Making data markets: Assetization,valuation,and proxy work in a digital health start-up

Part 1: Innovating the future of care

Digital technologies play an increasingly important role in academic knowledge production, where AI, smart devices, sensors, and other software and hardware can be mobilized in pursuit of different goals (Delvenne et al., 2023; Falkenberg & Fochler, 2024; Lee & Helgesson, 2020; Pinel, 2021). The technology that became the focus of this particular case originated in doctoral coursework and eventually formed part of a dissertation in the biomedical sciences. As a trained MD/PhD, Daniel, the student-turned-investigator/founder noted that smart technologies and inertial sensors were increasingly used to address clinical problems, and one of the problems he regularly encountered in clinical practice was sub-standard performance of at-home physiotherapy exercises. Often, patients who did not complete their physiotherapy exercises frequently or effectively enough would require more invasive treatments, which were both harmful to the patient and costlier to the health care system. This has led to a system-wide emphasis on compliance, where patients are encouraged in different ways to perform their prescribed exercises, sometimes without success, as Daniel describes here:

We were using smart watches in the lab for some other applications, for inertial sensors. There was one of the lab members, [name], a junior scientist who was using inertial sensors for assessing the knee kinematics of athletes who were at risk of or anterior cruciate ligament injury. I had some exposure to that technology and I thought this might be applicable .... It’s widely understood that the quality of treatment that patients receive, due to their own financial circumstances, or their own motivations, or beliefs about the quality of it really varies …. And so, that got me thinking about, what would be a good way to first of all, assess that, but also, to really make sure that patients got the most out of this treatment.

Here, Daniel shares his perspective on the potential value of the technology as a tool to support patients’ existing goals. The need for compliance with treatment regimens, and the perception that the technology could support those aims, represents the approach he brings to the project as a clinician-scientist. Already, we can appreciate the notions of care embedded in these conceptions of clinical value (Mol, 2008), such as the imperative to improve patients’ outcomes using the tools he has available, which represent some of the best his discipline has to offer. The problem informing Daniel’s approach is explicitly conceived of as a clinical challenge solvable by an already available technology, particularly as a more precise evidence-based intervention with the ability to ‘nudge’ patients when needed.

When an initial prototype was determined to be acceptable by research standards, the team applied for funding to clinically validate the technology with patients, eventually winning a grant competition sponsored by a large public insurer. The research team then leveraged that funding to secure additional research funding from a federal agency, while exploring commercialization avenues. As a ‘cutting-edge’ technology, the journey from clinical tool to commercial opportunity was a short one, buoyed by university and hospital translational research policies and resources supporting such efforts. Adam, one of the hospital’s business development executives, reflects on his role within the organization and the metrics used to track his office’s performance:

I’d say our metrics really start with invention disclosures that come in, which is when scientists or clinicians or students create new technologies and see potential, and then they file an invention disclosure. That’s kind of a leading indicator of research that may have commercial potential. And then we track our patenting activities, so provisional patents [interim ‘patent pending’ filings] and then through to patent cooperation treaties and national phase filings [international patent filings made in specific countries]. And then at the end of the day, how many of those are issued. So, that’s the assets building stage. And then on the other side … we need that transfer piece … so where we look to actually have the intellectual property leave the institute and be put in the hands of people who are advancing it. So, on that front, we track active engagement. We track term sheets [a nonbinding agreement that sets out basic terms and conditions of an investment]. When we enter that phase of term sheets, we track how many weeks of contracting, and then at the end of the day, how many licensing deals we do … [Hospital name’s] vision or motto is [Innovating the Future of Care]. …

We’re an academic institution effectively. We’re funded by the public sector. My feeling is the reason why the government funds things like universities and colleges and research institutions is not because there is some fundamental interest in basic research. It’s because it should have the potential to grow Canada’s economy at the end of the day. So, that’s, I think, a core piece there. If we look at a project that is taken on or a new company that’s formed, those will generally attract investment. It will lead to more job creation and help the Canadian ecosystem grow.

To Adam and others, academic research is a key pillar of economic growth, but practices of scientific production do not always map neatly onto commercial objectives. As such, a significant part of Adam’s day-to-day work involves finding partnerships, offering advice on commercialization pathways, supporting patent filing, and connecting researchers to commercial opportunities. Adam represents a first point of contact in the translation of research products from the lab to the clinic, which involves separate but increasingly overlapping activities in support of broader commercial objectives associated with contemporary biomedicine.

At these earliest phases in the lifecycle of the technology, the inertial data generated by the smartwatch and exercise scores generated by the algorithm were the primary data products, as the team called them. This move from the lab to the clinic impelled the team to think like entrepreneurs rather than investors (Birch & Muniesa, 2020), with a focus on creating mostly clinical value. This had important implications for the sustainability of the business, however, on which Jane, the teams’ strategy and finance consultant, reflects:

It definitely is more focused on bringing in data-driven decision-making to physical therapy, right, and I’m sure you’ve heard this pitched many ways. … I think for me it was really surprising to learn that you can do all this research, you have all this data behind it saying, hey, this is actually going to help you, improve you, help you do a better job. And doctors just were like, no, I don’t want that, I know better, even though you can show them all the data in the world.

As Jane suggests, the disentanglement of the technology from a scientific milieu to a clinical one was not a straightforward process. Achieving commercial success would require a translation of clinical and scientific aims into commercial ones, while appealing to potential users. In the context of the transition from lab to patient care, this meant a focus on treatment adherence and data-driven decision-making. These successive translations enrolled a diverse and ever-growing number of actors and interests, each accompanied by competing practices of valuation. As these grew in number, so too did potential conflicts, including over the perceived usefulness of digital data in outpatient physiotherapy practice.

Still, in spite of its longer-term commercial objectives, conflicts at this phase were limited. Data products were primarily academic and aimed at defining an acceptable or desirable clinical outcome or scientific knowledge claim (Fuller, 2019). Accumulation strategies and ownership and control rights were primarily derived from and supportive of academic work, without the need to demonstrate value outside of the clinical encounter or the context of academic knowledge production. The technology does not yet rely on investors or even users to perform its intended functions. IP filings, publications, grants, or other new sources of funding were important primarily in relation to the activity of commercializing the data-generating technology as an outcome itself, in alignment with forms of value associated with contemporary academy (Fochler, 2016), particularly in health and biomedical technology. However, this was only a first step towards future translations, entailing other asset forms and valuations, as the following sections illustrate.

Part 2: From the lab to the clinic

Through the establishment of a start-up the core research group recruited a larger team, comprised of a Chief Executive Officer (CEO) and several other experts from across finance, tech, engineering, and corporate strategy. In this subsequent phase of commercialization, selling the technology directly to individual physiotherapists or patients was determined to be too difficult, and so the team pursued chains of clinics as potential buyers of the technology, as the CEO Ken describes in his outline of the company’s approach:

I know from my background that it’s very difficult to identify recently injured patients, or people who have had surgeries, and try to market directly to them in some way, or to try to market to them through things they might be searching for. Some of our competition are now doing that. It’s an extraordinarily large marketing challenge. We will be focusing a lot of our marketing dollars on reaching those clinics, those physiotherapists, and they’re incredibly easy to identify online as a group and be able to be targeting them using social media, other targeted online techniques.

The team considered different approaches, including a ‘freemium’ model, where patients and providers could access certain features of the technology for free with paid upgrades. The team also simultaneously recognized that the potential value of the technology was not strictly clinical or scientific, and a clearer emphasis on its administrative benefits was articulated. This translation of clinical value to administrative value accompanied the team’s pivot in commercialization strategy, which also required a translation of the technology and the data it produced from a scientific product and clinical tool to an enabler of cost-effectiveness and transparency. The commercialization strategy that accompanied this shift also resulted in different data products, as described by one of the team’s commercialization experts:

Data comes across, as far as I can tell, three scales. And user testing has revealed that effectively everyone is comfortable sharing all of their data with their care provider like the physiotherapist. We see that being the smallest scale data product. The physiotherapist sees what an individual person is doing and can use that information to inform that person’s treatment. So, the next scale up of that is the one where there is huge quantities of ethics and regulatory concern. And that is when you take individual data that is being fed to a physiotherapist and group that together like via a policy number or some type of larger bidding structure. Maybe they need to have some type of, take some type of action when it comes to Bob’s Wrench company because it turns out that everyone there is not doing their physiotherapy. Something else is going on. You need to have a conversation with Bob and say you need to provide additional time for employees to have their physiotherapy because they need to do this during working hours, no one is any doing physio at home. The next step up is all people just grouped together in what would be a totally de-identified data set. At that point you could then say certain parameters are available for de-identification, so you’re allowed to have age still by year. So, you could say alright, this is the average recovery pathway for a 42-year-old. It just has to be de-identified. So individual data great, that’s probably quite useful to just people on an individual scale. Is it going to be worth any more than a core product which is helping people to do physiotherapy exercises? Probably not.

In this passage, the commercialization expert clearly outlines the three possible data products envisioned for the technology, each of which implicates different ethical and regulatory regimes, data architectures, and valuation practices. For something to be assetized, it must be identified and framed by credible experts so that it can be governed effectively (Birch, 2024). Margaret, the team’s data monetization consultant, is aware of the controversies surrounding different data valuation and assetization practices, and situates these in relation to the task she has been assigned:

I'm kind of constantly challenging and thinking through, if I was the consumer of this data or of this product on the corporate side, what is it that I would be looking for and how do we set the product up so that we continue to have trust by the customer and understand what kind of value we can provide to organizations?

While the earliest iterations of the technology exemplified forms of academic entrepreneurialism (i.e., patents, publications, grants, and eventually a spin-off company), subsequent phases brought about competing ideas about how best to produce financial value for more diverse actors, without losing sight of the other forms of value that made the technology desirable to those who were intended to use it. This required freeing the data from their associated milieu (Callon, 2021; Simondon et al., 1980) (in this case, a clinical-scientific one) so that they could become exchangeable while still being functionally useful in a clinical setting. This process of translation must be done in such a way that functionality is preserved, while facilitating a transaction and transfer of rights that commercial exchange requires. These exchanges could pertain to patients (who does or does not complete their exercises?), physiotherapists (whose patients improve faster?), companies (where are people getting injured?), clinics (which clinics perform better than others?), or all of the above. Achieving success this way would require larger actors, more users, more clinics, and more data, however. We elaborate on this below.

Part 3: Plug and play

The significance of large corporate actors in digital health ecosystems has been extensively discussed. These discussions typically focus on ‘Big Tech’ and the implications of monopolistic power and Silicon Valley logics for public health care systems (Anderson et al., 2022; Birch et al., 2021; Birch & Cochrane, 2022; Ozalp et al., 2022; Sharon, 2018). Less frequently examined in empirical work, however, is how the practices of large organizations, Big Tech and otherwise, practically shape the activities and valuation practices of other smaller organizations as they try to make a market for themselves. Generally, these dynamics have been represented in the existing literature in two distinct but complementary ways. First, is the observation that large organizations monopolize a particular arena in order to prevent other players from entering. This is often accomplished through the mobilization of existing resources and infrastructures which would be difficult or impossible to access by other actors in order to make a ‘first move’. Second, larger companies may pursue markets that have been ‘de-risked’ by government, start-ups, or other smaller players (Mazzucato, 2015), by either purchasing those companies, or forming their own divisions that compete with or control other players through ownership or control of essential infrastructure or resources.

Similarly, with respect to our own case, we found that as different commercialization strategies were considered and tested, it became clearer to the team that success through larger-scale adoption would require infrastructure and resources that depended on other, much larger players, as Sam, the co-founder explains:

So, it started with physical therapy, it went onto insurers upon the realization that physical therapy wasn’t really going to work out as a direct sale approach. Then it went to wearable adoptions and adoption rates. Then it would have to go on to actual development costs, what did we think it would cost to build this thing out as a technology and would we be able to make enough money if we could get into any major insurers doors, to justify spinning up a company to do it. Those were the principal steps in getting to the key points for whether or not it was worth pursuing this in the size of the market for [company name].

As a co-founder, Sam was tasked with conducting market research, liaising with partners, and devising potential business models that reflected the ideas that other team members contributed. He was present for much of the technology’s journey, and recognized that a partnership with a large, vertically integrated United States-based insurer/provider could deliver what they wanted. This would involve linking algorithmically generated performance scores to other proprietary infrastructures, like electronic medical records and insurance claims records, to generate savings for the licensee. This would generate more financial security for the start-up, as they could plug in directly to that organization’s ecosystem. This, however, came with its own risks, as the CEO, Ken, outlines:

The concern with that was from a business perspective that it’s kind of like a big bang thing, right? You can work with one guy for a year and you may or may not actually get through, so it’s a little more risky. But also, if you tend to work with one insurer, you can kind of be swayed by what they want. If you’re working with an insurer, they’re like your biggest customer, your main customer, and you don’t have many of them. And they ask you for, I want access to this or I want to build this or I want to do this a certain way, it’s really risky to say no to them.

The data assets generated by such an approach are distinct and associated with very different use cases for the technology. This new business model privileged the scrutability of clinical and administrative indicators for both individuals and groups, with the associated transfer of risk from the insurer or the provider, to an individual clinic, patient, or physiotherapist. These represent personalization at scale, where personalization can be used to individuate groups and populations through the creation of new risk categories or pools, and therefore new products and business strategies that transfer risk from the insurer to the individual (Cevolini & Esposito, 2020; Kampmann, 2024; McFall, 2019). By pursuing such partnerships, the team recognized that they could benefit from locked-in markets indirectly, through participation in ecosystems controlled by a small number of very large players, where contractual terms with those partners dictated access rights through licensing agreements. The potential pitfalls of such an approach, however, are also large. Patients would not use a technology if they thought that it could be to their disadvantage to do so, introducing challenges to the scale that would be needed to make a data asset viable in the first place (Geiger & Gross, 2021). Some patients also had personal reasons for not being able to use the technology, and if the technology was to become a condition of care, equity concerns also arose. Finally, the team also had its own attachments to the data and the technology and their uses, as an engineer, John, describes:

You could easily see a situation where they roll out an app with one major feature. They acquire a ton of data through private clinics. But then they put a new feature in the app that says, if you want, click here if you’re not doing your exercises and you’re doing this other thing. Basically add a pathway for another label to the data basically and more data to be collected, they would have an easy way to collect a whole bunch of new data on another thing and they could use that to train a new model and grow the company. Add a new feature with this new model and so on, grow the company that way. I don’t actually think that’s a bad thing. I don’t see really privacy concerns with that. But I have this weird feeling about that kind of expansion that I can’t really articulate. That kind of thing and it does happen with a huge company, they can kind of easily create a monopoly on human activity data basically, because they have the means to collect it, it kind of snowballs, if that makes sense. Where they have this app that’s popular, they can add features by collecting data with the app. So, they get more features, they get more users, and it just keeps growing. I can’t articulate why but I feel like that’s not a great situation.

These controversies demonstrate that ‘thinking like an investor’ (Birch & Muniesa, 2020) rather than like an entrepreneur (i.e., a focus on investment return over the development of new products and services) required a shift than that not all team members, including John the engineer, were necessarily comfortable with or capable of pursuing. Moreover, the technology’s translation from one phase to another would only work if it could continue to maintain an appropriate set of relations amongst its basic parts, in this case, the core team, the functionality of the technology itself, the data resulting from its use, practical business requirements, and the assets those afforded.

Although the story continued to unfold beyond our three-year engagement, this latest phase best exemplifies the challenges associated with helping innovations ‘find their publics’ (Callon, 2021), particularly where competing valuations are at play. The technology, its data assets, and their perceived value regularly overflowed their boundaries, carrying traces of their past with them as they became enrolled in new and transformed ventures. For example, the team’s data monetization consultant, Margaret, was aware of the controversies surrounding different data valuation and assetization practices, and had to manage these conflicts while meeting the future-oriented expectations of the investors she was hired to attract. At the same time, this had to be accomplished without losing sight of the earlier forms of value that made the technology both desirable and useful to those who were intended to benefit from it, including patients and physiotherapists. This became increasingly difficult, however, as the data assets relied on and generated very different use cases for the technology, potentially breaking the set of relations that permitted the identification and pursuit of those assets to begin with.

Put another way, while each style of valuation and its associated practices, beneficiaries, and data assets can be considered distinct (Table 1), they must also be considered in light of the activities that came before them. Clinical value was related to administrative value insofar as health, vitality, and research excellence were made to stand in for and support broader management goals, such as treatment adherence, or cost reduction. Clinical and administrative value also supported financial valuations, where investors would base their assessments of potential future growth on the technology’s ability to fulfil the clinical and administrative roles assigned to it, as well as the ‘added value’ made possible by combining new data with existing datasets made possible by large corporate actors. These overflows also signal that each style of valuation, and associated mode of assetization, always contains opportunities to intervene with different effects along the way. We discuss the implications of these framings and overflows in the discussion sections that follow.

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Table 1.

The valuation and assetization of digital health data.

	Style of valuation	Data assets	Reliant on …	Primary beneficiaries
Part 1	Clinical-Scientific • Research excellence • Technological sophistication • Health and vitality	Academic • Focused primarily on the data and technology themselves, represented by IP filings, but also publishable results, and training datasets for other AI products.	• Research questions • A health care ‘problem’ • Research funding • Inertial sensors • Movement data • AI generated scores • Graduate students • Commercialization offices	• Researchers • Research institutes • Patients • Physiotherapists
Part 2	Administrative • Treatment adherence • Data-driven decision making • Efficiency • Cost reduction	Engagement • Generated through the collection, sorting, and provision of data products, which relied on the differentiation of users based on their engagement with the technology.	• Patients • Physiotherapists • Hospital budgets • Clinical practice guidelines • Billing data • Healthcare delivery policies • Program administrators	• Hospitals • Clinics • Administrators
Part 3	Financial • Personalization • Growth • Scale	Enclave • Dependent on participation in ecosystems controlled by a small number of very large actors, where contractual terms dictate access to proprietary infrastructures, services, or products in exchange for licensing.	• Large providers and payers • Electronic medical records • Linked databases • Insurance policies	• Investors • Insurers • Partners