The ophthalmic surgical backlog associated with the COVID-19 pandemic: a population-based and microsimulation modelling study

Felfeli, Tina; Ximenes, Raphael; Naimark, David; Hooper, Philip L.; Campbell, Robert J.; El‐Defrawy, Sherif; Sander, Beate

doi:10.9778/cmajo.20210145

Cited by 12 publications

(14 citation statements)

References 39 publications

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“…5,18,19 However, ophthalmic surgeries are often grouped into the "other" category. 5,18 Recently, Felfeli et al 20 predicted the ophthalmic surgical backlog due to COVID-19 using the Ontario Wait Time Information System database and a microsimulation model. The authors reported that "by 2 years and 3 years since the start of the pandemic, the overall estimated number of patients awaiting surgery grew by 129% and 150%, respectively."…”

Section: Discussionmentioning

confidence: 99%

Backlog in ophthalmic surgeries associated with the COVID-19 pandemic in Ontario 2020

Jin

Cañizares²,

El‐Defrawy³

et al. 2023

Canadian Journal of Ophthalmology

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Section: Discussionmentioning

confidence: 99%

Backlog in ophthalmic surgeries associated with the COVID-19 pandemic in Ontario 2020

Jin

Cañizares²,

El‐Defrawy³

et al. 2023

Canadian Journal of Ophthalmology

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“…2 However, increasing case volume and backlogs in ophthalmic surgery will place unprecedented pressures on healthcare systems, particularly those with a limited ability to increase throughput. 3 Data regarding demand and supply of surgery is required to inform surgical policies for optimal outcomes and equitable care. 3 In many jurisdictions including the USA, there is a lack of population-based ophthalmic surgical data, and therefore, the unmet demand is unknown.…”

Section: Introductionmentioning

confidence: 99%

“…3 Data regarding demand and supply of surgery is required to inform surgical policies for optimal outcomes and equitable care. 3 In many jurisdictions including the USA, there is a lack of population-based ophthalmic surgical data, and therefore, the unmet demand is unknown. The Canadian universal healthcare context is uniquely positioned to provide an overview of the demand for ophthalmic surgeries in a developed country.…”

Section: Introductionmentioning

confidence: 99%

“… 3 Data regarding demand and supply of surgery is required to inform surgical policies for optimal outcomes and equitable care. 3 …”

Section: Introductionmentioning

confidence: 99%

“…Cataract surgery and other vision saving surgeries are among the most cost-effective healthcare interventions currently available 2. However, increasing case volume and backlogs in ophthalmic surgery will place unprecedented pressures on healthcare systems, particularly those with a limited ability to increase throughput 3. Data regarding demand and supply of surgery is required to inform surgical policies for optimal outcomes and equitable care 3…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Temporal trends in ophthalmic surgical demand in a universal healthcare system: an Ontario population-based study of over two decades

Felfeli

Austria²,

Menalo³

et al. 2022

BMJ Open Ophth

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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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The ophthalmic surgical backlog associated with the COVID-19 pandemic: a population-based and microsimulation modelling study

Cited by 12 publications

References 39 publications

Backlog in ophthalmic surgeries associated with the COVID-19 pandemic in Ontario 2020

Backlog in ophthalmic surgeries associated with the COVID-19 pandemic in Ontario 2020

Temporal trends in ophthalmic surgical demand in a universal healthcare system: an Ontario population-based study of over two decades

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

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