Intelligent Risk Detection in Health Care: Integrating Social and Technical Factors to Manage Health Outcomes

  • Hoda Moghimi
  • Nilmini Wickramasinghe
  • Monica Adya
Part of the Healthcare Delivery in the Information Age book series (Healthcare Delivery Inform. Age)


The rapid increase of service demands in healthcare contexts today has reignited the importance of a robust risk assessment framework supported by real-time service handling in order to ensure superior decision-making and successful healthcare outcomes. Big data and analytics have the potential to provide numerous opportunities in healthcare for the application of information technology (IT) and decision sciences to real-time intelligent risk detection and management. In this article, we suggest that this intersection of decision sciences and IT should be the focus when looking to the future of health risk management. To demonstrate the power and benefits of integrating these domains, this exploratory study develops a solution framework that combines a real-time intelligent risk detection solution with decision support for a specific healthcare context. An intelligent risk detection model called HOUSE (Health Outcomes around Uncertainty, Stakeholders, and Efficacy) is proffered for risk detection and management in the context of congenital heart disease (CHD) surgeries in children. The model builds on the principles of user-centered design, network-centric healthcare operations, and intelligence continuum. The article elaborates on elements of this model, describes the fundamental research that supports its design, and concludes with a research agenda and design recommendations for extension into other healthcare domains.


Risk management Intelligence continuum Intelligent risk detection Clinical decision support Value-based healthcare 


  1. Abidi, S. S. R., & Goh, A. (1998). Applying knowledge discovery to predict infectious disease epidemics. In PRICAI’98: Topics in artificial intelligence (pp. 170–181). Berlin/Heidelberg: Springer.Google Scholar
  2. Agarwal, R., Gao, G., DesRoches, C., & Jha, A. K. (2010). Research commentary—The digital transformation of healthcare: Current status and the road ahead. Information Systems Research, 21(4), 796–809.CrossRefGoogle Scholar
  3. Agoritsas, T., Heen, A. F., Brandt, L., Alonso-Coello, P., Kristiansen, A., Akl, E. A., Neumann, I., Tikkinen, K. A., Van Der Weijden, T., Elwyn, G., & Montori, V. M. (2015). Decision aids that really promote shared decision making: The pace quickens. British Medical Journal, 350, 7624.CrossRefGoogle Scholar
  4. Åhlfeldt, R. M., Persson, A., Rexhepi, H., & Wåhlander, K. (2016). Supporting active patient and health care collaboration: A prototype for future health care information systems. Health Informatics Journal, 22(4), 839–853.PubMedCrossRefGoogle Scholar
  5. Anderson, C. L., & Agarwal, R. (2011). The digitization of healthcare: Boundary risks, emotion, and consumer willingness to disclose personal health information. Information Systems Research, 22(3), 469–490.CrossRefGoogle Scholar
  6. Anderson, O., Brodie, A., Vincent, C. A., & Hanna, G. B. (2012). A systematic proactive risk assessment of hazards in surgical wards: A quantitative study. Annals of Surgery, 255(6), 1086–1092.PubMedCrossRefGoogle Scholar
  7. Ash, J. S., Stavri, P. Z., & Kuperman, G. J. (2003). A consensus statement on considerations for a successful CPOE implementation. Journal of the American Medical Informatics Association, 10(3), 229–234.PubMedPubMedCentralCrossRefGoogle Scholar
  8. Avison, D. E., & Fitzgerald, G. (2008). Information systems development: Methodologies, techniques and tools (4th ed.). Berkshire: McGraw-Hill.Google Scholar
  9. Banerjee, A. K., & Ingate, S. (2012). Web-based patient-reported outcomes in drug safety and risk management. Drug Safety, 35(6), 437–446.PubMedCrossRefGoogle Scholar
  10. Bardhan, I., Oh, J. H., Zheng, Z., & Kirksey, K. (2014). Predictive analytics for readmission of patients with congestive heart failure. Information Systems Research, 26(1), 19–39.CrossRefGoogle Scholar
  11. Barrett, J., Gifford, C., Morey, J., Risser, D., & Salisbury, M. (2001). Enhancing patient safety through teamwork training. Journal of Healthcare Risk Management, 21(4), 61–69.CrossRefGoogle Scholar
  12. Batal, I., & Hauskrecht, M. (2010). Mining clinical data using minimal predictive rules. In American Medical Informatics Association annual symposium, pp. 31–35.Google Scholar
  13. Baxter, G., & Sommerville, I. (2011). Socio-technical systems: From design methods to systems engineering. Interacting with Computers, 23(1), 4–17.CrossRefGoogle Scholar
  14. Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code development. Thousand Oaks: Sage.Google Scholar
  15. Boyd, 1987. available at
  16. Carter, E. L., Nunlee-Bland, G., & Callender, C. (2011). A patient-centric, provider-assisted diabetes telehealth self-management intervention for urban minorities. Perspectives in Health Information Management/AHIMA, American Health Information Management Association, 8(Winter), 1b.PubMedCentralPubMedGoogle Scholar
  17. Ceglowski, A., Churilov, L., & Wassertheil, J. (2005, January). Knowledge discovery through mining emergency department data. In System sciences, 2005. HICSS’05. Proceedings of the 38th annual Hawaii international conference (pp. 142c–142c).Google Scholar
  18. Cerrito, P. B. (2011). Data mining to determine risk in medical decisions. Amsterdam: IOS Press.Google Scholar
  19. Cerrito, P., & Cerrito, J. C. (2006). Data and text mining the electronic medical record to improve care and to lower costs. In Proceedings of SUGI (Vol. 31, pp. 26--29). Retrieved from
  20. Chandola, V., Sukumar, S. R., & Schryver, J. C. (2013, August). Knowledge discovery from massive healthcare claims data. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1312–1320).Google Scholar
  21. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.CrossRefGoogle Scholar
  22. Cios, K. J., Pedrycz, W., Swiniarski, R. W., & Kurgan, R. A. (2007). Date mining and knowledge discovery approach. New York: Springer.Google Scholar
  23. Clemer, T., & Spuhler, V. (1998). Developing and gaining acceptance for patient care protocols. New Horizon, 6(1), 12–19.Google Scholar
  24. Cleveringa, F. G., Gorter, K. J., Van Den Donk, M., & Rutten, G. E. (2008). Combined task delegation, computerized decision support, and feedback improve cardiovascular risk for type 2 diabetic patients a cluster randomized trial in primary care. Diabetes Care, 31(12), 2273–2275.PubMedPubMedCentralCrossRefGoogle Scholar
  25. Couët, N., Desroches, S., Robitaille, H., Vaillancourt, H., Leblanc, A., Turcotte, S., et al. (2015). Assessments of the extent to which health-care providers involve patients in decision making: A systematic review of studies using the OPTION instrument. Health Expectations, 18(4), 542–561.PubMedCrossRefPubMedCentralGoogle Scholar
  26. Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98.PubMedPubMedCentralGoogle Scholar
  27. De Backere, F., De Turck, F., Colpaert, K., & Decruyenaere, J. (2012). Advanced pervasive clinical decision support for the intensive care unit. In 6th International conference on pervasive computing technologies for healthcare 2012. Google Scholar
  28. DeMeester, R. H., Lopez, F. Y., Moore, J. E., Cook, S. C., & Chin, M. H. (2016). A model of organizational context and shared decision making: Application to LGBT racial and ethnic minority patients. Journal of General Internal Medicine, 31(6), 651–662.PubMedPubMedCentralCrossRefGoogle Scholar
  29. Durst, C., Viol, J., & Wickramasinghe, N. (2013). Online social networks, social capital and health related behaviours: a state-of-the-art analysis CAIS(Communications of the AIS), 32, Retrieved from
  30. Eichner, J. S., & Das, M. (2010). Challenges and barriers to clinical decision support (CDS) design and implementation experienced in the Agency for Healthcare Research and Quality CDS demonstrations. Rockville: Agency for Healthcare Research and Quality.Google Scholar
  31. Elbaum, S., Rothermel, G., Karre, S., & Fisher, M. (2005). Leveraging user-session data to support web application testing. IEEE Transactions on Software Engineering, 31(3), 187–202.CrossRefGoogle Scholar
  32. Elwyn, G., Edwards, A., & Kinnersley, P. (1999). Shared decision-making in primary care: The neglected second half of the consultation. The British Journal of General Practice, 49(443), 477–482.PubMedPubMedCentralGoogle Scholar
  33. Fichman, R. G., Kohli, R., & Krishnan, R. (2011). The role of information systems in healthcare: Current trends and future research. Information Systems Research, 22(3), 419–428.CrossRefGoogle Scholar
  34. Gago, P., et al. (2005). INTCare: A knowledge discovery based intelligent decision support system for intensive care medicine. Journal of Decision Systems, 14(3), 241–259.CrossRefGoogle Scholar
  35. Garwood, V., Claydon-Platt, D., Wickramasinghe, N., Mackay, J. R., & Smart, P. J. (2018). Smart phones and surgeons: Privacy and legal issues in the Australian healthcare. International Journal of Networks and Virtual Organisations., 19(1), 21.CrossRefGoogle Scholar
  36. Glesne, C., & Peshkin, A. (1992). Becoming qualitative researchers: An introduction. New York: Longman White Plains.Google Scholar
  37. Greenland, P. (2012). Should the resting electrocardiogram be ordered as a routine risk assessment test in healthy asymptomatic adults? JAMA, 307(14), 1530–1531.PubMedCrossRefGoogle Scholar
  38. Grundy, S., Pasternak, R., Greenland, P., Smith, S., Jr., & Fuster, V. (1999). Assessment of cardiovascular risk by use of multiple-risk-factor assessment equations: A statement for healthcare professionals from the American Heart Association and the American College of Cardiology. Journal of the American College of Cardiology, 34(4), 1348–1359.PubMedCrossRefGoogle Scholar
  39. Guikema, S. D., & Quiring, S. M. (2012). Hybrid data mining-regression for infrastructure risk assessment based on zero-inflated data. Reliability Engineering & System Safety, 99, 178–182.CrossRefGoogle Scholar
  40. Han, J., Kamber, M., & Pei, J. (2006). Data mining: Concepts and techniques. San Francisco: Morgan Kaufmann.Google Scholar
  41. Heisler, M., Bouknight, R. R., Hayward, R. A., Smith, D. M., & Kerr, E. A. (2002). The relative importance of physical communication, participatory decision making, and patient understanding in diabetes self-management. Journal of General Internal Medicine, 17(4), 1525–1497.CrossRefGoogle Scholar
  42. Holzinger, A., et al. (2012). Computational sensemaking on examples of knowledge discovery from neuroscience data: Towards enhancing stroke rehabilitation. In Information technology in bio-and medical informatics (pp. 166–168). Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  43. Jeffery, R., Iserman, E., & Haynes, R. (2013). Can computerized clinical decision support systems improve diabetes management? A systematic review and meta-analysis. Diabetic Medicine, 30(6), 739–745.PubMedCrossRefGoogle Scholar
  44. Kane, G. C., & Labianca, G. (2011). Is avoidance in health-care groups: A multilevel investigation. Information Systems Research, 22(3), 504–522.CrossRefGoogle Scholar
  45. Kaplan, R., & Porter, E. (2011). How to solve the cost crisis in health care. In Harvard business review. Boston: Harvard Business School Publishing.Google Scholar
  46. Karaolis, M. A., Moutiris, J. A., Hadjipanayi, D., & Pattichis, C. S. (2010). Assessment of the risk factors of coronary heart events based on data mining with decision trees. IEEE Transactions on Information Technology in Biomedicine, 14(3), 559–566.PubMedCrossRefGoogle Scholar
  47. Kaur, H., & Wasan, S. K. (2006). Empirical study on applications of data mining techniques in healthcare. Journal of Computer Science, 2(2), 194–200.CrossRefGoogle Scholar
  48. Kelley, H., Chiasson, M., Downey, A., & Pacaud, D. (2011). The clinical impact of eHealth on the self-management of diabetes: A double adoption perspective. Journal of the Association of Information Systems, 12(Special issue), 208–234.CrossRefGoogle Scholar
  49. Kent, B., Redley, B., Wickramasinghe, N., Nguyen, L., Taylor, N. J., Moghimi, H., & Botti, M. (2015). Exploring nurses’ reactions to a novel technology to support acute healthcare delivery. Journal of Clinical Nursing.
  50. Kersten, R. F., Stevens, M., van Raay, J. J., Bulstra, S. K., & van den Akker-Scheek, I. (2012). Habitual physical activity after total knee replacement. Physical Therapy, 92(9), 1109–1116.PubMedCrossRefGoogle Scholar
  51. Kim, K. K., & Michelman, J. E. (1990). An examination of factors for the strategic use of information systems in the healthcare industry. MIS Quarterly, 14, 201–215.CrossRefGoogle Scholar
  52. Kim, G., El Rouby, S., Thompson, J., Gupta, A., Williams, J., & Jobes, D. (2010). Monitoring unfractionated heparin in pediatric patients with congenital heart disease having cardiac catheterization or cardiac surgery. Journal of Thrombosis and Thrombolysis, 29(4), 429–436.PubMedCrossRefGoogle Scholar
  53. Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of Healthcare Information Management, 19(2), 65.Google Scholar
  54. Kraft, M. R., Desouza, K. C., & Androwich, I. (2003, January). Data mining in healthcare information systems: Case study of a veterans’ administration spinal cord injury population. In System sciences, 2003. Proceedings of the 36th annual Hawaii international conference (pp. 9–13).Google Scholar
  55. Kripalani, S., LeFevre, F., Phillips, C. O., Williams, M. V., Basaviah, P., & Baker, D. W. (2007). Deficits in communication and information transfer between hospital-based and primary care physicians. Implications for patient safety and continuity of care. JAMA., 297(8), 831–841. Scholar
  56. Krones, T., Keller, H., Sönnichsen, A., Sadowski, E. M., Baum, E., Wegscheider, K., et al. (2008). Absolute cardiovascular disease risk and shared decision making in primary care: A randomized controlled trial. The Annals of Family Medicine, 6(3), 218–227.PubMedCrossRefGoogle Scholar
  57. Kronick, R., Gilmer, T., Dreyfus, T., & Ganiats, T. (2002). CDPS-Medicare: The chronic illness and disability payment system modified to predict expenditures for Medicare beneficiaries. Final Report to CMS.Google Scholar
  58. Kuhn, K., Wurst, S., Bott, O., & Giuse, D. (2006). Expanding the scope of health information systems. IMIA Yearbook of Medical Informatics, 43–52.Google Scholar
  59. Kuntz, K. M., & Goldie, S. J. (2002). Assessing the sensitivity of decision-analytic results to unobserved markers of risk: Defining the effects of heterogeneity bias. Medical Decision Making, 22(3), 218–227.PubMedCrossRefGoogle Scholar
  60. Lacour-Gayet, F. (2002). Risk stratification theme for congenital heart surgery. Pediatric cardiac surgery annual of the seminars in thoracic and cardiovascular surgery. American Society for Thoracic Surgery, 5(1), 148–152.Google Scholar
  61. Laine, C., Horton, R., DeAngelis, C. D., Drazen, J. M., Frizelle, F. A., Godlee, F., Haug, C., Hébert, P. C., Kotzin, S., Marusic, A., Sahni, P., & Schroeder, T. V. (2007). Clinical trial registration — looking back and moving ahead. New England Journal of Medicine, 356, 2734–2736. Scholar
  62. Lawler, E. K., Hedge, A., & Pavlovic-Veselinovic, S. (2011). Cognitive ergonomics, socio-technical systems, and the impact of healthcare information technologies. International Journal of Industrial Ergonomics, 41(4), 336–344.CrossRefGoogle Scholar
  63. LeRouge, C., & Wickramasinghe, N. (2013). A review of user-centered design for diabetes-related consumer health informatics technologies. Journal of Diabetes science and Technology, 7(4), 1–18.CrossRefGoogle Scholar
  64. Liu, J., Wyatt, J. C., & Altman, D. G. (2006). Decision tools in health care: focus on the problem, not the solution. BMC Medical Informatics and Decision Making, 6(1), 4.PubMedPubMedCentralCrossRefGoogle Scholar
  65. Long, S. H., Galea, M. P., Eldridge, B. J., & Harris, S. R. (2012). Performance of 2-year-old children after early surgery for congenital heart disease on the Bayley Scales of Infant and Toddler Development. Early Human Development, 88(8), 603–607.PubMedCrossRefPubMedCentralGoogle Scholar
  66. Marino, B. S., Lipkin, P. H., Newburger, J. W., Peacock, G., Gerdes, M., Gaynor, J. W., Mussatto, K. A., Uzark, K., Goldberg, C. S., & Johnson, W. H. (2012). Neurodevelopmental outcomes in children with congenital heart disease: Evaluation and management a scientific statement from the American Heart Association. Circulation, 126(9), 1143–1172.PubMedCrossRefPubMedCentralGoogle Scholar
  67. Marschollek, M., Gövercin, M., Rust, S., Gietzelt, M., Schulze, M., Wolf, K.-H., & Steinhagen-Thiessen, E. (2012). Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups. BMC Medical Informatics and Decision Making, 12(1), 19.PubMedPubMedCentralCrossRefGoogle Scholar
  68. Meyer, G., Adomavicius, G., Johnson, P. E., Elidrisi, M., Rush, W. A., Sperl-Hillen, J. M., & O’Connor, P. J. (2014). A machine learning approach to improving dynamic decision making. Information Systems Research, 25(2), 239–263.CrossRefGoogle Scholar
  69. Oborn, E., Barrett, M., & Davidson, E. (2011). Unity in diversity: Electronic patient record use in multidisciplinary practice. Information Systems Research, 22(3), 547–564.CrossRefGoogle Scholar
  70. Ozdemir, Z., Barron, Z., & Bandyopadhyay, S. (2011). An analysis of the adoption of digital health records under switching costs. Information Systems Research, 22(3), 491–503.CrossRefGoogle Scholar
  71. Patrick, J. D., Nguyen, D. H., Wang, Y., & Li, M. (2011). A knowledge discovery and reuse pipeline for information extraction in clinical notes. Journal of the American Medical Informatics Association, 18(5), 574–579.PubMedPubMedCentralCrossRefGoogle Scholar
  72. Peleg, M., & Tu, S. (2006). Decision support, knowledge representation and management in medicine. Yearbook of Medical Informatics, 45, 72–80.Google Scholar
  73. Peng, Y., Bin, Y., & Jiang, J. (2006). Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis. Artificial Intelligence in Medicine, 37(1), 43–53.PubMedCrossRefGoogle Scholar
  74. Porter, M., & Teisberg, E. (2009). Redefining health care. In Harvard business review. Boston: Harvard Business School Publishing.Google Scholar
  75. Quintana, J. M., Escobar, A., Aguirre, U., Lafuente, I., & Arenaza, J. C. (2009). Predictors of health-related quality-of-life change after total hip arthroplasty. Clinical Orthopaedics and Related Research®, 467(11), 2886–2894.CrossRefGoogle Scholar
  76. Restuccia, J. D., Cohen, A. B., Horwitt, J. N., & Shwartz, M. (2012). Hospital implementation of health information technology and quality of care: are they related? BMC Medical Informatics and Decision Making., 12, 109. Scholar
  77. Rizzo, V. M., & Kintner, E. (2013). The utility of the behavioral risk factor surveillance system (BRFSS) in testing quality of life theory: An evaluation using structural equation modeling. Quality of Life Research, 22(5), 987–995.PubMedCrossRefGoogle Scholar
  78. Rodríguez, L. V., et al. (2003). Discrepancy in patient and physician perception of patient’s quality of life related to urinary symptoms. Urology, 62(1), 49–53.PubMedCrossRefGoogle Scholar
  79. Ryan, P. B., Madigan, D., Stang, P. E., Marc Overhage, J., Racoosin, J. A., & Hartzema, A. G. (2012). Empirical assessment of methods for risk identification in healthcare data: Results from the experiments of the observational medical outcomes partnership. Statistics in Medicine, 31(30), 4401–4415.PubMedCrossRefGoogle Scholar
  80. Safran, C., Bloomrosen, M., Hammond, W. E., Labkoff, S., Markel-Fox, S., Tang, P. C., & Detmer, D. E. (2007). Toward a national framework for the secondary use of health data: An American Medical Informatics Association White Paper. Journal of the American Medical Informatics Association, 14(1), 1–9.PubMedPubMedCentralCrossRefGoogle Scholar
  81. Sanchez, E., Toro, C., Artetxe, A., Graña, M., Sanin, C., Szczerbicki, E., et al. (2013). Bridging challenges of Clinical Decision Support Systems with a semantic approach. A case study on breast cancer. Pattern Recognition Letters., 34(14), 1758–1768.CrossRefGoogle Scholar
  82. Schectman, J. M., & Plews-Ogan, M. L. (2006). Physician perception of hospital safety and barriers to incident reporting. Joint Commission Journal on Quality and Patient Safety, 32(6), 337–343.PubMedCrossRefGoogle Scholar
  83. Schuele, M., Widmer, T., Premm, M., Criegee-Riech, M., & Wickramasinghe, N. (2015). Using a multiagent organisational approach to improve knowledge provision for shared decision making in patient-physician relationships: An example from Germany. Health and Technology, 5(1), 13–23. Scholar
  84. Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie, or die. Hoboken: Wiley.Google Scholar
  85. Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C. (2001). Clinical decision support systems for the practice of evidence-based medicine. Journal of the American Medical Informatics Association, 8(6), 527–534.PubMedPubMedCentralCrossRefGoogle Scholar
  86. Simon, A., Ebinger, M., Flaiz, B., Heeskens, K., & Wickramasinghe, N. (2015). A multi-centred empirical study to measure and validate user satisfaction with hospital information services in Australia and Germany. In iHEA congress (pp. 12–15).Google Scholar
  87. Srinivas, K., Kavihta Rani, B., & Govrdhan, A. (2010). Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering (IJCSE), 2(2), 250–255.Google Scholar
  88. Staal, I. I., Hermanns, J., Schrijvers, A. J., & van Stel, H. F. (2013). Risk assessment of parents’ concerns at 18 months in preventive child health care predicted child abuse and neglect. Child Abuse & Neglect, 37(7), 475–484.CrossRefGoogle Scholar
  89. Story, M. T., et al. (2002). Management of child and adolescent obesity: Attitudes, barriers, skills, and training needs among health care professionals. Pediatrics, 110(1), 210–214.PubMedGoogle Scholar
  90. Sulaiman, H., & Wickramasinghe, N. (2014). Assimilating healthcare information systems in a Malaysian hospital. Communications of the Association of Information Systems (CAIS), 34, 1291–1318.Google Scholar
  91. Sullivan, R., Peppercorn, J., Zalcberg, J., Meropol, N., Amir, E., & Khayat, D. (2011). Delivering affordable cancer care in high-income countries. The Lancet Oncology, 12(10), 933–980.PubMedCrossRefGoogle Scholar
  92. Thomas, J. C., Moore, A., & Qualls, P. E. (1983). The effect on cost of medical care for patients treated with an automated clinical audit system. Journal of Medical Systems, 7(3), 307–313.PubMedCrossRefGoogle Scholar
  93. Thompson, C., Cullum, N., McCaughan, D., Sheldon, T., & Raynor, P. (2004). Nurses, information use, and clinical decision making—The real world potential for evidence-based decisions in nursing. Evidence Based Nursing, 7(3), 68–72.PubMedCrossRefGoogle Scholar
  94. Tiwari, R., Tsapepas, D. S., Powell, J. T., & Martin, S. T. (2013). Enhancements in healthcare information technology systems: Customizing vendor-supplied clinical decision support for a high-risk patient population. Journal of the American Medical Informatics Association: JAMIA, 20(2), 377–380.PubMedCrossRefGoogle Scholar
  95. Trucco, P., & Cavallin, M. (2006). A quantitative approach to clinical risk assessment: The CREA method. Safety Science, 44, 491–513.CrossRefGoogle Scholar
  96. Tseng, C.-W., Carter, G., Kapur, K., Keeler, E., & Rastegar, A. (2003). Medicare calibration of the clinically detailed risk information system for cost: University College Dublin.Google Scholar
  97. Turban, E., Sharda, R., Aronson, J. E., & King, D. (2008) Business intelligence: A managerial approach. Pearson/Prentice Hall. ISBN: 978-0-13-234761-7.Google Scholar
  98. Venkatesh, V., Zhang, X., & Sykes, T. A. (2011). Doctors do too little technology: A longitudinal field study of an electronic healthcare system implementation. Information Systems Research, 22(3), 523–546.CrossRefGoogle Scholar
  99. Vincent, C., Taylor-Adams, S., & Chapman, E. J. (2000). How to investigate and analyse clinical incidents: Clinical risk unit and association of litigation and risk management protocol. British Medical Journal, 320(7237), 777–781.PubMedCrossRefGoogle Scholar
  100. Virshup, B. B., Oppenberg, A. A., & Coleman, M. M. (1999). Strategic risk management: Reducing malpractice claims through more effective patient-doctor communication. American Journal of Medical Quality, 14, 153–159.PubMedCrossRefGoogle Scholar
  101. Von Lubitz, D., & Wickramasinghe, N. (2006a). Healthcare and technology: The doctrine of network centric healthcare. International Journal of Electronic Healthcare, 2(4), 322–344.CrossRefGoogle Scholar
  102. Von Lubitz, D., & Wickramasinghe, N. (2006b). Network centric healthcare: Applying the tools, techniques and strategies of knowledge management to create superior healthcare operations. International Journal of Electronic Healthcare, 2(4), 415–429.CrossRefGoogle Scholar
  103. Von Lubitz, D., & Wickramasinghe, N. (2006c). Creating germane knowledge in dynamic environments. International Journal of Innovation and Learning, 3(3), 326–347.CrossRefGoogle Scholar
  104. Wetters, N. G., Murray, T. G., Moric, M., Sporer, S. M., Paprosky, W. G., & Della Valle, C. J. (2013). Risk factors for dislocation after revision total hip arthroplasty. Clinical Orthopaedics and Related Research, 471(2), 410–416.PubMedCrossRefPubMedCentralGoogle Scholar
  105. Weiner, J. P., Dobson, A., Maxwell, S. L., Coleman, K., Starfield, B., & Anderson, G. (1996). Risk-adjusted Medicare capitation rates using ambulatory and inpatient diagnoses. Health care financing review, 17(3), 77.PubMedPubMedCentralGoogle Scholar
  106. Wickramasinghe, N., & Gururajan, R. (2016). Innovation practice using pervasive mobile technology solutions to improve population health management: A pilot study of gestational diabetes patient care in Australia. Journal for Healthcare Quality, 38(2), 93–105.PubMedCrossRefPubMedCentralGoogle Scholar
  107. Wickramasinghe, N., & Schaffer, J. (2010). Realizing value driven patient centric healthcare through technology. Washington, DC: IBM Center for the Business of Government.Google Scholar
  108. Wickramasinghe, N., & Schaffer, J. L. (2006). Creating knowledge-driven healthcare processes with the intelligence continuum. International Journal of Electronic Healthcare, 2(2), 164–174.PubMedCrossRefGoogle Scholar
  109. Wickramasinghe, N., Bali, R., Gibbons, M., Choi, J., & Schaffer, J. (2008). A systematic approach: Optimization of healthcare operations with knowledge management. Journal of healthcare information management: JHIM, 23(3), 44–50.Google Scholar
  110. Wickramasinghe, N., Bali, R., Kirn, S., & Sumoi, R. (2012). Creating sustainable e-health solutions. New York: Springer.CrossRefGoogle Scholar
  111. Wright, A., & Sittig, D. F. (2006). Automated development of order sets and corollary orders by data mining in an ambulatory computerized physician order entry system. In Proceedings of the AMIA symposium (pp. 819–823).Google Scholar
  112. Wu, I., & Hu, Y. (2012). Examining knowledge management enabled performance for hospital professionals: A dynamic capability view and the mediating role of process capability. Journal of the Association of Information Systems, 13(2), 976, Article 3.CrossRefGoogle Scholar
  113. Yagi, M., Akilah, K. B., & Boachie-Adjei, O. (2011). Incidence, risk factors and classification of proximal junctional kyphosis: Surgical outcomes review of adult idiopathic scoliosis. Spine, 36(1), E60–E68.PubMedCrossRefPubMedCentralGoogle Scholar
  114. Yoshio H., Koji I., Hitoshi T., Masakazu I., Yasuo W., (2012), Value of general surgical risk models for predicting postoperative liver failure and mortality following liver surgery, Journal of Surgical Oncology, Early View (Online Version of Record published before inclusion in an issue), Online ISSN: 1096-9098.Google Scholar
  115. Yen, P.-Y., & Bakken, S. (2012). Review of health information technology usability study methodologies. Journal of the American Medical Informatics Association, 19(3), 413–422.PubMedCrossRefGoogle Scholar
  116. Yin, R. K. (2003). Case study research: Design and methods. Thousand Oaks: Sage.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hoda Moghimi
    • 1
  • Nilmini Wickramasinghe
    • 2
    • 3
  • Monica Adya
    • 4
  1. 1.Health Informatics Management, Epworth HealthCareRichmondAustralia
  2. 2.Epworth HealthCareRichmondAustralia
  3. 3.Swinburne University of TechnologyHawthornAustralia
  4. 4.Management, Marquette UniversityMilwaukeeUSA

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