Examining Failure in a Dynamic Decision Environment: Strategies for Treating Patients with a Chronic Disease

  • Gregory W. RamseyEmail author
  • Paul E. Johnson
  • Patrick J. O’Connor
  • JoAnn M. Sperl-Hillen
  • William A. Rush
  • George Biltz
Part of the Annals of Information Systems book series (AOIS, volume 19)


In this paper we investigate the dynamic decision-making task of primary care physicians treating patients with type 2 diabetes to achieve a blood glucose goal. The focus of the study is on developing and testing an information processing theory that can explain why some physicians more often succeed and others more often fail to achieve desirable clinical goals. The developed theory is represented in the form of two types of computational models, one employing a feedback decision-making strategy and the other a feedforward strategy. The models were implemented in software and tested using data from a previously reported experiment where physicians treated simulated patients with type 2 diabetes. The physician data were scored for a defined set of treatment errors. Computational processes were systematically examined to identify and specify processes to perturb in order to generate the observed errors. Models were created for each physician by introducing perturbations in computational processes based on errors that each physician committed during the experiment. These models treated the same simulated patients that the physicians treated; results from each model treating the patients were compared with the represented physician’s results to test the sufficiency of the models to explain observed errors. Process perturbations which explained observed errors took two characteristic forms, both of which resulted in delayed treatment action: (1) elevated thresholds for triggering action and (2) overestimating delayed effects of medications. Physician models made predictions for types and timing of subjects’ treatment errors: physician models generated 79 % of the same types of treatment errors as committed by physicians. As demonstrated by this study, developing task specific information processing theories (expressed as computational models) are useful for investigating patterns of decision making that lead to errors of performance. Studies of this nature can support the design of decision support systems intended to reduce errors associated with dynamic tasks, such as treating a chronic disease.


Computational models Physician decision making 


  1. American Diabetes Association (2008) Economic costs of diabetes in the U.S. in 2007. Diabetes Care 31(3):596–615CrossRefGoogle Scholar
  2. Bainbridge L (1981) Mathematical equations or processing routines? In: Rasmussen J, Rouse WB (eds) Human detection and diagnosis of system failures. Plenum, New York, pp 259–286CrossRefGoogle Scholar
  3. Bainbridge L (1997) The change in concepts needed to account for human behavior in complex dynamic tasks. IEEE Trans Syst Man Cybern Part A Syst Humans 27(3):351–359CrossRefGoogle Scholar
  4. Beaton SJ, Nag SS et al (2004) Adequacy of glycemic, lipid, and blood pressure management for patients with diabetes in a managed care setting. Diabetes Care 27(3):694–698CrossRefGoogle Scholar
  5. Brehmer B (1990) Strategies in real-time dynamic decision making. In: Hogarth R (ed) Insights in decision making: a tribute to Hillel J. Einhorn. University of Chicago Press, ChicagoGoogle Scholar
  6. Brehmer B (1992) Dynamic decision-making—human control of complex-systems. Acta Psychol 81(3):211–241CrossRefGoogle Scholar
  7. Broadbent D, Fitzgerald P et al (1986) Implicit and explicit knowledge in the control of complex systems. Br J Psychol 77:33–50CrossRefGoogle Scholar
  8. Cheney M (1997) Inverse boundary-value problems; from oil prospecting to medicine, the science of remote sensing benefits from interaction between mathematicians and computers. Am Scientist 85:448–455Google Scholar
  9. Conant R, Ashby WR (1970) Every good regulator of a system must be a model of that system. Int J Syst Sci 1(2):89–97CrossRefGoogle Scholar
  10. Doyle JK, Ford DN (1998) Mental models concepts for system dynamics research. Syst Dyn Rev 14(1):3–29CrossRefGoogle Scholar
  11. Dutta P, Biltz GR et al (2005) SimCare: a model for studying physician decision making activity. In: Henriksen K, Battle J, Marks E, Lewin D (eds) Advances in patient safety: from research to implementation: programs, tools, and products, vol 4. Agency for Healthcare Research and Quality, Rockville, pp 179–192Google Scholar
  12. Edwards W (1961) Behavioral decision theory. Ann Rev Psychol 12:473–498CrossRefGoogle Scholar
  13. Gale EAM (2006) Declassifying diabetes. Diabetologia 49:1989–1995CrossRefGoogle Scholar
  14. Garcia C, Morari M (1982) Internal model control. 1. A unifying review and some new results. Ind Eng Chem Process Des Dev 21:308–323CrossRefGoogle Scholar
  15. Garcia C, Prett D et al (1989) Model predictive control: theory and practice—a survey. Automatica 25(3):335–348CrossRefGoogle Scholar
  16. Gibson FP, Fichman M et al (1997) Learning in dynamic decision tasks: computational model and empirical evidence. Organ Behav Human Decis Process 71(1):1–35CrossRefGoogle Scholar
  17. Guven S, Kuenzi J et al (2005) Diabetes mellitus and the metabolic syndrome. In: Porth CM (ed) Pathophysiology: concepts of altered health states. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  18. Holman R, Paul S et al (2008) 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med 359(15):1577–1589CrossRefGoogle Scholar
  19. Hunt LM, Arar NH (2001) An analytical framework for contrasting patient and provider views of the process of chronic disease management. Med Anthropol Q 15(3):347–367CrossRefGoogle Scholar
  20. Johansson B (2003) Feedforward control in dynamic situations. PhD thesis. Linkoping UniversityGoogle Scholar
  21. Jordan M, Rumelhart D (1992) Forward models: supervised learning with a distal teacher. Cognit Sci 16:307–354CrossRefGoogle Scholar
  22. Jordan M, Wolpert D (2004) Computational motor control. In: Gazzaniga M (ed) The cognitive neuroscience III. MIT, CambridgeGoogle Scholar
  23. Karniel A (2002) Three creatures named ‘forward model’. Neural Netw 15:305–307CrossRefGoogle Scholar
  24. Karter A, Moffet H et al (2007) Glycemic response to newly initiated diabetes therapies. Am J Manag Care 13:598–606Google Scholar
  25. Kirwan B, Ainsworth LK (1991) A guide to task analysis: the task analysis working group. Taylor & Francis, LondonGoogle Scholar
  26. Lee TH, Wang QG et al (1996) Robust smith-predictor controller for uncertain delay systems. AIChE J 42(4):1033–1040CrossRefGoogle Scholar
  27. Leigh JR (1997) Control theory: a guided tour. Peter Peregrinus, LondonGoogle Scholar
  28. Lin C-L, Su H-W (2000) Intelligent control theory in guidance and control system design: an overview. Proc Natl Sci Counc ROC(A) 24(1):15–30Google Scholar
  29. Martin M, Gonzalez C et al (2004) Learning to make decisions in dynamic environments: ACT-R plays the beer game. In: Sixth international conference on cognitive modeling. Carnegie Mellon University/University of Pittsburgh, PittsburghGoogle Scholar
  30. Mazze R, Strock ES et al (2005) Staged diabetes management: a systematic approach, 2nd edn. Wiley, HobokenGoogle Scholar
  31. Nathan DM, Buse JB et al (2009) Medical management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy. Diabetes Care 32(1):193–203CrossRefGoogle Scholar
  32. Navarro G (2001) A guided tour to approximate string matching. ACM Comput Surv 33(1):31–88CrossRefGoogle Scholar
  33. Newell A (1982) The knowledge level. Artif Intell 18:87–127CrossRefGoogle Scholar
  34. O’Connor PJ, Sperl-Hillen JM et al (2009) Simulated physician learning intervention to improve safety and quality of diabetes care: a randomized trial. Diabetes Care 32(4):585–590CrossRefGoogle Scholar
  35. Rouse W, Morris N (1986) On looking into the black box: prospects and limits in the search for mental models. Psychol Bullet 100(3):349–363CrossRefGoogle Scholar
  36. Seborg DE, Edgar TF et al (1986) Adaptive control strategies for process control: a survey. AIChE J 32(6):881–913CrossRefGoogle Scholar
  37. Soukoreff RW, MacKenzie IS (2001) Measuring errors in text entry tasks: an application of the Levenshtein string distance statistic. In: ACM conference on human factors in computing systems. ACM, New YorkGoogle Scholar
  38. Soukoreff RW, MacKenzie IS (2003) Metrics for text entry research: an evaluation of MSD and KSPC, and a new unified error metric. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, Ft. LauderdaleGoogle Scholar
  39. Stanton MW (2001) Improving care for diabetes patients through intensive therapy and a team approach. Research in action. U. S. D. o. H. H. Services. Agency for Healthcare Research and Quality, Rockville. November 2001, pp. 1–12Google Scholar
  40. Sterman JD (1989) Misperceptions of feedback in dynamic decision making. Organ Behav Hum Decis Process 43:301–335CrossRefGoogle Scholar
  41. Ungar L, Hartmann E et al (1996) Process modeling and control using neural networks. In: First international conference on intelligent systems in process engineering. American Institute for Chemical Engineers, Central Florida, pp 312–318Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Gregory W. Ramsey
    • 1
    Email author
  • Paul E. Johnson
    • 2
  • Patrick J. O’Connor
    • 3
  • JoAnn M. Sperl-Hillen
    • 3
  • William A. Rush
    • 3
  • George Biltz
    • 4
  1. 1.The Earl G. Graves School of Business and ManagementMorgan State UniversityBaltimoreUSA
  2. 2.Carlson School of ManagementUniversity of MinnesotaMinneapolisUSA
  3. 3.HealthPartners Institute for Education and ResearchMinneapolisUSA
  4. 4.School of KinesiologyUniversity of MinnesotaMinneapolisUSA

Personalised recommendations