Leveraging the Granularity of Healthcare Data: Essays on Operating Room Scheduling for Productivity and Nurse Retention

The primary objective of this dissertation is to provide insights for healthcare practitioners to leverage the granularity of their healthcare data. In particular, leveraging the granularity of healthcare data using data analytics helps practitioners to manage operating room scheduling for productivity and nurse retention. This dissertation addresses the practical challenges of operating room (OR) scheduling by combining the existing insights from the prior literature through various tools in data analytics. In doing so, this dissertation consists of three chapters that operationally quantify the operational characteristics of the operating room and surgical team scheduling to improve operating room outcomes, including OR planning and OR nurse retention. This dissertation contributes to healthcare operations research and practice by emphasizing the importance of using granular information from hospitals’ electronic health records. While the prior research suggests that different team compositions affect OR productivity and OR time prediction, the empirical insights on how the team composition information can be utilized in practice are limited. We fill this gap by presenting data-driven approaches to use this information for OR time prediction and nurse retention. The first and third chapters deal with OR time prediction with the granular procedure, patient, and detailed team information to improve the OR scheduling. The second chapter deals with the OR nurse retention problem under OR nurses’ unique work scheduling environment. The first chapter, which is a joint work with Ahmet Colak, Lawrence Fredendall, and Robert Allen, examines drivers of OR time and their impact on OR time allocation mismatches (i.e., deviations of scheduled OR time from the realized OR time). Building on contemporary health care and empirical methodologies, the chapter identifies two mechanisms that spur scheduling mismatches: (i) OR time allocations that take place before team selections and (ii) OR time allocations that do not incorporate granular team and case data inputs. Using a two-stage estimation framework, the chapter shows how under- and over-allocation of OR times could be mitigated in a newsvendor ii setting using improved OR time predictions for the mean and variance estimates. The chapter’s empirical findings indicate that scheduling methods and the resulting scheduling mismatches have a significant impact on team performance, and deploying granular data inputs about teams—such as dyadic team experience, workload, and back-to-back case assignments—and updating OR times at the time of team selection improve OR time predictions significantly. In particular, the chapter estimates a 32% reduction in absolute mismatch times and a more than 20% reduction in OR costs. The second chapter, which is a joint work with Ahmet Colak and Lawrence Fredendall, addresses the turnover of OR nurses who work with various partners to perform various surgical procedures. Using an instrumental variable approach, the chapter identifies the causal relationship between OR nurses’ work scheduling and their turnover. To quantify the work scheduling characteristics—procedure, partner, and workload assignments, the chapter leverages the granularity of the OR nurse work scheduling data. Because unobserved personal reasons of OR nurses may lead to a potential endogeneity of schedule characteristics, the chapter instruments for the schedule characteristics using nurse peers’ average characteristics. The results suggest that there are significant connections between nurse departure probability and how procedures, partners, and workload are configured in nurses’ schedules. Nurses’ propensity to quit increases with high exposure and diversity to new procedures and partners and with high workload volatility while decreasing with the workload in their schedules. Furthermore, these effects are significantly moderated by the seniority of nurses in the hospital. The chapter also offers several explanations of what might drive these results. The chapter provides strategic reasoning for why hospitals must pay attention to designing the procedure, partner, and workload assignments in nurse scheduling to increase the retention rate in the ongoing nursing shortage and high nurse turnover in the U.S. The third chapter, which is a joint work with Ahmet Colak, Lawrence Fredendall, Babur De los Santos, and Benjamin Grant, systematically reviews the literature to gain more insights into addressing the challenges in OR scheduling to utilize granular team information for OR time prediction. Research in OR scheduling—allocating time to surgical procedures—is entering a new phase of research direction. Recent studies indicate that utilizing team information in OR scheduling can significantly improve the prediction accuracy of OR time, reducing the total cost of idle time and overtime. Despite the importance, utilizing granular team information is challenging due to the multiple decision-makers in surgical team scheduling and the presence of hierarchical structure in surgical teams. Some studies provide some insights on the relative influence of team members, which iii partly helps address these challenges, but there are still limited insights on which decision-maker has the greatest influence on OR time prediction and how hierarchy is aligned with the relative impact of surgical team members. In its findings, the chapter confirms that there are limited empirical insights in the existing literature. Based on the prior insights and the most recent development in this domain, this chapter proposes several empirical strategies that would help address these challenges and determine the key decision-makers to use granular team information of the most importance

Using topography to aid cell phone geolocation

In daily life, people demand accuracy of the Global Positioning System (GPS) receiver. The current problem of GPS on mobile phones is that it is not available in areas such as urban, natural canyons, forests, and indoor environments. Several methods have been developed to obtain more accurate position estimation over the past years. The received signal strength (RSS) and time difference of arrival (TDOA) are the main approaches to use available mobile signals and errors around 4 ~ 12 dB and 10 ~ 60 meters, respectively. Another approach to make a better performance of the sensor is to use radio frequency identification (RFID) with indoor Wi-Fi. A new method from our group shows that using magnetic field intensity maps based on interval analysis can perform better than the RSS, TDOA and RFID and reduce error for geolocation in some areas where GPS is not accessible. In our study, we develop a novel algorithm where sensor measurements on the cell phone are used to construct the topographic maps and aid cell phone geolocation which focuses on the angles of inclination in user\u27s pathway when GPS is spotty. This can be particularly useful on uneven terrain outdoors. For sensor characterization, we use application in android operating system of smartphone by name of sensor stream IMU+GPS. The sensor stream allows for users to observe, select or record the current values of various measurements such as accelerations, angular rates (gyroscope), magnetic fields, GPS position and received signal strength indication (RSSI) in 3-dimensional coordinate system. We firstly develop algorithms of fast fourier transform and low pass filter to find the accurate vertical acceleration measurements which impact values are corresponded to step occurrences. Before analyzing position estimation, we use the relationship between the stride length and stride interval and the methodology of detecting peak values to find the user\u27s step. In order to reduce uncertainty and find the user\u27s walking direction in our navigation system, we apply the Kalman filter and rotation matrix. We then develop optimization algorithms to bound the local position estimation into small 2-dimensional intervals using the interval analysis and dynamic estimation. After transforming the history of gravitational vectors to a fixed local-coordinate frames, we are able to construct a topographical map of pathway. We test our methodology in controlled conditions on an instrumented treadmill and also outdoors where GPS is available. We then use our topographic mapping to augment the results from pedometry and magnetic mapping to obtain better geolocation

BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition

Despite the remarkable progress achieved on automatic speech recognition, recognizing far-field speeches mixed with various noise sources is still a challenging task. In this paper, we introduce novel student-teacher transfer learning, BridgeNet which can provide a solution to improve distant speech recognition. There are two key features in BridgeNet. First, BridgeNet extends traditional student-teacher frameworks by providing multiple hints from a teacher network. Hints are not limited to the soft labels from a teacher network. Teacher's intermediate feature representations can better guide a student network to learn how to denoise or dereverberate noisy input. Second, the proposed recursive architecture in the BridgeNet can iteratively improve denoising and recognition performance. The experimental results of BridgeNet showed significant improvements in tackling the distant speech recognition problem, where it achieved up to 13.24% relative WER reductions on AMI corpus compared to a baseline neural network without teacher's hints.Comment: Accepted to 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

Measuring the Length of Period for the Long-Run Equilibrium in a Cointegration Relation

In economics the period of "long-run" often signifies the length of time within which transient fluctuations disappear, and a system comes back to an equilibrium state (or path). Among some interesting cases of long run analysis, the concept of cointegration is a relatively new concept of the long run equilibrium. This paper discusses how to determine the length of the long-run period for a cointegration relation. In an application to a consumption-income relation for three countries. U.S., Germany and Japan, we found that the length of the long-run period for the relation for these countries is about two to three years

Do Consumption and Income Have a Long Run Relationship?

This paper provides some new empirical evidence on the consumption-income relation which is one of the most thoroughly studied subjects in economics. According to the recent literature in economics the two variables should be co integrated for many theoretical results in economics, such as the permanent income hypothesis, to be meaningful. Our initial empirical results, however, show that cointegration between income and consumption is not well confirmed for U.S. quarterly data for extended postwar periods. This is an important problem that has to be addressed in the literature. In this paper we conjecture that failure of confirming cointegration for the consumption-income relation is due to nonstationarity fluctuations in some relatively short period(s) although the relation prevails in the majority of data period. Our empirical result confirms our conjecture. Two periods of "short-run" nonstationarity are identified for an extended postwar era of the U.S. economy: One is the Volker era in the early 1980's and the other consists of the recent years of unusually low interest rate. Our result has important implication for empirical analysis in economics where consumption and income variables are involved