Queen's University Belfast

Academic Institute

R.M. Dimuthu Wasana Rathnayake

E058

The proposed research seeks to identify feasible performance management strategies and interventions to reduce unreasonable waiting lists in NI hospitals using a variety of evidence-based methodologies. Demand for elective surgeries is escalating and waiting times have been taken as a key performance indicator of HSC services to encourage performance in hospitals towards high-quality care without unnecessary delay (Finkenstädt, 2015). Irrespective of the underlying strategies, waiting times for elective surgeries have increased dramatically over the Admissions and Dischargest few years (Siciliani and Hurst, 2005). The ability of hospitals to increase in performance have often been restricted due to resource constrains. As a result, most NHS Trusts in the UK have not achieved their minimum waiting targets in recent years (Morris et al., 2018). Growing waiting times for health care has led to public dissatisfaction with health authorities (Harrison and Appleby, 2009, Triggle, 2019). Overwhelming workload with the unprecedented pandemic of COVID-19 is producing an incalculable threat to this problem. Transforming the current situation into a research problem, this health systems research seeks to determine the scale of the opportunity to shorten waiting times for elective surgery, with a particular focus on the situation in NI hospitals. The waiting times for different elective surgeries in NI will be analysed in detail using population-based data of admissions and discharges for the last 10 years. Alongside this quantitative study, the systematic review and the qualitative research will allow to identify the strategies that could be recommended for uptake in NI to ease this burden.

Waiting times for different surgical specialties and different types of surgeries will be analysed for individual NI HSC Trusts and hospitals. This information would provide a much clearer picture on surgical case load for each hospital for various surgical specialties to support policy-decision making. In addition, the estimates from the study may be used in modelling the back log of surgery lists which were not carried out due to COVID-19 pandemic (2020), and also would help to seek alternative strategies to address the recovery of elective surgery.

23/07/2020

TRE