Business Process Redesign in the Perioperative Process: A Case Perspective for Digital Transformation

This case study investigates business process redesign within the perioperative process as a method to achieve digital transformation. Specific perioperative sub-processes are targeted for re-design and digitalization, which yield improvement. Based on a 184-month longitudinal study of a large 1,157 registered-bed academic medical center, the observed effects are viewed through a lens of information technology (IT) impact on core capabilities and core strategy to yield a digital transformation framework that supports patient-centric improvement across perioperative sub-processes. This research identifies existing limitations, potential capabilities, and subsequent contextual understanding to minimize perioperative process complexity, target opportunity for improvement, and ultimately yield improved capabilities. Dynamic technological activities of analysis, evaluation, and synthesis applied to specific perioperative patient-centric data collected within integrated hospital information systems yield the organizational resource for process management and control. Conclusions include theoretical and practical implications as well as study limitations

Card-Based Remittances: A Closer Look at Supply and Demand

Analyzes the supply and demand for card-based transfers among Latin American and Caribbean immigrants sending remittances. Outlines card features and fee structures, and examines usage by country of origin, legal status, location, and card type

The Return of the Rogue

The “rogue trader”—a famed figure of the 1990s—recently has returned to prominence due largely to two phenomena. First, recent U.S. mortgage market volatility spilled over into stock, commodity, and derivative markets worldwide, causing large financial institution losses and revealing previously hidden unauthorized positions. Second, the rogue trader has gained importance as banks around the world have focused more attention on operational risk in response to regulatory changes prompted by the Basel II Capital Accord. This Article contends that of the many regulatory options available to the Basel Committee for addressing operational risk it arguably chose the worst: an enforced selfregulatory regime unlikely to substantially alter financial institutions’ ability to successfully manage operational risk. That regime also poses the danger of high costs, a false sense of security, and perverse incentives. Particularly with respect to the low-frequency, high-impact events—including rogue trading—that may be the greatest threat to bank stability and soundness, attempts at enforced self-regulation are unlikely to significantly reduce operational risk, because those financial institutions with the highest operational risk are the least likely to credibly assess that risk and set aside adequate capital under a regime of enforced self-regulation

How Does Law Affect Finance? An Empirical Examination of Tunneling in an Emerging Market

This paper documents that law affects finance in emerging markets through the methods used by controlling shareholders to “tunnel” wealth out of the firm. We find that Bulgarian securities law enabled financial tunneling via dilution and freeze-out tender offers. During the period 1999- 2001, about two-thirds of the 1,040 firms on the Bulgarian Stock Exchange were delisted. Freeze-out tender offers for minority shares averaged about 25% of the shares’ intrinsic value. Bulgarian securities law changes in 2002 made financial tunneling more costly for controlling shareholders. Subsequent increases in stock market valuations and liquidity suggest that controlling shareholders have shifted from financial tunneling to less value-destroying methods, such as transfer pricing, to extract wealth from firms.http://deepblue.lib.umich.edu/bitstream/2027.42/40128/3/wp742.pd

STV-based Video Feature Processing for Action Recognition

In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end