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Our AI impact: personalised patient pathways

This is the second in a series of blogs around PRO-MAPP’s (and the wider healthcare industry’s) actionable uses of AI. You can find the introductory background article here, as well as Part One around waiting list monitoring and management here.

How does a healthcare system achieve ultimate customer satisfaction? These days, it’s achieved through personalising a patient’s journey, from understanding an initial health problem to finding a beneficial solution.

Determining that pathway goes beyond knowing exactly what their symptoms are at that point in time. Instead whole medical histories outline lifestyle choices, personal preferences for treatment, past assessments and surgeries, and everything that providers need to be able to tailor any patient’s care requirements with a personal touch. This personalisation marks a huge difference to make every patient feel thought about; it opens a transparent, comfortable dialogue between them and a healthcare professional from the pathway’s very outset.

But since the arrival of Electronic Medical Records (EMRs), there’s been an overwhelming explosion of data in the healthcare industry for every registered citizen. When you apply that to a nationwide database, such as that of the NHS, it shows the extent of how much personal data is out there. Collating pathways has been a major challenge, and this is where we see a pivotal use case for healthcare AI.

Traditional technology’s insufficient impact

Given today’s potential for advanced data usage, patients have a weight of expectation for the level of bespoke care they receive. Unfortunately this only compounds the difficulty level of operational efficiency faced by the NHS blighted by backlogs and budget constraints.

A streamlined end-to-end clinical workflow per patient comprises resource and task allocations across whole teams. That begins with a clean data repository to make every pathway matter. The trouble with old-school healthcare IT being monolithic systems is not only their large-scale and costly implementations, but their role for storing EMR information still being ineffectual due to siloed and underutilised patient data.

Their lack of flexibility and precision has pervaded as patient backlogs have extended. This is particularly true in the heavy-hit specialism of orthopaedic surgeries; helping complex patients receive pre-habilitation assessment, surgery and post-surgical analysis cries out for real-time data gathering and visualisation. Understanding successes and failures of past surgeries helps clinicians delivery a greater level of service for those in lengthy orthopaedic pathways – and when these insights are collated in nationwide databases, a framework can be applied for better patient outcomes en masse and remove clinic-to-clinic variations.

Surgical evidence offered by AI

The future for standardised pathway success lies in the metrics still, just using speedier automation technology to collect it, and simpler ways to surface and cross-reference each patients’ preferences and health histories immediately. When performance data audits can be collated by AI and housed in a comprehensive database, different arms of the NHS can use such information as a gold standard for orthopaedic surgery pathways.

AI is therefore a boon for registries in its tracking capabilities. It can scan demographics, as well as each patient’s preferences when it comes to each stage of treatment, then segment them for healthcare providers to view when necessary. Particularly when this data is gathered via patient self-reporting, such personalised choices are accurate; in fact, recent studies have shown that patients preferred the empathy and quality of AI-generated social media replies than that of clinicians, identifying the tool’s growing sophistication in increasing satisfaction.

The predictive analytics of AI may feel one of its more alien features. Essentially AI is able to identify potential risks for patients pre-surgery to make sure expectations are laid out upfront, as well as supply directions for seeking further advice, or tweaking lifestyle choices, according to collected medical notes and discharge summaries in post-operative stages. So-called ‘prepared lists’ get automated, ready for trained staff to educate waiting list patients on treatment benefits, risks and alternatives. It limits the operational times spent on producing reports, while laying out accurate predictions for continued care.

PRO-MAPP’s AI journey

Human-led one-to-one care is the ultimate driver of patient satisfaction, and that can be made seamless with administrative tasks eased by AI. PRO-MAPP offers an end-to-end AI-backed solution that enables the design and execution of tailored patient pathways to address high-levels of detail to individual needs:

  • The platform collates eOpNote, Patient-Reported Outcome Measures (PROMs) and barcode scans to craft dynamic dataset visualisations.
  • Our review by the York Health Economics Consortium (YHEC) identified the strength of AI applications in collected self assessments and diagnostics to ensure complex patients receive treatment sooner and ease unnecessary appointment numbers.
  • Complex patients can be determined with an accuracy rating of 98%.
  • AI is able to capture every insight from a surgical workflow in real-time, from resource utilisation to outcome-affecting variables, to offer opportunities for clinical support and pathway improvement.
  • We’ve achieved a target reduction in length of stay of half a day for pre-hab patients, saving £423,500.
  • Our platform has helped increase physiotherapy appointments, adding more than 320 than standard pathways.

AI’s automation is so key for a number of integral changes to improve the NHS. Fast-tracking in-need orthopaedic patients reduces the spend on unnecessary appointments that have stifled the system, and sees the right resources being allocated to the correct surgical practices. More productive healthcare teams are able to make their level of care go further with administrative burdens taken on by AI, and focus on what matters – improving the outcomes and results for patients. A happier health system means happier patients, and AI is marking a turning point in evolving orthopaedic surgery to meet those expectations for advanced care.

Next in the series, we’ll be delving into AI’s speedy adaptability to help health centres conduct same day pre-assessment. To find out more about how PRO-MAPP’s AI use drives personalised patient pathways, get in touch!

Our AI impact: waiting list monitoring and management

This is the first in a series of blogs around PRO-MAPP’s (and the wider healthcare industry’s) actionable uses of AI. You can find the introductory background article here.

Where waiting list management has halted

Most of us have been patients in the hospital waiting room. Whether sitting directly in the lounge for emergency services or being in the backlog for a more intense planned operation, a lot of us unfortunately have tales to tell about waiting times. Some can be horror stories.

In the UK, the National Health Service has carried a huge load; a mountain of intensive human and material resource usage on both critical and non-critical patients. The average GP surgery has more than 2000 registered patients, where any tailored assessments have to account for tests, evaluations, hospital space, and rehabilitation. Orthopaedics are commonly slow due to infamous multiple assessments for a patient’s surgery validity, implant costs and staff training. So much so that the speciality comprises the nation’s longests lists. Hip and knee replacement costs 1.5% of the NHS’ entire budget. There’s also spinal surgery or life-altering trauma cases to think about.

Restricting waiting list bottlenecks and ensuring every urgent matter gets prioritised calls for operational efficiency in triage – basing assessment or event treatment on how urgent a matter is. This has a positive domino effect on secondary or tertiary care freeing up resources for trauma and orthopaedic surgery. Understanding what their issue or condition is, where they should go and who they should see takes time to guarantee that every need is met. Managing such a data-heavy task is where artificial intelligence tools can step in.

Gaining assessments in real-time

Monitoring patients takes into account their health records, past surgeries or assessments, tests, need for surgery, and ideal pathways (what preferred service they expect during their health journey). It’s not that hospitals do not have this data to hand, but legacy technology has been tough to maintain – growing monolithic structures that lose or silo patient data, and misallocate resources or operational plans to staff. There are also inconsistencies between referrals at regional practices, as outlined by the NHS’ Getting It Right First Time initiative.

Managing these queues becomes unfocused and assessing-low risk cases amasses delays that can have a drastic effect on patients: seeking private service, skipping care altogether, depending on opiates to deal with pain, and perhaps worsening their symptoms.

One true supportive behaviour of AI is its swift data-gathering techniques. Any inputs – including patient questionnaires or assessment or surgery notes – can be collated immediately into digitised databases that are simple to find, amend and share. This can flag critical patients to ensure they’re fast-tracked through workflows that can be easily communicated to both clinicians, support staff and patients themselves. In that regard, AI’s unification of data not only supports risk assessment for triage, but boosts satisfaction in exemplary patient outcomes.

AI-driven waiting list monitoring and management has already reaped rewards in making data-backed decisions in a fraction of human time. In Scotland in 2019, automated AI triage was compared to clinician’s valued opinion on referrals for gastroenterology with positive results, including facilitated communication between primary and secondary care. AI triage has also seen a 77.1% acceptance rate among researched medical staff in China.

In trauma cases where fractures or other serious injuries require immediate surgery, these reactive demands require AI to surface pathway data for smooth scheduling and resource allocation for planned elective surgery, even at short notice.

PRO-MAPP’s AI journey

GIRFT is prioritising standardisation as a way to halt unnecessary delays, where integrating this technology on a nationwide scale lies with healthcare providers supported by AI automations, including here at PRO-MAPP:

  • The platform utilises AI for digital scheduling; collating results from patient intake forms to craft an assessment system for nursing staff to coordinate paths for surgery.
  • AI underlines automated patient-reported outcomes, satisfaction surveys, and operative data reporting without the need for dictation.
  • Assessment for surgical readiness can be decreased from around half an hour to 5 minutes, where operative reports can be billed on the same day as service.
  • Through waiting list monitoring and management, we target to reduce lengths of stay for prehabilitation, as well as wasted secondary care appointments, looking to save the NHS over £1.5 million and counting.

Speeding up triage through digital AI tools is not just down to switching telephone questionnaires to online consultations through apps. It’s about putting data input into the patients’ hands, removing the need for unnecessary in-person checks with doctors and prioritising critical patients for surgery or rehabilitation.

Waiting lists are the major operational dilemmas for the NHS – where AI’s help revolves around identifying critical clients and reducing resources of staff costs that take away from efficient orthopaedic surgeries. When those are saved, staff training classes and patient accommodations can be met more suitably which, when rolled out to other institutions, can standardise data collection and usage and reduce the burden of queues all around the nation.

Next in the series, we’ll be delving into AI’s personalisation capabilities to streamline patient pathways. If you’d like to know more about how AI drives our waiting list monitoring and management, contact us today!

4 key healthtech areas PRO-MAPP have impacted through AI

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.