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Journal of Medical Systems

, Volume 36, Issue 3, pp 1389–1401 | Cite as

Determining Patient Preferences for Remote Monitoring

  • Nuri Basoglu
  • Tugrul U. DaimEmail author
  • Umit Topacan
ORIGINAL PAPER

Abstract

This paper presents the patient preferences for an application in remote health monitoring. The data was collected through a mobile service prototype. Analytical Hierarchy Process and Conjoint Analysis were used to extract the patient preferences. The study was limited to diabetes and obesity patients in Istanbul, Turkey. Results indicated that sending users’ data automatically, availability of technical support, and price are key factors impacting patient’s decisions. This implies that e-health service providers and designers should focus on the services that enable users to send measurement results automatically instead of manually.

Keywords

Wireless Information technology Remote health monitoring Analytical hierarchy process Conjoint analysis Diabetes and obesity Turkey 

References

  1. 1.
    Ad, S., Barriers to accepting e-prescribing in the U.S.A. Int. J. Health Care Qual. Assur. Inc. Leadersh. Health Serv. 19:158–180, 2006.Google Scholar
  2. 2.
    Al-Qirim, N., Championing telemedicine adoption and utilization in healthcare organizations in New Zealand. Int. J. Med. Inform. 76:42–54, 2007.CrossRefGoogle Scholar
  3. 3.
    Aubert, B. A., and Hamel, G., Adoption of smart cards in the medical sector: the Canadian experience. Soc. Sci. Med. 53:879–894, 2001.CrossRefGoogle Scholar
  4. 4.
    Basoglu, N., Daim, T., Atesok, H. C., and Pamuk, M., Exploring Health Information Seeking Behaviour. Int. J. Bus. Inf. Syst. 5(3):291–308, 2010.Google Scholar
  5. 5.
    Basoglu, N., Daim, T., and Kerimoglu, O., Organizational adoption of enterprise resource planning systems: A conceptual framework. J. High Technol. Manage. Res. 18(1):73–97, 2007.CrossRefGoogle Scholar
  6. 6.
    Basoglu, N., Tanoglu, I., and Daim, T., Innovation diffusion: Case of IT products. Int. J. Inf. Technol. Decis. Mak. 9(2):195–222, 2010.zbMATHCrossRefGoogle Scholar
  7. 7.
    Berghout, R. M., Eminovic, N., Keizer, N. F., and Birnie, E., Evaluation of general practitioner’s time investment during a store-and-forward teledermatology consultation. Int. J. Med. Inform. 76:384–391, 2007.CrossRefGoogle Scholar
  8. 8.
    Bruner, G. C., and Kumar, A., Explaining consumer acceptance of handheld Internet devices. J. Bus. Res. 58:553–558, 2005.CrossRefGoogle Scholar
  9. 9.
    Chae, Y. M., Lee, J. H., Ho, S. H., Kim, H. J., Jun, K. H., and Won, J. U., Patient satisfaction with telemedicine in home health services for the elderly. Int. J. Med. Inform. 61:167–173, 2001.CrossRefGoogle Scholar
  10. 10.
    Chang, I. C., Hwang, H. G., Hung, M. C., Lin, M. H., and Yen, D. C., Factors affecting the adoption of electronic signature: Executives’ perspective of hospital information department. Decis. Support Syst. 44(1):350–359, 2007.CrossRefGoogle Scholar
  11. 11.
    Chau, P. Y., and Hu, P. J., Investigating healthcare professionals' decisions to accept telemedicine technlogy:an emprical test of competing theories. Inf. Manage. 39:297–311, 2002.CrossRefGoogle Scholar
  12. 12.
    Chen, C., Chang, R., Hung, M., and Lin, M., Assessing the quality of a web-based learning system for nurses. J. Med. Syst. 33:317–325, 2009.CrossRefGoogle Scholar
  13. 13.
    Chen, I. J., Yang, K. F., and Tang, F. I., Applying the technology acceptance model to explore public health nurses' intentions towards web-based learning: A cross-sectional questionnaire survey. Int. J. Nurs. Stud. 45(6):869–878, 2006.CrossRefGoogle Scholar
  14. 14.
    Cusack, C. M., Pan, E., Hook, J. M., Vincent, A., Kaelber, D. C., and Middleton, B., The value proposition in the widespread use of telehealth. J. Telemed. Telecare 14:167–168, 2008.CrossRefGoogle Scholar
  15. 15.
    Daim, T., Abu Taha, R., and Gu, Q., Comparing personal and organizational preferences in the acquisition of information technologies. Int. J. Decis. Sci. Risk Manage. 1(1–2):142–160, 2009.Google Scholar
  16. 16.
    Daim, T., and Intarode, N., Assessment & acquisition of green technology: Case of a Thai building material company. Energy Sustain. Dev. 13(4):280–286, 2009.CrossRefGoogle Scholar
  17. 17.
    Daim, T., Schweinfort, W., and Kayakutlu, G., Technology assessment of two energy portfolios using a hierarchical decision model. Int. J. Energy Sect. Manage. 4(1):24–43, 2010.CrossRefGoogle Scholar
  18. 18.
    Daim, T., Yates, D., Peng, Y., and Jimenez, B., Technology assessment for clean energy technologies. Technol. Soc. 31(3):232–243, 2009.CrossRefGoogle Scholar
  19. 19.
    Davis, F. D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3):319–340, 1989.CrossRefGoogle Scholar
  20. 20.
    Demiris, G., Speedie, S., and Hicks, L. L., Assessment of patients’ acceptance of and satisfaction with teledermatology. J. Med. Syst. 28(6):575–579, 2004.CrossRefGoogle Scholar
  21. 21.
    Dishaw, M. T., and Strong, D. M., Extending the technology acceptance model with task-technology fit constructs. Inf. Manage. 36:9–21, 1999.CrossRefGoogle Scholar
  22. 22.
    Dixon, D. R., The Behavioral side of information technology. Int. J. Med. Inform. 56:117–123, 1999.CrossRefGoogle Scholar
  23. 23.
    Edvardsson, B., and Olsson, J., Key concepts for new service development. he Serv. Ind. J. 16(2):140–164, 1995.CrossRefGoogle Scholar
  24. 24.
    Essen, A., and Conrick, M., New e-service development in the homecare sector: Beyond implementation a radical technology. Int. J. Med. Inform. 77:679–688, 2008.CrossRefGoogle Scholar
  25. 25.
    Gagnon, M. P., Godin, G., Gagne, C., Fortin, J. P., Lamothe, L., Reinhar, D., et al., An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians. Int. J. Med. Inform. 71:103–115, 2003.CrossRefGoogle Scholar
  26. 26.
    Goodhue, D. L., Understanding user evaluations of information systems. Manage. Sci. 19(4):561–570, 1995.Google Scholar
  27. 27.
    Green, P. E., and Srinivasan, V., Conjoint analysis in consumer research: issues and outlook. J. Consum. Res. 5:103–123, 1978.CrossRefGoogle Scholar
  28. 28.
    Gururajan, R., Factors influencing the intention to use wireless technology in health care: a study in India. J. Telemed. Telecare 13:40–41, 2007.CrossRefGoogle Scholar
  29. 29.
    Hailey, D., Jacobs, P., Simpson, J., and Doze, S., An assessment framework for telemedicine applications. J. Telemed. Telecare 5:162–170, 2010.CrossRefGoogle Scholar
  30. 30.
    Hallum, D., and Daim, T., A hierarchical decision model for optimum design alternative selection. Int. J. Decis. Sci. Risk Manage. 1(1-2):2–23, 2009.Google Scholar
  31. 31.
    Harrel, G., and Daim, T., HDM modeling as a tool to assist management with employee motivation. Eng. Manage. J. 22(4):74–85, 2010.Google Scholar
  32. 32.
    Kahen, G., and Sayers, B. M., Health-care technology transfer: Expert and information systems for developing countries. Meth. Inf. Med. 36:69–78, 1997.Google Scholar
  33. 33.
    Karahanna, E., Straub, D. W., and Chervany, N. L., Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly 23(2):183–213, 1999.CrossRefGoogle Scholar
  34. 34.
    Kargin, B., Basoglu, N., and Daim, T., Adoption factors of mobile services. Int. J. Inf. Syst. Serv. Sect. 1(1):15–34, 2009.CrossRefGoogle Scholar
  35. 35.
    Kargin, B., Basoglu, N., and Daim, T., Factors affecting the adoption of mobile services. Int. J. Serv. Sci. 1(2):29–52, 2009.Google Scholar
  36. 36.
    Kerimoglu, O., Basoglu, N., and Daim, T., Organizational adoption of information technologies: Case of enterprise resource planning systems. J. High Technol. Manage. Res. 19(1):21–35, 2008.CrossRefGoogle Scholar
  37. 37.
    Kifle, M., Mbarika, V. W., and Datta, P., Interplay of cost and adoption of tele-medicine in Sub-Saharan Africa: The case of tele-cardiology in Ethiopia. Inf Syst Front 8:211–223, 2006.CrossRefGoogle Scholar
  38. 38.
    Kilic, M., Arslan, M., Leblebici, D. N., Aydin, M. D., and Oktem, M. K., Managerial control Vs professional autonomy: An empirical study on perceptions and xpectations of physicians at teaching hospitals in Turkey. J. Med. Syst. 32(2):157–165, 2008.CrossRefGoogle Scholar
  39. 39.
    Kim, D., and Chang, H., Key functional characteristics in designing and operating health information websites for user satisfaction: An application of the extended technology acceptance model. Int. J. Med. Inform. 76:790–800, 2007.CrossRefGoogle Scholar
  40. 40.
    King, G., Richards, H., and Godden, D., Adoption of telemedicine in Scottish remote and rural general practices: a qualitative study. J. Telemed. Telecare 13:382–386, 2007.CrossRefGoogle Scholar
  41. 41.
    Kuziemsky, C. E., Laul, F., and Leung, R. C., A review on diffusion of personal digital assistants in healthcare. J. Med. Syst. 29(4):335–342, 2005.CrossRefGoogle Scholar
  42. 42.
    Kwak, N. K., McCarthy, K. J., and Parker, G. E., A human resource planning model for hospital/medical technologists: An analytic hierarchy process approach. J. Med. Syst. 21(3):173–187, 1997.CrossRefGoogle Scholar
  43. 43.
    Lee, H. J., Lee, S. H., Ha, K. S., Jang, H. C., Chung, W. Y., Kim, J. Y., et al., Ubiquitous healthcare service using Zigbee and mobile phone for elderly patients. Int. J. Med. Inform. 78(3):193–198, 2009.CrossRefGoogle Scholar
  44. 44.
    Lemire, M., Sicotte, G. P., and Harvey, C., Determinants of Internet use as a preferred source of information on personal health. Int. J. Med. Inform. 77(11):723–734, 2008.CrossRefGoogle Scholar
  45. 45.
    Liu, L., and Ma, Q., The impact of service level on the acceptance of application service oriented medical records. Inf. Manage. 42:1121–1135, 2005.CrossRefGoogle Scholar
  46. 46.
    Liu, L., and Ma, Q., Perceived system performance: A test of an extended technology acceptance model. Database Advences Inf. Syst. 37:51–59, 2006.CrossRefGoogle Scholar
  47. 47.
    Mathieson, K., Peacock, E., and Chin, W. W., Extending the technology acceptance model: The influence of perceived user resources. Databe Base Adv. Inf. Syst. 32(3):86–112, 2001.CrossRefGoogle Scholar
  48. 48.
    Moorman, C., and Matulich, E., A model of consumers’ preventive health behaviors: The role of health motivation and health ability. J. Consum. Res. 20:208–228, 1993.CrossRefGoogle Scholar
  49. 49.
    Pare, G., Sicotte, C., and Jacques, H., The effects of creating psychological ownership on physicians’ acceptance of clinical information systems. J. Am. Med. Inform. Assoc. 13(2):197–205, 2006.CrossRefGoogle Scholar
  50. 50.
    Peterson LT, Ford EW, Eberhardt J, Huerta TR & Menachemi N (2009) Assessing differences between physicians’ realized and anticipated gains from electronic health record adoption. Journal of Medical Systems, in printGoogle Scholar
  51. 51.
    Phan, K., Daim, T., Basoglu, N., and Kargin, B., Comparing factors affecting the adoption of mobile services in developed versus developing countries: Case of Turkey versus United States. Int. J. Serv. Sci. 3(2/3):216–231, 2010.Google Scholar
  52. 52.
    Ramsay, J., Barbesi, A., and Preece, J., A psychological investigation of long retrieval times on the World Wide Web. Interact. Comput. 10(1):77–86, 1998.CrossRefGoogle Scholar
  53. 53.
    Saaty, T. L., A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15:234–281, 1977.MathSciNetzbMATHCrossRefGoogle Scholar
  54. 54.
    Saaty, T. L., The analytic hierarchy process. RWS Publications, Pittsburgh, 1996.Google Scholar
  55. 55.
    Schaper, L. K., and Pervan, G. P., ICT and OTs: A model of information and communication technology acceptance and utilisation by occupational therapists. Int. J. Med. Inform. 76:212–221, 2007.CrossRefGoogle Scholar
  56. 56.
    Schaupp, C. L., and Bélanger, F., A conjoint analysis of online customer satisfaction. J. Electron. Commer. Res. 6(2):96–111, 2005.Google Scholar
  57. 57.
    Scheuing, E. E., and Johnson, E. M., A proposed model for new service development. J. Serv. Mark. 3(2):25–34, 1989.CrossRefGoogle Scholar
  58. 58.
    Sears, A., Jacko, J. A., and Dubach, E. M., International aspects of World Wide Web usability and the role of high-end graphical enhancements. Int. J. Hum. Comput. Interact. 12(2):241–261, 2000.CrossRefGoogle Scholar
  59. 59.
    Seneler, C. O., Basoglu, N., and Daim, T., Exploring the contribution of information systems user interface design characteristics to adoption process. Int. J. Bus. Inf. Syst. 4(5):489–508, 2009.Google Scholar
  60. 60.
    Seneler, C. O., Basoglu, N., and Daim, T., Interface feature prioritization for web services: Case of online flight reservations. Comput. Hum. Behav. 25:862–877, 2009.CrossRefGoogle Scholar
  61. 61.
    Shi, H. J., Nakamura, K., and Takano, T., Health values and health-information-seeking in relation to positive change of health practice among middle-aged urban men. Prev. Med. 39:1164–1171, 2004.CrossRefGoogle Scholar
  62. 62.
    Simon, J. S., Rundall, T. G., and Shortell, S. M., Adoption of order entry with decision support for chronic care by physician organizations. J. Am. Med. Inform. Assoc. 14(4):432–439, 2007.CrossRefGoogle Scholar
  63. 63.
    Spaulding, R. J., Russo, T., Cook, D. J., and Doolittle, G. C., Diffusion theory and telemedicine adoption by Kansas health-care providers: Critical factors in telemedicine adoption for improved patient access. J. Telemed. Telecare 11:107–109, 2005.CrossRefGoogle Scholar
  64. 64.
    Tam, M. C., and Tummala, V. M., An application of the AHP in vendor selection of a telecommunications system. Int. J. Manage. Sci. 29:171–182, 2001.Google Scholar
  65. 65.
    Taylor, S., and Todd, P., Assessing IT usage: The role of prior experience. MIS Quarterly 19(4):561–570, 1995.CrossRefGoogle Scholar
  66. 66.
    Thompson, R. L., Higgins, C. A., and Howell, J. M., Personal computing: Toward a conceptual model of utilization. MIS Quarterly 15(1):125–143, 1991.CrossRefGoogle Scholar
  67. 67.
    Topacan, U., Basoglu, N., and Daim, T. U., Exploring health information service adoption. Int. J. Inf. Syst. Serv. Sect. 2(1):71–93, 2010.CrossRefGoogle Scholar
  68. 68.
    Topacan, U., Basoglu, N., and Daim, T., AHP Application on evaluation of health information service attributes, technology management in the age of fundamental change, Portland International Conference on Management of Engineering and Technology Proceedings, Portland, Oregon, pp 486–493, 2009.Google Scholar
  69. 69.
    Topacan, U., Basoglu, N., and Daim, T., Evaluating health information services: A patient perspective analysis, Developments in Healthcare Information Systems and Technologies by J Tan, IGI Global, Hershey New York, pp 1–13, 2011.Google Scholar
  70. 70.
    Tung, F. C., and Chang, S. C., A new hybrid model for exploring the adoption of online nursing courses. Nurse Educ. Today 28:293–300, 2008.CrossRefGoogle Scholar
  71. 71.
    Tung, F. C., Chang, S. C., and Chou, C. M., An extension of trust and TAM model with IDT in the adoption of the electronic logistic information system in HIS in the medical industry. Int. J. Med. Inform. 77(5):324–335, 2008.CrossRefGoogle Scholar
  72. 72.
    Turi, J. J., Program eases decision making. Health Prog. 69:40–44, 1988.Google Scholar
  73. 73.
    Vaidya, O. S., and Kumar, S., Analytic hierarchy process: An overview of applications. Eur. J. Oper. Res. 169:1–29, 2006.MathSciNetzbMATHCrossRefGoogle Scholar
  74. 74.
    van den Brink, J. L., Moorman, P. W., de Boer, M. F., Pruyn, J. F., Verwoerd, C. D., and van Bemmel, J. H., Involving the patient: A prospective study on use, appreciation and effectiveness of an information system in head and neck cancer care. Int. J. Med. Inform. 74:839–849, 2005.CrossRefGoogle Scholar
  75. 75.
    Venkatesh, V., and Davis, F. D., A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manage. Sci. 46(2):186–204, 2000.CrossRefGoogle Scholar
  76. 76.
    Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D., User acceptance of information technology: Toward a unified view. MIS Quarterly 27(3):425–478, 2003.Google Scholar
  77. 77.
    Vuononvirta, T., Timonen, M., Keinänen-Kiukaanniemi, S., Timonen, O., Ylitalo, K., Kanste, O., and Taanila, A., The attitudes of multiprofessional teams to telehealth adoption in northern Finland health centres. J. Telemed. Telecare 15:290–296, 2009.CrossRefGoogle Scholar
  78. 78.
    WHO. (2009). http://apps.who.int/bmi/index.jsp?introPage = intro_3.html. Retrieved 05 30, 2009, from World Health Organization: http://www.who.int
  79. 79.
    Wilson, V. E., and Lankton, N. K., Modeling patients’ acceptance of provider-delivered E-health. J. Am. Med. Inform. Assoc. 11(4):241–248, 2004.CrossRefGoogle Scholar
  80. 80.
    Yu, P., Li, H., and Gagnon, M. P., Health IT acceptance factors in long-term care facilities: A cross-sectional survey. Int. J. Med. Inform. 78(4):219–229, 2009.CrossRefGoogle Scholar
  81. 81.
    Yusof, M. M., Kuljis, J., Papazafeiropoulou, A., and Stergioulas, L. K., An evaluation framework for Health Information Systems: human, organization, and technology-fit factors (HOT-fit). Int. J. Med. Inform. 77(6):386–398, 2007.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Bogazici UniversityIstanbulTurkey
  2. 2.Portland State UniversityPortlandUSA

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