BioLum Sciences is introducing new chemistry that has the potential to completely change the way we test for respiratory illnesses, like asthma, and analyze blood samples to identify hypertension. CEO Edward Allegra talks through the science behind both their breath and blood tests, both of which are patent-pending and have the ability to completely bend the cost-curve when it comes to identifying and monitoring these two common chronic conditions. What’s next for the early-stage health startup? A range of applications to detect everything from COPD to lung cancer and more.
Filmed at Bayer G4A Signing Day in Berlin, Germany, October 2019.
I am a clinician and a clinical trialist. Medical research in some form or another (performing it, consuming it, reviewing it, editing it, etc.) occupies much of my time. Therefore, you can imagine my excitement while watching Apple’s product announcement yesterday when they introduced a new open source software platform called ResearchKit. Apple states ResearchKit could:
“revolutionize medical studies, potentially transforming medicine forever”
ResearchKit allows clinical researchers to have data about various diseases collected directly from a study participant’s iPhone (and perhaps other devices in the future — see below). The software is introduced as a solution to several important problems with current clinical studies, such as:
limited participation (the software allows everyone to participate; anyone with an iPhone can download a specific app for every study they want to participate in)
frequent data entry (patients can enter data as often as required/desired, rather than only at limited opportunities such as hospital or clinic visits)
data fidelity (currently-used paper patient “diaries” are prone to entering implausible or impossible values — the iPhone can limit the range of data entered)
Specifically, the website states:
ResearchKit simplifies recruiting and makes it easy for people to sign up for a study no matter where they live in the world. The end result? A much larger and more varied study group, which provides a more useful representation of the population.
This is a bold claim. We’ll see below that it doesn’t yet ring true.
We’ve seen shorter abstracts, and we’ve seen abstracts with more curious findings, but we’ve never seen a shorter abstract with more curious findings than this one, done by Dignity Health and Dr. Rajan Merchant, and financed by the California Healthcare Foundation, evaluating a gadget made by Propeller Health.
The study group’s use of inpatient care for asthma declined by a whopping 62% vs. the control group. You might think this result violates Dr. John Ioannidis’ well-known conclusion that large treatment effects are usually wrong, but you’d be mistaken. You see, there was no treatment here.
There was only an effect. Dr. Ioannidis’ result applies only to actual comparisons of effects due to different treatments, not to random changes in effects using the same treatment. In this study, the actual treatment protocol was the same and the inhalers were the same.
The only thing different was frequency of drug use. Whereas the conventional wisdom for disease management states that hospitalizations can be avoided by more adherence and hence more drug use, in this case the study group used less medication than the control group, reaching for their rescue inhalers 25% less– once every 6.3 days vs. every 4.7 days for the control group.
With the rise of cell phone usage, smart and otherwise, many health care providers, researchers and entrepreneurs alike have assumed that this ubiquitous technology can be used to improve health and wellbeing. Entrepreneurs have led the charge and so the common catch phrase “there’s an app for that” underscores the fact that nearly 17, 000 health related apps are available either for free or a small charge for Android or Apple users. Young people in the US are perhaps the best targets of our mhealth efforts because they are eager users of mobile technology. However two questions arise naturally: 1) does data show that these apps lead to improved outcomes? 2) is there a theory of how we might use cell phones to improve health outcomes?
In a series of studies, we found that simply responding to text messages over a 3-month period led to improved quality of life and pulmonary function in pediatric asthma patients. In both studies, the researchers randomly assigned 30 asthmatic children, 10 to 17 years old, into three groups – a control group that did not receive any SMS messages; a group that received text messages on alternate days and a group that received texts every day. The children that received messages everyday between two scheduled appointments had the improved psychological and physical outcomes. Thus, our data does indicate that cell phones can be used effectively to improve health outcomes.
Perhaps more compelling is that we may have evidence of a possible mechanism that can lead to improved outcomes. The Health Belief Model is a cognitive theory of behavior change that espouses the notion that a critical pillar of behavior modification is that the individual must make the connection between the severity of the symptoms and the disease itself. In the case of asthmatic patients, we found that many times they attributed their symptoms to other causes. For example, they would say that they couldn’t exercise in the afternoon because they had a heavy lunch or that they couldn’t sleep the night before because they had seen a movie that had made them anxious— rather than attributing these symptoms (inability to exercise or sleep) to their asthma. The Health Belief Model also places value on acquiring knowledge about the disease. Thus, we sent patients texts messages that either asked about symptoms they had experienced or about asthma myths. Thus, our studies also indicate that improving symptom awareness and knowledge about their disease led them to have better medication adherence which in turn led to improved health outcomes.
Politicians and pundits everywhere call for more disease prevention as a way to reduce healthcare costs. Certainly you cannot argue with the logic that “an ounce of prevention is worth a pound of cure.”
Or can you? It turns out that you can not only argue against that so-called logic, but – just as with cancer detection, which may have been done to excess in some protocols — you can mathematically prove that, at least for asthma, it takes a pound of prevention to avoid an ounce of cure.
The database of the Disease Management Purchasing Consortium Inc. (www.dismgmt.com) tracks both asthma drugs and visits to the emergency room (ER) and hospital stays associated with asthma. The average cost of an attack requiring an ER visit or inpatient stay is about $2000. The average cost to fill a prescription to prevent or recover from an asthma attack is about $100. It turns out that asthma attacks serious enough to send someone to the ER or hospital are rare indeed. In the commercially insured population, these attacks happen only about 3-4 times a year for every thousand people. (The rate is much greater for children insured by Medicaid; additional resources spent on prevention could very well be cost-effective for them.)
For a million-member health plan, that might be 3000 or 4000 attacks Yet that same million-member health plan is paying for hundreds of thousands of prescriptions designed to prevent or recover from asthma attacks. Depending on the health plan, the ratio of drugs prescribed to asthma events serious enough to generate an ER or hospital claim ranges from 60-to-1 to 133-to-1. Using those statistics of $2000 per event and $100 per prescription, a health plan would pay, on average, anywhere from $6000 to $13,300 to prescribe enough incremental drugs to enough incremental people to prevent a $2000 attack.
Averages lump together people at all risk levels. Surely some of those people really are at high enough risk of an attack that they are already inhaling their drugs regularly to prevent one, and have a “rescue inhaler” nearby. By definition their risk of attack is much greater than for low-risk people. Assume, very conservatively, that low-risk patients have a risk of attack which is half that of the average patient. This means that putting most low-risk patients on drugs costs $12,000 to $26,600 for every $2000 attack prevented.
Twenty years ago, in order to keep presidential candidate Bill Clinton’s campaign on message, James Carville hung a sign in their “war room” that read:
Change vs. more of the same
The economy, stupid
Don’t forget health care
While point number two swiftly entered the national vernacular, the other two slogans have equally influenced the U.S. political landscape, especially since 2008. Four years ago, the country was on the precipice of transformation. Meaningful change was promised, and opportunities for significant, long-lasting reforms were abundant. Americans, particularly the millennial generation, turned out in record numbers to vote, and hope for the future was palpable. America, like a patient suffering from a debilitating chronic disease, seemed finally ready to put in the time and do the hard work to get healthy before that fatal heart attack occurred. After decades of procrastination, we heeded Carville and health care system overhaul became a top priority.
Pause: The State of America’s Health
Obesity prevalence increased 137 percent over 20 years, from 11.6 percent to 27.5 percent of the population. In 2008, more than one-third of children and adolescents were overweight or obese. The medical care costs of obesity in the U.S. totaled about $147 billion in 2008 dollars.
Diabetes has almost doubled in prevalence since 1996, rising from 4.4 percent to 8.7 percent of the adult population. For children, the prevalence of Type 2 diabetes increased 21 percent from 2001-2009, while Type 1 diabetes rose 23 percent. Estimated total diabetes costs in the U.S. were $174 billion in 2007.
Asthma diagnoses grew by 4.3 million from 2001 to 2009, and 9.4 percent of children currently have asthma. Asthma costs in the U.S. grew from about $53 billion in 2002 to about $56 billion in 2007.