Can we improve healthcare with centralized management systems, supported by information technology, predictive analytics, and real-time data?: A review

Abstract

This narrative review discusses the effects of implementing command centers, centralized management systems, supported by information technology, predictive analytics, and real-time data, as well as small-scale centralized operating systems, on patient outcomes, operation, care delivery, and resource utilization. Implementations of command centers and small-scale centralized operating systems have led to improvement in 3 areas: integration of both multiple services into the day-to-day operation, communication and coordination, and employment of prediction and early warning system. Additional studies are required to understand the full impact of command centers on the healthcare system.

Application of a Human Factors Systems Approach to Healthcare Control Centres for Managing Patient Flow: A Scoping Review

“…In general, literature describing control structures for the facilitation of patient management is diverse, and guidance on the implementation and operation of such systems is lacking [ 8 , 11 , 12 , 43 ]. Papers such as Appelbaum et al provide some guideance on the establishment of an incident command center to manage a healthcare crisis [ 75 ].…”

“…Research indicates that information and communication technology can potentially support patient coordination by improving data dissemination processes [ 8 , 40 , 104 ]. A consistent theme we identified was the importance of information flow for efficient patient flow management, whether across the different elements of a healthcare system [ 11 , 63 , 66 , 80 , 92 , 93 ] or between healthcare entities and outside organisations for disaster management [ 49 , 55 , 62 , 69 , 70 , 72 , 73 , 95 ].…”

“…Healthcare is a data-rich environment and articifial intelligence and machine learning algorithms can be integrated into systems to predict current and short-term future state of the system to manage patient coordination and resource allocation. Predictive analytics can be used for bed calculator applications [ 8 , 16 , 41 ]. Salehhnajed and Proudfoot (2023) describe more advanced predictive applications – the implementation of a healthcare command centre developed with collaboration between healthcare consultancies, clinicians, and data scientists, and which uses algorithms for complex data integration to provide real-time data, status predictions and prescriptive suggestions tailored to end users’ needs [ 108 ] They outline the use of digital twin control centres that allow for real-time data delivery, as well as predictive functions that help frontline practioners care for patients and executives manage organisational risks.…”

“…Technology exists for remote patient monitoring, predicting patient deteriorisation, illness and outcomes, activating early warning systems, and resource allocation. A full accounting of such developments was beyond the scope of our review but limited accounts can be accessed within the literature [ 8 , 108 , 111 ] Despite these limitations, the review identifies factors related to the function of healthcare control rooms for managing patient flow and provides a starting point for further research.…”

“…However, they reviewed research up to 2019 across only 8 reports and since then the field has grown. A more recent narrative review surveyed 18 reports of the impact of command centres and smaller-scale centralised operating systems on healthcare capacity, from the perspective of business as usual [ 8 ]. Our review takes a broader systems perspective to investigate the following: (1) the concept of virtual and physical control HCCCs, their operational focus, and their situation in terms of the RMF, (2) existing approaches to the design and operation of HCCCs, and (3) identification of human factors issues that might be applied to HCCCs operations and that may offer opportunities for development of further guidelines for HCCC design and operation.…”