism, International Journal of Person Centered Medicine 2 (3) 329-333.
Holmes, D.R., Jr. (2011). President’s page: Too much of a good thing, Journal of the American College of Cardiology 57 (18) 1857-1858.
Feblowitz, J.C., Wright, A., Singh, H., Samal, L. & Sittig, D.F. (2011). Summarization of clinical information: A conceptual model. Journal of Biomedical Informatics 44 (4) 688-699.
O’Grady, L. (2012). What is knowledge and when should it be implemented? Journal of Evaluation in Clinical Practice 18 (5) 951-953.
Goodhue, D.L. & Thompson, R. (1995). Task-technology fit and individual performance. MIS Quarterly 19 (2) 213-236.
Tsang, S. (2012). Quantifying judicious use of health information technology. Journal of Evaluation in Clinical Practice doi:10.1111/j.1365-2753.2012.01842.x.
Tsang, S. (2013). Health information technology use decisions of altruistic physicians, International Journal of Person Centered Medicine 3 (2) xxx-xxx .
Stempsey, W.E. (2009). Clinical reasoning: New challenges. Theoretical Medicine and Bioethics 30, 173-179.
Keller, K.L. & Staelin, R. (1987). Effects of quality and quantity of information on decision effectiveness. Journal of Consumer Research 14 (2) 200-213.
eHealth Initiative. (2006). Improving the quality of healthcare through health information exchange. selected findings from eHealth initiative’s third annual survey of health information exchange activities at the state, regional and local levels. 25 September. http://www.cdpublications.com/etc2/docfiles/chf/eHI2006HIESurveyReportFinal09.25.06.pdf (last accessed 10 December 2012).
European Communities(EC). (2007). eHealth priorities and strategies in European countries. eHealth ERA Report Brussels, March: http://ec.europa.eu/information_ society/activities/health/docs/policy/ehealth-era-full-report.pdf (last accessed 10 December 2012).
Wilson, T.D. (2002) The nonsense of ‘knowledge management’ Information Research 8 (1). http://informationr.net/ir/8-1/paper144.html (last accessed 10 December 2012).
Sivo, S.A., Saunders, C., Chang, Q. & Jiang, J.J. (2006). How low should you go? Low response rates and the validity of inference in is questionnaire research. Journal of the Association for Information Systems 7 (6) 351-414.
Aydin, C.E. (2005). Survey methods for assessing social impacts of computers in healthcare organizations. In: Evaluating the Organizational Impact of Health Care Information Systems, 2nd edn. (J. Anderson & C. Aydin eds.), pp. 75-128. New York: Springer Verlag.
Petter, S., DeLone, W. & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships, European Journal of Information Systems 17 (3) 236-263.
Zmud, R.W. & Boynton, A. (1991). Survey measures and instruments in MIS: Inventory and appraisal. In: The Information Systems Research Challenge: Survey Research Methods, Vol. 3 (K. L. Kraemer, ed.), pp. 187-195. Boston, MA: Harvard Business School Press.
Office of Technology Assessment (OTA). (1995). Bringing health care online: The role of information technologies. Washington, DC: US Government Printing Office.
Taylor, P. (2006). From patient data to medical knowledge: The principles and practice of health informatics. BMJ Books. Oxford: Blackwell Publishing.
Greenhalgh, T. & Hurwitz, B. (1999). Narrative based medicine: Why study narrative? British Medical Journal 318, 48-50.
van der Lei, J. & van Bemmel, J. (2002). Diagnostic decision support: contributions from medical informatics. In: The Evidence Base of Clinical Diagnosis. (J.A. Knottnerus, ed.), pp. 167-177. London: BMJ Books.
Sharma, S.K., Wickramasinghe, N. & Gupta, J.N.D. (2009). Knowledge management in healthcare In: Medical Informatics: Concepts, Methodologies, Tools, and Applications. (J. Tan, ed.), pp. 186-197. Hershey, PA: Idea Group Publishing.
DeLone, W.H. & McLean, E.R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems 19 (4) 9-30.
Raghavan, S. (2009). Medical decision support systems and knowledge sharing standards In: Medical Informatics: Concepts, Methodologies, Tools, and Applications. (J. Tan, ed.), pp. 276-293. Hershey, PA: Idea Group Publishing.
de Leeuw, E.D., Hox, J. & Huisman, M. (2003). Prevention and treatment of item nonresponse. Journal of Official Statistics 19 (2) 153-176.
R Development Core Team. (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. http://www.R-project.org (last accessed 10 December 2012).
Revelle, W. (2011). psych: procedures for psychological, psychometric, and personality research. Northwestern University. Evanston, Illinois, R package version 1.1.12 edn. http://personality-project.org/r/psych.manual.pdf (last accessed 10 December 2012).
Rosseel, Y. (with contributions from those listed in the lavaan website). (2012). Lavaan: Latent variable analysis. http://CRAN.R-project.org/package=lavaan (last accessed 10 December 2012).
van Buuren, S. & Groothuis-Oudshoorn, K. (2011). MICE: Multivariate imputation by chained equations in R. Journal of Statistical Software 45 (3) 1-67.
Horton, N.J. & Lipsitz, S.R. (2001). Multiple imputation in practice: Comparison of software packages for regression models with missing variables. The American Statistician 55 (3) 244-254.
Loehlin, J.C. (2004). Latent variable models: An introduction to factor, path and structural equation analysis, 4th edn. Mahwah, NJ: Lawrence Erlbaum Associates.
Hair, F.J., Jr., Anderson, R.E., Tatham, R.L. & Black, W.C. (1998). Multivariate data analysis, 5th edn. Upper Saddle River, NJ: Prentice Hall.
Falk, R.F. & Miller, N.B. (1992). A primer for soft modeling. Akron, OH: University of Akron Press.
Straub, D., Boudreau, M-C. & Gefen, D. (2004). Validation guidelines for IS positivist research, Communications of the Association for Information Systems 13, 380-427.
Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika 16 (3) 297-334.
Nunnally, J. (1968). Psychometric theory, 2nd edn. New York: McGraw-Hill.
Nunnally, J. & Bernstein, I. (1994). Psychometric theory. New York: McGraw-Hill.
Satorra, A. & Bentler, P.M. (1988). Scaling corrections for chi-square statistics in covariance structure analysis. In: ASA 1988 Proceedings of the Business and Economic Statistics Section, Vol. 1 pp. 308-313. Alexandria, VA: American Statistical Association.
Curran, P.J., West, S.G. & Finch, J.F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods 1 (1) 16-29.
Bentler, P.M. (2010). Sem with simplicity and accuracy. Journal of Consumer Psychology 20 (2) 215-220.
Rafaeli, A. (1986). Employee attitudes toward working with computers. Journal of Organizational Behavior 7 (2) 89-106.
Segars, A.H. (1997). Assessing the unidimensionality of measurement: A paradigm and illustration within the context of information system research. Omega 25 (1) 107-121.
Bagozzi, R.P., Yi, Y. & Phillips, L.W. (1991). Assessing construct validity in organizational research. Adminstrative Science Quarterly 36 (3) 421-458.
Herzog, W. & Boomsma, A. (2009). Small-sample robust estimators of noncentrality-based and incremental model fit. Structural Equation Modeling 16 (1) 1-27.
Hu, L. & Bentler, P. (1999). Cutoff criterion for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling 6 (1) 1-55.
Steiger, J.H. (1989). EzPATH: Causal modeling. Evanston, IL: SYSTAT Inc.
Loughlin, M., Bluhm, R., Buetow, S., Upshur, R.E.G., Goldenberg, M.J., Borgerson, K., Entwistle, V. & Kingma, E. (2012). Reason and value: making reasoning fit for practice. Journal of Evaluation in Clinical Practice 18 (5) 929-937.
Rutkowski, A-F. & Saunders, C.S. (2010). Growing pains with information overload. Computer 43 (6) 96-95.
DOI: http://dx.doi.org/10.5750/ejpch.v1i2.683
Refbacks
- There are currently no refbacks.