The impact of big data and predictive analytics on patient agility in hospitals

Background
There is a wealth of attention for information technology (IT) adoption and IT-enabled transformation in healthcare research. However, there is still a limited understanding of big data’s role and its associated predictive models as a crucial enabler of the hospital’s ability to adequately ‘sense’ and ‘respond’ to patient needs and wishes, i.e., patient agility. We refer to these ‘sense’ and ‘respond’ capabilities in hospitals’ context as ‘patient agility.’ As such, this Ph.D. proposal embraces the dynamic capabilities view (DCV), a foundational strategic framework within both the management sciences and information sciences field, when it comes to the innovative use of big data and predictive analytics in hospitals.

Big data typically refers to datasets whose size is beyond the ability of conventional database software tools to capture, store, manage, and analyze both structured and unstructured data. Modern hospitals are actively exploring new digital options and data-driven innovations using big data to drive clinical care quality and strengthen patient relationships and interactions using digital technologies. Predictive analytics refers to the skills, practices, applications, and techniques to analyze current and historical information to predict future or even unknown events. It heavily relies on extensive use of data, statistical and quantitative analysis, and fact-based management to drive integrated decision-making. Predictive analytics is particularly relevant for hospitals from both a medical point-of-view as well as a managerial point-of-view. From the medical point of view, predictive analytics can be used to identify defects in care and risk factors for patient safety issues, occurrence patterns, and statistical testing of intervention strategies, evidence-based medicine / comparative effectiveness, and analyses to measure the impact of using clinically substitutable supply items on patient outcomes. From the managerial point of view, predictive analytics can be used to determine the time required to perform key patient care activities (e.g., passing medication) and to coordinate (and exchange) capacity within and between hospitals (or with other care providers, e.g., general practitioners). Despite these opportunities, hospitals have not yet fully grasped the value of predictive analytics.

Objectives
This Ph.D. aims to extend the DCV literature by showing how predictive analytics drive hospitals’ patient agility and embraces an open-innovation ‘lens’ to identify the key principles for predictive analytics to work in clinical practice. This open innovation lens is essential in this research as predictive analytics requires gathering data from a wide variety of data sources and various stakeholders within the hospital ecosystem (e.g., general practitioners, pharmacists, rehabilitation services, insurance companies, other hospitals, informal carers).