Putting the needs of the patient at the forefront of decision making by hospital executives is hard – there’s a lot going on and the decision to do something for someone can come at a cost to someone else.
Everyone has their own explanation of why delays occur, or targets are missed. Often these are not tested or validated with data or fail to involve input from front line teams. Anecdote goes head to head against data, rather than working with data to uncover what is really happening and what solutions would work locally.
Here are five examples where local insights come together with the data:
1. Stroke patients and ski slopes
At one hospital, the medical director noticed a recurring stroke bed crisis each February – there were never enough beds. Escalation beds cost the Trust a lot, and patients weren’t getting the best care. The data confirmed this belief (the yellow area shows the seasonality of bed usage), but only hinted at the cause, which was entirely driven by changes in the length of stay (the green area is a simulation that removes seasonality by artificially capping maximum length of stay). Many hypotheses were proposed and a few tested, but it was only when the data were shared with ward staff that the problem became clear – the discharge coordinator’s annual ski trip.
2. Critical cancellations
At a large tertiary hospital in the north-west, specialty teams did the best thing for their sickest patients and booked them in for major surgery (e.g. oesophagostomy) early in the week, so that they could be guaranteed continuity of care through the week with their surgeon. This was foxed by other specialty teams doing the same thing, which led to peaks in demand on a Tuesday and lack of critical care beds on a Wednesday and Thursday. The result was many last-minute cancellations or sending critically ill patients to surgery “at risk” of not having a critical care bed. It was only when presented with the data that teams could see the impact of their booking practice.
3. Inventory management
Amazon has turned stock control into a dark art involving state of the art algorithms that track expected demand against supply. But in one hospital the stock control involved huge inventories worth £7m and last-minute deliveries (via taxi) for the delivery of vital equipment discovered to be missing on the morning of surgery. While the stock control was linked to forecasts, these were routinely poor and not linked to planned activity. The data on which operations were delayed due to inventory problems revealed the scale of this opportunity and led to cross-department solution development.
4. Winter pains
Spinal deformity surgery can change a child’s life for the better. It requires a lot of preparation, so it is extremely distressing if it gets cancelled. This sometimes happens due to a lack of paediatric high dependency beds. It turns out that this happens much more frequently between November and March each year – coinciding with the Respiratory Syncytial Virus season, which affects children and can require high dependency beds. But until seeing the data, spinal surgeons routinely booked similar numbers of patients throughout the year.
5. Shifting patterns
In one hospital, staffing rotas had been cast in stone so that shifts changed at 8 pm. Anecdotally, everyone found this a very busy and often distressing period of the day – many people leaving work late, and people tripping over each other. Not surprising given that 7 pm to 9 pm was by far the busiest time for admissions to the ward. This leads to a real problem for patients, who amidst the confusion don't get discharged on time, or end up having to stay in recovery due to an apparent lack of beds. When the data are seen the problem (and its solution) become really clear.
All the examples above are symptoms of the complexity of hospitals and pressures faced by staff with high workloads. When things don’t work (ward too busy, performance dropping, etc) everyone forms a view on what is going wrong – either based on anecdote, or just data.
While the plural of anecdote might be "data" (as I was once told), a more intelligent use of actual data can inform on the causes and solutions to many of the problems faced locally. This requires data, time with frontline teams, and a degree of curiosity.