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Learning Curves of Agents with Diverse Skills in Information Technology-Enabled Physician Referral Systems

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  • Tridas Mukhopadhyay

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • ParamVir Singh

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Seung Hyun Kim

    (Department of Information Systems, National University of Singapore, Singapore 117417)

Abstract

To improve operational efficiencies while providing state of the art healthcare services, hospitals rely on information technology enabled physician referral systems (IT-PRS). This study examines learning curves in an IT-PRS setting to determine whether agents achieve performance improvements from cumulative experience at different rates and how information technologies transform the learning dynamics in this setting. We present a hierarchical Bayes model that accounts for different agent skills (domain and system) and estimate learning rates for three types of referral requests: emergency (EM), nonemergency (NE), and nonemergency out of network (NO). Furthermore, the model accounts for learning spillovers among the three referral request types and the impact of system upgrade on learning rates. We estimate this model using data from more than 80,000 referral requests to a large IT-PRS. We find that: (1) The IT-PRS exhibits a learning rate of 4.5% for EM referrals, 7.2% for NE referrals, and 12.3% for NO referrals. This is slower than the learning rate of manufacturing (on average 20%) and more comparable to other service settings (on average, 8%). (2) Domain and system experts are found to exhibit significantly different learning behaviors. (3) Significant and varying learning spillovers among the three referral request types are also observed. (4) The performance of domain experts is affected more adversely in comparison to system experts immediately after system upgrade. (5) Finally, the learning rate change subsequent to system upgrade is also higher for system experts in comparison to domain experts. Overall, system upgrades are found to have a long-term positive impact on the performance of all agents. This study contributes to the development of theoretically grounded understanding of learning behaviors of domain and system experts in an IT-enabled critical healthcare service setting.

Suggested Citation

  • Tridas Mukhopadhyay & ParamVir Singh & Seung Hyun Kim, 2011. "Learning Curves of Agents with Diverse Skills in Information Technology-Enabled Physician Referral Systems," Information Systems Research, INFORMS, vol. 22(3), pages 586-605, September.
    • Handle: RePEc:inm:orisre:v:22:y:2011:i:3:p:586-605
    DOI: 10.1287/isre.1110.0359
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    References listed on IDEAS

    as
    1. Wai Fong Boh & Sandra A. Slaughter & J. Alberto Espinosa, 2007. "Learning from Experience in Software Development: A Multilevel Analysis," Management Science, INFORMS, vol. 53(8), pages 1315-1331, August.
    2. Tridas Mukhopadhyay & Sunder Kekre, 2002. "Strategic and Operational Benefits of Electronic Integration in B2B Procurement Processes," Management Science, INFORMS, vol. 48(10), pages 1301-1313, October.
    3. Eelke Wiersma, 2007. "Conditions That Shape the Learning Curve: Factors That Increase the Ability and Opportunity to Learn," Management Science, INFORMS, vol. 53(12), pages 1903-1915, December.
    4. Yves Atchade, 2005. "An Adaptive Version for the Metropolis Adjusted Langevin Algorithm with a Truncated Drift," RePAd Working Paper Series LRSP-WP1, Département des sciences administratives, UQO.
    5. Scott M. Shafer & David A. Nembhard & Mustafa V. Uzumeri, 2001. "The Effects of Worker Learning, Forgetting, and Heterogeneity on Assembly Line Productivity," Management Science, INFORMS, vol. 47(12), pages 1639-1653, December.
    6. Eric D. Darr & Linda Argote & Dennis Epple, 1995. "The Acquisition, Transfer, and Depreciation of Knowledge in Service Organizations: Productivity in Franchises," Management Science, INFORMS, vol. 41(11), pages 1750-1762, November.
    7. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    8. Ray Reagans & Linda Argote & Daria Brooks, 2005. "Individual Experience and Experience Working Together: Predicting Learning Rates from Knowing Who Knows What and Knowing How to Work Together," Management Science, INFORMS, vol. 51(6), pages 869-881, June.
    9. Nile W. Hatch & David C. Mowery, 1998. "Process Innovation and Learning by Doing in Semiconductor Manufacturing," Management Science, INFORMS, vol. 44(11-Part-1), pages 1461-1477, November.
    10. Gavin Sinclair & Steven Klepper & Wesley Cohen, 2000. "What's Experience Got to Do With It? Sources of Cost Reduction in a Large Specialty Chemicals Producer," Management Science, INFORMS, vol. 46(1), pages 28-45, January.
    11. Linda Argote & Bill McEvily & Ray Reagans, 2003. "Managing Knowledge in Organizations: An Integrative Framework and Review of Emerging Themes," Management Science, INFORMS, vol. 49(4), pages 571-582, April.
    12. Sarv Devaraj & Rajiv Kohli, 2003. "Performance Impacts of Information Technology: Is Actual Usage the Missing Link?," Management Science, INFORMS, vol. 49(3), pages 273-289, March.
    13. Gary P. Pisano & Richard M.J. Bohmer & Amy C. Edmondson, 2001. "Organizational Differences in Rates of Learning: Evidence from the Adoption of Minimally Invasive Cardiac Surgery," Management Science, INFORMS, vol. 47(6), pages 752-768, June.
    14. Dennis Epple & Linda Argote & Kenneth Murphy, 1996. "An Empirical Investigation of the Microstructure of Knowledge Acquisition and Transfer Through Learning by Doing," Operations Research, INFORMS, vol. 44(1), pages 77-86, February.
    15. C. Lanier Benkard, 2000. "Learning and Forgetting: The Dynamics of Aircraft Production," American Economic Review, American Economic Association, vol. 90(4), pages 1034-1054, September.
    16. Melissa A. Schilling & Patricia Vidal & Robert E. Ployhart & Alexandre Marangoni, 2003. "Learning by Doing Something Else: Variation, Relatedness, and the Learning Curve," Management Science, INFORMS, vol. 49(1), pages 39-56, January.
    17. Yuqing Ren & Kathleen M. Carley & Linda Argote, 2006. "The Contingent Effects of Transactive Memory: When Is It More Beneficial to Know What Others Know?," Management Science, INFORMS, vol. 52(5), pages 671-682, May.
    18. Yves F. Atchadé, 2006. "An Adaptive Version for the Metropolis Adjusted Langevin Algorithm with a Truncated Drift," Methodology and Computing in Applied Probability, Springer, vol. 8(2), pages 235-254, June.
    19. Linda Argote & Sara L. Beckman & Dennis Epple, 1990. "The Persistence and Transfer of Learning in Industrial Settings," Management Science, INFORMS, vol. 36(2), pages 140-154, February.
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