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

Information Systems Research, Forthcoming

39 Pages Posted: 6 Feb 2009 Last revised: 31 Aug 2012

See all articles by Tridas Mukhopadhyay

Tridas Mukhopadhyay

Carnegie Mellon University - David A. Tepper School of Business

Param Vir Singh

Carnegie Mellon University - David A. Tepper School of Business

Seung Hyun Kim

National University of Singapore (NUS)

Date Written: February 6, 2009

Abstract

To improve operational efficiencies while providing state of the art healthcare services, hospitals rely on IT 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), non-emergency (NE), and non-emergency out of network (NO). Further, the model accounts for complementarities 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 complementarities 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. The learning curve estimation of 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.

Keywords: Business Value of IT, Economics of IS, Econometrics, Physician Referral System, Learning Curves, IT Enabled Call Centers.

JEL Classification: M53, M54

Suggested Citation

Mukhopadhyay, Tridas and Singh, Param Vir and Kim, Seung Hyun, Learning Curves of Agents with Diverse Skills in Information Technology Enabled Physician Referral Systems (February 6, 2009). Information Systems Research, Forthcoming, Available at SSRN: https://ssrn.com/abstract=1338884 or http://dx.doi.org/10.2139/ssrn.1338884

Tridas Mukhopadhyay

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States
412-268-2307 (Phone)

HOME PAGE: http://web.gsia.cmu.edu/display_faculty.aspx?id=102

Param Vir Singh (Contact Author)

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States
412-268-3585 (Phone)

Seung Hyun Kim

National University of Singapore (NUS) ( email )

1E Kent Ridge Road
NUHS Tower Block Level 7
Singapore, 119228
Singapore

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