The United Kingdom’s public healthcare system faces significant pressure, with long waiting lists of seven million people making headlines. 1.5 million of these patients are waiting for diagnostic tests such as MRI and CT scans, and there is a side to this story that is sometimes overlooked: the diagnostic gap.
There is a significant void between having a health concern and being able to access the necessary treatment, and a diagnosis is the bridge across. Unfortunately, the processes to get a diagnosis are slow, opaque, and usually out of patients’ hands.
As many as one in five UK individuals attend four or more GP appointments before being referred for hospital tests or treatment. And, once on the waiting list for diagnostics, clogged workflows, the sheer number of people waiting, and the perceived urgency of their cases leaves 30% of people waiting longer than six weeks for an appointment, and some even over a year.
This problem isn’t unique to the UK – there are also complexities within the medical imaging market in the United States. While waiting times are generally shorter, issues such as opaque pricing, confusing insurance processes, and lost communications between patients, providers, insurers, and imaging clinics are prevalent. All of these factors combine to cause missed exams, delayed results and treatments, and a shock when the bill comes in.
What can be done to solve these problems?
Innovation in physical infrastructure is one way to improve access to imaging and speed up treatment pathways in the UK. More scanners should equal more scans, but this is a slow-moving and expensive solution. Plus, it doesn’t solve the slow administrative processes leading up to getting a patient into the clinic.
In a study of 21 countries by the Organisation for Economic Co-operation and Development (OECD), the UK has the lowest number of MRI scanners per capita, with many of our existing machines considered ‘technologically obsolete’. While specialised Community Diagnostic Hubs are being launched to tackle the backlog, this isn’t a mountain that can move overnight, especially with shortages of staff to operate the machinery and interpret the results.
We are seeing some new physical infrastructure players entering the market, many of whom we partner with at Scan.com. Examples include Living Room Health and Vitalscan, who are setting up high-street-style independent clinics with quick turnaround times and affordable prices.
Another option is using technological innovation to streamline imaging referral workflows, and this solution is applicable globally. If we can reduce admin, open up underutilised scanner capacity, speed up referrals by empowering more patients and clinicians with online tools, and automate the delivery of results, we can bridge the diagnostic gap and open the door to treatment much sooner.
That’s our focus at Scan.com. We’re removing friction in the diagnostic imaging workflow for both patients and their referring doctors. Managing referrals, scheduling appointments, informing patients, collating reports and images, and sharing them with healthcare professionals is slow and manual without a technology interface between all of the moving parts and parties involved. What began as a UK marketplace for patients to book scans, similar to booking a hotel room using Booking.com or Expedia, is now an end-to-end imaging platform in the UK and USA.
As an integration engine, we use APIs to seamlessly plug-in imaging within various healthcare journeys, ranging from sports and injury rehabilitation to surgery, and even medico-legal cases. We also connect the dots between imaging centres, patients, and physicians for booking and results delivery in real-time, with complete pricing and scheduling transparency.
Innovation in imaging results
Additionally, AI and machine learning can be used to predict diseases earlier and identify anomalies present in imaging before any symptoms are visible. This could reduce the need for further testing and repeated admin, while also making sure opportunities for treatment aren’t missed while patients are on waiting lists for diagnoses further down the line.
Of course, many of these innovations are in their early stages and still require access to timely imaging and large datasets, but there have been promising developments so far. For example, the use of machine learning to spot early biomarkers of Alzheimer’s’ disease to provide effective early interventions.
If diagnostics such as imaging are not accessible, individuals face unanswered questions, uncertainty about the progression of their condition, and the fear that opportunities for successful treatment and recovery will slip away. Technology has great potential to bridge the diagnostic gap, by reducing backlogs, speeding up administrative processes to deliver results sooner, spotting problems earlier, and empowering patients with greater choice and faster peace of mind. All of this combines to open the door to treatment and improve overall healthcare outcomes.
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