At a recent KPMG Robotic Innovations event, Futurist and friend Gerd Leonhard delivered a keynote titled “The Digital Transformation of Business and Society: Challenges and Opportunities by 2020”. I highly recommend viewing the Video of his presentation. As Gerd describes, he is a Futurist focused on foresight and observations — not predicting the future. We are at a point in history where every company needs a Gerd Leonhard. For many of the reasons presented in the video, future thinking is rapidly growing in importance. As Gerd so rightly points out, we are still vastly under-estimating the sheer velocity of change.
With regard to future thinking, Gerd used my future scenario slide to describe both the exponential and combinatorial nature of future scenarios — not only do we need to think exponentially, but we also need to think in a combinatorial manner. Gerd mentioned Tesla as a company that really knows how to do this.
He then described our current pivot point of exponential change: a point in history where humanity will change more in the next twenty years than in the previous 300. With that as a backdrop, he encouraged the audience to look five years into the future and spend 3 to 5% of their time focused on foresight. He quoted Peter Drucker (“In times of change the greatest danger is to act with yesterday’s logic”) and stated that leaders must shift from a focus on what is, to a focus on what could be. Gerd added that “wait and see” means “wait and die” (love that by the way). He urged leaders to focus on 2020 and build a plan to participate in that future, emphasizing the question is no longer what-if, but what-when. We are entering an era where the impossible is doable, and the headline for that era is: exponential, convergent, combinatorial, and inter-dependent — words that should be a key part of the leadership lexicon going forward. Here are some snapshots from his presentation:
Source: B. Joseph Pine II and James Gilmore: The Experience Economy
Gerd then summarized the session as follows:
The future is exponential, combinatorial, and interdependent: the sooner we can adjust our thinking (lateral) the better we will be at designing our future.
My take: Gerd hits on a key point. Leaders must think differently. There is very little in a leader’s collective experience that can guide them through the type of change ahead — it requires us all to think differently
When looking at AI, consider trying IA first (intelligent assistance / augmentation).
My take: These considerations allow us to create the future in a way that avoids unintended consequences. Technology as a supplement, not a replacement
Efficiency and cost reduction based on automation, AI/IA and Robotization are good stories but not the final destination: we need to go beyond the 7-ations and inevitable abundance to create new value that cannot be easily automated.
My take: Future thinking is critical for us to be effective here. We have to have a sense as to where all of this is heading, if we are to effectively create new sources of value
We won’t just need better algorithms — we also need stronger humarithms i.e. values, ethics, standards, principles and social contracts.
My take: Gerd is an evangelist for creating our future in a way that avoids hellish outcomes — and kudos to him for being that voice
“The best way to predict the future is to create it” (Alan Kay).
My Take: our context when we think about the future puts it years away, and that is just not the case anymore. What we think will take ten years is likely to happen in two. We can’t create the future if we don’t focus on it through an exponential lens
Source : https://medium.com/@frankdiana/digital-transformation-of-business-and-society-5d9286e39dbf
Digital health innovation continues moving full-force in transforming the business of healthcare. For pharma and medtech companies in particular, this ongoing shift has pushed them to identify ways to create value for patients beyond the drugs themselves. From new partnerships between digital health and life science companies to revamped commercial models, collecting and extracting insights from data is at the core of these growth opportunities. But navigating the rapidly evolving terrain is no simple task.
To help these companies effectively incorporate and utilize digital health tools, Rock Health partner ZS Associates draws on over 30 years of industry expertise to guide them through the complex digital health landscape. We chatted with Principal Pete Masloski to discuss how he works with clients to help identify, develop, and commercialize digital health solutions within their core businesses—and where he sees patients benefiting the most as a result.
Note: This interview has been lightly edited for clarity.
Where does ZS see the promise of data- and analytics-enabled digital health tools leading to in the next five years, 10 years, and beyond?
Data and analytics will play a central role in the digital health industry’s growth over the next five to ten years. Startups are able to capture larger, novel sets of data in a way that large life science companies historically have not been able to. As a result, consumers will be better informed about their health choices; physicians will have more visibility into what treatment options work best for whom under what circumstances; health plans will have a better understanding of treatment choices; and pharmaceutical and medical device companies will be able to strategically determine which products and services to build.
We see personalized medicine, driven by genomics and targeted therapies, rapidly expanding over the next few years. Pharmaceutical discovery and development will also transition to become more digitally enabled. The ability to match patients with clinical trials and improve the patient experience will result in lower costs, faster completion, and more targeted therapies. The increase in real-world evidence will be used to demonstrate the efficacy of therapeutics and devices in different populations, which assures payers and providers that outcomes from studies can be replicated in the real world.
How is digital health helping life sciences companies innovate their commercial models? What is the role of data and analytics in these new models?
The pharmaceutical industry continues to face a number of challenges, including the increasingly competitive markets, growing biosimilar competition, and overall scrutiny on pricing. We’ve seen excitement around solutions that integrate drugs with meaningful outcomes and solutions that address gaps in care delivery and promote medication adherence.
Solving these problems creates new business model opportunities for the industry through fresh revenue sources and ways of structuring agreements with customers. For example, risk-based contracts with health plans, employers, or integrated delivery networks (IDNs) become more feasible when you can demonstrate increased likelihood of better outcomes for more patients. We see this coming to fruition when pharma companies integrate comprehensive digital adherence solutions focused on patient behavior change around a specific drug, as in Healthprize’s partnership with Boehringer Ingelheim. In medtech, digital health tools can both differentiate core products and create new profitable software or services businesses. Integrating data collection technology and connectivity into devices and adding software-enabled services can support a move from traditional equipment sales to pay-per-use. This allows customers to access the new equipment technology without paying a large sum up front—and ensures manufacturers will have a more predictable ongoing source of revenue.
That said, data and analytics remain at the core of these new models. In some cases, such as remote monitoring, the data itself is the heart of the solution; in others, the data collected helps establish effectiveness and value as a baseline for measuring impact. Digital ambulatory blood pressure monitors capture an individual’s complete blood pressure profile throughout the day, which provides a previously unavailable and reliable “baseline.” Because in-office only readings may be skewed by “white coat hypertension,” or stress-induced blood pressure readings, having a more comprehensive look at this data can lead to deeper understandings of user behaviors or conditions. Continuous blood pressure readings can help with diagnoses of stress-related drivers of blood pressure spikes, for example. These insights become the catalyst for life science companies’ new product offerings and go-to-market strategies.
What are some examples of how data sets gathered from partnerships with digital health companies can be leveraged to uncover new value for patients and address their unmet needs?
As digital health companies achieve a certain degree of scale, their expansive data sets become more valuable because of the insights that can be harnessed to improve outcomes and business decisions. Companies like 23andMe, for example, have focused on leveraging their data for research into targeted therapies. Flatiron Health is another great example of a startup that created a foundational platform (EMR) whose clinical data from diverse sources (e.g., laboratories, research repositories, and payer networks) became so highly valued in cancer therapy development that Roche acquired it earlier this year for close to $2B.
It’s exciting to think about the wide array of digital health solutions and the actionable insight that can be gleaned from them. One reason partnerships are important for the industry is few innovators who are collecting data have the capabilities and resources to fully capitalize on its use on their own. Pharma companies and startups must work together to achieve all of this at scale. Earlier this year, Fitbit announced a new partnership with Google to make the data collected from its devices available to doctors. Google’s API can directly link heart rate and fitness activity to the EMR, allowing doctors to easily review and analyze larger amounts of data. This increase in visibility provides physicians with more insight into how patients are doing in between visits, and therefore can also help with decision pathways.
Another example announced earlier this year is a partnership between Evidation Health and Tidepool, who are conducting a new research study, called the T1D Sleep Pilot, to study real-world data from Type 1 diabetics. With Evidation’s data platform and Tidepool’s device-agnostic consumer software, the goal is to better understand the dynamics of sleep and diabetes by studying data from glucose monitors, insulin pumps, and sleep and activity trackers. The data collected from sleep and activity trackers in particular allows us to better understand possible correlations between specific chronic conditions, like diabetes, and the impact of sleep—which in the past has been challenging to monitor. These additional insights provide a more comprehensive understanding of a patient’s condition and can lead to changes in treatment decisions—and ultimately, better outcomes.
How do you assess the quality and reliability of the data generated by digital health companies? What standards are you measuring them against?
Data quality management (DQM) is the way in which leading companies evaluate the quality and reliability of data sources. ISO 9000’s definition of quality is “the degree to which a set of inherent characteristics fulfills requirements.” At ZS, we have a very robust DQM methodology, and our definition goes beyond the basics to include both the accuracy and the value of the data. Factors such as accuracy and absence of errors, and fulfilling specifications (business rules, designs, etc.), are foundational, but in our experience it’s most important to also include an assessment of value, completeness, and lack of bias because often these factors can lead to misleading or inaccurate insights from analysis of that data.
However, it’s not easy assessing the value of a new data source, which presents an entirely different set of challenges. One very important one is the actual interpretation of the data that’s being collected. How do you know when someone is shaking their phone or Fitbit to inflate their steps, or how do you interpret that the device has been taken off and it’s not tracking activity? How do you account for that and go beyond the data to understand what is really happening? As we get more experience with IOT devices and algorithms get smarter, we will get better at interpreting what these devices are collecting and be more forgiving of underlying data quality.
What are the ethical implications or issues (such as data ownership, privacy, and bias) you’ve encountered thus far, or anticipate encountering in the near future?
The ethical stewardship and protection of personal health data are just as essential for the long-term sustainability of the digital health industry as the data itself. The key question is, how can the industry realize the full value from this data without crossing the line? Protecting personal data in an increasingly digitized world—where we’ve largely become apathetic to the ubiquitous “terms and conditions” agreements—is a non-negotiable. How digital health and life science companies collect, manage, and protect users’ information will remain a big concern.
There are also ethical issues around what the data that is captured is used for. Companies need to carefully establish how to appropriately leverage the data without crossing the line. For example, using de-identified data for research purposes with the goal of improving products or services is aligned with creating a better experience for the patient, as opposed to leveraging the data for targeted marketing purposes.
Companies also face the issue of potential biases that may emerge when introducing AI and machine learning into decision-making processes around treatment or access to care. Statistical models are only as good as the data that are used to train them. Companies introducing these models need to test datasets and their AI model outputs to ensure gaps are eliminated from training data, the algorithms don’t learn to introduce bias, and they establish a process for evaluating bias as the models continue to learn and evolve.
Source : https://rockhealth.com/the-data-driven-transformation-of-the-life-sciences-industry-a-qa-with-zs-associates-pete-masloski/
The digital health rocket seems to have gotten supercharged lately, at least when it comes to fundraising. Depending on who you ask, either $1.62 billion (Rock Health’s count) or $2.5 billion (Mercom) or $2.8 billion (Startup Health’s count) was plowed into digital health companies in just the first three months of 2018. By any measure Q1 2018 was the most significant quarter yet for digital health funding. This headline has been everywhere. Digital health: to infinity and beyond! But what is the significance of this? Should investors and customers of these companies be excited or worried? It’s a little hard to tell.
But if you dig a little deeper, there are some interesting things to notice.
Another thing to notice: some deals are way more equal than others, to misquote a book almost everyone was forced to read in junior high. Megadeals have come to digital health (whatever that is), defined as companies getting more than $100 million dropped on them in a single deal. For instance, according to Mercom Capital, just five deals together accounted for approximately $936 million, or more than a third of the entire quarter’s funding (assuming you’re using the Mercom numbers) If you use the Rock Health numbers, which include only three of the mega deals, we are talking $550 million for the best in class (bank account wise, anyway). Among the various megadeal high fliers are Heartflow ($240 million raised), Helix ($200 million raised), SomaLogic ($200 million raised), PointClickCare ($186 million raised), and Collective Health ($110 million raised); three others raised $100 million each.
First of all, the definition of “digital health” is getting murkier and murkier. Some sweep in things that others might consider life sciences or genomics. Others include things that may generally be considered health services, in that they are more people than technology. Rock Health excludes companies that are primarily health services, such as One Medical or primarily insurance companies, such as Oscar, including only “health companies that build and sell technologies—sometimes paired with a service, but only when the technology is, in and of itself, the service.” In contrast, Startup Health and Mercom Capital clearly have more expansive views though I couldn’t find precise definitions. My solution is this: stop using the term “digital health”. Frankly, it’s all healthcare and if I were in charge of the world I would use the following four categories and ditch the new school monikers: 1) drugs/therapeutics 2) diagnostics in vivo, in vitro, digital or otherwise; 3) medical devices with and without sensors; and 4) everything else. But I’m not in charge of the world and it isn’t looking likely anytime soon, so the number and nomenclature games continue. My kingdom for a common ontology!
It used to be conventional wisdom that the reason healthcare IT deals were appealing, at least compared to medical devices and biotech, is because they needed far less capital to get to the promised land. Well, property values in the digital health promised land have risen faster than those in downtown San Francisco, so conventional wisdom be damned. These technology-focused enterprises are giving biotech deals a run for their money, literally.
But here’s what it really means: if you take out the top 10 deals in Rock Health’s count, which take up 55 percent of the first quarter’s capital, the remainder are averaging about $10.8 incoming capital per deal. If you use the Mercom numbers, the average non-megadeal got $8.6 million. This is a far more “normalish” amount of capital for any type of Series A or Series B venture deal, so somewhere in the universe, there is the capacity to reason.Another note on this topic: the gap between the haves and have not’s is widening dramatically. Mercom reports that the total number of deals for Q1 2018 was 187, which is the lowest total number of digital health deals for five quarters. Rock Health claims there were 77 deals in the quarter; Startup Health, always the most enthusiastic, claims there were 191 deals in the digital health category. I don’t know who has the “right” definition of digital health; what I do know is that either way, this is a lot of companies.
I think that the phenomenon of companies proliferating like bunnies on Valentine’s Day has another implication: too many damn companies. Perhaps it’s only me, but it’s getting harder and harder to tell the difference between the myriad of entrants in a variety of categories. Medication adherence deals seem to be proliferating faster than I can log them. Companies claiming to improve consumer engagement, whatever that is, are outnumbering articles about AI, and that’s saying something. Companies claiming to use AI to make everything better, whether it’s care delivery or drug development or French Fries are so numerous that it’s making me artificially stupider. I think that this excess of entrepreneurship is actually bad for everyone in that it makes it much harder for any investor to pick the winner(s) and makes it nearly impossible for customers to figure out the best dance partner. It’s a lot easier for customers to simply say no than to take a chance and pick the wrong flavor of the month. It’s just become too darn easy to start a company.
And with respect to well-constructed clinical studies to demonstrate efficacy, nothing could be more important for companies trying to stand out from the crowd. We keep seeing articles like this one, that talks about how digital health products often fail to deliver on the promise of better, faster, cheaper or any part thereof. And there’s this one by a disappointed Dr. Eric Topol, a physician who has committed a significant amount of his professional life to the pursuit ofhigh quality digital health initiatives – a true believer, as it were, but one who has seen his share of disappointment when it comes to the claims of digital health products. I’m definitely of the belief that there are some seriously meaningful products out there that make a difference. But there is so much chaff around the wheat that it’s hard to find the good stuff.
Digital health has become the world’s biggest Oreo with the world’s thinnest cream center. But well-constructed, two-arm studies can make one Oreo stand out in a crowd of would-be Newman-Os. One way that investors and buyers are distinguishing the good from the not-so-much is by looking for those who have made the effort to get an FDA approval and who have made an investment in serious clinical trials to prove value. Mercom Capital reports that there were over 100 FDA approvals of digital health products in 2017. Considering that there were at least 345 digital health deals in 2017 (taking the low-end Rock Health numbers) and that only a fraction raised money in that year, it is interesting to think that a minority of companies are bothering to take the FDA route.
Now, this is a SWAG at best, but it feels about right to me. I often hear digital health entrepreneurs talking about the lengths they are going to in order to avoid needing FDA approval and I almost always respond by saying that I disagree with the approach. Yes, there are clearly companies that don’t ever need the FDA’s imprimatur (e.g., administrative products with no clinical component), but if you have a clinically-oriented product and hope to claim that it makes a difference, the FDA could be your best friend. Having an FDA approval certainly conveys a sense of value and legitimacy to many in the buyer and investor community.
It will be interesting to see if the gravity-defying digital health activity will continue ad infinitum or whether the sector will come into contact with the third of Newton’s Laws. Investors are, by definition, in it for the money. If you can’t exit you shouldn’t enter. In 2017 there were exactly zero digital health IPOs. This year there has, so far, been one IPO: Chinese fitness tracker and smartwatch maker, Huami, which raised $110 million and is now listed on the New York Stock Exchange, per Mercom Capital. In 2017 there were about 119 exits via merger or acquisition, which was down from the prior year. This year has started off with a faster M&A run rate (about 37 companies acquired in Q1 2018), but what we don’t know is whether the majority of these company exits will look more like Flatiron (woo hoo!) or Practice Fusion (yikes!). Caveat Emptor: Buyer beware is all I have to say about that.