Hybrid Healthcare is the Future of Healthcare
By: Sophie Smith, MBA
I am frequently chastised for making bold statements about the future without the use of the conditional. So I adapt. “Could women’s health be a massive market opportunity?” I ask. “Should TACo be the recommended model for telehealth? Might Hybrid Healthcare be the future of healthcare?”.
A little caution is always advisable. We are not oracles after all, and to insist on certainty when really only death is certain, is arguably to lack some essential humility. But where Hybrid Healthcare is concerned, I really do have no doubts.
Hybrid Healthcare is the future of healthcare and Nabta is going to prove it.
What is Hybrid Healthcare?
At Nabta, we define Hybrid Healthcare as the integration of digital health components into traditional care pathways to improve clinical outcomes.
We coined the phrase Hybrid Healthcare last year after I discovered (thanks to the use of the OvuSense Realtime Fertility Monitor) that our attempts to conceive Baby №2 were not going anywhere — I was not ovulating. No egg, no baby.
Armed with this information, I went and saw my local OB/GYN, and two days and a bunch of tests later, received a diagnosis of insulin-resistant Polycystic Ovary Syndrome (PCOS). It appeared, much to my surprise, that I had developed secondary infertility. Not to worry, said my OB/GYN — she had spotted a single maturing follicle on the ultrasound, and she reckoned she could induce ovulation. I was given a trigger shot to take at home, and instructed to get cosy with my husband and then wait at least seventeen days before taking a pregnancy test.
I did as I was told. Exactly seventeen days later, I was confirmed pregnant. Eleanor is now five months old and competing with my son for the rule of the roost.
What my mercifully short battle with infertility taught me was this: without the digital health component — the OvuSense Realtime Fertility Monitor — I would never have known with certainty (a) that I had a problem, and (b) what that problem was — long, irregular, anovulatory cycles. But without the “traditional” care — the in-clinic appointment, transvaginal ultrasound, blood tests, and hCG shot — it would have taken me a lot longer to fall pregnant, if indeed I fell pregnant at all.
Digital-only would not have sufficed, but neither would traditional-only.
Clearly an alternative to the “one or t’other” approach was needed; a new, hybrid model that seamlessly combined digital with traditional care to deliver improved clinical outcomes in the most efficient way possible.
Combining digital and traditional care
Of course, creating a hybrid model of healthcare is easier said than done.
The incorporation of medical technologies into the fabric of the healthcare ecosystem has, thus far, been a slow and painful process — hampered by strict regulations, the competing interests of players within the ecosystem, and a refusal by those same players to acknowledge the shifting socioeconomic dynamics of our increasingly connected, technology-enabled world. Or to put it another way: lay people are increasingly connected and informed, and are actively seeking ways to effectively and proactively manage their own health without having to defer entirely and always to the medical profession. Fearful for their survival, the medical profession resists.
To date, what we term Hybrid Healthcare has been mostly referred to as “digital health” by thought leaders in the space. mHealth defines “digital health” as:
“The cultural transformation of how disruptive technologies that provide digital and objective data accessible to both caregivers and patients leads to an equal level doctor-patient relationship with shared decision-making and the democratization of care”.
A somewhat convoluted definition, but the point mHealth is trying to make here is that in order to effect a transformation from traditional to digital healthcare (or, as we prefer to think of it, from traditional to Hybrid Healthcare), a cultural transformation is required; an evolution of the relationship between doctor and patient to put the two parties on a more equal footing.
And this requires care pathways to shift from being provider-centric and provider-led to patient-centric and patient-led.
Building personalised, patient-centric care pathways
But first, what exactly is a care pathway?
In 2007, Vanhaecht et al. defined a care pathway as “a complex intervention for the mutual decision-making and organisation of care processes for a well-defined group of patients during a well-defined period”. Or in layman’s terms, a care pathway is a standardised way of diagnosing and treating people who present with the same symptoms. Vanhaecht listed the defining characteristics of a care pathway as: an explicit statement of the goals and key elements of care based on evidence, best practice, and patient expectations; facilitating communication between patients, doctors and family; and, coordinating the care process.
Traditionally, care pathways have been owned by (/led, facilitated, and wholly visible to) healthcare providers rather than patients. To support this provider-led model, NICE (National Institute for Health and Care Excellence) has produced evidence-based guidelines for a wide range of care pathways, for everything from the assessment and treatment of fertility problems, to the recognition and management of cancers, with defined quality standards and quality measures for each pathway.
The problem with provider-led care pathways is that the “how” of moving patients from symptom assessment to diagnosis to treatment is invisible to patients. This does not mean that patients are unable to assess their symptoms, or research diagnostic methods, or investigate treatment options, it means they do not have the ability to connect all three elements together. So we have a partially informed populace who believe themselves to be fully informed, but they are not. One of the (many, negative) consequences of this is over-testing — patients requesting more tests than are strictly necessary to cover off all possible eventualities.
How then do you solve the problem of a partially informed populace?
The only logical solution, given the fact that withdrawing or curtailing access to information really is not an option, is to make care pathways patient-centric and patient-led instead. If patients have the tools at their disposal to see, understand and even define their own care pathways, they will not feel the need to be tested for things they do not have or be prescribed drugs they do not need.
But patient-centric care pathways means personalised care pathways, and how can personalised care pathways be possible when the very definition of a care pathway is a standardised model for diagnosis and treatment?
That is where machine learning comes in.
Creating an augmented intelligence using machine learning
Until recently, it was assumed that machines in the form of carefully crafted, highly specialised neural networks would one day replace doctors. Today, this assumption has been largely replaced by the belief that “AI” and other advanced digital technologies will instead be used to create an “augmented intelligence”, which enhances and complements the talents of the medical profession instead of replacing them.
“Augmented Intelligence is the intersection of machine learning and advanced applications, where clinical knowledge and medical data converge on a single platform.”
Machine learning in its most basic form is the ability of a computer to automatically learn and improve from experience without being explicitly programmed. At Nabta, we believe the most useful (immediate) application of machine learning in healthcare to be in the personalisation of care pathways through the introduction of predictive analytics.
And what exactly are predictive analytics?
Predictive analytics are defined as the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. In the context of care pathways, this means comparing the clinical outcomes of patients who have been treated using a traditional model of care with the clinical outcomes of patients who have been treated using a hybrid model of care. Once the mechanisms for arriving at a particular clinical outcome have been established, the underlying data, statistical algorithms and machine learning work through interactions with real patients to continuously refine the personalisation of each care pathway in real-time.
And that, ladies and gentlemen, is Hybrid Healthcare in a nutshell. Personalised, patient-centric, patient-led care pathways, powered by machine learning, with the aim of continuously improving clinical outcomes.
I could go on, but I won’t.
Nabta Health is a hybrid healthcare platform for women, empowering women to effectively manage their health using personalised care pathways powered by machine learning. For more information, visit www.nabtahealth.com.
References
Digital health is a cultural transformation of traditional healthcare, mHealth, 2017
The care pathway: concepts and theories: an introduction, International Journal of Integrated Care, 2012
Augmented Intelligence: The Next Frontier, HIMSS, 2019
