Assessing Strategic Readiness for Healthcare Analytics: System and Design Theory Implications

  • Sathyanarayanan Venkatraman
  • Rangaraja P. Sundarraj
  • Ravi Seethamraju
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10844)


The adoption of analytics solutions in hospitals is a recent trend aimed at fact-based decision making and data-driven performance management. However, the adoption of analytics involves diverse stakeholder perspectives. Currently, there is a paucity of studies that focus on how the practitioners assess their organizational readiness for health analytics (HA) and make informed decisions on technology adoption given a set of alternatives. We fill this gap with our study by designing a strategic assessment framework guided by a DSRM approach that iteratively extends our past artifact. Our approach first entails the use of many in-depth case-studies, as well as embedded experts from the industry to inform the objective setting and design process. These inputs are then supported by two multi-criteria decision-making methods. We also evaluate our framework with healthcare practitioners for both design validity and future iterations of this project. Implications of our work for theory of design and action are also highlighted.


DSRM IPA DEMETAL Health-Analytics Theory for design and action 


  1. 1.
    Groves, P., Kayyali, B., Knott, D., Van Kuiken, S.: The “big data”revolution in healthcare. McKinsey Q. 22 (2013)Google Scholar
  2. 2.
    Ammenwerth, E., Brender, J., Nykänen, P., Prokosch, H.U., Rigby, M., Talmon, J.: Visions and strategies to improve evaluation of health information systems: reflections and lessons based on the HIS-EVAL workshop in Innsbruck. Int. J. Med. Inform. 73, 479–491 (2004)CrossRefGoogle Scholar
  3. 3.
    Raghupathi, W., Tan, J.: Information systems and healthcare: charting a strategic path for health information technology. Commun. Assoc. Inf. Syst. 23, 501–522 (2008)Google Scholar
  4. 4.
    Venkatraman, S., Sundarraj, R.P., Seethamraju, R.: Healthcare Analytics Adoption-Decision Model: A Case Study. In: 2015 Proceedings of the PACIS (2015)Google Scholar
  5. 5.
    Ward, M.J., Marsolo, K.A., Froehle, C.M.: Applications of business analytics in healthcare. Bus. Horiz. 57, 571–582 (2014)CrossRefGoogle Scholar
  6. 6.
    Lavalle, S., Hopkins, M.S., Lesser, E., Shockley, R., Kruschwitz, N.: Analytics: the new path to value. MIT Sloan Manag. Rev. 52(1), 1–24 (2010)Google Scholar
  7. 7.
    Sherer, S.A.: Advocating for action design research on IT value creation in healthcare. J. Assoc. Inf. Syst. 15, 860–878 (2014)Google Scholar
  8. 8.
    Cortada, J.W., Gordon, D., Lenihan, B.: The value of analytics in healthcare. IBM Institute for Business Value Healthcare (2010)Google Scholar
  9. 9.
    Venkatraman, S., Sundarraj, R.P., Mukherjee, A.: Prototype design of a healthcare-analytics pre-adoption readiness assessment (HAPRA) instrument. In: Parsons, J., Tuunanen, T., Venable, J., Donnellan, B., Helfert, M., Kenneally, J. (eds.) DESRIST 2016. LNCS, vol. 9661, pp. 158–174. Springer, Cham (2016). Scholar
  10. 10.
    Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research. J. Manag. Inf. Syst. 24, 45–77 (2008)CrossRefGoogle Scholar
  11. 11.
    Martilla, J.A., James, J.C.: Importance-performance analysis. J. Mark. 41, 77–79 (1977)CrossRefGoogle Scholar
  12. 12.
    Gabus, A., Fontela, E.: The DEMATEL observer - DEMATEL 1976 Report - Battelle Geneva Research Center, Geneva, Switzerland (1976)Google Scholar
  13. 13.
    Shneiderman, B., Plaisant, C., Hesse, B.W.: Improving healthcare with interactive visualization. IEEE Comput. Soc. 46, 58–66 (2013)CrossRefGoogle Scholar
  14. 14.
    Songthung, P., Sripanidkulchai, K., Luangruangrong, P., Sakulbumrungsil, R.C., Udomaksorn, S., Kessomboon, N., Kanchanaphibool, I.: An innovative decision support service for improving pharmaceutical acquisition capabilities. In: 2012 Annual SRII Global Conference, pp. 628–636 (2012)Google Scholar
  15. 15.
    Peck, J.S., Benneyan, J.C., Nightingale, D.J., Gaehde, S.A.: Characterizing the value of predictive analytics in facilitating hospital patient flow. IIE Trans. Healthc. Syst. Eng. 4, 135–143 (2014)CrossRefGoogle Scholar
  16. 16.
    Aktaş, E., Ülengin, F., Önsel Şahin, Ş.: A decision support system to improve the efficiency of resource allocation in healthcare management. Socio-Econ. Plann. Sci. 41, 130–146 (2007)CrossRefGoogle Scholar
  17. 17.
    Davenport, T.H., Harris, J.G.: Competing on Analytics: The New Science of Winning. Harvard Business Press, Boston (2007)Google Scholar
  18. 18.
    Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: theory and results (1986)Google Scholar
  19. 19.
    Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage. Sci. 46, 186–204 (2000)CrossRefGoogle Scholar
  20. 20.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478 (2003)CrossRefGoogle Scholar
  21. 21.
    DeLone, W.H., McLean, E.R.: The DeLone and McLean model of information systems success: a ten-year update. J. Manag. Inf. Syst. 19, 9–30 (2003)CrossRefGoogle Scholar
  22. 22.
    Hikmet, N., Bhattacherjee, A., Menachemi, N., Kayhan, V.O., Brooks, R.G.: The role of organizational factors in the adoption of healthcare information technology in Florida hospitals. Health Care Manag. Sci. 11, 1–9 (2008)CrossRefGoogle Scholar
  23. 23.
    Yu, P.: A multi-method approach to evaluate health information systems. Stud. Health Technol. Inform. 160, 1231–1235 (2010)Google Scholar
  24. 24.
    Brooks, P., El-Gayar, O., Sarnikar, S.: A framework for developing a domain specific business intelligence maturity model: application to healthcare. Int. J. Inf. Manage. 35, 337–345 (2015)CrossRefGoogle Scholar
  25. 25.
    Yusof, M.M., Kuljis, J., Papazafeiropoulou, A., Stergioulas, L.K.: An evaluation framework for health information systems: human, organization and technology-fit factors (HOT-fit). Int. J. Med. Inform. 77, 386–398 (2008)CrossRefGoogle Scholar
  26. 26.
    Davis, M.W.: The seven stages of EMR adoption: majority of hospitals are in stage 3 and rising. Healthc. Exec. 25, 18–19 (2010)Google Scholar
  27. 27.
    Sanders, D., Burton, D., Protti, D.: The healthcare analytics adoption model (HAAM): a framework and roadmap.
  28. 28.
    Malladi, S.: Adoption of business intelligence & analytics in organizations – an empirical study of antecedents. In: 2013 Proceedings of the AMCIS, vol. 2016, pp. 1–11 (2013)Google Scholar
  29. 29.
    Ghosh, B., Scott, J.E.: Antecedents and catalysts for developing a healthcare analytic capability. Commun. Assoc. Inf. Syst. 29, 395–410 (2011)Google Scholar
  30. 30.
    Myers, B.L., Kappelman, L.A., Prybutok, V.R.: A comprehensive model for assessing the quality and productivity of the information systems function. Inf. Resour. Manag. J. 10, 6–26 (1997)CrossRefGoogle Scholar
  31. 31.
    Lee, Y.W., Strong, D.M., Kahn, B.K., Wang, R.Y.: AIMQ: a methodology for information quality assessment. Inf. Manag. 40, 133–146 (2002)CrossRefGoogle Scholar
  32. 32.
    Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2, 192–222 (1991)CrossRefGoogle Scholar
  33. 33.
    Ebner, K., Mueller, B., Urbach, N., Riempp, G., Krcmar, H.: Assessing IT management’s performance: a design theory for strategic IT benchmarking. IEEE Trans. Eng. Manag. 63, 113–126 (2016)CrossRefGoogle Scholar
  34. 34.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28, 75–105 (2004)CrossRefGoogle Scholar
  35. 35.
    Zhang, N.J., Seblega, B., Wan, T., Unruh, L., Agiro, A., Miao, L.: Health information technology adoption in U.S. acute care hospitals. J. Med. Syst. 37(2), 9907 (2013)CrossRefGoogle Scholar
  36. 36.
    Tornatzky, L.G., Fleischer, M., Chakrabarti, A.K.: The processes of technological innovation (1990)Google Scholar
  37. 37.
    Purao, S., Storey, V.C.: Evaluating the adoption potential of design science efforts: the case of APSARA. Decis. Support Syst. 44, 369–381 (2008)CrossRefGoogle Scholar
  38. 38.
    Han, T., Purao, S., Storey, V.C.: Generating large-scale repositories of reusable artifacts for conceptual design of information systems. Decis. Support Syst. 45, 665–680 (2008)CrossRefGoogle Scholar
  39. 39.
    Skok, W., Kophamel, A., Richardson, I.: Diagnosing information systems success: importance-performance maps in the health club industry. Inf. Manag. 38, 409–419 (2001)CrossRefGoogle Scholar
  40. 40.
    Ahmadi, H., Nilashi, M., Ibrahim, O.: Organizational decision to adopt hospital information system: an empirical investigation in the case of Malaysian public hospitals. Int. J. Med. Inform. 84, 166–188 (2015)CrossRefGoogle Scholar
  41. 41.
    Amiri, M., Salehi, J., Payani, N., Shafieezadeh, M.: Developing a DEMATEL method to prioritize distribution centers in supply chain. Manag. Sci. Lett. 1, 279–288 (2011)CrossRefGoogle Scholar
  42. 42.
    Gregor, S., Hevner, A.R.: Positioning and presenting design science research for maximum impact. MIS Q. 37, 337–355 (2013)CrossRefGoogle Scholar
  43. 43.
    Gregor, S., Jones, D.: The anatomy of a design theory. J. Assoc. Inf. Syst. 8, 312–335 (2007)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sathyanarayanan Venkatraman
    • 1
  • Rangaraja P. Sundarraj
    • 1
  • Ravi Seethamraju
    • 2
  1. 1.Department of Management StudiesIIT MadrasChennaiIndia
  2. 2.Business SchoolThe University of SydneySydneyAustralia

Personalised recommendations