Tackling the Elective Surgery Backlog: Does Data Science Hold the Answers?
While the NHS delivered a remarkable amount of elective treatment during the second wave of the pandemic, the pressure of caring for large numbers of patients seriously unwell with COVID-19 has led to long delays for the growing number of patients on the waiting list. Particularly for those whose diagnosis and treatment cannot be treated remotely, where a hospital setting is essential.
Consequently, the NHS waiting list is currently at 4.6 million – the highest level since comparable records began (in 2007). And with 6 million fewer people being referred into consultant-led elective care in 2020 vs 2019, this number is only expected to increase. Bringing together IT, Data and Operational leaders across the NHS, this webinar will how data analytics and data science can best support NHS leaders and clinical decision-makers by providing the right information at the right time, to overcome the complex challenges ahead.
With this ongoing uncertainty and the effects of the pandemic still playing out operationally in the day-to-day, restoring elective care to pre-pandemic levels and addressing the growing backlog of patients is likely to take months, if not years. It will require a multi-faceted approach to increasing capacity, optimising collaboration, managing demand and prioritising patients.
Key discussion points include:
- How will approaches to waiting list management change over the coming months and years & what new systems are required to manage this challenge?
- What role does predictive analytics and scenario planning play in identifying operational and financial efficiencies?
- In the context of fluctuating demand, capacity and the potential for shared patient lists between Trusts, how can managers access real-time information?
- In what new ways can patients be systematically prioritised, for best outcomes?
- What role can algorithms play in identifying health inequalities and helping to ensure we take forwards the lessons of the pandemic?