Healthtech startups come in many forms. You have Electronic Health Record (EHR) platforms, at-home test kits and AI image analysis tools, to name a few. Spend enough time speaking with healthtech founders, though, and you will soon realise 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 major stakeholders of the healthcare ecosystem.

Data is the new oil, they say, and in the world of tech, drilling has been 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 valuations are 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 accumulate certain types of data.

Startups selling consumer-grade DNA tests, for example, might gather huge amounts of direct, first-party genetic data in a short period of time. But such data will also likely be episodic (from a single point in time), which is less appealing and useful to insurance and pharma companies compared to longitudinal data (from the same patient over a period of time).

Besides the data from analysing 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 some companies are starting to offer complementary services, like genetic counselling, that enable them to build longer-term, repeated patient interactions and acquire data from that same patient over time.

On the flip side, startups focusing on EHRs, especially in emerging markets, will likely struggle with their initial go-to-market. Driving EHR adoption can be challenging as it requires convincing entire clinics and/or hospitals to overhaul legacy systems and implement software to manage financial, clinical, and administrative operations.

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The raw data acquired, however, will likely be longitudinal and more reliable as they are collected from clinical tests done by the same patients rather than primarily self-reported. While the initial onboarding can be challenging, there are potential mitigants like easy onboarding for care settings trying to adopt an EHR for the first time and partnership agreements with data exclusivity terms.

Everyone has the same end goal of data aggregation, but there are different means of getting there. In the end, though, it all comes down to three attributes: breadth, depth, and exclusivity. As in, the breadth of the data set when it comes to population size and demographic diversity, the depth of each patient’s healthcare profile, and exclusivity in terms of access and ownership to more unique data.

The rig operators and rig operability

The second consideration is the human element. Who operates the rig has a huge impact on whether the machine is used to its full potential. We think about usability in two ways.

First, user experience encourages usage among trained medical staff. In theory, workflow software and diagnostic support algorithms can save physicians a lot of time through automation.

In reality, however, automation is not as useful if the number of conditions that can be identified and diagnosed by the algorithm is limited. For example, take an AI tool that helps diagnose lung cancer. Radiologists still have to spend the same amount of time examining each scan or X-ray to check for possible conditions that the AI can’t identify.

In the end, adopting these diagnostic tools can be challenging if the new technology doesn’t add much to the existing workflow of medical professionals.

Second, technology enables us to tap into lower-skilled resources. AI guidance is especially helpful in ultrasound, where operator skills can impact results. Unlike MRIs or X-rays, ultrasounds are taken using a wand held by an operator, who decides the angle and depth from which the recording is taken.

With AI-powered workflow software that can tell you whether the device is placed correctly and guide you step-by-step, even untrained staff that are unfamiliar with taking echos can use the machine. Such software can also produce high-quality and therapeutic-area-specific data, though access to and exclusivity to quality data at scale depends greatly on partnerships with medical institutions and providers.

These features are highly valuable, especially in rural areas in Southeast Asia cities that have limited access to specialised expertise and equipment. For healthtechs operating in this area, they would need to look at partnership agreements that allow them to continue to commercialise their algorithm, which was built based on borrowed data during the partnership.

The data refinery: From raw to useful

Data preparation is a key next step to ensure the final product can be useful to the acquirer. In this case, we’re talking about the big players in the healthcare ecosystem: large medtechs, clinical research organisations, pharmaceutical companies and insurers. Instead of raw data, they want their data sets cleaned, curated, and structured, ready to answer the questions they want to ask of it.

But how much are they willing to pay for that data? That depends as the potential use case for the data influences its premium in price. Exits have been few and far between, but some examples we’ve found include general EHR/claims data ranging from US$15 to US$50 per record and genomic data ranging from US$2,900 per record for general data to US$26,000 for oncology-focused data.

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These examples are a good starting point for us to understand how and where premiums accrue across different data types. At first glance, we can see how genomic data is a hotter commodity than EHR data. Still, oncology-focused data sets are more in demand than less curated general data.

When data is not oil

Unlike crude which gets processed and separated, data becomes more valuable when amalgamated and layered on top of each other. Another point we should make is around the reusability of data and how it affects the price.

Simply put, reusability is largely determined by ownership rights and exclusivity. Who gets to mine the data? Who gets access to the mined data?

Although data wells are pretty much inexhaustible, different rigs mining from the same well over and over again commoditise the data extracted, resulting in lower prices.

At the other end of the spectrum, we can see that precision health companies that own and guard the gates to the genomic data that they harvest enjoy a frothy price premium. Ultimately, it’s about controlling the access to high-demand supply.

Putting it all together

Now, back to the overarching question, we discussed at the start: how does everything we’ve discussed translate to exits for healthtechs in Southeast Asia? While there’s no straightforward answer, we can start to piece together some rules of thumb on how we can think about it.

In order to reach the endgame of accumulating sufficient, and sufficiently high-quality data, healthtechs that accumulate data across the three buckets of breadth, depth, and exclusivity are surely heading in the right direction. Ultimately, however, we think that the key to healthtech exits will come down to breadth even as depth and exclusivity are table stakes.

Achieving regional breadth is likely the most challenging to accomplish out of the trifecta and, therefore, will be the biggest differentiator among healthtechs, especially in Southeast Asia, where there’s great cultural, infrastructural, and political diversity.

Whoever manages to build an oil rig that taps on the many wells across the region will stand a much better chance of getting the attention of these global healthcare giants.

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Image credit: Theodore Ng, Analyst at Integra Partners

This article was first published on September 13, 2022

The post Healthtech data: The race for new oil in Southeast Asia appeared first on e27.