2016 INFORMS Annual Meeting Program

SD69

INFORMS Nashville – 2016

SD68 Mockingbird 4- Omni Process Monitoring, Diagnosis, and Prognosis in Complex Systems Sponsored: Quality, Statistics and Reliability Sponsored Session Chair: Qiang Zhou, City University, 1, Kowloon, 1, Hong Kong, q.zhou@cityu.edu.hk Co-Chair: Li Zeng, Texas A&M University, College Station, TX, United States, lizeng@tamu.edu 1 - High Dimensional Process Monitoring Using Sparse Principal Component Analysis Mohammad Nabhan, Georgia Institute of Technology, nabhan@gatech.edu, Jianjun Shi Dimension reduction techniques, such as PCA and PLS, have been used for process monitoring in statistical process control. However, in high dimensional settings they suffer from inconsistency and interpretability issues. Sparse principal component analysis (SPCA) has been shown to be more consistent in these settings. Due to its sparse nature, it allows for better interpretation. This article proposes a monitoring and diagnostics scheme utilizing SPCA to reduce the dimensionality of the data while improving interpretability. The method is effective under certain stipulations on the spatial structure of the data streams. The proposed method is validated through simulation and a case study. 2 - Monitoring Low-e Glass Manufacturing Using Optical Profiles Qian Wu, Texas A&M University, College Station, TX, 77840, United States, hi_qianwu@tamu.edu, Li Zeng In this study we develop a method for process monitoring using optical profiles collected from low-E glass products. The proposed method uses a piecewise polynomial mixed-effect model to characterize the complex shape of optical profiles and a T2 control chart to monitor the estimated random effects for change detection. We investigate a potential problem caused by high correlations of random effects in implementing this method and propose a remedy based on regressor transformation for this issue. A case study will be shown, indicating the proposed method fits the real optical profiles and performs well in process monitoring. 3 - Remaining Useful Life Prediction In Populations With Heterogeneity Raed Kontar, University of Wisconsin - Madison, Madison, WI, United States, alkontar@wisc.edu Degradation signal data used for prognosis are often imbalanced as most units are reliable and only few tend to fail at early stages of their life cycle. Such imbalanced data may hinder accurate remaining useful life (RUL) prediction especially in terms of detecting pre-mature failures as early as possible. . In this paper, we propose a degradation signal-based RUL prediction method to address the imbalance issue in the data. The proposed method introduces a mixture prior distribution to capture the characteristics of different groups within the same population and provides an efficient and effective online prediction method for the in-service unit under monitoring. 4 - Statistical Monitoring And Fault Diagnosis Of Vibration Signal Based On Wavelet Transform Wei Fan, City University of Hong Kong, Kowloon, Hong Kong, weifan8-c@my.cityu.edu.hk, Qiang Zhou To effectively monitor and detect the early fault of rolling bearing, a wavelet- based statistical process control method is proposed and studied. The vibration signal is decomposed by orthonormal wavelet transform. The generalized likelihood ratio test is taken into consideration to detect the shift of the wavelet coefficients. To increase the detection power of the small shift, the proposed control chart takes the exponentially weighted moving average of the logarithm of the likelihood ratio. Both the simulation studies and the experimental cases show the effectiveness of the proposed method.

SD69 Old Hickory- Omni

Pierskalla II Award Session

Chair: Baris Ata, University of Chicago, Booth School of Business Co-Chair: Anton Skaro, Northwestern University, Feinberg School of Medicine Co-Chair: Sridhar Tayur, Carnegie Mellon University, Tepper School of Business 1 - Pierskalla Award Vikram Tiwari, Vanderbilt University Medical Center, Nashville, TN, Contact: vikram.tiwari@vanderbilt.edu The Health Applications Society of INFORMS sponsors an annual competition for the Pierskalla Award, which recognizes research excellence in the field of health care management science. The award is named after Dr. William Pierskalla to recognize his contribution and dedication to improving health services delivery through operations research. The Pierskalla award information can be found on the website at: https://www.informs.org/Community/HAS/Pierskalla-Award 2 - Online Decision-Making with High-Dimensional Covariates Hamsa Bastani, Mohsen Bayati, Stanford University, Stanford, CA, bayati@stanford.edu Big data has enabled decision-makers to tailor treatment decisions based on their clinical information. This involves learning a model of decision rewards conditional on individual patient covariates. These covariates are high- dimensional; typically only a small subset of the observed features are predictive of a decision’s success. We formulate this problem as a multi-armed bandit with high-dimensional covariates, and present a new efficient bandit algorithm based on the LASSO estimator. Our analysis establishes that our algorithm achieves near-optimal performance in comparison to an oracle that knows all the problem parameters. The key step in our analysis is proving a new oracle inequality that guarantees the convergence of the LASSO estimator despite the non-i.i.d. data induced by the bandit policy. We illustrate the practical relevance of our algorithm by evaluating it on a real-world clinical problem of warfarin dosing. A patient’s optimal warfarin dosage depends on the patient’s genetic and medical records. We show that our algorithm outperforms existing bandit methods as well as physicians to correctly dose patients. 3 - Do Mandatory Overtime Laws Improve Quality? Staffing Decisions and Operational Flexibility of NursingHomes Lauren Xiaoyuan Lu, University of North Carolina, Chapel Hill, NC, lauren_lu@unc.edu, Susan Feng Lu During the 2000s, over a dozen U.S. states passed laws that prohibit health care employers from mandating overtime for nurses. Using a nationwide panel dataset from 2004 to 2012, we find that these mandatory overtime laws reduced the service quality of nursing homes, as measured by an increase in deficiency citations. This outcome can be explained by two undesirable changes in the staffing hours of registered nurses: decreased hours of permanent nurses and increased hours of contract nurses per resident day. We observe that the increase in deficiency citations concentrates in the domains of administration and quality of care rather than quality of life, and the severity levels of the increased citations tend to be minor rather than major. We also find that the laws’ negative effect on quality is more severe in nursing homes with higher percentage of Medicare- covered residents. These observations are consistent with the predictions of a stochastic staffing model that incorporates demand uncertainty and operational flexibility. Further, we rule out an alternative hypothesis that the quality decline is induced by an increase in nurse wages. 4 - Data-Driven Incentive Design in the Medicare Shared Savings Program Anil Aswani, UC Berkeley, Berkeley, CA, aaswani@berkeley.edu, Zuo-Jun Shen, Auyon Saddiq The Medicare Shared Savings Program (MSSP) was created to control escalating Medicare spending by incentivizing providers to deliver healthcare more efficiently. Providers that enroll in the MSSP earn bonus payments for reducing spending to below a risk-adjusted financial benchmark. To generate savings, a provider must invest to improve efficiency, which is a cost that is absorbed entirely by the provider under the current contract. This has proven to be challenging for the MSSP, with a majority of participating providers unable to generate savings. In this paper, we formulate the MSSP as a principal-agent model and take a data-driven approach to redesigning the MSSP contract. We propose a new type of contract that includes a performance-based subsidy that partially reimburses the provider’s investment. We prove that there exists a subsidized contract that dominates the current MSSP contract by producing a strictly higher expected payoff for Medicare and the provider. We then present a maximum likelihood approach for estimating the parameters of the principal-agent model, using a dataset containing the financial performance of providers.

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