Researchers at MIT and Brigham and Woman’s Hospital in Boston published an intriguing theory: base NIH funding similarly to how investment companies handle portfolios, complete with return on investment calculations. But what would a rate of return be in biomedical research? Years of life saved per dollar spent, say Andrew Lo and his colleagues in their paper published in PLoS One.

But as the authors and others admit, “years of life saved” is probably an overly simplistic measure. Perhaps the authors were trying to make a point in the most extreme way possible.

The paper does illustrate that NIH’s current criteria for funding should be questioned. The agency currently rates projects according to public needs, scientific quality of the proposal, likelihood of scientific progress, need for diversified research, and the need to support people, equipment and facilities. All but the first criteria are not really related directly to improving healthcare, at least not on the individual patient level.

The “portfolio theory” article succeeded in prompting me not only to think about the shortcomings of the current funding formula but also to question the wisdom of the authors’ proposed alternative:

• Many disease states—rare diseases, and chronic but painful forms of cancer, for example–would suffer after changing to a “portfolio theory” for allocation of funding, either due to the relatively low number of affected patients or as a result of isolating years of life saved to the exclusion of important benefits like pain reduction.
• Finance theory tries to maximize one thing; profit, free cash flow, or return on investment (ROI), for example. You can choose which metric to maximize, but you do choose just one.
• To comprehend other outcomes than years of life saved would require a weighted formula, looking at reduced pain and suffering, quality of life, as well as years of life saved. The determination of these weights would require value judgments that would to some extent reduce the benefit of the new quantitative approach to allocating funds.

Nonetheless, the “portfolio theory” discussion is important, and hopefully can lead to an improvement over the status quo of NIH funding.

Does application of the portfolio theory at NIH work for you? Are there other metrics beyond years of life saved? If you wanted to create a weighted formula for matching funding to healthcare outcomes, how would you do it? We’d love to hear your ideas.

To discuss ways to resolve the puzzles inherent in regulating digital health opportunities, Paul Sonnier and I met with Keith Studdard, the Legislative Director to U.S. Representative Marsha Blackburn (R-Tenn), who has been advocating a clearer, more streamlined regulatory approach to new developments in digital health.  What was our reaction coming home? Instead of scratching our heads, we were pleasantly surprised by the level of engagement and dedication we found.

Our meeting included an acknowledgement that the Republicans in the U.S. House needed to address digital health regulations in a way that involved Democrats. Despite persistent accusations of partisan gridlock in Congress, I came away sensing a high degree of sincerity, at least judging from the candid remarks coming from Blackburn’s office. One of the biggest problems to legislators and regulators in digital health is uncertainty within the industry, a point Studdard reinforced by citing the old maxim that, while “no” is often an answer that people don’t want to hear, “maybe” is worse, particularly in terms of its impact on business planning. And at the moment, digital health’s regulatory future is replete with “maybes.”

Blackburn has been involved in regulating digital health for at least two years, when a Memorandum of Understanding (MOU) was signed between the Food and Drug Administration (FDA) and the Federal Communications Commission (FCC) to help eliminate the “maybes” and coordinate regulations of mobile health apps, devices and other innovations in digital health. The FDA and FCC agreed that wireless and digital health is a brave new world and admitted they were not equipped to deal with this fast-moving area. The agencies admitted that no policies that existed in 2010 could be written in such a way that comprehends what the mobile health space would mean for consumers or patients. The MOU promised to focus on this.

Blackburn was one of six members of Congress who recently crafted a letter asking for a progress report from the FDA and FCC on this MOU. What should be regulated? How should they determine what gets regulated? What regulations would apply? What’s happened since the signing of the MOU in 2010?

Certainly, patient safety, medical record security, and consumer protection are very important areas to consider from a regulatory perspective. But how much regulation is necessary? And how can these regulations provide safeguards without slowing innovation? What types of regulations do you think are necessary in this area? And how would they keep up with the ever-changing digital health world? Let us know, and we commit to continue engaging with those – like Mr. Studdard – who can help make a difference!

While the report focused on many areas outside of healthcare, the following three points struck me as significant for those of us who spend our time thinking about life sciences:

1. We must facilitate the transition of bioinventions from research lab to market, including an increased focus on translational and regulatory sciences.  If a national “bioeconomy” is to be successful, it must be based on a steady flow of new products and services that address the needs of American and global customers. Ensuring this steady flow will mean that policies must be developed—and taxpayer dollars used responsibly—to foster an environment that supports discovery, innovation and commercialization.
2. We must integrate approaches across fields. Modern research questions have become so complex that traditional boundaries between fields of study have become permeable. Today, programs must concentrate expertise from diverse disciplines around society’s greatest challenges. The Administration should prioritize (fund) additional multidisciplinary efforts to enable biological research at the boundaries of fields, such as physics, chemistry, engineering, computer sciences, and mathematics, as well as genomics, imaging, biosensors and wireless technologies that will all support the new bioeconomy. The success of systems biology in using genetic data and other big data sets to link and thus redefine disparate diseases is an example of how these efforts are starting to pay off. The bioeconomy can borrow from cross-disciplinary approaches used in other industries like construction and defense.
3. Moving forward—improved regulatory processes will help rapidly and safely achieve the promise of the future bioeconomy.

• Agencies should improve their predictability and reduce uncertainty in their regulatory processes and requirements. Part of this predictability includes coordinating efforts among agencies that hadn’t operated in the healthcare space; recently Rep. Marsha Blackburn (R-Tenn) and five other members of Congress asked the Federal Communications Commission and FDA to clarify their ongoing efforts to improve the regulatory process for development of wireless medical devices.
• To reduce costs and impediments, but without compromising safety, attention should be given to drug and device application review times, coordinating reviews by multiple agencies to allow parallel (instead of sequential) reviews, and specific guidance that should be issued in response to stakeholder needs.
• When emerging technology enters the regulatory process, agencies must have a solid framework to identify lead agency responsibilities, clarify supporting agency roles, and deliver timely, specific guidance for applicants. At the very least, these new moves should update and streamline the 1986 Coordinated Framework among federal agencies.

In future posts, we will be talking more about how the federal government is planning on aligning itself with the new bioeconomy, including initiatives in digital health and takeaways from some of our meetings with federal government representatives.

What was your key reaction to the White House’s blueprint for a bioeconomy? Are there points that you honed in on? For what other programs should the OSTP advocate? Is a future “bioeconomy” even a realistic concept? Share your thoughts with us.

Today, I’d like to explore the growing power of the healthcare consumer.

Overall, healthcare costs – both on the societal and the individual level – are increasing. Consumers are required, one way or another, to pay a greater share. So they are starting, albeit slowly, to ask questions about value. And they are interested in the value to themselves, individually, not to the population as a whole. How the consumer perceives healthcare value is an area that needs a lot of further exploration.

At Popper and Company, we have observed that an empowered consumer, armed with more information than ever before, is using this information to demand more tailored, customer-centric treatment from practitioners and institutions and from the tools and technology used. This customer demand is starting to move information from large centers accessible only to physicians, researchers or engineers to mobile devices, web sites and social media platforms accessible to nearly everyone.

How might healthcare innovators respond to such changes in customer demand?

• Create new metrics, like the popular Consumers Union of the U.S. rankings, that measure how customers rate a product (high/low acquisition costs, maintenance costs, product lifespan) according to their preferences. This would require healthcare providers and technology developers to consider customer input in their products and services. More significantly, it would require them to be able to describe their products and services in a way that’s comparable to a competitor’s (and in ways that consumers will understand).
• This new “report to consumers” would require the development of information systems that can read customer behavior, wants, and needs. Seeing patients as customers means considering how these customers will react to products or services, and taking those reactions into account when designing a product or service, or when developing a new treatment strategy.

Certainly, this would require quite a paradigm shift in the life sciences industry, but it’s the way our colleagues in the automobile, electronics, and durable goods industries work every day. Now, as in these other industries, consumers can educate themselves and retrieve information easily. Therefore, it’s probably time that our industry joined those other industries in putting consumer perceived needs first or a the very least on a par with what the health care providers think is “good for them.”

Do you think a consumer ranking system is possible broadly in healthcare? How readily are you now able to capture your customer’s ratings and opinions of your company or its products? Are there any other ways those developing healthcare solutions can integrate and embrace the changing role of the customer? Please share your thoughts with us.

This is the digital revolution in healthcare: not only are Microsoft® products ceasing to become the predominant platform for healthcare employees, providers and consumers, the decisions to adopt certain technologies are being made by employees, providers, and customers (and less often by the corporate IT department). For example:

• Pharmaceutical companies are buying more Apple® iPad® tablets for their sales representatives and executives to use to communicate, download data, and otherwise manage their accounts remotely. The number of life sciences companies planning to buy Apple hardware jumped 220% since 2009.
• While eight percent of hospitals fully enable access for user-owned (BYOD) devices right now, 58 percent plan to give iPad users remote access to hospital applications, and 85 percent provide some degree of access (Internet-only, limited applications, etc.). Those are big first steps for a highly security-conscious business.
• Pfizer has launched the world’s first virtual clinical trial, using mobile phones and web-based technology so the trial’s 600 participants can enter data and manage their activity without reporting in person to a clinical site. The trial, testing a drug to treat overactive bladder, allows patients to use mobile devices to keep an electronic diary and report results directly. Researchers can monitor activity and report results to participants, all across the Internet.

This is a major shift from paper-based systems used just a decade ago, through pushing browser-based apps to mobile devices (with predictably disappointing results from customers), to integrating customer-, employee- and provider-based experiences into new technology design and strategy. This shift has resulted in greater penetration into specific markets, higher employee engagement, and ultimately, could provide a better, more customer-driven healthcare system.

Could this convergence of technology and consumer use be a perfect storm, with blue skies in the forecast? Can traditional healthcare IT embrace this new world of customer-driven, BYOD technology? What’s the best way to manage security and privacy issues? Is there a strong hesitancy to embrace a disruptive innovation? Tell us what you’re thinking and how your life science company is tracking rapid changes in the digital health space.

We at Popper and Company believe that many of these recommendations are fairly obvious, such as not ordering a CT scan or antibiotics for someone with uncomplicated sinus inflammation, or forgoing routine annual electrocardiograms for low-risk, asymptomatic patients.

But these medical society recommendations addressing patient care point to an important issue for life science companies in the business of developing new health products—a need to get away from building revenue projections based on a population-based “screening” mode, and an urgency to shift to ensuring desired ROI based on a personalized one.

The idea of personalized medicine always leaves me with the indelible impression that developers and deliverers should return to Marketing 101—Lesson 1: separate and segment your markets. At the 40,000-foot level, personalized medicine is little more than a specialized way to segment a market. This kind of marketing segmentation—whether it’s ordering tests only for patients who exhibit a strong need or designing a regimen based on a patient’s individual proteomic profile—can be a boon for the whole diagnostics industry, which finds itself at the sharp end of the segmentation spear.

For new product developers, the focus on personalization opens the door to integrate a new information infrastructure. New information system products should be able to capture and analyze costs on an “episode of care”  basis, and integrate patient outcomes. Rather than evaluate cost-effectiveness of an individual procedure, an information system should be able to track quality, outcome and cost over the whole disease episode, from the patient’s initial reporting of symptoms to treatment.

This is not the future of health care; this is happening now. It would behoove us not to ignore the segmentation trend; instead, our industry can borrow techniques from others that routinely segment their markets (e.g., automobile manufacturers that make both electric vehicles for short commutes and large trucks for transport), consider total cost of use (e.g., energy consumption over the life of a dishwasher), and integrate outcome and customer feedback (e.g., a consumer report rating).

Have you started to segment your market? If not, how would you start? What impact do you think this will have on costs and their reimbursements? Tell us what you think.

How do we balance guidelines focused on standardizing physician behavior with individual consumer demands for testing?

A new study at Brigham and Women’s Hospital and the University of Michigan has begun to plumb the depth of this new consumer interest in genomics. The Personal Genomics (PGen) study is one of the first interdisciplinary inquiries to examine why people want genomic testing now. It will survey 1,000 volunteers. Then, physicians, scientists, attorneys, genetic counselors, psychologists and bioethicists – many of whom hope the study will guide public policy and business practices in this area – will analyze the results.

Knowing what is in your genome can empower patients, further reinforcing their role as “consumers of health care.” This new power, then, would – in theory – drive their demand for more tailored care. Earlier studies found that the earliest adopters of genetic testing were simply satisfying a vague curiosity; today, however, we do not know precisely why subsequent waves of consumers are interested in testing.

As life science industry strategists who are also healthcare professionals and consumers, we at Popper and Company are wondering whether the rise of the empowered consumer will or will not be accompanied by a commensurate rise in consumer knowledge of genetics. We are also thinking about how this new consumer will interact with potentially less information-empowered health care providers, and how this interaction might impact health care delivery within current care guidelines and standards and the historic doctor-patient relationship.

How well are consumers internalizing the information provided by genomic tests? Why are they demanding it, and what are they looking for? How can physicians weave this new demand into their practice? How can a win-win situation be created? How do physicians and patients become comfortable with concepts of “relative risk” and “probability”? What opportunities and responsibilities for a broad education do test developers bear? What does all of this mean to those of you developing the tests? Are the disciplines of human behavior, biology and assay development about to converge? Share your thoughts with us.

The technology Topol recommends to stratify patients could also streamline drug development. In fact, the FDA is suggesting that faster approvals of antibiotics could result from smaller clinical trials that test antibiotics targeted against drug-resistant bacteria.

We now know enough about molecular biology that we can apply genomics/proteomics/metabolomics to drug development (DNA screening, identifying and validating surrogate markers, or characterizing tumors by genetic makeup). However, tailored treatments can run into challenges, including  heterogeneity in tumor cells as recently described in the New England Journal of Medicine, that resist even targeted biological treatments.

But as our pace of understanding accelerates, even knowing that tumor cell heterogeneity exists can allow us to use animal models to find correlations between gene expression and proteomics that show the progression of cancer.

There is currently a tsunami of genetic information that is being applied to diagnostics and disease management. Genetic testing is available for 2,000 conditions, and that number is expected to rise rapidly. Better preclinical characterization of drugs, more targeted applications of new and existing compounds, and a focus on actionable genes like EGFR, BRAF and many others could make tailored, cost-effective treatments a reality.

Do you think the life sciences industry has enough biological knowledge at our disposal to create a new world of tailored treatments? Or does the tumor heterogeneity study show that what we don’t know can stymie our efforts? What other methods can tailor treatment development? Share your ideas and thoughts with us here.

Our core team provides diversity and expertise to resolve problems and create strategies in the health arena for our life science clients. Paul, who has recently been described as a catalyst in the wireless and mobile health field, brings his familiarity, insights and experience to our existing team of experts whose operational backgrounds span clinical, technology, marketing and finance in a variety of capacities in the diagnostics, medical device and pharmaceutical industries.

You can read Paul’s full bio here. Please join me in welcoming him, and I hope you’ll reach out to one of us directly or comment below if you have questions or issues pertaining to digital health that Paul can help to address. We’re excited to offer you solutions at the cross-section of life sciences and digital technology.

Dr. Eric Topol, a cardiologist and scientist at the Scripps Research Institute, recently published a short article on “How to Change Medicine.” The article, which is excerpted from Dr. Topol’s book, The Creative Destruction of Medicine, provides nine key steps to changing health care delivery: from changing focus from populations to individual patients, to using genomic data to help “fit” treatments for each patient, to redesigning the way doctors are reimbursed.

We agree with all of Dr. Topol’s suggestions, but suggest that there should be a 10^th step. Controlling the spiraling costs of drug development is essential to realizing the other changes to the U.S. health care system. Today, it costs more than $1 billion for the discovery and development of new treatments. Since “blockbuster” drugs are so called because they generate $1 billion or more in revenue, even an immensely successful drug may no longer be profitable (and blockbuster drugs are becoming increasingly rare). Some of Dr. Topol’s steps do hint at ways to address these costs:

• By changing health care’s focus from the population to the individual, tailored drug development could produce more effective drugs that work for smaller groups of patients, reducing clinical trial sizes, and cutting costs.
• Using genomic data to determine whether a drug benefits a certain patient should underlie the creation of companion diagnostics as well as tailored therapies; this pharmacogenomic approach would speed development (less time to market theoretically means sooner time to profit).
• “Step 7,” which advocates the rewarding of providers for frugal innovation (e.g., using treatments that improve outcomes and cut costs) can be applied to drug development that considers cost-effectiveness as well as efficacy when creating a new therapy.

Addressing health care costs without emphasizing the need to reduce R&D costs strikes us as akin to worrying about your grocery bill when your mortgage eats up the majority of your budget. Future posts on this blog will address drug development innovations that could help tame this billion-dollar monster, including “low-hanging” fruit (like smaller trials and targeted therapies) that could easily reduce costs in a short period of time.

Do you agree with Dr. Topol’s nine steps? Do you think that reducing drug development costs is a key factor in revolutionizing health care? Could there be enough “low-hanging” solutions to make a difference now? Please share your thoughts with us.