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The Impact of Data Analytics on Patient Flow Management and Resource Allocation: A Resource-Based View
Authors: Sidney Anderson (Texas State University, United States of America)
Given the proliferation of healthcare information systems, electronic medical records (EHRs), smart devices, wearables, and mobile apps, the healthcare industry is experiencing an increasingly rapid digital transformation. As a result, healthcare organizations now possess enormous amounts of digital health services-related data that has enormous potential to improve the delivery of care. Unlike industries such as banking and retail industries, most healthcare organizations have traditionally not viewed their data as “a central asset source for competitive advantages” (Murdoch and Detsky, 2016). Considering the increasingly competitive nature of the healthcare industry, healthcare organizations have recently begun to rely on analytics to transform raw data into actionable insights (Goli-Malekabadi et al., 2016). Analytics is generally referred to as the “systematic use of technologies, methods, and data to derive insights and to enable fact-based decision-making for planning, management, operations, measurement, and learning” (Deloitte, 2015). A Deloitte Center for Health Solutions (2015) report indicates that fewer than 50% of healthcare organizations “have a clear, integrated analytics strategy,” while the vast majority (approximately 80%) recognize analytics as key to delivering value-based care (VBC).
The purpose of this research project is to examine how data analytics can be used to optimize patient workflow management and resource allocation in hospitals in an effort to enhance their performance. The theory used for this study is Resource-Based View (RBV) theory, which has been effective in analyzing the impact of process improvement initiatives in healthcare (Burton and Rycoft-Malone, 2014). In addition, this study employs a conceptual framework developed by Yang and Hajli (2017), which has been effectively applied in prior investigations into various health technologies. The measures used in this study come from two data sources, the Centers for Medicare and Medicaid Services (CMS) and American Hospital Directory (AHD). The CMS database provided the initial sample of approximately 4,600 hospitals and was matched to the AHD database. Using a unique CMS identifying number that exists in both databases, approximately 3,200 hospitals were matched in the CMS and AHD sources.
To conduct this study’s empirical examination, Tableau Business Intelligence software is being used to analyze and “visualize” the two sources of secondary data mentioned above. This research design uses a multi-step approach to conduct the empirical analyses, which begins with analyzing patient flow using hospital-level data (e.g., EHRs, level of care, arrivals, length of stay, discharges), followed by an analysis of patient flow on resource allocation. The preliminary results suggest that hospitals can improve on both performance dimensions, which is visualized by a dual performance efficiency frontier figure that plots hospitals’ efficiency across both performance dimensions. Managerial implications are provided by identifying the unique characteristics possessed by hospitals that are achieving dual efficiency on both patient flow and resource allocation.