Abstract
Health care in the U.S. is notoriously inefficient and ineffective, in part because the various influences affecting the different classes of participant stymie traditional top-down analysis and management techniques. This chapter presents a novel application of complex, multi-agent simulation methods to a study of how informed patient choices can influence the distribution of surgical volume for complex procedures. The simulation suggests that payer networks can have the positive effect of changing the distribution of surgical volume so that there are more highervolume providers, but can also have the negative effect of increasing the cumulative complication rates for surgeries, likely resulting from lower maximum volumes.
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References
Auchincloss, A.H., Diez Roux, A.V.: A New Tool for Epidemiology: The Usefulness of Dynamic-Agent Models in Understanding Place Effects on Health. American Journal of Epidemiology 168(1), 1–8 (2008)
Axelrod, R., Axelrod, D., Pienta, K.J.: Evolution of cooperation among tumor cells. Proceedings of the National Academy of Sciences 103(36), 13474–13479 (2006)
Basole, R., Rouse, W.: Complexity of Science Value Networks: Conceptualization and Empirical Investigation. IBM Systems Journal 47(1), 53–70 (2008)
Begg, C.B., Cramer, L.D., Hoskin, W.J., et al.: Impact of hospital volume on operative mortality for major cancer surgery. JAMA 280, 1747–1751 (1998)
Begg, C.B., Riedel, E.R., Bach, P.B., et al.: Variations in morbidity after radical prostatectomy. N. Engl. J. Med. 346, 1138–1144 (2002)
Birkmeyer, J.D., Finlayson, S.R.G., Tosteson, A.N.A., et al.: Effect of hospital volume on in-hospital mortality with pancreaticoduodenectomy. Surgery 125, 250–256 (1999)
Birkmeyer, J.D., Siewers, A.E., Finlayson, E.V.A., et al.: Hospital volume and surgical mortality in the United States. N. Engl. J. Med. 346, 1128–1137 (2002)
Castiglione, F., Pappalardo, F., Bernaschi, M., Motta, S.: Optimization of HAART with genetic algorithms and agent-based models of HIV infection. Bioinformatics 23(24), 3350–3355 (2007)
Dimick, J.B., Pronovost, P.J., Cowan, J.A., et al.: Surgical volume and quality of care for esophageal resection: do high-volume hospitals have fewer complications? Ann. Thorac. Surg. 75, 337–341 (2003)
Dudley, R.A., Johansen, K.L., Brand, R., Rennie, D.J., Milstein, A.: Selective referral to high-volume hospitals: estimating potentially avoidable deaths. JAMA 283, 1159–1166 (2000)
Gordon, T.A., Burleyson, G.P., Tielsch, J.M., et al.: The effects of regionalization on cost and outcome for one general high-risk surgical procedure. Ann. Surg. 221, 43–49 (1995)
Gouma, D.J., van Geenen, R.C.J., van Gulik, T.M., et al.: Rates of complications and death after pancreaticoduodenectomy: risk factors and the impact of hospital volume. Ann. Surg. 232, 786–795 (2000)
Halm, E.A., Lee, C., Chassin, M.: Is volume related to outcome in health care? A systemic review and methodologic critique of the literature. Ann. Intern. Med. 137, 511–520 (2002)
Hannan, E.L., Radzyner, M., Rubin, D., et al.: The influence of hospital and surgeon volume on in-hospital mortality for colectomy, gastrectomy, and lung lobectomy in patients with cancer. Surgery 131, 6–15 (2002)
Crossing the Chasm: A New Health System for the 21st Century. Institute of Medicine (March 1, 2001)
To Err is Human: Building a Safer Health System. Institute of Medicine (November 1, 1999)
The Learning Healthcare System: Workshop Summary. Institute of Medicine (April 2, 2007)
Koopman, J.S.: Modeling Infection Transmission- The Pursuit of Complexities That Matter. Epidemiology 13(6), 622–624 (2002)
Lieberman, M.D., Kilburn, H., Lindsey, M., et al.: Relation of perioperative deaths to hospital volume among patients undergoing pancreatic resection for malignancy. Ann. Surg. 222, 638–645 (1995)
Rouse, W.B.: Healthcare as A Complex Adaptive System: Implications for Design and Management. National Academy of Engineering Website
Schrag, D., Panageas, K.S., Riedel, E., et al.: Hospital and surgeon procedure volume as predictors of outcome following rectal cancer resection. Ann. Surg. 236, 583–592 (2002)
Sosa, J.A., Bowman, H.M., Tielsch, J.A., et al.: The importance of surgeon experience for clinical and economic outcomes from thyroidectomy. Ann. Surg. 228, 320–330 (1998)
Wilensky, U.: NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999), http://ccl.northwestern.edu/netlogo/
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Studnicki, J., Eichelberger, C., Fisher, J. (2009). Complex Adaptive Systems: How Informed Patient Choice Influences the Distribution of Complex Surgical Procedures. In: Ras, Z.W., Ribarsky, W. (eds) Advances in Information and Intelligent Systems. Studies in Computational Intelligence, vol 251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04141-9_1
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DOI: https://doi.org/10.1007/978-3-642-04141-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04140-2
Online ISBN: 978-3-642-04141-9
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