Background Policymakers have proposed risk-adjusted bundled payment as the single-most promising method of linking reimbursement to value rather than to quantity of service. Our objective was to assess the relationship between risk and cost to develop a model for forecasting cardiac surgery costs under a bundled payment scheme. Methods All patients undergoing adult cardiac surgery operations for which there was a Society of Thoracic Surgeons (STS) risk score over a 5-year period (2008–2013) at a tertiary care, university hospital were reviewed. Patients were stratified into 5 groups based on preoperative risk as a basis for negotiating risk-adjusted bundles. A multivariable regression model was developed to analyze the relationship between risk and log-transformed costs. Monte Carlo simulation was performed to validate the model by comparing predicted to actual FY2013 costs. Results Among the 2514 patients analyzed, preoperative risk was strongly correlated with costs (p<0.001) but was only able to explain 28% (R2=0.28) of the variation in costs between individual patients. Using bundling to diffuse and adjust for risk improved prediction to only 33% (R2=0.33). Actual costs in 2013 were $21.6M compared to predicted costs of $19.3M (±$350K), which is well outside the forecast’s 95% confidence interval. Conclusion Even among the most routine cardiac surgery operations using the most widely validated surgical risk score available, much of the variation in costs cannot be explained by preoperative risk or surgeon. Consequently, policymakers should re-examine whether individual practices or insurers are best suited to manage the residual financial risk.
The advent of big data has created opportunities for firms to customize their products and services to unprecedented levels of granularity. Using big data to personalize an offering in real time, however, remains a major challenge. In the mobile advertising industry, once a customer enters the network, an ad-serving decision must be made in a matter of milliseconds. In this work, we describe the design and implementation of an ad-serving algorithm that incorporates machine-learning methods to make personalized ad-serving decisions within milliseconds. We developed this algorithm for Vungle Inc., one of the largest global mobile ad networks. Our approach also addresses other important issues that most ad networks face, such as user fatigue, budget restrictions, and campaign pacing. In an A/B test versus the company's legacy algorithm, our algorithm generated a 23 percent increase in revenue per 1,000 impressions. Across the company's network, this increase represents a $1 million increase in monthly revenue.
In the early months of the 2007-08 financial crises, a loan manager faces a real estate financing decision. Should he approve a bullet structure three-year loan to a longstanding client, a legendary Texan developer? The developer, who near retirement downsized his business, is seeking financing for his only project: residential or commercial development on an attractive piece of land in suburban Houston. The loan manager considers the decision in light of the mortgage market turmoil, seeing commercial projects as safer, but also factoring that the residential market could bring higher returns if the market stabilizes soon. The manager collects the data and asks an analyst to assess the risks; that ultimately requires assessing the economics of both projects from both the bank’s and the developer’s perspectives. The bank could still change the interest rate on the loan to receive adequate compensation for the risk it carries, but the loan manager knows that doing so will change their long-term client willingness to take on the loan.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.