Quantitative Evidence Supporting Distributed Situation Awareness Model of Patient Flow Management

Alhaider, Abdulrahman A.; Lau, Nathan; Davenport, P. B.; Morris, Melanie K.

doi:10.1177/2327857920091000

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…Eight papers discuss the benefits of physically co-locating staff with respect to patient management parameters [16,23,41,[43][44][45][46][47]. For example, Kane et al analysed the existing patient capacity in a health care system and set out to enhance patient flow by reducing patient boarding in the ED, and improving critical transfer care processes from outside facilities [41].…”

Section: Resultsmentioning

confidence: 99%

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

Paterson,

Chari,

McCormack

et al. 2024

J Med Syst

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Over the past decade, healthcare systems have started to establish control centres to manage patient flow, with a view to removing delays and increasing the quality of care. Such centres—here dubbed Healthcare Capacity Command/Coordination Centres (HCCCs)—are a challenge to design and operate. Broad-ranging surveys of HCCCs have been lacking, and design for their human users is only starting to be addressed. In this review we identified 73 papers describing different kinds of HCCCs, classifying them according to whether they describe virtual or physical control centres, the kinds of situations they handle, and the different levels of Rasmussen’s [1] risk management framework that they integrate. Most of the papers (71%) describe physical HCCCs established as control centres, whereas 29% of the papers describe virtual HCCCs staffed by stakeholders in separate locations. Principal functions of the HCCCs described are categorised as business as usual (BAU) (48%), surge management (15%), emergency response (18%), and mass casualty management (19%). The organisation layers that the HCCCs incorporate are classified according to the risk management framework; HCCCs managing BAU involve lower levels of the framework, whereas HCCCs handling the more emergent functions involve all levels. Major challenges confronting HCCCs include the dissemination of information about healthcare system status, and the management of perspectives and goals from different parts of the healthcare system. HCCCs that take the form of physical control centres are just starting to be analysed using human factors principles that will make staff more effective and productive at managing patient flow.

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Section: Resultsmentioning

confidence: 99%

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

Paterson,

Chari,

McCormack

et al. 2024

J Med Syst

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“…For border control (orange bordered circle in Fig. 1), the CECC chiefly coordinates with four ministries and agencies strategically to minimize the spread and support contract tracing of COVID-19 [29]. The CECC acquires SA on infected countries from the TCDC which uses WHO electronic reports and media briefings to identify countries from where visitors could create public health risks.…”

Section: Resultsmentioning

confidence: 99%

“…The DSA three-part method helped to identify functions and knowledge/SA of the CECC as well as the government ministries and agencies pertinent in their communication. Due to the lack of access to the CECC, data elicitation only involved documentation review of published articles and public video recordings rather than internal documentation review, interviews, and observations [14], [28], [29]. Data extraction involved identifying how the CECC operates (i.e., task elements), what information or SA is generated or transmitted (i.e., knowledge elements), and which ministries and agencies are involved for each operation (i.e., social elements).…”

Section: B Dsa Modelling Methodologymentioning

confidence: 99%

Resilience to the COVID-19 Pandemic: A Distributed Situation Awareness Perspective

Alhaider

Lau

2020

2020 Resilience Week (RWS)

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Provisionary command centers for containing disease outbreaks can be an effective strategy in coordinating efforts by different government ministries and agencies to provide resilience that limits human and economic losses. This paper applies the Distributed Situation Awareness (DSA) framework to investigate the fundamental design and operational principles of provisionary command centers that contribute to the resilience against disease outbreaks. The investigation involves a case study on modeling the DSA of the Taiwan Central Epidemic Command Center (CECC) that coordinates multiple government ministries and agencies to contain COVID-19. Task, knowledge, and social networks were developed and combined into one DSA network model to depict how the CECC operations in processing and distributing information enhance situation awareness (SA) of all the government agencies and citizens for a coordinated response to the pandemic. The DSA network model illustrates an interorganizational system architecture that can enable graceful extensibility of various government functions and sustained adaptability of the agencies against disease outbreaks.

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“…Alhaider et al, (2020a) presented a descriptive DSA network model combining social, task, and knowledge elements of patient transport to identify deficient SA transactions between agents that appeared to be associated with re-scheduling, cancelations, and delays, thereby prolonging minimum length of the patient journeys. These promising results motivate investigations into quantitative methods to study SA distribution and transaction for systems improvements (Alhaider et al, 2020b). Thus, we seek to quantify the knowledge distribution and transaction for assessing the impacts of various DSA deficiencies and interventions for improving patient flow in hospitals.…”

Section: Introductionmentioning

confidence: 99%

Modeling and simulation for situation awareness distribution and transactions for intrahospital patient transports

Alhaider,

Lau,

Davenport

et al. 2023

Proceedings of the International Symposium on Human Factors and

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Intrahospital transports rely on effective transmission of information between medical staff and information systems to support Distributed Situation Awareness (DSA) on patient status and transport requirements. This study presents the preliminary results a simulation model combining discrete event simulation (DES) and agent-based modeling (ABM) to quantify SA distribution and transactions for intrahospital transportation. The simulation model captured the transport requests, transport processes, and knowledge distribution and transactions between (human or machine) agents for intrahospital transports. Specifically, this paper presents the simulation results for the successful patient transport from 17 hospital inpatient units to the radiology department for (1) transport times and (2) transport cancellations. One-way t-tests did not reveal any significant differences in average transport time and number of cancelations between outputs of 100 simulation replications and historical data for 18-month operation. These results indicate that the simulation model represents real-world operation that can subsequently be used to test potential intervention to improve DSA in patient flow.

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Quantitative Evidence Supporting Distributed Situation Awareness Model of Patient Flow Management

Cited by 3 publications

References 20 publications

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

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

Resilience to the COVID-19 Pandemic: A Distributed Situation Awareness Perspective

Modeling and simulation for situation awareness distribution and transactions for intrahospital patient transports

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