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Pharma meets digital healthcare

I recently attended a small Pharma-meets-academia working group on digital health and some excellent opportunities were highlighted that could be of value to the wider community so I have written up the key points here.

Why does Pharma care?

It’s all about new ways of solving problems, it has moved past the hype of apps for apps sake and is much more about hybrid or convergent digital / hardware / drug solutions that form live rather than static solutions (for example, Tesla can now perform live updates to car software).
One of the major insights is that the pharma business model still works (despite hype saying that it’s dead) but development time is increasing and profits are being eroded due to price transparency. This is overlaid with an increasing focus on outcomes by both the groups that make purchases and the patients themselves, particularly in the US. This means that the whole industry is shifting towards a consumer-focused paradigm.

How does this change the current pathway?

Prevent > diagnosis > treatment decision > treatment > long term care
Pharma is currently in the treatment zone and looking to move outwards.

How does pharma see the segmentation of the market?

1. Medication adherence (e.g. RF pills, adherence as low as 10% in some categories)
2. Treatment awareness (23&me)
3. Engage and activate patients (e.g. Soma analyticsQualcomm lifeCellscope)
4. Improved access (e.g. virtual GP)

What are the key top-level opportunity areas?

1. Efficiency in the drug development and trials process
2. Improving participation and outcomes
3. Patient experience and brand loyalty beyond patent cliffs

Some specific opportunities noted

1. Detecting upcoming changes in condition can make a huge difference over set routines: people are dynamic after all.
2. 25% of people drop out of trials due to adherence issues, which often invalidates entire trials!
3. Data is still incredibly siloed and everyone is doing everything from scratch.
4. Not just about symptoms but which symptoms matter to the patient: qualitative measures can be very important.
5. Oncology can be tough because specific side effects are rare so rarely seen by one individual: this is one area where large data sources are valuable.
6. Pharma is very interested in “closed loop technologies”, understanding “right time, right dose, right day” which are able to correct dynamically in order to meet target results, perhaps with integration of sensors and rapid diagnostics.
7. Information only results in actions if it aligns with patient beliefs or if you can change beliefs.
8. Data aggregation often fails due to compliance, because tech companies don’t understand the often stringent requirements imposed by pharma.

Key challenges

1. Pilots are easy, scale is very hard, lots of companies stuck at the stage of more and more pilots. It typically costs £50m to get to 10k patients — scaling is very expensive: need better efforts at partnering with existing sales forces in pharma or better systems.
2. Regulators still struggling with hybrid products as different bits move at different speeds, the software might be updated every day whilst the device is only refreshed once a year.
3. Patient > payers > practitioners > care-givers > pharma — Needs to tick a box for all.