Dr. Anna Buckingham shares how, as part of ICHP’s team working on the North West London Mission to enable more days at home for our patients and residents, she’s bridging the strategic and the operational as a Clinical Innovation Fellow.
From clinician to clinical innovation
As a clinician in the NHS, I am pretty good at problem solving – I can reset a jammed printer, find tea and biscuits for exhausted relatives, and round up pigeons out of a ward. Reacting to the constant stream of crises is the bread and butter of NHS staff. It is also the reason that we can struggle to get involved in improving the services we provide. The idea of having the time to proactively work on a project seems like a dream.
Becoming a Clinical Innovation Fellow at Imperial College Health Partners (ICHP) has given me the opportunity and space to take my experience with the daily challenges and consider their opportunities. Having been on the receiving end of “change”, I recognise the criticisms of the NHS as being slow to implement developments and resistance to new technologies. In this role, I want to act as a “translator” between my front-line colleagues and those who are offering improvement.
The innovation Mission
One of our North West London (NWL) Missions for Research and Innovation focuses on enabling more days at home for our patients. We recognise that delays and inconsistencies in the discharge process can lead to patients remaining in hospital longer than medically necessary. This not only impacts bed occupancy and Emergency Department (ED) wait times, but also patients’ outcomes, with increased risk of deconditioning, delirium and hospital acquired infections.
The daily flow in an acute hospital is a series of repetitive conversations: “Is bed 4 going home?”, “Has bed 10 had their physio assessment?”, “Bed 12 needs a dossett box so they’re delayed”, oh and “we have ten wating in ED”. I’ve worked in hospitals where patient flow is managed effectively and others where it’s more of a challenge, but the fundamental principles remain the same. Improvement can only achieve so much, mostly asking more and better of an already stretched team. What if there was a way for the staff to do less? Less chasing, checking and guessing. What if innovation could give back the hours of time spent on these repetitive tasks.
Innovation opportunities in patient flow and AI
One of the Mission’s approaches is to consider how Artificial Intelligence (AI) and Machine Learning approaches can use health data to predict aspects of patient flow at the point of discharge.
- What if we can predict the length of a patient’s stay, more accurately than current practice?
- What if it was a dynamic prediction, updated with data from the admission, integrated with the discharge process?
- What if we could predict discharge pathways and allow prioritisation of other resources like pharmacy and therapies time?
- What if the discharge predictions allowed for more dual planning or identified those who were a risk of long stays from admission?
These what ifs have the potential to change the conversation from just finding the information to using it to give patients, staff and management visibility of a patient’s journey through hospital.
Industry engagement and bringing staff with us
Part of my role with the ICHP team has been to work with hospital staff to understand the way predictive modelling would integrate with their roles. Identifying how different staff may have different needs and priorities, and how these different use-cases would impact the ask from industry.
This is a new area of innovation and, therefore, AI technologies and companies in this field are mostly in an early stage of development and design, with rollout only at limited sites. This is the time when clinicians and operational staff are most needed to highlight their demands, needs and requirements in the interest of co-designing solutions with suppliers.
We met with several companies and used the feedback from our Mission Innovation Forums (in-person stakeholder events, bringing together system colleagues with innovators) and workshops in NWL to inform, probe and test the products on the market. These discussions are essential to ensure the clinical voice is heard, and that expectations and assumptions on both sides can be challenged. We arranged live demonstrations by two companies, giving a range of staff the opportunity to see these new technologies in action and reconnect them with the ideas they had originally contributed. It was a rare opportunity to connect developers to their future users and was a productive conversation.
Frustrations and challenges
This process has not been easy, from either side. There are significant differences in the priorities of different parts of the NHS system. The current market solutions don’t tick all the boxes and come with timeframes that can seem endless. The frequent complaint from staff has been why add more “tech” to my day. The fragmented approach to the digitalisation of healthcare has been one of its main weaknesses and staff are wary of adding another layer to the already complex system.
Funding and opportunities to test these new technologies is very limited. The scale of the infrastructure requirements needed to develop AI within healthcare is a barrier to companies in this sector making progress, a single acute trust is unlikely to meet the needs. The understandable concerns around privacy and data security are another limiting factor, making this one of the most challenging areas for NHS improvement. However, the prize is equally great.
From one tool to the creating the toolbox
An AI generated discharge date may seem like a small change for such a demanding project, but ultimately this is a project to release back time. More time for the patient at home, for the nursing staff to provide care, not sit on the phone to another care provider, for the discharge team to stop checking on Estimated Dates of Discharge(EDDs) with endless ward checks.
It is also a test of the way for us to work with industry on the future pipeline of AI tools that could soon be ubiquitous. Predictive modelling is starting to be used across the health system often at a population health level, but it’s use in the day-to-day tasks of staff is the next area of potential. It also links to work on predicting patient deterioration. By learning from how to develop this tool, and set up the structures for sustainable finance, and infrastructure we can improve the efficiency of future projects.
Given the previous challenges in tech implementation within healthcare, it is more important than ever that industry has access to the range of clinical and operational voices that will support design and implementation that works. The ability to bring industry into more of our discussions around innovation and improvement, to frontline environments and staff is vital to enable effective design.
Clinical Innovation Fellows can be a small part of that ongoing mission.
Further reading and get in touch
- Find out more about the NWL Mission to enable more days at home
- Want to work with us to tackle discharge optimisation in our region? Get in touch.
- Interested in learning more about our Clinical Innovation Fellows programme? Drop us a line.
