The advent of Community Diagnostic Centres is welcome. But without their integration into pathways, systems and patients will struggle to benefit. Innovative Scottish company Lenus Health has evidenced a better way, using its plug-in digital diagnostic pathways to coordinate and automate the patient diagnostic journey from referral to treatment plan.
Access to timely and effective elective diagnostic services is critical to providing high-quality care, reducing waiting times for treatment and future hospitalisation.
Current approaches to diagnosing chronic diseases such as heart failure, COPD and asthma remain unsatisfactory, dispersed across medical specialties and healthcare settings.
However, the rollout of Community Diagnostic Centres (CDCs) and the recent strengthening of guidance on improving GP Direct Access to diagnostic services represents a major step forward in addressing the diagnostics waiting times challenge.
To capitalise on these developments, these resources must be fully integrated within transformed diagnostic pathways.
If not, there is a risk that CDCs will simply feed more capacity into existing dysfunctional pathways that are fragmented and complex for patients to navigate, particularly those with multi-morbidity.
Wider statistics reveal the growing scale of the problem.
According to NHS England, only 15 per cent of patients on waiting lists have had a decision to admit for hospital treatment.
As the King’s Fund has pointed out, that leaves 85 per cent of seven million patients waiting for a test or a clinical decision.
And if we look at the impact of this on specific clinical categories – cardio-respiratory conditions, for example – the challenge is illuminated further.
Asthma and Lung UK’s 2022 annual COPD report revealed that 25 per cent of patients are waiting more than five years for a confirmed diagnosis – and one in eight have waited more than a decade.
Many of these challenges are often the consequence of poorly coordinated pathways, compounded by inappropriate referrals, a lack of staff to address backlogs (particularly specialist nurses and trained diagnostics staff), and an ever-growing volume of valuable but fragmented data languishing in silos.
More testing capacity will not be enough to address the backlog on its own.
What if digital tools were to provide a catalyst for transforming diagnostic pathways into a joined-up system that can reduce time to treatment for patients?
Such a system could enable a co-located and data driven approach to testing in community settings, integrate patient generated health data, and improve care coordination and data access across care settings and between medical specialities.
It could also automate the creation of patient and GP communications to reduce clinical and administrative burden.
This is the concept at the heart of the Lenus Digital Diagnostics Product – a plug-in clinical workflow automation system for improving diagnostic pathways for a range of chronic conditions.
If digital workflow tools are used as a mechanism to streamline existing processes, diagnostic services can evolve in ways that improve efficiency and co-ordination at every touch point in the pathway: from clinical workflows to the targeting of resources where they are most needed, the co-location of diagnostic testing, quicker asynchronous digital diagnosis, and an improved patient experience.
Indeed, following the development of this digital approach for a heart failure diagnostic pathway in the West of Scotland, there was a 78 percent reduction in waiting times and a 72 per cent reduction in time to treatment.
An independent study revealed a 20 percent cost saving in the provision of the pathway service compared to standard care.
This is attributed to fewer healthcare contacts and emergency admissions as earlier treatment of patients becomes feasible.
Building on this success, the service now provides a range of cardio-respiratory pathways.
By collecting data at each step, it generates a more comprehensive understanding of the patient’s journey through referral, triage, testing, diagnosis, and treatment planning.
This rich dataset, encompassing test results, observable traits, symptoms, and patient-reported outcomes, offers a more detailed characterisation of patients with chronic conditions.
Such detailed phenotyping enables the categorisation of patients into subgroups, facilitating the delivery of more targeted and effective treatments and advancing the goals of precision medicine.
Moreover, the digitisation of diagnostic pathways is a catalyst for developing AI-powered risk prediction models.
These models can stratify patient populations, helping care teams identify and intervene with high-risk patients sooner.
They also pave the way for the adoption of AI-enabled diagnostic devices that support clinical decision-making.
These improvements are achieved through process optimisation rather than extensive innovation.
Redesigning diagnostic pathways for conditions like COPD, Severe Asthma, or breathlessness streamlines the process, reducing time between steps and eliminating bottlenecks.
This approach minimises the need for repeat tests and additional outpatient consultations, and it supports the shift towards home-based diagnostic testing.
Standardising and digitising services not only mitigate care variation but also generate insights for future service enhancements.
They help address data interoperability challenges between primary and secondary care.
The cloud-based software can also be customised to reflect local pathway variations, meeting the workflow, data collection, and integration needs of specific specialties.
A standardised, coordinated approach allows for the evaluation of pathways against various metrics, including reductions in waiting time, outpatient visits, patient and clinician experience, and projected decreases in hospitalisations and other health outcomes.
Nationally, this new digital tooling can support richer patient registries and enhance audit reporting.
In conclusion, the digital transformation of diagnostics is not just about enhancing service efficiency and reducing treatment times for the millions of patients on waiting lists.
It’s also a crucial step towards leveraging future innovations in diagnostic technology, including point-of-care and home-based testing, and harnessing structured data for advancements in AI.
AI helps cut nuisance alerts for healthcare teams
Stabilising the NHS – it’s going to take a lot more than AI
Answer Digital enables speedy deployment of AI with strategic partnerships
Transforming NHS procurement: How good data holds the key to integrated care
British tech firm gets green light on fibroids patent
Cancer leaders pen ‘letter to the world’ calling for urgent investment
Generative AI used to create translatable paediatric care videos for LMIC hospitals
NHS Forth Valley goes live with RCPCH GrowthAPI integrated with Morse Mobile EPR
Mid and South Essex ICS building new shared care record with Orion Health
Red light can reduce blood glucose levels, study finds
- Medtech4 weeks ago
UK’s MHRA announces two new Approved Bodies to certify medical devices
- AI2 weeks ago
AI-powered personalised medicine project launches in UK and EU
- Deals3 weeks ago
UK gov invests £45 million in quantum tech
- Medtech1 week ago
Leeds health tech research centre awarded grant for surgical technologies and rehabilitation