Tachycardia

Matt Lambert

Tachycardia

  He had lived long enough, and the world had changed enough, for the last two truths of his childhood to become untrue. The first of these truths was that the 6:00am Staten Island ferry would depart from the St. George Terminal and arrive at the Whitehall terminal on the southern tip of Manhattan at precisely 6:25 am. Yet here it was, at 6:29 am on the morning of November 14, 2029, and the ferry was just arriving at the makeshift dock atop Pier 15 along the East River. The water level had risen so much with the changing climate that for a few months each fall it overcame what the three adjustable landings at the Whitehall terminal could accommodate. While City Hall debated on where to find the funding to rebuild the pulleys and platforms, they had to find a seasonal spot for the ferry. The new route hugged closer to Governors island on the right and then river left past the Battery Maritime landing, Coenties and Old slips dating back to the days of the Dutch, and by the helipad terminal used by CEOs, Popes, and Presidents. The FDR freeway, much of which was built on fill from World War II European rubble returned to the states as ballast on Marshall plan era boats, was too low to get a ferry under until Pier 15. There were two cranes with aluminum walkways placed on the top deck of the pier and the seawall was lined temporarily with rusted pilings adjacent to the historical sign that marked the high-water level from Superstorm Sandy back in 2012. On this particular day, all but the top of the sign was under water. He was anxious to get off the boat because the guy next to him was singing the chorus of Christopher Cross’s “Ride Like the Wind” (while drinking a PBR tall boy) the entire trip. He was struggling to get Michael McDonald’s falsetto part, “Such a long way to go…..” out of his head as he walked down the runway, down the steps, continued diagonally under the highway and started up Maiden Lane. He stopped at a bodega and got some Tums for a little nausea and heartburn that he attributed to taking his blood pressure medication on an empty stomach, his third cup of coffee and the unsolicited serenade. He was walking to the New York Federal Reserve Bank building, to go to work where he actively undermined the other childhood truth that had become untrue.

  This truth was a little more significant than the seasonal re-route of his commute, but still tied to the Staten Island Ferry. For it was on the boat, that he learned it. His father captained the ferry for thirty years and he would ride back and forth with him on days when he didn’t have school or hop a one-way ride when he was going into the city to hang out with his friends. The conversations they had on these trips were some of his most vivid childhood memories. The truths he learned here took a deep hold in his young mind, as ideas delivered early in life by adults in positions of authority tend to do. But when they come from the father, who is literally cussing like a sailor while steering a big boat with the Statue of Liberty over his left shoulder, they have an even greater impact.

  The father’s lesser truths fell one by one over the years. Bill Russell had been replaced by Michael Jordan as the best basketball player ever and Mariano Rivera had replaced Whitey Ford as the all-time best Yankees pitcher. You couldn’t just “aim her at the twin towers” anymore when you were given the ferry captain’s wheel. Rudy Giuliani > Ed Koch. But the most consistent truth espoused to him from the time he was a boy on the boat was “As long as you can pay for own shit, nobody can fuck with you.” It was his father’s way of expressing the importance of personal accountability and it was the way in which he lived his life throughout his career and retirement until his passing. At every critical time in his own life, the son had fallen back on this advice and it guided him through an honest, hardworking journey. He was born in 1967 and after graduating high school he went for paramedic training. He worked the worst shifts in the worst parts of the city during the worst days of the crack cocaine era and then went to nursing school. From there he was an ER nurse for twenty years and then took on roles in nursing administration and education. He continued to ride an early ferry to this job every day at the age of 62, still guided by the last truth of the father.

  He never tired of walking to the old Federal Reserve Building and he usually stopped at the adjacent Louise Nevelson Plaza to have a cigarette while he admired the architecture. Built in 1924 from sandstone and with its turret, gothic doors, and barred windows he always felt like he was approaching the castle or strolling along the Seine. For decades, its five underground floors housed gold owned by countries from around the world which was wheelbarrowed from room to room based on that day’s international trading. But after the US left the gold standard in the seventies and Wall Street went to electronic trading, the gold went to Fort Knox and the traders moved up to Midtown. The neighborhood was hit hard by 9/11 and by the rising sea water that started with Sandy. But just like other parts of the island such as the meat packing and garment districts, the financial district had reinvented itself. The old banks and offices had been converted into shared work spaces for young professionals who couldn’t afford anything else in Manhattan, some had become movie studios, and the ultra-secure, subterranean levels of the Fed building became a perfect home for the medical surveillance state.

  It didn’t start with Trump era deregulation, but that was the accelerant. In the years leading up to his administration, there was widespread adoption of information and remote monitoring technology in the healthcare sector and a trend of mergers and acquisitions between health systems, retail pharmacies, and insurance companies. This was all done in the name of efficiencies and economy of scale, but it was really done so that the increasingly corporate models of healthcare could show growth to board members and pay dividends to shareholders. When Trump dissolved the Federal Trade Commission, most industries became dominated by massive monopolies that favored executives and speculators over employees and consumers. In the healthcare space, the health insurance companies bought the life insurance companies, and then the hospitals, and the pharmacies, and the doctors, and the device companies, and the software and consulting companies. There was no distinguishable line between anything anymore, cradle to grave healthcare was managed as an internal transaction in the bowels of the New York Fed building, just like gold trading was done previously. Artificial intelligence and predictive modeling were used to determine who was going to become ill and need medical services. Surveillance was used to track patients. Gone were the industry compartments and competition for market share that increased short term costs but created some level of patient protection and choice. Now, the “pure alignment” was there to allow for cost control of not just medical care, but of your entire existence. The cost of providing services was then calibrated against computer modeled cost ceilings and revenue projections from premiums. In blatant violation of the father’s law, this is where they fucked with people who paid for their own shit.

  Once he cleared the scanner at security he got on the elevator and pushed B5. The first floor was for administrators and public relations. The second was for IT and the third was where they maintained their internal cloud computing. The fourth floor was for Research & Development, while what they called the monitors, worked on the lowest floor. Even though this was the future of healthcare, it had retained many of the old ways since most of the monitors were former paramedics and nurses. They were comfortable with the old 7 to 7 schedule and the building took on the same sort of rhythm, structure, and attitude of an ER shift change with coffee maker chats about interesting cases, chain smoking and the like. After he received report from the night shift, he settled down at his work station and got ready for the day by logging in and putting on his headset just like the hundred or so other monitors who were tracking members across the country. He took another Tums. The monitors were currently working on the fourteenth iteration of the Combined Registry Model, known internally as the CRM 1.14 and it worked liked this.

  All of your medical information was combined into a registry and constantly updated. It took another decade and another $25-billion-dollar federal incentive investment in the health IT market to achieve this level of interoperability, but it was executed with tax payer dollars and influence from the insurance lobby. No matter where you went to the doctor, filled a prescription, or bought an over the counter medicine, the result of the test or the record of a transaction was collected here. This medical registry was combined with other data from every other part of your life that could be tracked in real time. Your movements were tracked by security cameras and facial recognition technology. Your location was also tracked from your cell phone via the location monitoring feature running in the background of weather and navigation apps. People were still very keen to track themselves with social media by posting photos or checking-in from certain locations. Uber was complicit and true to form, always charged the highest rates to buyers of their tracking data. Another registry tracked your online activity, and since streaming services had replaced cable, it was possible to know what people were viewing on televisions, computers, laptops and mobiles at all times. Every part of your life was tracked and, when combined with your health history, was used for predictive modeling of when you would become ill in the same way they can predict what movie you might choose on Netflix or when you would buy a plane ticket. But since the healthcare delivery organizations were now the same as the payer organizations, they could predict when you were going to incur costs against your insurance policy, which were capitated with yearly and lifetime limits.

  The problem was how to operationalize all of this tracking data. Early computer models couldn’t sort out the data, so they finally broke down and asked some clinicians, who directed them to the vital signs. The temperature, blood pressure, respiratory rate, and what proved to be most useful for this model- the heart rate. In a face to face medical encounter, the heart rate is tested by taking a patient’s pulse or by connecting them to a cardiac monitor or an EKG. A normal resting heart rate is between sixty and one hundred beats per minute and any acute care provider will tell you that the presence of a heart rate greater than 100, known as tachycardia, is a very good predictor of disease. You become tachycardic when you have a fever. Or when you are in pain. Your heart rate goes up when you are anxious or have a sense of impending doom from a heart attack or a pulmonary embolism. It is the first sign of dehydration, significant blood loss and sepsis. There are tachycardic rhythms that can make you pass out, like supraventricular tachycardia and others, like atrial fibrillation, that increase your risk of stroke. Stable ventricular tachycardia has to be treated with powerful intravenous medications or defibrillation, while unstable ventricular tachycardia is also known as sudden cardiac death. With the advancement of remote monitoring technology and the ability to integrate it with electronic health records, it was easy to track your pulse remotely. Consumers will often track their own heart rate with wearable technology and collect this information with apps or share it with their doctor through their patient portal. These methods proved to be very useful for the CRM, but since it didn’t have enough market penetration, particularly amongst the sickest cohorts, they looked for other sources. Since the insurers owned all the medical device companies, they began collecting all the pulse rates recorded by implantable pacemakers and defibrillators. They funded a study printed in The American Journal of Cardiology that expanded the indications for implantable loop recorders, so more cardiologists would place them in more patients. They added heart rate monitoring technology to all implantable medical devices such as hip and knee replacements, orthopedic screws and plates, internal surgical staples, valve replacements and cardiac stents. They placed electrical wire into suture material used for deep sutures and they lobbied the AAMC to change medical student curriculum increasing the indications for subcutaneous sutures. The implantation of pulse tracking technology into glasses, sunglasses, t-shirts, and underwear was not as effective.

  The earliest CRM models were done solely by computers tracking millions of patients and when indicated, executing one of four interventions based on algorithms from your heart rate, real-time surveillance data, and revenue from your insurance premiums versus your cost to insure. Most alerts they received simply needed to be dismissed, normal people living their lives, such as people training for marathons or walking up the stairs, but those who needed an intervention received one of the following. A level one intervention, known also as a proposed intervention, was a seemingly random automated text message sent to the patient reminding them about inexpensive care options such as a telehealth visit or encouraging them that there was an opening in the clinic schedule tomorrow if they were feeling ill. A young patient with a pulse of 110 who had no significant medical history who had logged in at work was a good candidate for a level one intervention. A second level intervention (also known as an active intervention) sent a message to the patient’s care manager who would then actively call the patient to check on them. If indicated, the care manager could dispatch a health drone, capable of testing blood and performing x-ray imaging, directly to the patient at home. Think of a middle school teacher who was still young and healthy but had tachycardia and had called in sick to work that day. A third level intervention, or a mobile intervention, sent a physician assistant and a nurse to your home via a mobile medical unit. The mobile unit was able to provide the same medical care as an inpatient hospital stay and the patient was managed there unless they needed surgery or ICU level care at a hospital. These patients were often older or with complex medical histories who had significant disruptions in their recent meta data such as few phone calls, decreased online activity combined with a more serious tachycardic rhythm. A typical third level patient was a 70-year-old male with pneumonia who could be treated at home with IV antibiotics and oxygen while the mobile team also observed his diabetes and hypertension while communicating with a physician working from home. A fourth level intervention was called a suppression. Patients were suppressed when they were old, had terminal diseases, or had surpassed their yearly or lifetime cost quotas. Any attempts by these patients to call 911, call their doctors, or to contact loved ones was blocked. Obviously, these were the most robust algorithms that also accounted for whether or not the patient was in the company of a friend or family member who would raise suspicions about rationing care and forced end of life decisions.

And it was the suppressions that got them into trouble early on. The computer misinterpreted the heart rhythm of a young woman who had previously had a very expensive Trisomy 18 baby who had passed away before the age of 2. She went into SVT, which could have easily been treated with adenosine delivered via a mobile intervention, but the computer counted the cost of her deceased child against her spend and misread the tracing as ventricular tachycardia. After her husband drove her to the emergency department where she received treatment, he complained to the hospital and to a friend of his who was a reporter for The New York Times. The subsequent investigation involved the 911 dispatcher, the ambulance company and was getting closer to the insurer. Luckily, the investigative reporter found her “dead peasant” policy before they found out about the CRM. Dead peasant insurance, is where the employer takes out life insurance on their employees, without their knowledge while naming themselves as the beneficiary. Also known as corporate life insurance, it is usually applied selectively to employees who have the lowest premiums and highest payouts, such as women of child bearing age. These policies are illegal in most states and the courts and the media got so distracted by the employer’s policy, and subsequent lawsuit, that they didn’t look any deeper into the practices of the insurance company. There were other similar near misses, but the company was able to pin one on a paramedic with a history of drug problems and another on a mid-level city manager in Chicago.

  It turned out, however, that this new technology was still constrained by the old, basic limitations of a medical test, sensitivity versus specificity. Tachycardia, as described above, is an incredibly sensitive test meaning that it very rarely absent in acute illness. On the downside, it is not very specific. Tachycardia is present in a variety of disease states described above and can also be present in non-disease states. A certain level of tachycardia when you exercise has been part of training regimen goals for decades. The commercial exercise equipment in the gym can track it. Your heart rate goes up during sex or when your favorite team scores late to win the game. Some people have tachycardia at rest. As they iterated on CRM logic, the software developers once again brought in the clinicians, this time to approve certain decisions in real time. To keep an eye on people performing such sensitive work, they required employees to work onsite, in the basement of the Fed building.

  Back in the basement, he popped another Tums by the water cooler and under his breath, he cussed the tomato sauce at Rao’s for once again giving him heartburn. It had been a pretty slow morning and over the last few months, he had noticed that fewer cases were coming up for human review, a sign that the computer algorithms were managing more cases on their own. When he did get a pop-up, he would get a brief description of the case and a computer suggested intervention. He would then toggle to review the heart rhythm and the medical history and, as emphasized by his supervisor, pay special attention to the real time tracking feed before signing off on the computer-generated intervention or selecting his own. He dismissed a young man with sinus tachycardia who was streaming porn. He did the same for a tachycardic woman with a right bundle branch block secondary to her mitral valve prolapse who had checked in at the gym, using her fingerprint, twenty minutes prior. He dispatched a mobile intervention to a 43-year-old father of three who was in atrial fibrillation. He would have selected an active intervention but the natural language processing from the EMR revealed that he didn’t feel the palpitations due to his diabetes. He suppressed someone on Phoenix and texted a patient in Portland.

  This part of the job reminded him of the old days in the ER, moving from one acute case to another, and still doing it at this age was the reward for a life lived by doing the right thing. But doing it from a basement and selectively denying care to certain people was his new truth. And truths, like lives, come and go. He reached again for the Tums, but this time he felt nauseous and got diaphoretic. He had been tachycardic all morning. He stood to get another drink of water and maybe some fresh air when he fell to the ground and clutched his chest. He yelled for help and he was sure someone had heard him crash to the ground and knock his chair across the cubicle. In his years as a clinician, he observed that people lose consciousness relatively quickly when dying as if it were some sort of protective adaptation of evolution. He could feel that he was about to go out. The only light he saw was not from the divine, but from the glow of his monitor. With his last thought on earth, he realized that he had been suppressed.