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

Extensions of SNOMED taxonomy abstraction networks supporting auditing and complexity analysis

The Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) has been widely used as a standard terminology in various biomedical domains. The enhancement of the quality of SNOMED contributes to the improvement of the medical systems that it supports. In previous work, the Structural Analysis of Biomedical Ontologies Center (SABOC) team has defined the partial-area taxonomy, a hierarchical abstraction network consisting of units called partial-areas. Each partial-area comprises a set of SNOMED concepts exhibiting a particular relationship structure and being distinguished by a unique root concept. In this dissertation, some extensions and applications of the taxonomy framework are considered. Some concepts appearing in multiple partial-areas have been designated as complex due to the fact that they constitute a tangled portion of a hierarchy and can be obstacles to users trying to gain an understanding of the hierarchy’s content. A methodology for partitioning the entire collection of these so-called overlapping complex concepts into singly-rooted groups was presented. A novel auditing methodology based on an enhanced abstraction network is described. In addition, the existing abstraction network relies heavily on the structure of the outgoing relationships of the concepts. But some of SNOMED hierarchies (or subhierarchies) serve only as targets of relationships, with few or no outgoing relationships of their own. This situation impedes the applicability of the abstraction network. To deal with this problem, a variation of the above abstraction network, called the converse abstraction network (CAN) is defined and derived automatically from a given SNOMED hierarchy. An auditing methodology based on the CAN is formulated. Furthermore, a preliminary study of the complementary use of the abstraction network in description logic (DL) for quality assurance purposes pertaining to SNOMED is presented. Two complexity measures, a structural complexity measure and a hierarchical complexity measure, based on the abstraction network are introduced to quantify the complexity of a SNOMED hierarchy. An extension of the two measures is also utilized specifically to track the complexity of the versions of the SNOMED hierarchies before and after a sequence of auditing processes

Medical Informatics

Information technology has been revolutionizing the everyday life of the common man, while medical science has been making rapid strides in understanding disease mechanisms, developing diagnostic techniques and effecting successful treatment regimen, even for those cases which would have been classified as a poor prognosis a decade earlier. The confluence of information technology and biomedicine has brought into its ambit additional dimensions of computerized databases for patient conditions, revolutionizing the way health care and patient information is recorded, processed, interpreted and utilized for improving the quality of life. This book consists of seven chapters dealing with the three primary issues of medical information acquisition from a patient's and health care professional's perspective, translational approaches from a researcher's point of view, and finally the application potential as required by the clinicians/physician. The book covers modern issues in Information Technology, Bioinformatics Methods and Clinical Applications. The chapters describe the basic process of acquisition of information in a health system, recent technological developments in biomedicine and the realistic evaluation of medical informatics

The linguistic construction of business reasoning: Towards a language-based model of decision-making in undergraduate business

This thesis reports on research whose aim is to arrive at a linguistically theorised understanding of the process of decision-making in undergraduate business studies. The use of ‘real-life’ tasks such as country reports – the major assessment task of the interdisciplinary unit Business in the Global Environment at a metropolitan Australian university – is intended to prepare students for the skills of ‘problem-solving’, ‘decision-making’ and professional report writing in international business environments. However, as indicated by the large number of students failing this task, few students possess the sophisticated linguistic resources necessary to build the generic complexity and persuasive rhetoric this high-stakes task demands. This study is concerned with identifying the linguistic demands of demonstrating decision-making in country reports. Current modelling of ‘big texts’ in SFL (Martin, 1994, 1995) is insufficient for understanding longer texts stretching across the many pages tertiary students are generally required to write. This thesis will show through fine-grained linguistic analyses of High Distinction student assignments that not all ‘big texts’ are macrogenres made up of elemental genre complexes and illustrate that embedded genres play a fundamental role in enabling texts of the length of business country reports to grow bigger than a page. Drawing on discourse semantics (Martin, 1992; Martin & Rose, 2007; Martin & White, 2005), this thesis also will also show how business reasoning is construed in undergraduate business reports through different types of grammatical structures and how successful student writers construct cause-effect relations and three major types of rhetorical moves in these texts. By making visible the academically valued meanings by which skillful writers demonstrate the process of decision-making in undergraduate business country reports, this research has pedagogical implications for academic literacy interventions aimed at making explicit the basis of achievement in business studies. It is hoped that this study will open up future research directions for the continued study of knowledge-building in undergraduate business studies

Android security: analysis and applications

The Android mobile system is home to millions of apps that offer a wide range of functionalities. Users rely on Android apps in various facets of daily life, including critical, e.g., medical, settings. Generally, users trust that apps perform their stated purpose safely and accurately. However, despite the platform’s efforts to maintain a safe environment, apps routinely manage to evade scrutiny. This dissertation analyzes Android app behavior and has revealed several weakness: lapses in device authentication schemes, deceptive practices such as apps covering their traces, as well as behavioral and descriptive inaccuracies in medical apps. Examining a large corpus of applications has revealed that suspicious behavior is often the result of lax oversight, and can occur without an explicit intent to harm users. Nevertheless, flawed app behavior is present, and is especially problematic in apps that perform critical tasks. Additionally, manufacturer’s and app developer’s claims often do not mirror actual functionalities, e.g., as we reveal in our study of LG’s Knock Code authentication scheme, and as evidenced by the removal of Google Play medical apps due to overstated functionality claims. This dissertation makes the following contributions: (1) quantifying the security of LG’s Knock Code authentication method, (2) defining deceptive practices of self-hiding app behavior found in popular apps, (3) verifying abuses of device administrator features, (4) characterizing the medical app landscape found on Google Play, (5) detailing the claimed behaviors and conditions of medical apps using ICD codes and app descriptions, (6) verifying errors in medical score calculator app implementations, and (7) discerning how medical apps should be regulated within the jurisdiction of regulatory frameworks based on their behavior and data acquired from users

The Gene Ontology Handbook

bioinformatics; biotechnolog

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe