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

Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

Fault detection in operating helicopter drive train components based on support vector data description

The objective of the paper is to develop a vibration-based automated procedure dealing with early detection of mechanical degradation of helicopter drive train components using Health and Usage Monitoring Systems (HUMS) data. An anomaly-detection method devoted to the quantification of the degree of deviation of the mechanical state of a component from its nominal condition is developed. This method is based on an Anomaly Score (AS) formed by a combination of a set of statistical features correlated with specific damages, also known as Condition Indicators (CI), thus the operational variability is implicitly included in the model through the CI correlation. The problem of fault detection is then recast as a one-class classification problem in the space spanned by a set of CI, with the aim of a global differentiation between normal and anomalous observations, respectively related to healthy and supposedly faulty components. In this paper, a procedure based on an efficient one-class classification method that does not require any assumption on the data distribution, is used. The core of such an approach is the Support Vector Data Description (SVDD), that allows an efficient data description without the need of a significant amount of statistical data. Several analyses have been carried out in order to validate the proposed procedure, using flight vibration data collected from a H135, formerly known as EC135, servicing helicopter, for which micro-pitting damage on a gear was detected by HUMS and assessed through visual inspection. The capability of the proposed approach of providing better trade-off between false alarm rates and missed detection rates with respect to individual CI and to the AS obtained assuming jointly-Gaussian-distributed CI has been also analysed

The next frontier: Fostering innovation by improving health data access and utilization

Beneath most lively policy debates sit dry-as-dust theoretical and methodological discussions. Current disputes over the EU Adaptive Pathways initiative and the proposed US 21st Century Cures Act may ultimately rest on addressing arcane issues of data curation, standardization, and utilization. Improved extraction of inform ation on the safety and effectiveness of drugs-in-use must parallel adjustments in evidence requirements at the time of licensing. To do otherwise may compromise safety and efficacy in the name of fostering innovation

Implications of customer participation in outsourcing noncore services to third parties

Purpose Focal service providers increasingly involve customers in the decision-making about outsourcing parts of the service delivery process to third parties. The present study investigates how customers' outsourcing decisions affect the formation of the waiting experience with the focal service provider, by which the objective waiting time, environmental quality and interactional quality act as focal drivers. Design/methodology/approach To test our hypotheses in the context of cancer care, we gathered process data and experience data by means of a patient observation template (n = 640) and a patient survey (n = 487). The combined data (n = 377) were analyzed using Bayesian models. Findings This study shows that opting for a service triad (i.e. outsourcing non-core services to a third party) deduces customers' attention away from the objective waiting time with the focal service provider but not from the environmental and interactional quality offered by the focal service provider. When the type of service triad coordination is considered, we observe similar effects for a focal service provider-coordinated service triad while in a customer-coordinated service triad the interactional quality is the sole experience driver of waiting experiences that remains significant. Originality/value By investigating the implications of customer participation in the decision-making about outsourcing parts of the service delivery process to third parties, this research contributes to the service design, service triad and service operations literature. Specifically, this study shows that customer outsourcing decisions impact waiting experience formation with the focal service provider.Purpose Focal service providers increasingly involve customers in the decision-making about outsourcing parts of the service delivery process to third parties. The present study investigates how customers' outsourcing decisions affect the formation of the waiting experience with the focal service provider, by which the objective waiting time, environmental quality and interactional quality act as focal drivers. Design/methodology/approach To test our hypotheses in the context of cancer care, we gathered process data and experience data by means of a patient observation template (n = 640) and a patient survey (n = 487). The combined data (n = 377) were analyzed using Bayesian models. Findings This study shows that opting for a service triad (i.e. outsourcing non-core services to a third party) deduces customers' attention away from the objective waiting time with the focal service provider but not from the environmental and interactional quality offered by the focal service provider. When the type of service triad coordination is considered, we observe similar effects for a focal service provider-coordinated service triad while in a customer-coordinated service triad the interactional quality is the sole experience driver of waiting experiences that remains significant. Originality/value By investigating the implications of customer participation in the decision-making about outsourcing parts of the service delivery process to third parties, this research contributes to the service design, service triad and service operations literature. Specifically, this study shows that customer outsourcing decisions impact waiting experience formation with the focal service provider.A

A Primer for Monitoring Water Funds

This document is intended to assist people working on Water Funds to understand their information needs and become familiar with the strengths and weaknesses of various monitoring approaches. This primer is not intended to make people monitoring experts, but rather to help them become familiar with and conversant in the major issues so they can communicate effectively with experts to design a scientifically defensible monitoring program.The document highlights the critical information needs common to Water Fund projects and summarizes issues and steps to address in developing a Water Fund monitoring program. It explains key concepts and challenges; suggests monitoring parameters and an array of sampling designs to consider as a starting-point; and provides suggestions for further reading, links to helpful resources,and an annotated bibliography of studies on the impacts that result from activities commonly implemented in Water Fund projects

Analyzing the determinants of e-commerce in small and medium-sized enterprises: a cognition-driven framework

The increasing use of information technology in enterprises’ daily operations has led to multiple innovative ways to run a business, including electronic commerce (hereafter, e-commerce). However, firms with fewer resources, such as small and medium-sized enterprises (SMEs), are more reluctant to use electronic channels during transactions. This aversion to contemporary business models is a result of these companies’ lack of knowledge and capabilities regarding e-commerce. To improve their businesses, SMEs’ managers and decision makers could benefit from a methodological framework that fosters a deeper understanding of the determinants of e-commerce. This study sought to explore the use of fuzzy cognitive mapping to address this need. The results are grounded in the knowledge and experience of a panel of experts in e-commerce. The fuzzy cognitive map (FCM) developed shows that entrepreneur profile, market, operational management, marketing and promotions, website and digital platform, and products present the highest centrality indices as determinants of SME e-commerce. The findings offer a better understanding of the cause-and-effect relationships between these determinants. The advantages, limitations, and shortcomings of our constructivist proposal are also discussed.info:eu-repo/semantics/publishedVersio

Relation of Swine Industrial Livestock Operation Air Emissions Exposures to Sleep Duration and Time Outdoors in Residential Host Communities

Residents of communities hosting swine industrial livestock operations (ILOs) in North Carolina are exposed to mixtures of air pollutants originating from animal confinements, waste lagoons, and waste spray-field systems. To add to the understanding of swine ILO impacts on nearby community residents, I estimated the impact of swine ILO air emissions on sleep and time outdoors. These outcomes have not been formally assessed using epidemiologic methods, but are important components of quality-of-life, have implications for health and disease, and have been raised as concerns by community members. Acute exposure effects on sleep and time outdoors were estimated by applying discrete-time hazard models to data collected in the Community Health Effects of Industrial Hog Operations (CHEIHO) study. CHEIHO was a community-based, participatory research study that coupled continuous monitoring of pollutant plume markers with twice-daily odor and activity diaries. Dynamic Bayesian network models were used to estimate the total chronic effect of exposures accounting for potential feedback between subsequent exposures and outcomes. Detectible swine ILO pollutants at night was associated with an average sleep deficit of approximately 15 minutes. Exposure to outdoor odors was associated with decreased odds of being outdoors during the following hour (OR 0.62, 95% interval 0.44 to 0.89). Dynamic models estimated that the total effects of exposures exceeded the expected total effect calculated by summing individual acute effects, suggesting the importance of a feedback mechanism. The results demonstrate measurable and important impacts of ILO air emissions on sleep and time outdoors among those living nearby. The modeling approaches used were robust to bias from factors that remained constant for each participant over the course of the study and to factors that varied with the time-of-day or the weather, suggesting a causal effect. Policy interventions to reduce community exposures to swine ILO emissions from lagoon-and-sprayfield systems could have positive impacts on public health in rural North Carolina communities.Doctor of Philosoph

Building and testing necessity theories in supply chain management

This article contributes to the Emerging Discourse Incubator initiative by presenting how supply chain management scholars can contribute to theory development by means of necessity theories. These are unique theories that inform what level of a concept must be present to achieve a desired level of the outcome. Necessity theories consist of concepts that are necessary but not sufficient conditions for an outcome, where the absence of a single causal concept ensures the absence of the outcome. The theoretical features of necessary conditions have important implications for understanding supply chain management phenomena and providing practical applications. In 2016, Necessary Condition Analysis (NCA) became available for building and testing necessity theories with empirical data. However, NCA has not yet been used for the development of supply chain management theories. Therefore, we explain how necessity theories can be built and tested in a supply chain management context using necessity logic and the empirical methodology of NCA. We intend to inspire scholars to develop novel necessity theories that deepen or renew our understanding of supply chain management phenomena