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he most exciting part of delivering some form of innovation

in the healthcare arena is the empowering thought of it

potentially changing, or certainly impacting, the current

standard of care for patients and providing a step change in the

thinking for a particular understood concept or theory, making it

one step closer to an eventual solution. At that moment in time,

the focus and enthusiasm tend to narrow quickly on the scientific

novelty of what has been unearthed, and a belief begins to take

control that this idea is unique and if it can be developed, the

market will follow. To take it to this next logical step will require a

level of financial funding to deliver the required data and evidence

that will ensure utility in a commercial setting.

The financing journey

The start of any healthcare innovation looking to receive financial

support, which could include various types of grants, will require,

to varying degrees, a clear idea of what the innovation is intending

to deliver to the ‘universal healthcare system’. This usually takes

the form of a clinical unmet need that this technology is intending

to address to a sufficient enough degree that would make it an

option of choice and potentially a standard of care for clinicians.

To the team in the lab, many of these embryonic concepts or

ideas may feel a million miles away from ever being used in

clinical practice, so understanding the contextual environment

could feel too early to consider or maybe not relevant to what is

being proposed, but this is exactly where the fundamental

challenge lies for the innovator.

Regardless of whether the proposed innovation is healthcare

related or not, the challenge for anyone trying to eventually

commercialise an opportunity is firstly to ensure there is a

significant enough demand, in terms of patients, that only your

offering would be able to satisfy sufficiently. Initially, this could

appear to be a moot point as a low likelihood of success

combined with the many years required to develop the concept

may make the realisation feel very distant. But it’s crucial for there

to be enough addressable market at the beginning to make this a

worthwhile opportunity to pursue. Using the right patient

epidemiology data is critical in understanding this demand. In

many cases, it may require identification of a specific sub-

population of a larger prevalence or incidence disease population

that would align with the innovative solution.

If you are unclear about the specifics of the patient population,

you can easily overestimate eligibility. A good example would be in

acute coronary syndrome (ACS), where there is data to suggest

that approximately 350,000 males in the USA currently suffer

from this syndrome. However, in the changing healthcare industry,

this basic information is no longer adequate. Accurate, publicly

available patient data could have limitations and be difficult to

obtain if it’s for a rare condition or an orphan disease that will

require the aggregation across several markets to get sensible

population numbers.

It is now vital to further segment a patient population to better

understand a disease area. In this case, of the male patients with

ACS, 73% experienced a heart attack. By looking deeper into this

sub-population, you can then see that 36% experienced an ST-

elevated heart attack while 64% experienced a non-ST-elevated

heart attack. This quickly demonstrates that the majority of male

patients who experience a heart attack will be found within the

non-ST-elevated heart attack sub-population. You may also need

to refine the target population based on associated risk factors. In

the case of ACS, the number of patients with a blood pressure

over 140mmHg (high blood pressure) will need to be segmented.

Publicly available data sources can be used to help arrive at

these numbers but should always be used with caution. A number

of issues can affect the integrity of these sources; this can include

caveats around methodology, representative sampling and data

representation. Secondly, even with high quality data sources, they

may not always provide the correct perspective or insight required

for the data. A good example of this is GLOBOCAN cancer

epidemiology data, which is extremely high level but does not

provide insight as to staging or treatment rates. Other data

sources, such as the Surveillance Epidemiology and End Results

(SEER) programme from the USA, provide extensive coverage in

terms of patients, attributes and outcomes but require a

significant level of data interpretation and manipulation to be

used in a meaningful or insightful manner.

Alternatively, the availability of robust databases such as


( cle

arly shows not just the current

volume but also future numbers for a specific patient population

Black Swan Analysis works towards understanding the big picture when it comes

to healthcare innovation

Understanding the ‘big picture’



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