Doctor of Philosophy, The Ohio State University, 2024, Health and Rehabilitation Sciences

This dissertation presents the development and evaluation of the Agile Healthcare Performance Index (AHPI), a novel methodology designed to improve quality and measure performance within healthcare settings. It offers a framework designed to capture the complexities of healthcare delivery. Chapter 3 introduces the AHPI, emphasizing its significance in enhancing resource allocation and operational decision-making through an analysis of synthetic data across hospital service lines. The results underscore the adaptability and temporal sensitivity compared to static, unweighted indices, highlighting the potential to refine healthcare performance measurement. Chapter 4 extends the application of the AHPI to quality improvement (QI) initiatives, hypothesizing its effectiveness in aligning healthcare decision-making processes with the complex nature of care delivery. A simulation-based case study illustrates the alignment of the AHPI with the Cynefin framework's domains, demonstrating its strategic utility in navigating the dynamic challenges of healthcare. Chapter 5 focuses on the practical application of the AHPI in evaluating hip fracture care among the elderly, utilizing data from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP). The findings reveal the ability of the AHPI to accurately reflect variations in surgical outcomes, validating its role as a dynamic tool for quality improvement and policymaking across healthcare settings. Together, these studies advocate for the AHPI as a groundbreaking approach to healthcare performance assessment and QI. By integrating multidimensional metrics and a data-driven methodology, using the AHPI can provide a robust solution for enhancing care quality and operational efficiency, paving the way for a more adaptable and effective healthcare system.

Committee: Catherine Quatman-Yates (Advisor); Courtney Hebert (Committee Member); Lisa Juckett (Committee Member); Carmen Quatman (Committee Co-Chair) Subjects: Health Care; Health Sciences; Operations Research; Systems Design

PhD, University of Cincinnati, 2021, Business: Business Administration

This dissertation is motivated by the problem of crowding in the emergency department. A near-universal problem, crowding has been linked to negative outcomes for both patients and providers. A primary cause of crowding is the inherent stochasticity of patient arrivals. Stochasticity, while operationally problematic, is difficult to control in an emergency department where all patients seeking care must be seen. As it cannot be eliminated, accounting for stochasticity is critical to mitigating crowding in the emergency department. Because both crowding and its consequences occur in real time, any analytical model designed to support operational decisions must also provide insights in real time. A review of the literature reveals that, while many arrival forecasting models have been proposed, few have been assessed for their ability to support real-time decision-making during demand surges. This dissertation studies the design of such models with an eye towards operational support, such as the activation of backup staff when beneficial. Using a unique set of data --- made up of approximately 875,000 patient encounters from four hospitals across two health systems --- valuable insights as to the importance of distributional assumptions when forecasting during demand surges are identified. Namely, when quantifying the risk of a potential crowding event, discrete distributional forecasts (e.g., those with Poisson and Negative Binomial predictive distributions) will outperform typical Gaussian-based models. In addition, it is shown that proactive activation of backup staff, based on an appropriately constructed model, can lead to decreased patient waiting times compared to typical current practice. Importantly, this benefit to patients comes at a cost to schedule stability for providers. Intelligent management of this tradeoff presents opportunities for both improvements to practice and future research.

Committee: Craig Froehle Ph.D. (Committee Chair); Jeffrey Mills Ph.D. (Committee Member); Yichen Qin (Committee Member); Uday Rao Ph.D. (Committee Member) Subjects: Health Care

Doctor of Philosophy (PhD), Ohio University, 2020, Mechanical and Systems Engineering (Engineering and Technology)

The development of the self-evolving data collection engine through analytics and business intelligence (SEDCABI) research engine along with plug-in prediction module (PPM) is demonstrated for the prediction of cryptocurrency (specifically, Bitcoin). Leveraging all data proves increase the accuracy of the prediction when compared to using only structured data, or only using unstructured data alone.

Committee: Gary Weckman (Advisor) Subjects: Information Science; Information Systems

Doctor of Business Administration, Cleveland State University, 2020, Monte Ahuja College of Business

Business analytics (BA) continues to be one of the top technology trends in recent years as well as one of the top priorities for CIO's in many large enterprises. Business analytic tools can significantly help small businesses in quickly responding to changing market conditions and improving their organizational performance. However, prior studies report that the adoption rate of business analytics in small businesses is extremely low such that only 32 percent small businesses have adopted Business Intelligence (BI) and analytics solutions till now (SMB Group, 2018). As small businesses constitute a major force in the US economy, a slow rate of adoption of significant technological innovations, such as BA, may be a critical concern that can affect the economy in the longer run. Despite this, the extant small business literature as well as the information systems literature fails to provide an understanding of why small businesses are not receptive to current BA trends. Therefore, drawing upon the theoretical underpinnings of organizing vision theory, strategic orientation literature, and theory of upper echelon, this study investigates the willingness of small businesses to adopt newer innovations in BA. More specifically, this study investigates the impact of the reception of organizing vision of BA by owner-managers, learning orientation of small businesses, analytics orientation of small businesses, and personal characteristics of owner-mangers on small businesses' willingness to adopt BA. By drawing its motivation from prior strategic orientation and BA literature, this study is also among the first one to propose, formally develop, and validate the measurement construct of analytics orientation.

Committee: Radha Appan Dr. (Committee Chair); Raymond Henry Dr. (Committee Member); Sreedhar Madhavaram Dr. (Committee Member); Chieh-Chen Bowen Dr. (Committee Member) Subjects: Information Systems

Doctor of Philosophy, University of Toledo, 2018, Manufacturing and Technology Management

Today's small-market and mid-market sized manufacturers, competitively face increasing pressure to capture, integrate, operationalize, and manage diverse sources of digitized data. Many have made significant investments in data technologies with the objective to improve on organization performance yet not all have realized demonstrable benefits that create organization value. One simple question arises, do business-analytics make a difference on company performance in today's information intensive environment? The research purpose, to explore this question by looking through the lens of data-centric pressure placed on management driving the invested use of data-technologies; how these drivers impact on management influence to adopt a digitized organization mindset, effecting data practices, shaping key processes and strategies and leading to capabilities growth that impact on performance and culture. The terms `Big Data' and `Small Data' are two of the most prolific used phrases in today's world when discussing business analytics and the value data provides on organization performance. Big Data, being strategic to organization decision-making, and Small Data, operational; is captured from a host of internal and external sources. Studying how leveraging business-analytics into organizational value is of research benefit to both academic and practioner audiences alike. The research on `Big and Small Data, and business analytics' is both varied and deep and originating from a host of academic and non-academic sources; however, few empirical studies deeply examine the phenomena as experienced in the manufacturing environment. Exploring the pressures managers face in adopting data-centric managing beliefs, applied practices, understanding key value-creating process strategy mechanisms impacting on the organization, thus provides generalizable insights contributing to the pool of knowledge on the importance of data-technology investments impacting on organizational cul (open full item for complete abstract)

Committee: Paul Hong (Committee Chair); Thomas Sharkey (Committee Member); Wallace Steven (Committee Member); Cheng An Chung (Committee Member) Subjects: Information Systems; Information Technology; Management; Organization Theory; Organizational Behavior

Doctor of Business Administration, Cleveland State University, 2016, Monte Ahuja College of Business

Most organizations today use business analytics systems mainly for efficiency; reducing cost by contacting the right customer, generating revenue by reducing churn, etc. Nevertheless, business analytics holds promise in generating insights and in making users more creative in their decision making process. Analytics technology is becoming sophisticated with very advanced technical capabilities. However, behavioral aspects (i.e. user interaction) of using business analytics software have not reached the same level of sophistication. Very little research in this field discusses how to implement analytical systems and what outcomes will it produce. We are looking at conditions that can enhance user interaction with business analytics systems leading to certain performance outcomes. We propose that the fit between users' cognitive style (intuitive vs. rational), business analytics model representations (decision tree vs. clustering), and task type (convergent vs. divergent) can lead to efficiency but can have adverse effects on creativity because that might lead to mindlessness in the decision making process.

Committee: Raymond Henry PhD (Committee Chair); Radha Appan PhD (Committee Member); Amit Ghosh PhD (Committee Member); Robert Whitbred PhD (Committee Member) Subjects: Business Administration; Computer Science; Information Systems; Information Technology; Management; Statistics; Systems Design; Systems Science