107 Reducing delay to support patient flow; an innovative approach applying quality improvement - Clough, Stuart; Groop, Johan (BMJ open quality, 2025)
BackgroundOur intervention was hospital-wide, conducted as a ‘spread and scale’ approach aiming to reducing patient delay across a large NHS teaching hospital in the Northwest of England, covering two sites and a population of 390,000 people.Lancashire Teaching Hospitals NHS Foundation Trust (LTHTR) and Nordic Healthcare Group (NHG) attempted to reduce patient delay by applying the Theory of Constraints (ToC) to patient journeys, using innovative software to record delay and identify constraints.There are many reasons why patients experience delay, however, there needs to be a way to identify the bottlenecks which are responsible and remove them. Prior to embarking on a partnership with NHG, LTHTR didn’t have a data driven way to identify bottlenecks.MethodUsing an improvement science methodology to understand which bottlenecks were causing the main problems, LTHTR applied ToC. ToC is a concept which has been popularised in healthcare by Alex Knight, author of the book ‘Pride and Joy’. NHG have developed a set of processes enabled by software to support organisations to deploy the core principles. Software named ‘Flowful’ allowed LTHTR to quantify how many days were lost to delay per patients, for specific tasks which were ‘outstanding’. Using this data, across a small cohort of wards, LTHTR could understand where to focus improvement support to greatest effect.Staff groups were consulted in their professional groups and asked if they would test the software as a trial. Using spread and scale techniques, data was analysed and results shared across the participating wards and management forums.Using innovative software, the change to practice was for clinical teams to register all planned discharge dates (PDD) and associated tasks for each patient on the ward which were outstanding in their journey. Recording this information in such detail within Flowful enabled teams to quantify how many days were lost to delay once the task outstanding had surpassed the PDD. Centralised information on the top delayed patients was utilised in the daily bed meetings and using escalation principles to identify and resolve sources of delay expediting the time discharge of patients.LTHTR used a ‘spread and scale’ approach to iteratively test across 10 wards in a three-month period, then a further six wards in two-months across two hospital sites. Using pareto principles to understand the tightest constraint this enabled greater focus on the problem at unit level and organisational level. Staff were engaged by socialising Flowful at existing forums, known advocates of quality improvement where enlisted to commence testing. Weekly open development forums were available for staff to discuss results and improvements.ResultsLTHTR used time-series data, through Statistical Process Control charts to monitor change over time. This helped teams to understand the impact of their focussed improvement, using PDSA cycles to sequentially test ways to reduce patient delay. This allowed for sharing of good practice and build confidence in changes that were made.Over a 12-month period, wards taking part in Flowful realised a 0.5-day reduction in length of stay (LOS) in comparison to non-Flowful wards. Flowful supported changes to quickly reduce delay, it was a catalyst for LTHTR to truly quantify, how much delay was in the system. It helped to identify three of the top constraints, subsequently forming a key focus for the organisation’s strategy. Reducing LOS, leads to reducing harms, it provides better experiences of care in a more cost-effective way. Seasonal variation and other hospital initiatives made it difficult to demonstrate causation, though there is little doubt that the focus Flowful brought supported such outcomes.