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The COVID-19 pandemic tested how healthcare organisations allocate economic, staffing and infrastructural resources, demonstrating that we operate in a resource-constrained environment. The recent National Health Service (NHS) staffing crisis has highlighted the need to deliver our services most efficiently and to enable our systems to work better for our workforce.1
Complex challenges in healthcare require that leaders often make multiple decisions in parallel, for example, in each department decisions regarding how many nurses to roster, how much to pay temporary staff and the length of shifts. Considering all possible combinations by trial and error is, at best, slow and, at worst, infeasible.
Optimisation is a mathematical method of allocating resources to minimise or maximise the desired outcome. Many industries around the world have used optimisation techniques to modernise their services: airlines use optimisation to maximise profit when setting air fares2; Amazon uses optimisation to plan routes and minimise the time and cost of delivery3 and even dating services have made use of optimisation to maximise the chance of finding love.4 5
Technological innovations associated with rapid improvement in other industries have not been easily translated into healthcare. For example, fax machines, pagers and paper notes are still used by healthcare organisations worldwide, and risk aversion has slowed the adoption of changes.6 Many areas within healthcare could benefit from optimisation, including rota staffing, appointment booking and resource allocation.
How does it work?
Optimisation requires that a problem is deconstructed into four distinct components: decisions, constraints, parameters and objectives (figure 1). An algorithm can then identify the best combination of decisions to achieve your desired objective. Algorithms need not be complicated; options are available using the Microsoft Excel ‘Solver’ function, or more advanced versions …
Funding TH and MD are supported by graduate fellowship awards from Knight-Hennessy Scholars at Stanford University.
Disclaimer The fellowship had no role in the study design, in the writing of the report, and in the decision to submit the paper for publication.
Competing interests None declared.
Provenance and peer review Not commissioned; internally peer reviewed.