When I first went to Dr. Dan—for what was technically a checkup—he wanted what felt like about two liters of blood. I tell people that and they're surprised, but in Dr. Dan's words, “I wanted as much information as I could get, objective and subjective. So many of the recommendations for how we manage patients are based on population‐level stats, data about cost‐efficient recommendations based on someone turning a certain age, but what's missed are the individual blueprints.”1 He wanted to know not just how old I was, but who I was, from every perspective we're able to assess. “Especially with advancements like biomarkers, genomics, proteomics, every patient is different. We need to look at what's specifically going on with each individual.”
Dr. Dan looks at a patient's coronary calcium score as a measure of risk stratifying: are they more likely to have a heart condition that needs medication? He looks at sleep—“having good objective measures of sleep would be great,” he says—and diet and exercise. He looks at blood pressure variability—at a continuous measure of blood pressure instead of the staccato points of measurement in his office. “Having these additional points of data is always helpful,” Dr. Dan says. “They let me make better decisions about a patient's health care. Is a patient a ‘dipper,’ whose blood pressure drops every night, for example?”
“One of the biggest health care problems out there is heart failure,” Dr. Dan explains. “Trying to decrease heart failure readmissions to the hospital is so important, from a patient health perspective and also a cost perspective. One device out there uses impedance. When impedance decreases, it shows that heart failure is right around the corner. The patient hasn't manifested symptoms yet, but if a pacemaker or other device is showing that impedance is decreasing, I double up on the medication so the patient doesn't have to go to the ER. I can see it in the numbers before it happens.”
Seeing it in the numbers before it happens is exactly what patient equations are all about. And Dr. Dan wants as many useful numbers as he can get. “If there could be a composite set of data to tell me the continuous heart rate variability, the number of cigarettes smoked, the peaks and valleys in blood pressure, the patient's surrogate stress markers, the amount of mindfulness during the day, how much protected time someone has to not do anything…objective data about diet and nutrition and exercise, amount of REM sleep…the kind of information I could wish for to optimize patient care is endless. It would all absolutely influence my dialogue with every patient.”
Even better, he says, would be if insurance companies were measuring these numbers too, and paying him based on how much he was able to help his patients' data get better. “We have aligned interests,” he insists.
Dr. Dan is emphatically not scared of the data replacing him and his function. “The art of medicine is taking those data points, objective and subjective, and trying to optimize them—how do you use resources, in terms of approach and quality? It's not just a formula. My patients want more than the standard of care. They want to feel good, and if they are living longer, they want their cognitive faculties, they want their independence, but to make those decisions takes more than just the numbers. It takes looking beyond them.”
And to truly effect change with most patients takes more than just a data printout. The doctor‐patient relationship is also so important—and that's something the data can't re‐create on their own. “One of the things missing with technology,” says Dr. Dan, “is that doctor‐patient bond. I am afraid we will get good at diagnosing and preventing disease but not as good at taking care of the illness as far as actually treating the patient. Our role is to anticipate what might happen and have useful, meaningful discussions that might prevent it from happening in the first place. The data can't do that.”
Respecting the Unquantifiable
Indeed, as Dr. Dan says, doctors need to go beyond the data. Effective physicians in a patient equation‐powered world won't just be treating discrete episodes of illness—they'll be partnering with patients to manage their health and optimize their lives. They will be monitoring our trajectories as we move along the lines defined by patient equations in the multidimensional phase diagrams we've already discussed. The pitfall is relying too much on the data alone.
“Let's not confuse the exchange of data with the exchange of knowledge,” Michael Hodgkins, chief medical information officer at the American Medical Association, told an audience of medical leaders, at a 2017 conference reported on by MedCityNews.2 “In chronic disease, if we don't solve the problem of how to adapt these tools to clinical practice and patient care, we're not going to make much progress,” he said. It can be too much. The data alone only gets us so far.
But it can change the paradigm. Imagine a doctor's office of the future, as Glen Tullman, executive chairman of consumer digital health company Livongo, does in an article in Forbes. “Imagine receiving a suggestion on your smartphone,” he writes, “that your doctor would like to see you to determine whether that nagging cough is seasonal asthma or an exacerbation of your congestive heart failure.”3 The doctor of the future can order labs in advance, deploy a range of home‐based tests (perhaps through apps on the patient's phone or other devices) and collect whatever data is necessary to make the in‐person conversation more fruitful and productive.
Data, in the future, will be already loaded and ready to apply. It's an unreasonable expectation that a doctor can do everything in one office visit when they often start with nothing. The baseline will change. The expectations will change. And a side benefit: for better or worse (and, of course, when it comes to actually helping doctors make choices that improve our health, it's definitely for better), we can't lie to our digital doctors. The sensors know whether we exercised, what we ate, if we took our pills. The guarantee of objective honesty alone will enable huge amounts of progress in patient care.
Dr. Rajesh Pahwa at the University of Kansas Medical Center told mHealthIntelligence about how wearables, like a smartphone or smartwatch, can help him treat his Parkinson's patients: “[Pahwa] can track body movements over a period of several days, charting the instances and severity of tremors, and correlate those movements with the patient's prescribed treatment of L‐DOPA, a medication usually taken every four hours.”4 The digital device helps him refine the treatment, and improve life for his patients.
But Dr. Pahwa's clinical judgment is still critical in the process, and that's what we can't forget. There's an example from outside of health care that crystallized this idea for me. On September 26, 1983, Stanislav Petrov, a lieutenant colonel in the Soviet Union military, was on overnight duty monitoring early‐warning nuclear satellites. Suddenly, according to the Washington Post's account of the situation, “sirens began blaring. A red button on the panel in front of him flashed the word ‘Start.’ On a computer screen was the word ‘Launch,’ in red, bold letters.” The technology was telling him that the United States had just launched a nuclear strike.5
Five missiles were launched, according to the warning system. Petrov had to decide whether to tell his commanders to strike back or tell them the system was wrong. His gut told him the system was wrong. And that's what he told his commanders. “I had a funny feeling in my gut,” Petrov told the Post. “I didn't want to make a mistake. I made a decision, and that was it.”6
He was right, even though the machine was saying otherwise. Technology, whether in a potential nuclear war or in the doctor's office, is not always foolproof. It does not always have the answers. But it can certainly help doctors make better decisions, empower them in ways we couldn't even imagine a few years ago. Doctors like Dr. Dan, who embrace these new data sources, will thrive. Others will be left behind, particularly by patients who choose to take it upon themselves to get the most that they can from the new technologies.
Empowered Patients
The publication Elemental writes about entrepreneur Julia Cheek. “In 2017, [she] broke a record on ABC's Shark Tank: The show's judges awarded her a $1 million deal for her company, EverlyWell, ma
rking the largest investment a solo female entrepreneur had received in the show's history.”7 EverlyWell sells at‐home medical tests, of the same quality that would be used in a medical lab or doctor's office, for conditions and measurements ranging from Lyme disease to cholesterol to sexually transmitted diseases. Cheek's company is just one of many new entrants in the space. It's not that the technology they are selling is new—but the impulse for patients to be more involved in the kinds of information gathering they would typically see as exclusively owned by the health care system is tied inextricably to the topics we've been talking about in this book. Patients are able to do more for themselves—and so they are.
It comes back to the late Jack Whelan, the self‐tracking cancer patient and advocate I talked about in the Introduction—one of my first inspirations in thinking about patient equations as he showed me graphs of his biomarkers in Microsoft Excel. With the tools to track, patients can be part of their care, more than ever before. Jack knew as much about the clinical trial landscape as his doctors did, and as much about his own biomarkers. In fact, he probably knew more. He felt that his care would be better if he was at least partly at the wheel—and technology made that possible. The efforts to move patient equation–type thinking into the mainstream will only be helped by patients who understand the power of data and take it as seriously as their health care providers do.
Patients don't all need to be Jack Whelan, instrumenting themselves and tracking their biomarkers at home, but they do need to be willing to leverage their data, or willing to wear a device (or multiple devices) and engage through the digital world in their health care. They need education—and the pharmaceutical industry can certainly help here—about how apps and wearables can be combined with pills and procedures, and should be taken just as seriously. They need to understand that in the new world of health care, information is power, and information can help them. It can help them stay healthy between appointments, it can help them partner with the health care system to find the best treatments for whatever ails them, and it can ultimately help them live longer and healthier lives. And they also need to be scientifically skeptical. They need to understand and want measurable, objective outcomes. And they need to know the difference between those objective science‐tested facts and the promises of snake oil.
Patient advocate, entrepreneur, and author Robin Farmanfarmaian talks about the patient as the “CEO” of his or her health care team—bringing together doctors, devices, apps, and more to optimize their health and their lives. “The amount of information we have as patients now is staggering,” she told me in an interview.8 “We can wear a clinical‐grade EKG monitor that connects to our iPhone and sends the information into the cloud. Contact lenses can track our glaucoma progression, socks can measure our gait, subcutaneous sensors can measure glucose, and epidermal sensors can measure UV radiation. I don't want the raw data, but if the AI can produce a dashboard with actionable items for me to deal with—should I drink eight ounces of water because I'm dehydrated?—then I can go out and hire a doctor or other health care professional when I have a problem that benefits from their input, and otherwise I can be in charge myself.”
For Robin, the technology provides freedom and power. “It's not necessarily the doctor's fault if the outcome turns out badly,” she says. “Doctors are traditionally siloed in their home health care systems. If they work at, say, Stanford, they don't necessarily know anything outside of those four walls, they don't know about all of the devices, medications, and technologies available in the world, purely because there is too much information coming out every day.” Which means it's incumbent on us as patients to take charge. Scary, perhaps—but also reassuring in that it means that we can have more control over our own medical destinies. (And payers ought to be willing to incentivize patients to take as much control as they can, to track, to listen, to be fully engaged—because healthier patients are going to mean lower costs at all points along the way.)
A column in MedCityNews by Jeff Margolis, chairman and CEO of WellTok, a consumer health software company, puts this issue about as well as anyone: imagine two different tubes of health care data, he writes. The first concerns what doctors should do to “fix” their patients. The second is about what patients can do themselves “to achieve their highest health status in the context of their daily lives.…We've spent decades working to perfect the first tube,” Margolis writes, “but what about the second?”9 We can't ignore patients, and, in fact, we need to embrace the opportunities that technology gives us to touch them directly and help them manage their medical lives.
Glen Tullman's company Livongo, in fact, is already doing this—in the area of diabetes now, with plans to expand elsewhere in the future.10 He started Livongo believing that health care should be following the same path toward reduced complexity through technology as almost every other industry has over the past generation. “It's so much easier to ticket a flight than it used to be, for instance,” he told me in an interview, “and yet health care has become more confusing, more complex, and more costly.” Everything has become more consumer‐focused except health care, Tullman realized. Since that epiphany, he has been investing through his venture fund in companies that seek to create more intelligent, informed, and connected health consumers. He wants to use data to keep consumers out of the health care system and treat them as people, not patients.
“We believe people will make good decisions,” he says. “We just need to make it easy and cost‐effective. No one wants to be sick or get bad care. It's just too complex. We have to make it simpler to make the right decision.” With Livongo, he works with existing players in health care—insurers and employers—to get to their members and employees and give them data tools to improve behavior and decision‐making. In the area of diabetes, he is disrupting the traditional model by making money by providing actionable advice instead of by selling blood sugar test strips for exorbitant rates to patients who desperately need them. Livongo provides the test strips for free, with the confidence that patients aren't checking their blood sugar more than they need to, and that he needs to partner with them for health instead of fighting them every step of the way.
“Everything we do is about our members,” he says. “If you have a question, you touch the screen [on our app] and someone calls you, 24/7, within 60 to 90 seconds.” This keeps people out of the hospital, and it keeps them happy. He charges employers based only on whether the members use the system, so that if Livongo is not providing value, the employer doesn't pay. As the company realized that 70% of people with diabetes also suffered from hypertension, Livongo expanded into that domain as well, providing an integrated experience—one platform with advice for patients to stay healthy, manage their diabetes and hypertension, manage their weight, and manage their mental health. The company collects data—it has amassed the largest database of blood glucose information in the world—and uses predictive data‐driven science to figure out what information it needs to provide to patients to help them better manage their health, telling them when to check their blood sugar, when to eat something, when to drink more water, and more. The result has been a cost savings of more than $1,000 a year per user, with measurable improvements in hemoglobin A1c and other indicators of health.
It's not an artificial pancreas, but it's an end result far easier than wearing a clunky or invasive device that patients may never comply with—it's data, applied as intelligently as possible (Livongo calls it Applied Health Signals) toward improving health, in the patient's home and in the patient's pocket.
So where does all of this leave us? If the pieces all come together—we collaborate to share data, reimbursement models begin to reflect the advantages of a data‐driven system, and doctors and patients come on board to help lead the charge toward more robust patient equations—what does the future state health care system look like? In the Conclusion, I'll paint that vision for the life sciences industry, highlight one exciting example of patient equations in
action, and talk about how we can get to the future state faster and more effectively, with the maximum impact on our businesses and bottom lines. Yes, it is easy (for now) to stick with the models that have worked for decades. But change is already here, more is coming, and the industry has to evolve in order for our businesses to survive.
Notes
1. Daniel Yadegar, interview for The Patient Equation, interview by Glen de Vries and Jeremy Blachman, February 7, 2017.
2. Josh Baxt, “Data, Data Everywhere, Not a Drop of Insight to Glean?,” MedCity News, August 25, 2017, https://medcitynews.com/2017/08/data-data-everywhere-not-drop-insight-glean/.
3. Glen Tullman, “Health Care Doesn't Need Innovation—It Needs Transformation,” Forbes, December 21, 2016, http://www.forbes.com/sites/glentullman/2016/12/21/health-care-doesnt-need-innovation-it-needs-transformation/#7e9623525a4f.
4. Eric Wicklund, “A Parkinson's Doctor Explains How MHealth Is Changing Patient Care,” mHealthIntelligence, October 2, 2017, https://mhealthintelligence.com/news/a-parkinsons-doctor-explains-how-mhealth-is-changing-patient-care.
5. David Hoffman, “‘I Had A Funny Feeling in My Gut,’” Washington Post, February 10, 1999, https://www.washingtonpost.com/wp-srv/inatl/longterm/coldwar/soviet10.htm.
6. Ibid.
7. Erin Schumaker, “What's Driving the Boom in At‐Home Medical Tests?,” Medium (Elemental), May 15, 2019, https://elemental.medium.com/whats-driving-the-boom-in-at-home-medical-tests-2e9812e38a16.
8. Robin Farmanfarmaian, interview for The Patient Equation, interview by Glen de Vries and Jeremy Blachman, February 22, 2017.
The Patient Equation Page 24