Coping but not coping in health and social care: masking the reality of running organisations beyond safe design capacity

Wolstenholme, Eric; Monk, David; McKelvie, Douglas; Arnold, Steve

doi:10.1002/sdr.390

Cited by 46 publications

(35 citation statements)

References 23 publications

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“…We determined that the majority of the processes could be described as formal and had well defined rules that could be described by a range of statistical distributions. However, as identified by Wolstenholme et al (2007), we also discovered several informal processes, with rules that were less than rigid. This meant that although the algorithms in our model were able to identify patients close to breach and fast-track them, we could not reproduce exactly the number of patients that actually breached, and we must surmise that there are some aspects of the informal processes yet to be accurately modelled.…”

Section: Discussionmentioning

confidence: 65%

“…Further informal strategies were not explicitly captured within the model, including the practice of contacting the pathology laboratory to expedite diagnostic test results when patients were approaching breach. Wolstenholme et al (2007) point out that some of these informal processes, if continued over a period of time, can have detrimental effects on the very problems that they are trying to solve, and it is possible that the very metrics used to measure performance may disguise this fact. An example of this may be the use of the OBS ward as a 'waiting area' for patients who will be admitted to another ward when a bed becomes available.…”

Section: Discussionmentioning

confidence: 99%

“…Modelling the system with an attempt to understand the coping strategies, therefore, brings great benefit. Wolstenholme et al (2007) report on the difficulty in modelling the coping strategies adopted by healthcare staff to make the system work. One reason is that such strategies are, to some extent, unofficial, and therefore generally unrecorded.…”

Section: Literature Reviewmentioning

confidence: 99%

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Meeting the four‐hour deadline in an A&E department

Eatock

Clarke

Picton³

2011

Journal of Health Organization and Management

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Purpose: A&E departments experience a secondary peak in patient Length of Stay (LoS) at around 4-hours, caused by the coping strategies used to meet the operational standards imposed by government. We aim to build a discrete-event simulation model that captures the coping strategies and more accurately reflects the processes that occur within an A&E department.Design/methodology/approach: A Discrete-event simulation (DES) model was used to capture the A&E process at a UK hospital and record the LoS for each patient. Input data on 4150 arrivals over three one-week periods and staffing levels was obtained from hospital records, while output data was compared with the corresponding records. Expert opinion was used to generate the pathways and model the decision-making processes Findings : We were able accurately to replicate the LoS distribution for the hospital. The model was then applied to a second configuration which had been trialled there, again the results also reflected the experiences of the hospital. Practical implications:This demonstrates the coping strategies, such as re-prioritising patients based on current length of time in the department, employed in A&E departments have an impact on LoS of patients and therefore need to be considered when building predictive models if confidence in the results is to be justified. Originality/value:As far as the authors are aware this is the first time that these coping strategies have been included within a simulation model, and therefore the first time that the peak around the four-hour has been so accurately analysed using a model. Biography: Having completed his doctoral research in laser spectroscopy Terry Young moved into industrial research using Finite Elements and other simulation techniques to model photonic switches, filters and other components for optical communication networks. Moving up-system and through layers of management, he migrated to a role that informed corporate strategy in healthcare. In 2001 he became Professor of Healthcare Systems at Brunel, since when his research has embraced care delivery, organisationally and in technological terms. His grant portfolio has included both the MATCH and RIGHT projects, under which and through which, this research was performed. Keywords 1Meeting the four-hour deadline in an A&E Department Abstract Purpose: A&E departments experience a secondary peak in patient Length of Stay (LoS) at around 4-hours, caused by the coping strategies used to meet the operational standards imposed by government. We aim to build a discrete-event simulation model that captures the coping strategies and more accurately reflects the processes that occur within an A&E department.Design/methodology/approach: A Discrete-event simulation (DES) model was used to capture the A&E process at a UK hospital and record the LoS for each patient. Input data on 4150 arrivals over three one-week periods and staffing levels was obtained from hospital records, while output data was compared with the corresponding records. Expert opinio...

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Section: Discussionmentioning

confidence: 65%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Meeting the four‐hour deadline in an A&E department

Eatock

Clarke

Picton³

2011

Journal of Health Organization and Management

View full text Add to dashboard Cite

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“…The crowding problem has been a frequent target. [8][9][10][11][12][13][14][15] Morrison and Wears 16 used SD modeling to uncover inherent contradictions in an ED teamwork scheme. The broad utility of the SD approach is illustrated by the study of decision-making in crises in the operating theater by Rudolph et al, 17 although it seems highly applicable to emergency medicine.…”

Section: Sd Modelingmentioning

confidence: 99%

Understanding Emergency Care Delivery Through Computer Simulation Modeling

Laker

Torabi

France

et al. 2017

Academic Emergency Medicine

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In 2017, Academic Emergency Medicine convened a consensus conference entitled, "Catalyzing System Change through Health Care Simulation: Systems, Competency, and Outcomes." This article, a product of the breakout session on "understanding complex interactions through systems modeling," explores the role that computer simulation modeling can and should play in research and development of emergency care delivery systems. This article discusses areas central to the use of computer simulation modeling in emergency care research. The four central approaches to computer simulation modeling are described (Monte Carlo simulation, system dynamics modeling, discrete-event simulation, and agent-based simulation), along with problems amenable to their use and relevant examples to emergency care. Also discussed is an introduction to available software modeling platforms and how to explore their use for research, along with a research agenda for computer simulation modeling. Through this article, our goal is to enhance adoption of computer simulation, a set of methods that hold great promise in addressing emergency care organization and design challenges. W ith over 130 million annual ED visits,1 a declining number of EDs to provide emergency care, 2 and lengthening wait times to see providers, 3 EDs are operating under increasingly arduous conditions. One underutilized approach to addressing problems in health care quality and value, particularly in emergency care, is through the use of computer simulation modeling. Computer simulation is a method to build dynamic models that quantitatively abstract a system, such as a facility (e.g., ED) or a process (e.g., physician-in-triage). Not unlike "highfidelity patient simulation" for training clinicians in clinical care through the use of mannequins, computer simulation provides a platform to inform decision making prior to implementation in the real world.

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“…8,16,17 Once systems thinking and SD models are validated, and there is confidence in their structure and behavior, they can be put to the task of generating plausible, sustainable solutions by using the model for policy testing, scenario analyses, or intervention optimization. [18][19][20][21][22] SD modeling has been used to examine health care quality improvement; 11,[23][24][25][26][27][28] community-based systems of care; [29][30][31][32] epidemiology and disease surveillance; [33][34][35][36][37] global health care management; [38][39][40] behavioral health interventions in tobacco; 37,[41][42][43][44][45] substance abuse; [46][47][48][49] and mental health; 9,50 as well as in managing chronic illness. 32,51,52 SD modeling to examine HIV epidemiology, prevention, and treatment has also been conducted.…”

Section: Introductionmentioning

confidence: 99%

Simulating Patterns of Patient Engagement, Treatment Adherence, and Viral Suppression: A System Dynamics Approach to Evaluating HIV Care Management

Lounsbury

Schwartz

Palma

et al. 2015

AIDS Patient Care and STDs

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System dynamics (SD) modeling belongs to the rapidly evolving, interdisciplinary field of system science research. This field adds value to more traditional health research by contributing to the design and testing of complex integrated models of change, to examine health system performance and patient outcomes. Using selected milestones in HIV care management to frame our simulation research, we created a SD model to examine three patient subgroups of women of color (WOC) represented in our multi-site cohort, classified by their health care seeking status at baseline. Asked to reflect on their circumstance 6 months prior to enrollment in the MSE cohort, 53% noted they were receiving some care (In Care, n = 341), 31% that they had been seeking care (Seeking Care, n = 201), and 16% that they were undecided about seeking care (i.e., answered that they may or may not look for care) for treatment of their HIV (May or May Not Seek Care, n = 103). Our SD model compared simulated patterns of patient retention over 24 months in relation to: (1) access to antiretroviral therapy (ART), (2) adherence to ART, and (3) viral suppression. Assessed patterns yielded insights about system capacities and constraints in the context of the SPNS initiative under evaluation.

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Coping but not coping in health and social care: masking the reality of running organisations beyond safe design capacity

Cited by 46 publications

References 23 publications

Meeting the four‐hour deadline in an A&E department

Meeting the four‐hour deadline in an A&E department

Understanding Emergency Care Delivery Through Computer Simulation Modeling

Simulating Patterns of Patient Engagement, Treatment Adherence, and Viral Suppression: A System Dynamics Approach to Evaluating HIV Care Management

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