Crossing the valley of death of clinical trial monitoring

October 20, 2015

When hype exceeds adoption

As a matter of fact, hype always succeeds adoption and rightly so – because hype is a way of getting our attention and getting us to try out a new product or service.

But how can we discern substance from online marketing content?

As a new hire at Intel, I wrote weekly progress reports just like everyone else.   After a few weeks, my manager told me that I was not doing a good job writing my “weeklies”. Why?   I was not using Intel standard jargon – for example – running into difficulties in a software project should be called “traveling down rocky roads” and finishing a project under budget was called “executing with good news“. Over budget was of course “bad news“. He told me – “talk to Mike Moylan in Santa Clara – he does a great job on his weeklies, ask him for some good examples”.   I did that and looked at a number of examples and by the next week I had a list of 25 Intel-standard buzz-words that I sprinkled liberally through my weeklies.  I showed my list to my manager and suggested that I could write some code that would automate the process of writing an Intel-standard weekly and save me the time.   Suggestion was frowned upon.

Gartner’s well-known hype curve covers 5 phases of technology development and adoption:

  1. Innovation trigger
  2. Peak of inflated expectations
  3. Trough of disillusionment
  4. Slope of enlightenment
  5. Plateau of productivity

The Gartner hype curve is a corollary of another thing I learned at Intel (you can read it in Andy Grove’s book – “Only the paranoid survive”, and you don’t need to immerse yourself in Intel culture to figure this out or pay Gartner money) is that there is an inverse relationship between the amount of media buzz about some technology and the actual adoption.

A technology mainstreams when you don’t think about it anymore – like phones or smart phones. It’s part of your life and daily routine.

There is a tremendous amount of media buzz on patient monitoring.   Be it  mobile health monitoring in the personal health management space (a consumer play) or remote risk-based monitoring in the clinical trial space (a B2B play), the actual adoption on the ground seems to be well, on a “rocky road”.

Gartner has mobile health monitoring of patients in a trough of disillusionment as you can see in the graph below:


The problem with mobile health monitoring is not that there are not enough cool and innovative startups.  Nor is it a question of funding. 2015 so far has seen 136 digital health investment deals over $2 million, for a total of $2.1 billion in funding according to Rock Health’s mid-year report. Rock Health tracks a specific range of digital health companies and only follows deals over $2 million.

Considering that the average digital health startup is a 2-5 man team writing code, $2 Billion is actually a lot of money going into innovation.

So why is mobile health monitoring in a trough of disillusionment?

We might say that’s a statistical, weather-kind of phenomenon that goes through cycles with the exact physics not precisely understood.   But in the case of Gartner and weather forecasting, there is a lot of data and models that are well-proven so it’s hard to just pin this on natural cycles.

The problem, I believe is related to a lack of a solid business model.

We work with a fair number of Israeli mobile medical device startups. Whatever they do, there is almost always an element of remote patient monitoring and almost a vague unformed idea of monetizing  remote patient monitoring data.

Just storing and processing remote patient monitoring data in the cloud is a tricky proposition these days with the plethora of privacy concerns and changes in regulation (EU Data security court rulings not accepting Safe Harbor for example and a Snowden ripple effect on privacy regulation in places as disparate as France and New Zealand.

Once we store and reduce remote patient monitoring data in the cloud we need to turn the data into money.

Will remote patient monitoring data turn into clicks?

Monetizing patient monitoring data is an indirect model where the buyer is not the consumer.  This is a bit like Google and Facebook – we are all users and content generators but we are not the buyers – the buyers are the advertisers and they use our click and page visit behavior to target use with advertisements for products.

An online advertising model could be a great business model for monetizing patient monitoring data.

Imagine that you have a digital health app that monitors your insulin usage and physical activity, this data could be used by a third-party vendor to sell you services using targeted sales and advertising.

Patient privacy turns into a currency that can be exchanged for services.

People under 21 don’t know another world and just assume that their data is out there – perhaps in another 25 years, the privacy issue might become less of a global policy issue. In the meantime – I would not bet on monetizing patient monitoring data as a sustainable business model because I do not foresee less privacy regulation on the horizon only more and better-enforced.

Highest quality of life at the lowest possible cost?

We buy products and services that either save us money or save us time or help us do the same job for less money and less time. That’s not hype – that’s value for our money.

This is where we get back to Intel.

Several years ago, Intel developed a shift left framework for understanding the business and clinical benefits of health IT, outlining how to achieve the highest quality of life at the lowest possible cost.


As we we move care out of hospitals to low cost health providers and put more focus on individual wellness, we are, shifting our attention and financial investments “left,” away from institutional care and upstream toward better quality of life for consumers (patients).

To reap the full economic potential and quality benefit from this Shift Left model, healthcare is undergoing a business model re-design, using new technologies to realign the roles and responsibilities of doctors and patients, and develop entirely new ways of engaging empowered and informed consumers. Innovative digital tools are playing a key role in this shift by aiming to solve acute operational, economic and clinical problems.

I sort of get this.  And I love the idea of Shift Left (I’m a programmer – and I still remember that there was a SLI – shift left immediate instruction in the IBM 360 instruction set).

But – there are so many stumbling blocks to shifting left.  Incumbent players that don’t want to shift left. Insurance companies and health plans don’t want to shift left.   Patients that are afraid of leaving a traditional tiered Western medicine model don’t want to shift left.   And the government doesn’t really want the industry to shift left – there are regulatory blocks like HIPAA which require the entire supply chain to comply with the HIPAA Security Rule including cloud service providers that are grateful for the tail-winds from HHS that help them up-sell digital health providers with expensive dedicated cloud services.

So how do we make patient monitoring part of our life and daily routine?

Recall that technologies mainstream when we don’t pay attention to them anymore.

For chronic patients like people with CHF (congestive heart failure) all that is required is a little bit of shifting left; if the mobile medical device can help reduce readmission, maybe hospitals might pay for the cost of acquiring and operating mobile medical devices that save them high costs of readmission. An even better model shifts even further left – with the patient using a mobile business model and paying a mobile provider a little bit extra in the monthly service charge for monitoring the level of liquid in her lungs.

In the meantime -we will have to go through another Intel buzz-word towards new technology adoption – we will all have to cross “the valley of death”.


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