Systemic Design Approach to a Real-Time Healthcare Monitoring System: Reducing Unplanned Hospital Readmissions †

Abstract

Following hospital discharge, millions of patients continue to recover outside formal healthcare organizations (HCOs) in designated transitional care periods (TCPs). Unplanned hospital readmissions of patients during TCPs adversely affects the quality and cost of care. In order to reduce the rates of unplanned hospital readmissions, we propose a real-time patient-centric system, built around applications, to assist discharged patients in remaining at home or in the workplace while being supported by care providers. Discrete-event system modeling techniques and supervisory control theory play fundamental roles in the system’s design. Simulation results and analysis show that the proposed system can be effective in documenting a patient’s condition and health-related behaviors. Most importantly, the system tackles the problem of unplanned hospital readmissions by supporting discharged patients at a lower cost via home/workplace monitoring without sacrificing the quality of care.

Data Analytics and Its Advantages for Addressing the Complexity of Healthcare: A Simulated Zika Case Study Example

“…U.S. healthcare is a complex system, both from a clinical perspective and from a business perspective [4]. Governmental regulations, insurance policies, billing and payment agreements, and internal entity policies have certainly led to a complex business organization inside every healthcare entity.…”

“…One product of the complex healthcare industry are the large volumes of data captured, much of which have the potential to improve performance on both the business and clinical side if used appropriately. The management of this increasing amount of healthcare data is crucial for having efficient healthcare management and decision making [4,6]. Therefore, information systems are needed to organize and catalog the massive amounts of information in a way that makes the data easily accessible and highly efficient for anyone using a simple search criteria [4][5][6].…”

“…The management of this increasing amount of healthcare data is crucial for having efficient healthcare management and decision making [4,6]. Therefore, information systems are needed to organize and catalog the massive amounts of information in a way that makes the data easily accessible and highly efficient for anyone using a simple search criteria [4][5][6]. The evolution of information technology, particularly related to data analytics, has the potential to reduce some of the complexity of healthcare, however, researchers must be willing to explore that potential to its fullest [6].…”

“…The use of analytics in healthcare has increased rapidly and has been shown to reduce costs and increase efficiencies in both the business and clinical areas [4,5]. Research has noted that incorporating solutions using data analytics has highlighted some of the difficulties in providing healthcare solutions including uneven data loads, having heterogeneity in applications, ranges of user expectations that are not consistent and latency sensitivity for validating models [6].…”

“…Research has noted that incorporating solutions using data analytics has highlighted some of the difficulties in providing healthcare solutions including uneven data loads, having heterogeneity in applications, ranges of user expectations that are not consistent and latency sensitivity for validating models [6]. However, improvements in algorithms, data and text mining, and the use of big data offer tremendous potential to reduce much of the complexity that surrounds decision making in healthcare [4,5]. For example, recent research has shown that using a discrete event simulation model to assist in data driven decision making for populations with dementia in Australia generated valid estimates of dementia as well as future resource requirements needed based on those estimates [7].…”