By David Shaywitz

A few months ago, I heard a young design entrepreneur named Aza Raskin talk about his idea for a consumer health company, MassiveHealth, built around the concept of providing rapid feedback. For example, if you had a skin dye that faded a certain amount each time you took a dose of your antibiotic, you would be more likely to complete the full course.

Skip ahead not very far. Recently, MassiveHealth launched its first, free app (dubbed an experiment), called the Eatery. The idea is that you take a picture of your meal and rate its healthiness, which is then shared with other users. You benefit, as I understand it, by thinking more about your food and by getting input on your food from other users. What the company itself gets is not yet clear. They’ve shared some pretty maps of San Francisco and New York City showing where people are eating more vs. less healthy foods, and they’ve drawn some fairly general conclusions about how the supposed healthiness of our food changes during the day (good at breakfast, bad during the day, partial recovery at dinner).

At least as important, I’d imagine, they have an engaged group of users who seem (at least at this early stage) to be interested in interacting with the platform, and thus contributing to the development of the emerging data set; after only a week, more than one million food ratings were reportedly received.

As I’ve followed the evolution of MassiveHealth, I’ve been struck by some of the profound differences between a tech start-up (even one ostensibly in the healthcare space) and a biopharma start-up. In the standard biopharma model, you spend years developing a product, without having any real idea of (a) whether it will work, (b) whether it will be safe and well-tolerated, and (c) whether by the time you’ve demonstrated (a) and (b), anyone will care, or pay you for your efforts. When you develop a new drug, most of the relevant properties of the product are pretty much baked in at a fairly early stage; you can tweak the formulation a little bit (to make it longer-lasting, say), but otherwise, you have what you have, and the challenge is figuring out just what this something is, and determining who might benefit most from it. Many have compared the process to an unforgiving lottery: Make a mistake at any point (choose the wrong indication, the wrong study design, or the wrong study sites) and you’re out of the game; execute flawlessly and you buy yourself only a chance to see whether or not your number is drawn.

Unfortunately, getting to the drawing takes a lot of time and money: most of the cost of drug development (which generally exceeds $100 million for an individual program — and this figure doesn’t include the cost of all the failures) isn’t from coming up with the particular molecule, but rather in putting it through the increasingly expensive series of clinical studies required to see if it’s actually going to work. Of course, once you’ve successfully run this gauntlet, not only do you have something that’s demonstrably effective (at least in the setting of clinical trials — see here), but there’s also a pretty high barrier for potential competitors, at least until your patents expire.

In contrast, MassiveHealth has managed to get a product in customer hands after a few months of work. True, they’ve probably not made any money thus far, and (not insignificantly) it’s entirely unclear whether they have, or will ever impact anyone’s health, massively or otherwise. Nevertheless, they have an extraordinarily powerful opportunity, at this very early stage, and after spending (I suspect) very little money, to learn, gather feedback, iterate, and explore: In an organismic sense, they can carefully assess their environment and respond adaptively — evolve their product based on demonstrated customer needs. And they can do this very rapidly so that even if just one aspect of their platform is interesting, they can rapidly pivot and exploit it (similar to the way Twitter developed).

The ability to make cost-effective exploratory efforts is a powerful enabler of innovation, as Peter Sims has highlighted in Little Bets (my Wall Street Journal review here, implications for pharma discussed here and here), and the need for successful start-ups to rapidly collect data and adjust course is more the rule than the exception, as John Mullins and Randy Komisar thoughtfully discuss in Getting to Plan B (listen to this interesting podcast of Komisar at Stanford).

Unfortunately, drug development is far less conducive to this sort of exploration; as I’ve discussed elsewhere, the cycle times tend to be far too long, and the costs are way too high. As a result, it’s a lot more difficult to change things on the fly, and to rapidly pivot in response to a new appreciation of customer need.

Not surprisingly, there’s been a lot of interest in ways to streamline the drug development process. One especially attractive way is to identify new uses for existing drugs (as these have already been carefully vetted), an approach I’ve previously argued might be especially amenable to a crowdsourcing model. More detailed patient segmentation — more comprehensive phenotyping — could also be helpful, as many clinical studies could be smaller and in many cases shorter if you could more precisely target your intervention to the patients more likely to benefit, and thus boost your effect size. The development of new, highly predictive models, including in silico, preclinical (i.e. animal), and especially experimental measurements in healthy human volunteers, would be especially valuable, and would allow for more rapid iteration and optimization.

The challenge for consumer health companies, on the other hand, is somewhat different: the question for them is whether will they actually improve health, in a robust, measurable fashion, offering the disruptive innovation their founders usually promise, or will they essentially be the Vitamin Shoppes and Whole Foods Whole Body departments for tech-oriented Millennials, offering what I would characterize as generally benign placbos at extremely profitable margins.

My guess is that to the extent consumer health tech companies can make money delivering a vague notion of wellness, they will. For example, I can imagine MassiveHealth generating significant revenue by selling geographically-targeted advertisements from restaurants promoting healthy alternatives — a business model that wouldn’t require them to mess around with prickly medical product regulatory requirements.

I’m most excited, however, by the opportunity to bring consumer health products to bear in a serious way, and ask whether they can deliver measureable healthcare value, which I’d provisionally define as improving health while removing costs from the system within a five-year period — a standard I cribbed from Stanford healthcare guru Arnold Milstein, and thus I dub the “Milstein Metric.” (Milstein directs Stanford’s innovative “Clinical Excellence Research Center,” focused on developing better and cheaper health delivery models — see here.)

What I like about the Milstein Metric (obviously not the only way to evaluate a new medical innovation) is that it represents a surprisingly high bar, and has a way of immediately focusing our thinking on impactful healthcare innovations. Prioritizing innovations that remove costs from our system makes sense given the urgent national need to rein in healthcare costs, a bipartisan ambition. The time period selected (five years) is admittedly somewhat arbitrary, and reflects not only the fact that patients switch payors frequently (so an investment not recouped before patients switch out is difficult to justify on financial terms), but forecasting is an inherently fragile business, and projecting years into the future is tenuous to the point of rarely being credible. An intervention that maybe kinda sorta pays off in 50 years is usually too speculative to be useful.

So the question — highlighted in this recent Washington Post article — is: can you make money by saving money? Phrased differently: There are literally hundreds of new devices, apps, and companies jumping into the consumer health space (see here and here); some will succeed simply because they delight users, but will any deliver real health benefits, measured by the Milstein Metric?

In general, I suspect the answer is no; Most consumer health companies are unlikely to deliver measurable health benefits. Thousands of apps will be downloaded, perhaps used for a week, and then abandoned in favor of the lastest snazzy offering.

My suspicion is that it might take an entirely different set of people, and a very different mindset to start thinking seriously about how to deploy some of these new technologies in a fashion that actually impacts health. These technologies represent the raw tools, and it will require additional insight and effort to rigorously apply these tools to the practice of medicine and the delivery of health, and use them to extract measurable value.

That’s OK. I’m grateful for these tools. As I’ve advocated frequently (see here and here), improved patient measurement is the single most important mechanism we have to improve health, both in the short term (essentially by providing physicians and patients with more immediate feedback and enabling just the sort of iterative learning described earlier) and in the long term (by providing an extensively annotated view of human physiology, yielding fundamental new insights and enabling the development of powerful new therapeutics).

My guess is that the integration of consumer health and patient health will be driven by innovators in both the public and private sectors. Academic medical centers — a precious source of both inquisitive physician and involved patients — can play a critical role in orchestrating and driving this process. (Disclosure: I am co-founding the creation of one such non-profit institute, the Center for Assessment Technology and Continuous Health.) I also imagine some of the most important advances will continue to come from the private sector, extending from the start-ups I see all around me in the Bay Area, advancing promising technologies and data-analysis platforms, to the large medical products companies seeking to sharpen their focus and improve their offerings, to the payors always on the hunt for the elusive “cut costs but improve care” program, to regulators, who to their credit were among the first to recognize the need for improved patient-associated measurements (as noted here and here).

I am inspired by this integrative vision of the future, motivated by our urgent unmet healthcare needs, stimulated by the energy and passion of the entrepreneurs and inquisitive physicians and scientists I meet, and driven by the patients whose endurance has been sorely tested. It’s time to deliver consumer health — consumer health we can believe in.

David Shaywitz is co-founder of the Harvard PASTEUR program, a research initiative at Harvard Medical School. His a strategist at a biopharmaceutical company in South San Francisco. You can follow him at his personal website. This post originally appeared on Forbes.