How should hospitals manage the backlog of patients awaiting surgery following the COVID-19 pandemic? A demand modelling simulation case study for carotid endarterectomy

Martin, Guy; Clarke, Jonathan M.; Markar, Sheraz R.; Carter, Alexander W; Mason, Sam; Purkayastha, Sanjay; Darzi, Ara; Collaborative, PanSurg

doi:10.1101/2020.04.29.20085183

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…The pent-up demand for care alone has created a major imbalance between demand for treatment and the capacity to deliver. 8 Recognition of the finite scope for recruitment of migrant labour and the inevitable time lag in training new health professionals, combined with institutional worry over the likely magnitude of pandemicassociated exits, has given rise to an unprecedented focus of NHS policy makers on finding ways to stabilise/ enhance staff retention. This paper reports findings from a study of the relative salience and weighting of an array of widely cited push influences on health professional exit from NHS employment.…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

Determining the relative salience of recognised push variables on health professional decisions to leave the UK National Health Service (NHS) using the method of paired comparisons

Weyman,

O’Hara,

Nolan

et al. 2023

BMJ Open

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ObjectiveThe primary and secondary impacts from the COVID-19 pandemic are claimed to have had a detrimental impact on health professional retention within the UK National Health Service (NHS). This study set out to identify priorities for intervention by scaling the relative importance of widely cited push (leave) influences.DesignDuring Summer/Autumn 2021, a UK-wide opportunity sample (n=1958) of NHS health professionals completed an online paired-comparisons exercise to determine the relative salience of work-related stress, workload intensity, time pressure, staffing levels, working hours, work–homelife balance, recognition of effort and pay as reasons why health professionals leave NHS employment.SettingThe study is believed to be the first large-scale systematic assessment of factors driving staff exits from the NHS since the COVID-19 pandemic.ResultsAll professions gave primacy to work-related stress, workload intensity and staffing levels. Pay was typically located around the midpoint of the respective scales; recognition of effort and working hours were ranked lowest. However, differences were apparent in the rank order and relative weighting of push variables between health professions and care delivery functions. Ambulance paramedics present as an outlier, notably with respect to staffing level (F-stat 4.47, p=0.004) and the primacy of work–homelife balance. Relative to staffing level, other push variables exert a stronger influence on paramedics than nurses or doctors (f4.29, p=0.006).ConclusionFindings are relevant to future NHS health professional retention intervention strategy. Excepting paramedics/ambulance services, rankings of leave variables across the different health professional families and organisation types exhibit strong alignment at the ordinal level. However, demographic differences in the weightings and rankings, ascribed to push factors by professional family and organisation type, suggests that, in addition to signposting universal (all-staff) priorities for intervention, bespoke solutions for different professions and functions may be needed.

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Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

Determining the relative salience of recognised push variables on health professional decisions to leave the UK National Health Service (NHS) using the method of paired comparisons

Weyman,

O’Hara,

Nolan

et al. 2023

BMJ Open

View full text Add to dashboard Cite

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A reinforcement learning-based optimal control approach for managing an elective surgery backlog after pandemic disruption

Fang

Chou

et al. 2023

Health Care Manag Sci

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Contagious disease pandemics, such as COVID-19, can cause hospitals around the world to delay nonemergent elective surgeries, which results in a large surgery backlog. To develop an operational solution for providing patients timely surgical care with limited health care resources, this study proposes a stochastic control process-based method that helps hospitals make operational recovery plans to clear their surgery backlog and restore surgical activity safely. The elective surgery backlog recovery process is modeled by a general discrete-time queueing network system, which is formulated by a Markov decision process. A scheduling optimization algorithm based on the piecewise decaying -greedy reinforcement learning algorithm is proposed to make dynamic daily surgery scheduling plans considering newly arrived patients, waiting time and clinical urgency. The proposed method is tested through a set of simulated dataset, and implemented on an elective surgery backlog that built up in one large general hospital in China after the outbreak of COVID-19. The results show that, compared with the current policy, the proposed method can effectively and rapidly clear the surgery backlog caused by a pandemic while ensuring that all patients receive timely surgical care. These results encourage the wider adoption of the proposed method to manage surgery scheduling during all phases of a public health crisis.

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How should hospitals manage the backlog of patients awaiting surgery following the COVID-19 pandemic? A demand modelling simulation case study for carotid endarterectomy

Abstract: Background

Cited by 2 publications

References 25 publications

Determining the relative salience of recognised push variables on health professional decisions to leave the UK National Health Service (NHS) using the method of paired comparisons

Determining the relative salience of recognised push variables on health professional decisions to leave the UK National Health Service (NHS) using the method of paired comparisons

A reinforcement learning-based optimal control approach for managing an elective surgery backlog after pandemic disruption

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