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Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England.
Wood, Richard M.
  • Wood RM; Modelling and Analytics, UK National Health Service (BNSSG Integrated Care Board), Marlborough Street, BS1 3NX, Bristol, UK. richard.wood16@nhs.net.
Health Care Manag Sci ; 25(4): 521-525, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2059936
ABSTRACT
The recovery of elective waiting lists represents a major challenge and priority for the health services of many countries. In England's National Health Service (NHS), the waiting list has increased by 45% in the two years since the COVID-19 pandemic was declared in March 2020. Long waits associate with worse patient outcomes and can deepen inequalities and lead to additional demands on healthcare resources. Modelling the waiting list can be valuable for both estimating future trajectories and considering alternative capacity allocation strategies. However, there is a deficit within the current literature of scalable solutions that can provide managers and clinicians with hospital and specialty level projections on a routine basis. In this paper, a model representing the key dynamics of the waiting list problem is presented alongside its differential equation based solution. Versatility of the model is demonstrated through its calibration to routine publicly available NHS data. The model has since been used to produce regular monthly projections of the waiting list for every hospital trust and specialty in England.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Waiting Lists / COVID-19 Limits: Humans Country/Region as subject: Europa Language: English Journal: Health Care Manag Sci Journal subject: Health Services Year: 2022 Document Type: Article Affiliation country: S10729-022-09615-2

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Waiting Lists / COVID-19 Limits: Humans Country/Region as subject: Europa Language: English Journal: Health Care Manag Sci Journal subject: Health Services Year: 2022 Document Type: Article Affiliation country: S10729-022-09615-2