Healthcare systems are consistently under strain, and alleviating cost and resource pressures comes down to efficiency. Improving those workloads has to take into account every single key worker in the system – doctors, nurses, surgeons, and administrative staff in both primary and secondary settings – which is a conundrum better solved through a commodity helping operations in many sectors: data.
Data is everywhere. But its abstract form can be confounding, especially when advanced data usage nowadays brings artificial intelligence into the conversation. If we can even cast our minds back to the pre-AI age, ‘Expert System’ was a similar concept used to code rules (essentially cloning senior staff’s expertise and judgements) which, when applied to a given scenario, would aim to give medical staff repeatable support and success in decision making
As is typical of any new digital invention in fashion-led IT, AI carries the paradox of being an exceptional helper that requires a great deal of explanation to healthcare staff and patients. Its use for the everyday is more commonplace than ever, but most patients may still be put off by receiving recommended treatment from an output-generation machine.
The key idea to reinforce is that AI is a “supporting tool”, not a replacement for the services of expertly-trained doctors, or conscientious experiences carried out by healthcare staff. What we’ve read about AI’s most actionable utility – trawling through millions of datasets in seconds, such as patient records or imagery – can collect relevant high-quality insights and efficacies helping clinical decision-making to better satisfactory patient pathways and outcomes.
From the health professionals’ side, we see many FAQs around the technology and its effects on operational efficiency:
- Can we use AI enabled solutions safely with our patients and staff?
- What’s the impact of costs, implementation and (ultimately) patient outcome?
- How complicated is this to set up, and how quickly do we see the benefits?
These are all valid, and can be answered from PRO-MAPP’s two years of practical usage. When combined with our proven health platform, safe AI can enhance key functions to collect the right data at the right time and provide effective insights and actions for ‘next steps’; rapidly delivering solutions covering monitoring, same-day pre-assessment, and reducing large waiting lists without impacting timelines, IT resources or increasing costs.
We’ve had independent confirmation from the positive impact AI can have, and we’ll be sharing a series of case studies and key learning around how we’ve utilised AI solutions within a NHS environment, including:
Waiting list monitoring and management
Orthopaedics are the NHS’ hardest hit waiting lists for knee or hip replacement surgery. Restricting bottlenecks and ensuring every urgent matter gets prioritised calls for AI-backed operational efficiency in triage. Our targets to reduce lengths of stay for prehabilitation, as well as wasted secondary care appointments, look to save the NHS over £1.5 million and counting.
Personalised pathways
Understanding every care requirement and preference helps tailor experiences to every patient. AI steps in to collect self-reported surveys, medical histories, and grant post-assessment recommendations, where we’ve seen 100% of patients being ‘very satisfied’ or ‘satisfied’ with their new pathways.
Same day pre-assessment
Re-testing and progressive ailments are just two side-effects of fractured pre-assessments affected by the weight of backlogs involving crucial and less-crucial appointments. Streamlining necessary in-person appointments is possible – PRO-MAPP has helped decrease them by 75%, while consultants can see up to three patients in the same time as previous pathways to also save up to 75% in clinical resources.
Additionally, our Pre-Assessment Clinical Triage (PACT) programme with Oxford University Hospitals NHS Foundation Trust (OUH) helped raise identification of surgery-ready patients from 28% to 77%.
Future areas of benefit
Analysis by York Health Economics Consortium (YHEC) has spotted how annual costs per patient can be reduced by £749, and the NHS’ future relies on a larger AI rollout for these personalised, patient-centric care across the nation. Predictive analytics can determine critical patients and identify patterns in nationwide databases: improving care pathways without the operational hurdles of legacy healthcare systems.
We’re always striving at PRO-MAPP to rise to the ‘data challenge’ and improve our platform using this revolutionary technology. Check back soon to see our AI past, present and future across these 4 critical areas.
