Harnessing the Diagnostic Data Revolution
Rethinking our approaches to diagnosis and information management in the 21st century
David Wells | | Opinion
Accurate and timely diagnosis sits at the heart of patient care. And yet, despite myriad advances in our medical knowledge and systems, the perennial problem with diagnoses is the same as it ever was – human nature. We put off calling our doctors for as long as we can, producing every excuse under the sun. Put bluntly, humans are the first point of failure in our health system.
But this need no longer be true. Pathologists can lead a data-driven revolution that will bring health systems firmly into the 21st century, transforming patient outcomes globally via world-class interoperable diagnostics and data workflows. Using data-rich laboratory information management systems (LIMS), pathologists can reshape healthcare with patients firmly at the center of models of care.
Briefly consider these questions:
- What if our diagnostic and clinical systems were entirely flipped?
- What if your primary care provider developed a data-driven preventative care plan to keep you healthy and active before illness struck?
- What if your clinician identified your personalized risk profile based on your integrated pathology and fitness app history?
- And what if we ride the coattails of COVID-19’s lightning-fast innovation momentum to deliver all of this within the next three years?
As pathologists, we are uniquely positioned to lead this dramatic change. LIMS are at the forefront of large-scale improvements to patient flow across disparate disciplines and vast geographies. Innovative diagnosticians are already collaborating with clinicians across the traditional organizational silos throughout labs, hospitals, and trusts to deliver a higher quality of care that is increasingly integrated and multidisciplinary.
Mature pathology and radiology networks are successfully incorporating the fast-growing plethora of clinical AI apps, giving trusts access to more predictive analytics and putting more personalized care within reach.
Recently, NHS England announced new requirements to share results across previously rigid healthcare settings and organizational boundaries. To ensure future-proofed interoperability across their countrywide pathology network, NHS Wales scoured the globe before selecting a LIMS that has, over three decades, proven extraordinarily successful at aggregating diagnostic services and delivering equitable patient access over vast geographies in Australia. But to truly move diagnostics forward, we need to close the feedback loop and show clinicians the longer-term effectiveness of their recommendations on patient outcomes. We must accelerate the adoption of scalable data and interoperability standards, continually evolving virtual lab management to orchestrate data across patient journey settings.
So how do we drive the necessary pace of change? The pandemic proved that, where there is a will, there is a way. Specifically, it proved that high-quality healthcare system change can be rapidly delivered. The UK has exponentially grown its molecular diagnostic capability – from processing 300,000 molecular microbiology tests per year in England to performing more than that number in just one day for SARS-CoV-2.
In the early years of my career, we brought in troponin as a protein marker for heart attacks. This transformative blood test saves lives and is now the de facto diagnostic, but it took 14 years to become universally available because the NHS did not plan holistically or act collectively with urgency. But the decisions back then were driven by money and departmental budgeting, rather than by patient need. In contrast, when it came time to roll out the COVID-19 antibody test countrywide, we did so in just two weeks. Harnessing this can-do attitude and maintaining this appetite for collaboration across organizations, disciplines, and trusts could swiftly transform diagnostic networks on a national scale. There is already enough diagnostic patient data across radiology and pathology systems to provide life-saving patient insight. Unlocking the potential of this data and better integrating the information at our fingertips could prioritize patients appropriately, connecting them with the best clinicians for their needs.
Going further, plugging a clinical AI into this process could identify correlations of concern between image reporting and test results, helping clinicians optimize treatment plans based on similar patient cohorts.
By supporting clinicians with patient insight drawn from historical and real-time data, we can accelerate diagnosis, reduce the need for intervention, and keep more people healthy in the long run. For example, lung cancer is often only identified in a patient after they present in the middle of the night with shortness of breath. Clinicians will send the patient for tests, leading to consultant visits and hospital treatment with varying degrees of success. Yet proactive blood tests for at-risk patients could identify lung cancer before the disease has spread, vastly improving the prospects of a positive outcome.
More accurate and integrated diagnostics could serve as an early alarm for future pandemics or help uncover previously unknown genetic links to chronic conditions. Interconnecting wearable technology, mobile apps, or home cameras could even help predict mental health issues or the potential for falls based upon movement, gait analysis, or other indicators. The richness of data collected and analyzed from laboratory, radiology, and therapeutic systems, alongside the wealth of personal device data, would mean any change in patient pathways could be quickly identified and the most appropriate treatments and actions implemented.
One setting where this could be particularly useful is among our aging population. We can extend our diagnostic capability beyond healthcare – particularly into social and aged care, where slips, trips, and falls are the primary reason older people are brought to emergency rooms, shortening their lives and costing the NHS England £435 million every year.
The possibilities of better health data sharing are endless, as is the potential for better patient outcomes and a more sustainable, proactive healthcare system. Yes, we’ll need a new approach to determining who can access patients’ health data. But if you’d like better outcomes with more personalized treatment pathways, lowered healthcare costs, and better clinical teams, you can simply call the challenges the price of admission.
So what’s next? It’s time to build a coalition of the willing, rallying those armed with the revolutionary potential of a fully integrated LIMS and eager to be at the vanguard of change to embrace a proactive diagnostic system worthy of a 21st century healthcare system and all those who rely upon it.