Data, The New Oil, and The New Oil Rigs
Health tech startups come in many forms. You have the Electronic Health Record (EHR) platforms, at-home test kits, remote monitoring medical devices, and AI image analysis tools, to name a few. Spend enough time speaking with healthtech founders, though, and you will soon realize that no matter the sub-sector, most of them are playing towards the same endgame — to accumulate sufficient, and sufficiently high-quality data to be of interest to the major stakeholders of the healthcare ecosystem, insurers and pharmaceutical companies. Put in another way, for many of these healthtech startups, their ostensible products — the kits, the software platforms, the devices — are just the digital world’s equivalent of an oil rig, drilling for data.
Data is the new oil, they say, and in the world of tech, it’s been drill, baby, drill for quite some time now, fueled by the twin forces of Venture Capital (VC) and the growing abundance of connected devices. But how similar are oil and data, really? And what can their similarities and differences teach us, especially in the emerging healthtech sector in Southeast Asia, where talent has been proliferating, valuations rising, but exits remain somewhat unproven?
Different Machines, Different Strategies, Different Data
As when drilling for oil, the equipment itself is of paramount importance. Different acquisition methods will predispose startups to prioritizing and accumulating certain types of data, depending on where and how they interact with patients during their healthcare journeys. Startups selling consumer-grade DNA tests, for example, might gather huge amounts of direct, first-party genetic data in a short period of time (especially if they’re backed by deep-pocketed VCs who are happy to fund high customer acquisition costs). But such data will also likely be episodic (from a single point in time) rather than longitudinal (from the same patient over a period of time) in nature. Episodic data is also far less appealing (and useful) to insurance and pharma companies.
Other than the data from analyzing the test kits, medical history is usually collected as part of the process. However, the information is usually self-reported by consumers through online surveys and therefore patchy and less reliable. This is why as these types of companies mature, they start to offer complementary services like genetic counseling, which enables them to build longer-term, repeated interactions with patients — and acquire data from that same patient over time.
On the flipside, startups focusing on EHRs, especially in emerging markets, will likely struggle with their initial go-to-market. Driving EHR adoption isn’t as simple as convincing someone to take a saliva sample — it requires convincing entire clinics and/or hospitals to change the way they do things every day. However, the raw data you get access to will likely be longitudinal as the same patients visit, and acquired through clinical tests and examinations rather than primarily self-reported. Even so not all EHR systems are created equal. Depending on whether it’s used in an oncology center or a GP clinic, the type of patient data collected will look very different — and be valued differently as well.
Let’s take a look at the different types of machines we might find in the health tech space, and the implications of their various data acquisition strategies. Of course, these are generalizations — there are many startups playing in each of the categories below who have found their own ways to defy the limitations of their initial data acquisition strategies.
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Written and illustrated by Theodore Ng, Analyst at Integra Partners