On Tigers and Coronaviruses

Some of the most important engineering lessons were demonstrated on the tank battlefields of World War II when German Tigers faced off against Soviet T-34s. The Tiger tank was a technical masterpiece of for its time with many features that did not appear in allied tanks until after the war. Despite its much heavier armor it was able to match the speed of lighter enemy tanks and keep up with its own light tank scouts. The armor featured almost artisanally welded interlocking plates. The ammunition featured innovative eletric trigger primers and high penetration tungsten shells. The double differential steering system allowed the Tiger to rotate in place. A complex system of interleaving wheels distributed weight evenly, improved offroad mobility and even allowed mobility with damaged tracks. But while the Tiger was a star on the blueprints, it was a disaster on the Eastern front, not because of its combat performance but because it was a logistical and operational nightmare. The heavy armor made the tank a gas guzzler, which made tanks inoperable when supplies were low. The electric trigger primers would fail in cold weather. When rotating in place the gearbox would often break and German training manuals forbid the maneuver. The highly specialized internal mechanics made production slow and meant the tank often could not be repaired in the field but had to be sent back to Germany, and the great logistic costs meant that Tigers couldn’t drive to the front but had to be brought there by rail.

The Russian attitude toward the T-34, on the other hand, was that making a tank a work of art was a waste of time if its purpose was move a gun into position just before being destroyed. Systems were designed as simple as possible to improve mass production speed. Armor was often welded with gaps between plates that you could stick your fingers into. To prove the tank’s mobility, the T-34’s designer Mikhail Koshkin drove the tank through snowstorms from Kharkov to Moscow and back. The tank lasted the journey, but he died from pneumonia contracted during the trip. When German armor outclassed the tank’s 76 mm gun, instead of designing unique solutions the Russian simply stuck an 85mm anti-aircraft gun into the turret. The Germans built 1347 Tigers; the Russians built 84,070 T-34s.

The Soviet success provides many valuable lessons for those trying to design and implement solutions for the current coronavirus outbreak. It is critical to understand the problem that you’re trying to solve, the resources that you have available, and how those resources can be applied most directly towards solving that problem. Unnecessary complexity leads to increased failure and causes problems in operation through low reliability and throughout the supply chain by creating material bottlenecks. Production capacity is necessary for overpowering the problem and simple minimum-viable-product design improves total system throughput and allows for mobilization of non-technical personnel. Materiel, personnel and time are all limited resources that cannot be used on discreationary objectives. But most importantly, it is necessary to be radically honest because you need to design for the problem that you have, not the problem that you want.

Already we’re seeing these principles being violated in the US response to the growing COVID-19 pandemic. Time and initiative are some of the most limited resources available in critical situations like this. Napoleon said ‘space we can recover, lost time never’. Despite this, leadership has been absentminded at all levels of governance, from the local to the federal, even to the global. Most officials appear to be stuck in a daze unable to take action until natural events force their hand. The comparison to Stalin hiding in shame from the public during the first days of the German invasion is impossible to resist. Even on March 9th the WHO is still making absurd statements that the threat of a pandemic “has become very real“, a phrase that can only still make sense to a bureaucrat waiting for a case in the notorious Madagascar before declaring that the pandemic is in fact not in our imaginations. In the meantime the CDC has demonstrated inability to provide testing kits at the required capacity, furnishing only 2000 testing kits in the same time that South Korea mobilized over 100,000 tests. In fact, the CDC made matters worse by preventing hospitals from developing their own tests and requiring them to route all testing through the CDC, creating a capacity bottleneck, increasing system complexity, and adding the travel time as an additional lead time between testing and results. Furthermore, the CDC increased complexity of their tests by using three primers, causing the initial batch of the kits to fail validation because of varying performance between the primers. In the meantime, the President’s bizarre and false ‘everybody who needs a test gets one’ cheer campaign is deafening.

With nearly two months of time since the initial Wuhan outbreak wasted, the US is finally scrambling to develop a strategy that it can communicate to the public. Much of this scramble involves an academic debate over how deadly or how infectious this virus is, as if the pundits are trying to find some economic optimization point at which simply doing nothing would be justified. Lost in the debate is the fact that this point does not exist because we know from in vivo experience in China and Italy that this coronavirus is both highly infectious and highly deadly, far above the rates of the flu that it still keeps being compared to. The frailty of the modeling exercise is that it depends on information to model off, which we know is dramatically incomplete due to the testing bottleneck, a conclusion that is validated by the constant stream of surprise cases that are now cropping up across the country.

The engineer’s task of developing a solution to the problem begins with understanding the nature of that problem. First, accept that this is highly infectious and highly deadly virus and that the hyperparameter tuning of the modelers is effectively irrelevant at this point. Two Italian physician posted extensive descriptions of the situation in the northern Italian lockdown. Their reflections are mandatory reading and corroborate what we know about what happened in Wuhan weeks earlier. Now that transmission is indigenous to the US, proper risk management requires us to assume that without intervention the same will happen within the next few weeks. The exact CFR of COVID-19 will be irrelevant to the overflowing emergency rooms, the shortages of respiratory ventilators, and the insufficient clinician staff. The second thing to understand is that right now there is no vaccine or cure against the infection. Normally the healthcare system provides curative interventions that directly treat the infection, but in this case the only known countermeasure is our own immune systems, which means that the only thing the healthcare system can do is provide stabilizing interventions to critical cases until the immune system can do its job. Finally, it’s necessary to understand the unusual virulence of this infection, which devastates the elderly and chronically ill while leaving the youth relatively unaffeted.

Combining these parameters, we are able to make a clearer formulation of the problem. The primary mission before the nation is to save as many elderly and chronically ill as possible by preventing the healthcare system’s resources from being overwhelmed. When accounting for the ease with which this infection spreads, the recorded autochthonous cases and the timeline over which the infection has shown to operate, it can be assumed that containment is impractical, if not impossible, at this stage. We are already within the critical window. In the words of Martin Sheen’s Robert E. Lee, “there is no time for that“. The primary objective therefore becomes not limiting the total number of cases, but limiting the rate at which new patients enter the healthcare system, and most importantly the ICUs, such that this rate does not exceed the rate at which people recover and leave the system. If the intake rate outstrips the exit rate system performance begins to quickly degrade as patients are unable to enter the required facilities, materiel shortages prevent delivery of appropriate treatment, and exhausted clinical staff begin to make errors that put patients and themselves in danger. The healthcare system cannot increase the rate at which patients immune systems will function and it has limited ability to effect the intake rate. It can and should redirect resources from discretionary procedures towards COVID-19 treatment, but this only increases the treatment-in-progress capacity and has no effect on the intake rates. Furthermore, the capacity of the most important resources like ICU beds is highly inflexible because these are more complicated systems than simply shoving more stretchers into a space. Recovery for critical COVID-19 cases has been shown to be 3-6 weeks and 2 weeks for even mild cases.  This is much longer than the average 3.3 day ICU (+5 day non-ICU recovery) or 4.5 day regular hospital length of stay. From Little’s Law we know that the patient load is proportional to the intake rate and recovery time, which means that an increase in the overall ICU intake rate by only 25% would double ICU utilization. It’s easy to see how the system capacity quickly becomes exhausted. This will be especially devastating for highly optimized systems like the NHS where the ICUs operate at 90% utilization and only a 2% increase over the base intake rate will result in all current capacity being exhausted.

Without the ability to affect rates directly, this means that the control intervention has to extend beyond the healthcare system and be applied at the level of the entire national community. It must be again emphasized that at this point there is insufficient information to determine the spread of this disease and there is still insufficient testing capacity to improve this information. There is therefore a high non-zero probability that the US is ALREADY past the point where intake rates would outstrip current healthcare capacity. Furthermore, delaying significant action increases this probability with every day. Because of the speed with which this infection progresses it is therefore necessary to implement dramatic interventions to minimize further spread until testing capacity can catch up and information on the morbidity is more complete. In short, total mobilization of the society is necessary. Half-hearted measures such as recommendations to stay out of public spaces or to wash your hands are not sufficient. These solutions are in fact highly complex because it requires the cooperation of millions of independent actors. Such complexity only creates risk of failure in a time when insufficient information requires a restriction on the degrees of freedom to the problem. The only way to ensure the risk of further spread is minimized is by halting all public events, travel, commercial activity etc. Right now it is necessary to buy our clinicians as much time as possible. Mass quarantine is a simplistic, dumb, even brutal, tactic but it fits the requirements of the problem and desired outcome. These restrictions can be lifted as the healthcare system’s utilization by COVID-19 is validated as sustainable and testing capacity improves our information and enables effective containment and quarantine, but delaying them until the last moment implies a vulgar acceptance of risk to American lives for the sake of preserving economic activity.

On Three Doctors

Note: This is old, an assignment back from when I was still in undergrad. It’s interesting reading old things you’ve written and seeing how you have and have not changed.

The arrival of the Enlightenment throughout Europe during the 17th and 18th centuries heralded the rise of large bureaucracies in European governments that were meant to rule the people with the ideals of reason and justice. As the bureaucrats become more powerful in the 19th century, the logical ideal became closely associated with the government’s power, and therefore also became tainted by its corruption and incompetence. In Charlotte Perkins Gilman’s “The Yellow Wallpaper,” Leo Tolstoy’s “The Death of Ivan Ilyich” and Sir Arthur Conan Doyle’s “Scandal in Bohemia” doctors become symbols of authority and reason. Because of their training in the sciences and logic, they command great respect from the community and are characterized as infallible, their decisions being final. For Gilman and Tolstoy, however, because the doctors often make their patients more ill, the absolute authority of the physicians is largely undeserved. Doyle, however, portrays the character of Watson as a physician who uses his training in reason to do good deeds through solving crimes during his adventures. Seeking reform in their societies, Gilman and Tolstoy urge the reader to question the authority held by the physician and to think for himself, while Doyle emphasizes the physician’s heroism and his dedication to justice. This difference in views shows the difficulty faced by physicians to connect with their patients and the importance of communication in health care.

In “The Yellow Wallpaper” Gilman writes the diaries of a woman who is suffering from an unnamed malaise, and is made worse by her physician husband who insists that the only cure is more rest and that she must not worry about anything. Initially, Gilman’s character submits herself to the authority of her husband.  The narrator emphasizes that her husband, John, is a “physician of high standing,” and that her brother “is also a physician, and also of high standing, and he says the same thing” (Gilman 29), demonstrating how the two doctors represent a center of power that she must obey. It is important that her physician is also her husband, emphasizing that she submits to his judgment not only as a patient, but also as a wife, showing how completely overpowering is his influence over her. Although John “laughs at [her]” (29), despite her illness, she claims that “of course one expects that in a marriage” (29). John’s dual role as husband and doctor creates a scenario where, in addition to the husband mocking his wife, the doctor is mocking his patient. John treats her like a child, addressing her as a “little girl” (36) and patting her head. This condescending behavior creates a distance between the doctor and the patient, and the narrator withdraws emotionally, deciding that she will hide her thoughts in her diary. In addition, the description of her treatment of “phosphates and phosphites –whichever it is – and tonics” (29), reveals an intellectual barrier between her and John. The doctor doesn’t respect her enough to properly explain the treatment, and when she doesn’t seem to get better, he threatens to send her to yet another notorious doctor, Weir Mitchell, who is “just like John and my brother” (33). Although all these men are in agreement about the proper course of treatment, over time the narrator becomes more ill psychologically, turning mad at the end of the story. She becomes obsessed with the pattern of the yellow wallpaper in her room, which has “a lack of consequence, a defiance of law, that is a constant irritant to a normal mind” (37). The narrator is attracted to this pattern because it defies logic, and is the opposite of her physician husband, who “is practical in the extreme” (29). Because the yellow wallpaper unlocks her imagination, she can escape the strict limitations imposed on her by her husband’s treatment. She expresses a desire to creep around with disregard to how society perceives it, revealing the desire for action and freedom (42). When the wallpaper ultimately takes over her mind, the narrator locks the door and begins creeping along the walls of her room. She tells her husband defiantly that she has “pulled off most of the paper, so you can’t put me back” (42). John, who was portrayed as always in control, is so baffled by this that he faints, while the narrator continues creeping. John fainting is symbolical of the failure of reason to treat her malaise. In fact, her husband’s rest cure was detrimental to her condition. Gilman’s short story showed the need for reform in the way society treated women, starting with the need to question the absolute authority of physicians and a look at the impact their ineffective treatments had over women’s lives.

In “The Death of Ivan Ilyich” Tolstoy paints a similar portrayal of physicians and their futile treatments. All of the doctors are considered to be famous, with each new physician being more renowned than the one preceding him. Ivan is constantly visited by “yet another celebrity” who says “almost the same thing as the first, but put his questions different” (Tolstoy 64) with the only effect that “his questions and conjectures confused Ivan Ilyich still more and increased his doubts” (64), which reveals how in Tolstoy’s eyes the physicians protect each other’s authority, but fail to improve the condition of the patient who comes to them seeking help. Tolstoy’s physicians also create an intellectual distance between themselves and their patients. When Ivan goes for the first time to see a doctor about his pain, he finds the process to be “the same as it was in court” (61). The doctor is dismissive of the patient in the same way that Ivan, a judge, would be dismissive of a man on trial. When Ivan finally asks the doctor to explain the seriousness of his condition, he is met with a pompous “I’ve already told you what I consider necessary and appropriate” (62). Such mistreatment by the physicians causes Ivan to develop a resentment towards them that grows into a feeling that it’s a “lie, a lie for some reason acknowledged by everyone, that he is merely ill and not dying, and that he needed only to keep calm and be treated” (75). The famous doctors put him on a strict regimen and at first Ivan’s “main occupation since his visit to the doctor became the precise following of the doctor’s prescriptions” (63), showing how the doctor’s absolute authority can consume a person’s personal life. The treatments fail to improve his condition and ultimately the doctors can only give him increasingly “large doses of opium” (83) to control the pain. Ivan’s deteriorating condition symbolizes the failure of medical science, and therefore the failure of reason, to save not only his life, but also his soul. Despite being trained in logic as a judge, when Ivan runs the logical exercise that “Caius is a mortal” (70), he refuses to accept the logical conclusion that he himself is a mortal, and that he also will have to die. Tolstoy’s view of medicine criticizes doctors for not being able to connect to their patients on an emotional level and he seeks reform in the increasingly bureaucratic Russian society to solve social problems from a merciful Christian perspective instead of only using cold logic. This is shown when Ivan’s pain is comforted only by the peasant Gerasim, who “alone did not lie…he alone understood what it was all about” (76).

Sir Arthur Conan Doyle was himself a professional physician, and out of the three authors is the most positive about the characteristics of a doctor. In the story “Scandal in Bohemia” the Dr. Watson is portrayed as an adventurous man who dutifully serves the powers of reason and uses it for justice. To Watson, the character of his friend Sherlock Holmes represents logical perfection, because of his exacting “process of deduction” (Doyle Part I) with which he is able to uncover the most obscure truths. The attraction that Watson feels towards Holmes’ mind is almost erotic at times, with their interactions often being flirtatious in nature. Watson “could not help laughing” at Holmes’ brilliance and the two men communicate “with hardly a word spoken, but with a kindly eye” (I) like two lovers exchanging secret glances in public. Doyle goes on to show Watson as a courageous man who isn’t even afraid of “breaking the law” (II) in his quest to aid Holmes. Doyle also has Holmes emphasize that Watson’s help “was all-important” (II) and that none of his exploits would be possible without Watson. With this Doyle emphasizes the role of doctors as individuals who sacrifice themselves in order to enable others to succeed. Dealing with deadly diseases on a daily basis means physicians are constantly surrounded by danger, which is why they must have adventurous personalities much like Watson. Doyle also emphasizes the humanity of Watson, showing that the physician is an empathetic character in contrast to the cold Holmes who is so rude that he even insults a king (II). In addition, the choice to write the Adventures of Sherlock Holmes in first person shows the very personal impact each of these adventures have on Watson, much the same way each patient would have an impact on the doctor.

For Gilman and Tolstoy the physician represents how reason can become corrupted by its own power.  They portray doctors as centers of authority that are respected by the society but don’t deserve such praise because they often do more harm than good to the patient. For Doyle, however, the doctor is a heroic character who seeks to use reason for purposes of justice despite his own human flaws. The great difference between these two views of doctors as villains and heroes reveals the importance of doctors’ ability to communicate empathetically with their patients. Tolstoy and Gilman are frustrated that the physician doesn’t listen earnestly to the patients’ complains, and seek to reform that. Doyle approaches the divide between doctor and patient from the opposite perspective, showing that doctors aren’t the ideals of reason that society makes them out to be, but have their own human desires and fears that they have to overcome. These short stories demonstrate that because the doctor is placed in such a respectable position by society, they have a duty to offer the best care possible. They also show that such effective care can’t be achieved by only applying drugs and surgeries, but requires an honest connection between the doctor and his patient that makes the patient comfortable with his treatment.


Tolstoy, Leo, Richard Pevear, and Larissa Volokhonsky. “The Death of Ivan Ilyich.” The Death of Ivan Ilyich and Other Stories. New York: Vintage, 2010. Print.

Dock, Julie Bates., and Charlotte Perkins. Gilman. Charlotte Perkins Gilman’s “The Yellow Wall-paper” and the History of Its Publication and Reception: A Critical Edition and Documentary Casebook. University Park, PA: Pennsylvania State Univ., 1998. Print.

Doyle, Sir Arthur Conan. “A Scandal In Bohemia.” The Adventures of Sherlock Holmes. N.p., n.d. Web. 9 Dec. 2012.

Reality is Not Binary

A couple of weeks ago a research article being published in Science Advances made the rounds of healthcare policy Twitter. The article made the claim that there was a correlation between police shootings of unarmed black men and black infants being born underweight and premature. The research design took two datasets, one of fatal police shootings and another one of births in California, mapped them, and established a relationship for local police shootings that occured during an infants gestation based on a distance between the shooting location and the infant’s residence ranging from 1 to 3 kilometers. The working theory was that widespread perception of police discrimination against blacks, combined with exposure to local events of police brutality against blacks was causing undue stress on black mothers and having important consequences on the gestating infants. 

The research appeared to have found a significant result, showing police shootings of unarmed blacks being associated with negative effects on black infant gestation period and birth weight. Such a finding would have obvious serious implications and the signal was quickly magnified by the usual suspectsas well as Twitter in general. Once I looked at the article myself, however, things didn’t sit right. Whenever I’m mentoring people I tell them to always do a sanity check and make sure that things make sense. If you look at the results:


The first thing you notice is that unarmed black american victim / black infant is effectively the only combination that results in a significant result. Does this make sense? If the working effect is dependent on a perception of systemic injustice, why are infants not affected by shootings of armed blacks? Similarly, the police is not notorious for equitable treatment of hispanics, so why is there no effect for hispanic infants? In fact, shouldn’t we expect mothers being stressed by shootings of unarmed citizens regardless of their race? This didn’t pass my first sanity check. The second thing you’ll notice about the results is the size of the confidence intervals for black infants relative to the other groups. This made me wonder if there was something going on with the sample size and not being able to find any information in the main article about the number of each babies in each group, I looked into the supplemental materials.


I will leave it to the statisticians to debate whether these samples are sufficiently powered. The thing that I noticed is the ratio of infants exposed to unarmed vs armed black victim shootings – 3,296 : 3,888. I’ve looked into statistics police shootings and other general gun violence in the past when the “get in your lane” debate started (I encourage you to look at these publicly collected datasets yourself, you will be sure to come to surprising conclusions on the subject) and from the police shooting dataset maintained by the Washington Post I knew that the true ratio of armed:unarmed police shootings was much closer to 15:85. Fortunately the author supplied the data that they used for this analysis. This is obviously not common and I’m sure many of you also have had cases where you strongly doubted the findings of a research article but couldn’t contest it because the data was not available. After teaching myself R to open the file and matching the coding of the case ids in the research dataset against the case id in the Fatal Encounters database from which the data was drawn I was able to research the news stories about these cases and found numerous cases that were incorrectly coded as unarmed when there was concrete evidence that the victim was armed at the time of the shooting. I have my own theories of how these errors happened, but it’s all speculative (hint: there’s 2025 cases).

After bringing up these cases to the author’s attention and some back and forth the author stated that he redid the analysis and that there was no longer a significant effect and the paper was being retratected. I’m not sure what to make of the statement that he reviewed the data. It took me 4 hours to review the news stories and recode just 36 cases. Did the author really review all 2025 cases in the dataset in just a week? Did he have help? Whatever the case, it is true that the author must be commended for choosing to make the data available, for responding to the concerns, and for promptly issuing a retraction.

The reason for this post is that in the discussion that ensued after the retraction, a number of people have opined that the paper should not be retracted, but instead published with the null result. The basic argument is that a null finding is still a finding and in addition to fighting the academic bias of publishing only significant findings that it would further our knowledge and help build a foundation for future research. I disagree with the sentiment that publishing the null result furthers our knowledge and propose that this belief makes the same mistake as the original: erroneously making conclusions without a proper understanding of the underlying reality. As a system engineer in training my bias is to understand how data is collected and what it represents before even considering how to operationalize it into system improvement, but in my experience this is something that rarely happens in healthcare policy research. I’ve written previously about how data fails to accurately represent the underlying reality in comparative system research. By reducing a complex system to a set of binary or even continuous variables we are failing to accurately capture the complexity of interactions and behaviors inherent to that system. Furthermore, scientific analysis causes researchers to self-impose a required myopia focusing on only one variable while holding all else constant, an obvious conceit when dealing with organic human systems. I’ll use the cases between 2014-2017 that I recoded for this police shooting dataset to demonstrate the limitations of the scientific method at drawing conclusions about the real world. As a reminder, each case was originally coded as a simple binary ARMED/UNARMED and the hypothesis was that the stress of proximatory to discriminatory police shootings would impose stress on mothers and adversely affect infants. I will not go into the cases that I found to be coded clearly incorrectly (victim is known to have had a gun or knife) because that’s not relevant to the point I’m try ing to make here.

Before we start, let’s pay respect and remember the names of men and women that were unarmed and should have never been shot by the police. Many of these stories involve homelessness and mental illnesses and are a direct reflection of the mass alienation tolerated by our economic and political structures. These innocents are: Tommy Yancy Jr., Paul Ray Kemp Jr., Jacorey Calhoun, Charly Keunang, Brendon Glenn, Kris Jackson, Nathaniel Harris Pickett, Rodney Watts, Donnell Thompson, Alfred Olango, Nana Àdomako, Mark Roshawn Adkins, Keita Oneil, Elena Mondragon.

The first set of cases is those in which the definition of armed/unarmed explicitly fails to reflect the situation. In 2014 Andre Milton was in the process of publicly smashing his girlfriend’s head into a nearby vehicle when the female officer, unable to stop him, chose to use her firearm. Also in 2014, Michael Laray Dozer took the handle of a gas pump and after soaking a woman with gasoline tried to use a lighter when he was shot by the officer. In a strictly literal sense of the word, these cases could be coded as unarmed. But would any person say that it was unreasonable for the officers to use their firearm in these cases? In the context of the hypothesis, does it make any sense to say that women were stressed by systemic discrimination when a man brutally beating his girlfriend was stopped?

The second set of cases is those in which the concept of armed/unarmed comes under scrutiny to a much greater degree because the urgency so clear in the previous set are much more vague. The first category in this set are cases in which there is a physical struggle between the officer and the victim and at some point the victim grabs for the officer’s firearm or taser. This is the case for James McKinney, Ezell Ford, Anthony Ashford, and Leroy Browning. The police officers involved in these shootings were determined to have acted in self-defense or defense of a partner and did not have charges pressed against them. The author raised the legitimate issue that many of these cases only have other officers as witnesses and that there’s a known concern about police departments covering up to protect their own. But if we are correcting for systemic bias by assuming that the victim in all these cases never grabbed the firearm or that they were unarmed because the firearm in question was not theirs, are we sure that we’re not introducing an even greater bias into the data? If that’s the case, how should one handle cases in which the victim actually did get hold of a firearm or taser and even used it on an officer, such as Carl Blossomgame? And what about cases where a vehicle is being used to endanger officer and civilian lives such as the cases of Dion Ramirez? If you investigate the statistics on police shootings you’ll know that this is often how women become a police shooting victim. In many jurisdictions police officers are allowed to treat drivers who use their vehicle in a way that could endanger others as dangerous and are allowed to use their firearms to stop them. The issue of police self-reporting bias is present here, but how would you treat cases where there is civilian or recorded footage corroborating the victim using their vehicle to ram into officers themselves or into vehicles such as the case with Nephi Arriguin, Jessice Williams, Michelle Lee Shirley. Were these victims armed or unarmed? Is a car used as a weapon actually a weapon? What about corded electric clippers swung like a ball and chain? And what about the case of Marquintan Sandlin, who is not known to have had a weapon, but Kisha Michael, who died in the same shooting, did have a gun with her in the vehicle?

The next set of cases our understanding of what constitutes a police shooting of an unarmed victim is challenged. Dominic Andrew Hutchinson shouted that he was armed and upon the police approaching the door rushed the officers while gripping a set of binoculars like a firearm. There are many such cases of “suicide-by-police”. The victim didn’t have a weapon and was in principle harmless, but the research hypothesis was that unjustified shootings are causing stress on mothers. How are we to expect the police to react in this scenario? Should the case be coded as armed/unarmed based on reality or the information on which the police acted on? Similarly, Augustus Crawford was being chased for being the suspect in another case and was suspected to have a gun. The gun associated with the previous case was found in the area after the shooting. The author suggested that this case was up to interpretation. The interpretation seems to be whether Crawford discarded the weapon during the chase without the police knowing this, whether the police did know he discarded the weapon and still shot him, or even whether the police planted the weapon. How are we to interpret this? Washington Post interpreted it as a shooting of an armed victim. Similarly, how are we to interpret the case of Marquez Warren who broke into the home of off-duty police officer Vedder Li and was shot with a personal (not service) firearm. Is this a police shooting? Washington Post did not include the case in their database.

Finally, there are the cases where there’s simply not enough information. Damian Murray held a teacher hostage for 6 hours before being shot by SWAT, but the news does not confirm or deny whether he had a weapon. The news for Jeffrey Smith’s case says that it’s still under investigation whether the victim was armed. The cases were coded as unarmed, but is this appropriate? Should they be coded at all? Should more information be collected by reaching out to the local authorities? Or should the data be discarded?

The point I hope I’ve conveyed is that reducing complex events to a simple binary is completely insufficient when trying to provide evidence for a complex hypothesis like public perception of police brutality against a racial group resulting in long term health consequences for infants. The best that such evidence can conclude is an opinion, precisely because so much of the data is compromised and confounded in one way or another and the way that the data is coded often comes down precisely to opinion. In the above events, a reasonable case can be made for both the armed and unarmed option. That’s a major problem. Is a car driving at you a weapon? If you’re a strict legalist then it may not be, but if you’re in situ and the vehicle is coming directly at you, I bet you’re more than likely to interpret it as a weapon that’s a threat to you. The connection to healthcare system research is that this flattening of complex information and interpretation is pervasive in policy research. If you take Commonwealth Fund data and look at data saying that some percent of people were seen within 3 hours of arriving at a hospital and that this is a metric of healthcare accessibility, what does that actually mean? What is the underlying reality? Was the patient “seen” for 2 minutes which involved being told to come back next week for a scheduled clinic appointment or was the patient seen for a 2 hour full workdown? I don’t know, and I guarantee that the researchers don’t either. The bias of research to try to find significant differences causes the data to be flattened to a degree that it no longer represents any system from which it was drawn. The consequence of then making conclusions and policy based on this data is, in my opinion, flying close to fraud. I encourage healthcare system researchers to not only seek data but to deeply understand what this data means qualitatively, what it doesn’t mean, what its limitations are, and to be honest and explicit about these details. It’s the only way to avoid systemic disasters that are guaranteed when we try to reduce reality to a binary.

On Hahnemann and Noblesse Oblige

Healthcare Twitter has been focused this past week on the closure of the Hahnemann University Hospital (HUH) trying to rationalize the root causes of such a large system failure. (although perhaps it’s more appropriate that medical Twitter is focused on this subject, as the economists have been surprisingly silent).

Dr. Gaffney in particular wrote a piece in The Nation arguing along predictable ideological lines that the source of the problem is the for-profit organization of the healthcare system and that reorginizing the system around non-profit providers funded by a centralized Medicare-for-All single payer plan would prevent such closures. Notably, and crucially, missing from his analysis is that the the HUH came under for-profit private management as part of a restructuring of Allegheny Health Education and Research Foundation (AHERF), a large NON-PROFIT healthcare system in Pennsylvania that went bankrupt. This is not an unusual occurence. The non-profit St. Vincent’s Catholic Medical Center in New York was the main hospital for the downtown Manhattan community. This was the main hospital where 9/11 first responders were brought to. As a personal note this was also the hospital where my stepfather died. In 2005 St. Vincent’s declared bankruptcy and went through reorganization, but in 2010 declared bankruptcy again, shut down the hospital and sold off its various properties and outpatient facilities to other healthcare systems. The site sat fallow for a few years until the hospital building was demolished and an apartment building was built there. I think part of the building is also some kind of religious center now. The experience of AHERF and St. Vincent’s shows that being a non-profit is not a silver bullet solution to preventing such hospital closures. The St. Vincent’s case is also notable because unlike HUH, which was a safety-net hospital in a poor community, St. Vincent’s serviced downtown Manhattan, the district with the highest per capita income in the country. If a non-profit in a high income community couldn’t make it, who would? This doesn’t appear like something that can be solved simply by a single payer, something else is going on.

Dr. Gaffney’s critique of running a a safety-net hospital as a for-profit business has a logic to it, but as I have argued above simply eliminating the for-profit nature of the hospital does not eliminate the hospital closures we’re trying to prevent. To counter the Jacobin argument, I propose instead a Jacobite critique of capitalism. The fundamental problem with capitalism is not that it encourages profit-seeking, which is inherent to human nature, but that it separates the conduct of business from the social responsibility of the aristocracy (later bourgeoisie) to the local community that existed under feudalism and monarchism. While it’s important not to over-romanticize this noblesse oblige, it is nonetheless necessary to emphasize that social organization in pre-capitalist societies was dependent on responsibilities of the aristocracy and peasantry to each other and that these responsibilities were enshrined in the law of the time. The fundamental problem that Marx was highlighting in his critique of capitalism was the erosion of the aristocracy’s legal obligations to the peasantry as they simulatenously accumulated capital through new technologies and legal “reform”.

The primary mechanism for this deterioration of noblesse oblige is the accumulation of capital to the point where the capital becomes effectively international. As capital accumulates it reaches a point where investment in other communities becomes not only desirable, but even necessary. As the business expands outside of the community that gave birth to it, the original community becomes increasingly less critical to the operation of the business and therefore vulnerable to outsourcing as the capital is shifted between communities as part of various arbitrage opportunities. Just as capitalism causes the worker to be alienated from their labor, the aristocrat is alienated from their noblesse oblige by accumulating capital. This can be seen distinctly in the case of HUH. The non-profit AHERF that operated HUH until 1998 was a Pennsylvania-wide system based in Pittsburgh, not Philadephia. Tenet Healthcare acquired HUH from AHERF and is based in Texas. Paladin Healthcare acquired HUH from Tenet and is based in California. None of these organizations had a responsibility to Philadelphia as part of their organizational mission. Not even AHERF, which did not need HUH to continue existing as the rebranded Allegheny Health Network. Naturally HUH kept getting undermined by decisions made somewhere far away, usually in conflict with the interests of HUH’s Philadelphia patients. This failure of noblesse obligate to the community is also highlighted in the situation that HUH’s residents have been put in by the closure, left to frantically look for new facilities to host them. The situation in St. Vincent’s is related to this systemic degeneration of the aristocracy in a subtle way. Although St. Vincent’s had various outpatient clinics in other boroughs, it was inherently a local institution that only served the New York City community. But not its staff. Even though St. Vincent’s mission was focused on serving the community, that’s not necessarily true of its personnel. This is especially true for physicians who will often be uprooted from their own community to go somewhere else to university, then somewhere else to medical school, somewhere else again for residency, and somewhere else yet again to practice. As a less extreme example, my understanding is Dr. Gaffney was from New York, but practices in Boston. Although physicians are a functional aristocracy through their privilege and wealth, can we expect aristocratic loyalty from such ronins?  60% of a hospital’s expenses are labor costs, and even after its bankruptcy reorganization St. Vincent’s was not able to reduce its payroll. The hospital could have been saved if staff had reduced their salaries, but why should they if they have no loyalty to the community? St. Vincent’s closed and the staff dispersed to high paying jobs at the many other NYC hospitals (who are also slowly going into bankruptcy as they accumulate debt to pay competitive wages). Similarly, HUH staff could have saved HUH by taking paycuts. I wouldn’t expect them to take paycuts to generate profits for a private equity firm, but the point remains that they could have done it. These physicians will also find highly lucrative jobs in other hospitals.

A basic critique of for-profit schemes (or even non-profits behaving as for-profits) is insufficient to resolve the problem of hospital closures. The root cause of the issue is a much bigger society-wide problem where the public in general, and the functional aristocracy especially, are increasingly alienated from their local community and the responsibilities that bind community members together. Single payer insurance could make the situation even worse as the financing of healthcare is further removed from the local community to Washington DC. In addition, the St. Vincent example provides a haunting demonstration that having a community oriented organization may be insufficient to provide stability if clinicians are not themselves committed to that mission and are allowed to exploit the resources of the community and then leave once the system collapses. Hospital stability cannot be ensured until there is a more thorough social reevaluation of the fundamental responsibilities of the bourgeois aristocracy to the local communities that function as the source of their status and wealth.

On Taylorism, Healthcare, and The Goal

This was prompted by Kim Sullivan’s review of Jerry Muller’s The Tyranny of Metrics at The Healthcare Blog. Drs. Accad and Koka also had Mr. Muller on their podcast. Both are worth going through.

It’s always been unironically fascinating to me how primitive systems thinking is in healthcare. Frederick Taylor was a turn of the 19th century industrial engineer credited with the development of scientific management, known as Taylorism colloquially. Taylor’s main idea as described in his Principles of Scientific Management was to apply the scientific method and experimentation as a management tool to figure out what’s really going on inside the organization and what processes work best instead of relying on impulse, intuition, and habit. This on its own should not be controversial. Discord about Taylor arises from the way Taylorism has been applied in practice, through the meticulous measurement of employees in order to justify the creation of onerous compensation structures and unrealistic performance expectations. As Mr. Sullivan and Mr. Muller point out, this is increasingly a problem in healthcare through the rise of pay-for-performance measures and evidence-based medicine requirements that are detached from reality. Taylor himself is not entirely guilt-free of this association, as he did have a consulting business through which he showed a willingness to advise exactly this type of tyrannical measurement as long as it would sell his consulting product.  Unfortunately villains in the real world are rarely straightforward. In addition to proposing the scientific method and measurement as management tools, Principes of Scientific Management also instructs managers to treat employees as human beings by considering employee capabilities and motivations when assigning work  and for managers to be actively engaged in the training and development of their employees.

That being said, although Taylor was instrumental in introducing the scientific method to the practice of management, industrial engineering theory was already evolving beyond Taylorism and its flaws at the time of his death in 1915, and has made significant advances in both theory and practice since then. This is to say that Taylorism in industry has been out of fashion for 100 years already, so it’s both interesting and frustrating to see it so earnestly adopted in healthcare only now and in such a chimeric form. The work of W. Edwards Deming, Peter Drucker, Taiichi Ohno, and even Taylor’s contemporary Henry Ford made great strides to humanize and correct the errors of Taylorism. This work remains unknown and misunderstood in the healthcare field, but of course everybody must start somewhere.

However, although healthcare systems engineering is still in its nascent stage, there is little reason for optimism or hope for actual clinicians that the situation in general will change for the better, regardless of their attempts to fight this problem of tyrannical measurement. While industrial engineering advanced significantly beyond Taylorism, most of this knowledge is not being put in practice widely despite being around for quite some time. Bob Emiliani dives into the question of why this is the case in “The Triumph of Classical Management over Lean Management“. Mr. Muller is correct to point out that manager expertise is an issue, but this is only a symptom of the problem. Low level managers are less affected to do this problem insofar as they are directly engaged with the work, so even if they were not previously experienced or taught they are bound to learn quickly. The root of the problem lies in the scale of organizations and the implications that this has for increasingly higher levels of management. As you go up the levels of management, the managers are paradoxically vested with increasingly greater levels of resources while at the same time increasingly less knowledgeable of how to properly utilize those resources due to being more removed from the actual work. As the Russian proverb wisely says, God is high above and the Tsar is far away. This is compounded by the fact that high ranking management needs to justify its existence to their employees and the organizations funders (whether public or private), needs to manage conflict between different departments vying for the same resources, and has a perfectly human (surprisingly, managers appear to have limbic systems) need to minimize risk to the self from mistakes made downstream. Is it surprising then that the solution management uses to negotiate all these stresses are dumb, objective measurements that they can point to when something goes wrong? Downstream employees say they were following instructions to explain why a mistake is not their fault, upstream managers say they were following best practices.

This is all colorfully illustrated in Eli Goldratt’s “The Goal“, a delightful book that I highly recommend. It’s a cult hit among industrial engineers and operations consultants, butit’s also a good bridge for clinicians who have a visceral reaction to the specter of  Cheesecake Factory management insofar it shows that their frustrations with administration are shared in other industries and explains what good scientific management can look like. The book itself is a novel and not especially well written, the characters are blunt and the events can feel conveniently orchestrated, but the plot moves along at a rapid pace making one always wonder what happens next. In many ways it reminded me of a Dan Brown novel. The story follows Alex Rogo, an industrial engineer who is the head manager of a struggling factory producing a variety of widgets. Alex is a humane character in that it’s obvious he cares deeply about his factory and the employees that work there, but he is also limited in his ability to be patient, to keep promises, and to fully understand his situation. These challenges extend to his personal life. Alex’s main challenge in the story is that he did everything that he was supposed to: he went to the right schools, got the right internships, and once he became manager made sure that his factory did well on all the metrics that the central corporate offices believe to be important. Why, then, is his factory failing? Why are his costs always increasing? Why are his orders always overdue? All this should be a familiar setting to healthcare professionals.

Without spoiling the novel itself, and leaving Alex’s adventure for you to discover for yourself, the main point of the book is that blind measurement is not only ineffectively, but actively harmful to organizations’ ability to achieve their eponymous Goal. Measurement has to be intelligent and strategic, designed to analyze and solve specific problems in the organization rather than be applied haphazardly trying to use the measurement to find problems to solve. For frontline practitioners the book implies two key pieces of advice. The first is that although the Tsar is far away and measuring foolishly, this does not mean that you can’t use measurement appropriately within your local context to help you solve problems that you as a person immediately affected by these problems, the person with that dasein to use Heidegger’s term, are facing. The second piece of advice is to stop trying to convince whoever is making you do ineffective measurement to change, because the odds are that they are incompetent and won’t change until proven wrong. To that effect, rather than asking for permission, the frontline practitioner needs to take the initiative (and responsibility) to make the necessary change and then let the results speak for themselves.

I recommend reading Mrs. Muller, Emiliani, and Goldratt’s books for the rest.

A Case Against Free Markets in Medicine

In the final act of Shakespeare’s Richard III the eponymous villain king arrives on the battlefield to fight against Richmond, who will soon become Henry VII. During the battle Richard is dismounted as his horse is killed and in a mad frenzy wades through the battlefield screaming “A horse, a horse! My kingdom for a horse!” Richard shows us how  market value can change drastically depending on the circumstances, or your mental state, and even the most absurd exchange rate can become reasonable in a moment of crisis.

This presumably arbitrary nature of prices should be the first thing about the US healthcare market that catches the attention of any student of economics. Prices for the same procedure vary greatly between hospitals on opposite sides of the street, and even then appear to have no basis in reality. Further investigation reveals many other features of the healthcare market that economics teaches us will increase transaction costs and the misallocation of resources. The prices we discussed are generally not paid by the patient, but by a third party insurer. Often the patient isn’t even able to select the insurer, but is assigned one by his or her employer. What the patient thinks of the insurer’s ability as a steward of his or her premiums is irrelevant. Further, contracts between providers or pharmacies and the insurer completely hide the true price from the patient’s review. In addition, anti-competitive certificate of need laws limit competition between providers and expensive regulations compel providers to merge in order to compete in a nuclear arms race with the insurers, although the real victim is the patient’s wallet over which the providers and insurers fight their proxy wars. The best way to explain the US healthcare system is if you took every economic best practice and then to did the opposite. How does one get out of this mess?

Academics and physicians from places like Boston and San Francisco often argue that this situation is proof that free market healthcare has failed America and that the only solution is to implement a nationalized single-payer model. Writing in Medical Economics, Dr. Anish Koka makes the above point that “labeling this “the free market” is about as pure as labeling the offspring of a Great Dane and a Chihuahua a purebred” and retorts that the way out of this quagmire isn’t towards a centralized single payer, but to normalize a price driven medical market. When presented with this argument these Boston types profess their support of free markets in general, followed by a regretful proclamation that markets can’t work in healthcare because medicine is not a generic commodity or that people don’t shop.

But Dr. Koka is ready for this and preempts the argument by presenting the case of the Surgery Center of Oklahoma. Founded by Drs. Keith Smith and Steven Lantier in Oklahoma City in 1997, the Surgery Center does not accept insurance and operates on a cash basis directly with patients, or on a contract basis with businesses self-funding their employees’ healthcare. The prices for their procedures are available online and are binding, you won’t be surprised by unexpected items being added to you bill.  Their transparency and quality has earned the Surgery Center high praise from patients domestic and international, and their success is evidenced by their ability to survive for 20 years despite their unique model (unique for healthcare, that is, any fast food restaurant would be quite familiar with it, perhaps that’s why we’re more efficient at creating COPD than treating it?) Dr. Koka proposes that making the Surgery Center’s price model the standard will reveal and remove all the bureaucracy, middlemen and inefficient practices that are making the American healthcare system so expensive.

However, despite the success of the Surgery Center of Oklahoma, there really are features inherent to healthcare that make extension of the price mechanism to the rest of the market impossible or impractical. Every Russian schoolboy knows that one of the first criticisms of the market mechanics in medicine was the recently passed Nobel winner Ken Arrow’s “Uncertainty and the Welfare Economics of Medical Care“. From a libertarian perspective Arrow’s critique is a mixture of truth and opinion, but nonetheless it is a good starting point. He himself wrote that the short paper is an “exploratory and tentative study”.

Arrow’s analysis is built around the ubiquity of uncertainty in the medical field and proposes that “virtually all the special features” of medicine come from this origin. The first unique characteristic is that demand for medical care is irregular and unpredictable. Although there are other industries where demand is irregular, there is truth that relying on insurance financing schema would make a market less competitive as it adds a middleman between the patient and physician. This comment also shows the age of the analysis; written in 1963, when insurance was not as prevalent as it is today (although already a captured market with the employer sponsored insurance tax benefit in place) and medical care was still oriented around irregular acute cases rather than the reliable chronic diseases that are the greatest burden in medicine today.

Arrow next remarks that the physician has a responsibility for the patient’s welfare far above that of a typical salesman, such as a barber (I’ll write more about the Barberians at the Gates of healthcare later). While this is true of the profession in theory, in practice there is a robust medical malpractice industry that is a testament to the fact that in the end physicians are still human and there are good ones, bad ones, and sometimes even evil ones. I have no reason to believe that physicians are more saintly than barbers.

Arrow then makes his best point, which is that medical care is inherently different from commodities because the success of medical care is uncertain. In particular, this point is most relevant to the Surgery Center of Oklahoma’s model, which provides medical services that are most commodity like in their reliability and outcomes. Happy hip replacements are all like, every unhappy chemo response is unhappy in its own way.

Finally, Arrow concludes by pointing out that medicine is endemic with licensing restrictions on entry, uncompetitive pricing, and price discrimination based on income levels. This is a tautology that medicine can’t be a competitive free market because it hasn’t been a competitive free market. We’ve seen industries standardize as they mature (finance) and deregulate (airlines), so there’s no reason to believe medicine’s professional culture can’t change.

From Kenneth Arrow’s analysis we can see that there are some concerns about the uniqueness of medicine that aren’t true, some that are irrelevant, and some that are legitimate. It is these valid impediments to free market operation that need to be considered seriously before we propose that the price competition model should be extended to the rest of the market. Arrow’s most relevant point was that the success of medical services is uncertain, but in fact there are many more.

The first set of problems is cases where patients shopping is either impossible or socially undesirable. This includes shopping for medical services and health insurance, since the unpredictable incidence of disease makes the two inseparable, as Kenneth Arrow pointed out. The most obvious example is emergency medicine, which has already been culturally recognized in America as a universal service with the passage of EMTALA. An unconscious person brought into the ER by definition can’t price shop for services. Similarly, we don’t want insurers to be able to price shop by keeping ERs out of network. When I have an emergency, I need the ambulance to bring me to the closest ER able to take care of me, not the closest one that my insurer decided is a good value. Similarly, physicians are correct to be outraged with insurers trying to not cover care performed in the ER that they deem inappropriate for the ER setting. It’s absurd and dangerous to expect patients to self-diagnose when deciding whether to go to the ER. Finally, even the most brave libertarian would be hard pressed to say that the woman who recently pleaded not to have the ambulance called should not receive emergency care if she can’t afford it. I certainly can’t say it, and there are some on Twitter who have been impressed with my lack of empathy. If we as a society agree that emergency care is a public good and that shopping for it is impossible, shouldn’t coverage of ER services be nationalized?

Emergency Medicine is only 2-5% of healthcare spending, but it does demonstrate that there really are parts of medicine where free market competition is impossible. It’s a cornerstone from which the rest of my critique proceeds. Healthcare decisions that have negative externalities on the public are another example where shopping is undesirable. Mass vaccination of the public was one of the most successful medical developments of the 20th century, and the public health benefits provide a good reason for not only vaccination but all infectious disease treatment to be nationalized and available to the public at no cost. Coughing patients not going to the hospital because they’re afraid of the cost is how zombie movies and ebola epidemics start.

We also don’t want shopping to occur when the beneficiary of the medical care can’t shop for themselves. Here I am specifically referring to perinatal and child healthcare. Should infants have their entry into the world endangered because of women not being able to afford their prenatal care? Should children be required to not receive healthcare because their parents are not able to afford, or even worse choose not to purchase, child insurance? There are arguments to be made against John Rawls’ theory of justice, but they usually are based on adults using their agency to take advantage of such a system, which children by definition of their minor status can’t do. The national insurance coverage rate for children is 95.2%, it should be 100%. It is in the public’s interest that all children can receive appropriate care until they reach adulthood and are allowed to start screwing up their health however they wish.

The next set of problems are related to the nature of disease. Unlike televisions and cars, we don’t choose to buy the bodies that we are born into. As the existentialists argued, we are thrust into existence without our consent. This absurdist nature of our lives comes with many equity and justice problems, the most Rawlsian of which are pre-existing conditions, whether genetic disorders such as cystic fibrosis or more nuanced diseases like Type 1 diabetes. Disease like these, especially so when they are progressive, are a permanent tax on individuals’ ability to operate on a daily basis and impede their physiological, psychological, and financial wellbeing. The part about financial wellbeing is most important in this discussion, because these conditions directly decrease the patient’s ability to finance the healthcare that they need. Despite all the injustices and inefficiencies of cross-subsidization in healthcare, if we as a nation really are a unified community, then don’t the healthy among us owe it at least to those who are sick since their youth to support their healthcare financially as a thanks for taking the bullet during the genetic Russian roulette at birth? We did nothing to deserve to be healthy and  they did nothing to deserve to be ill; we can’t deny that we know this to be true.

The next problem is the mental health elephant in the room, which patients don’t talk about because of stigma and policy wonks avoid because there aren’t any clear solutions. In the past we used to have thick family structures to keep us insulated from life’s shocks and priestly confession to keep us from going crazy of guilt. The world developed and we became solitary atheists, but the problems of our minds remain.  Jordan Peterson often talks about how just about everybody either has or knows somebody with a mental disorder, but we still have to get up in the morning and go to work. The result is a society that is increasingly addicted, depressed, and suicidal. Similarly to genetic disorders, mental health disorders progressively decrease our ability to finance the healthcare that we would need, but to make matters worse they also inhibit our ability to even recognize that we need healthcare or to negotiate the prices for that healthcare, in a way that cystic fibrosis does not. There’s a perverse libertarian economic argument that an alcoholic with hepatitis who uses his money to buy more alcohol is making a welfare efficient decision because he is buying exactly what he wants. If we want society to remain even moderately functional, we have to reject this argument. Mental health is yet another area where price mechanisms are untenable.

Finally, we need to discuss chronic disease management and the problem it poses to the medical field. When Kenneth Arrow was writing in 1963, the majority of healthcare was infectious disease treatment and low-probability surgeries, or conditions amenable to insurance financing mechanisms. Back then obesity affected 10% of the population and childhood overweight was virtually non-existent. Today 75% of the population is overweight and 40% is obese. 50% of the population has at least one chronic disease. The obesity, addiction, and e-cig trends all indicate that the problem is only going to get bigger. It is increasingly becoming not a question of whether somebody will get a chronic disease, but when they will get it. Conservatives correctly raise the concern that insurance covering a known adverse event is no longer insurance. The problem is further complicated by the fact that although there are behaviors that can significantly improve their outcomes, there are many smokers who won’t develop COPD and there are many exercise junkies who will get diabetes. These diseases have too many causal variables to attribute blame to any specific reason. But we can’t throw up our hands and give up just because insurance isn’t going to be a viable financing mechanism. Is price driven medicine a viable solution? It doesn’t seem so. The problem is that chronic diseases are developed over decades from the accumulation of strain caused by daily living and normal aging, with the majority of costs rapidly (and somewhat reliably) materializing in the last 1-2 decades of a person’s life. People don’t consciously factor in the cost of diabetes 40 years from now when they decide to eat a slice of pie because they simply can’t forecast effectively over such a long horizon. The correct financing mechanism to solve this asymmetry is not an insurance (the outcome is known) or uninsured shopping (the cumulative cost is too high) but a long maturity bond, with the person paying into the bond over the course  of their life with the bond paying out a large lump of cash at maturity. Sound familiar? That’s exactly how pensions work, which is something that has always been nationalized (the problems with our pension schemes is a separate discussion).

To conclude, this is not a critique of the Surgery Center of Oklahoma’s model, which I praise and admire, but a beginning analysis of its limitations. The broader problem that I see is that much of the health insurance market is already nationalized through Medicaid, Medicare and the VA. The reality is that it is politically impossible to reverse from this position. If we then add to this list emergency medicine, perinatal healthcare, child healthcare, genetic diseases, mental health and chronic disease management, all of which I earnestly believe have a legitimate case to be nationalized, then what are we left with for free market shopping? Surgeries and generic drugs? At that point you might as well nationalize the rest just to simplify things and remove any regulatory asymmetries. I hate to say it, but it’s not clear to me that single payer (or at least universal coverage + optional private supplemental coverage) isn’t the right solution.

Social Medicine Infantilizes Patients

I came across a revealing  post by Dr. Hans Duvefelt on KevinMD discussing the game theory problem created by health insurance where hidden cross-subsidies incentivize individuals to make healthcare decisions that benefit the individual tremendously but are inefficient for the group as a hole. The traditional tragedy of the commons problem. In particular Dr. Duvefelt focuses on an example presented by Dr. Siddhartha Mukherjee of ‘Emperor of All Maladies’ fame in an earlier NYT article, the CV drugs Brilinta and Plavix.

Brilinta, at $6.50 per pill, twice a day, reduces cardiovascular events more than generic Plavix (clopidogrel), which costs 50 cents per pill, once a day. But only by a little: a 20% relative or 2% absolute risk reduction. The event risk was 10% with the more expensive drug and 12% with the one that costs 82% less.

Put differently, if 100 patients were treated with Brilinta for a year, at a cost of $4,680 for each patient, 10 patients would still have an event. With clopidogrel, 100 patients, each one at a cost of $180, 12 events would occur. That means two fewer events would happen per 100 patients on Brilinta at an extra cost of $450,000, or $225,000 per avoided cardiovascular emergency (number needed to treat, NNT=50).

There’s an important error in this back of the napkin calculation because it forgets to factor in the savings created by the avoidance of a cardiovascular event. One study reports that the average initial and follow costs of a cardiovascular event in the US are $16,981 per case, ranging from $6,669 to $56,024. This does not include any non-medical costs that are incurred after a cardiovascular event, including lost productivity, personal utility, etc. Some may say that this is a pedantic criticism of back-of-the-napkin math, but it is necessary to emphasize that there is more to all of this than initially meet’s the eye.

Drs. Duvefelt and Mukherjee thus try to reconcile the ethical conflict between patient welfare and social welfare. What is to be done? Which drug should the physician prescribe? Mukherjee points out the argument that the physician has a responsibility to provide the best care available to the patient. It’s arguable that cost containment is in conflict with the Hippocratic Oath. For Mukherjee the physician can’t do anything, “the solutions are abstract and political”. Dr. Duvefelt points out that although Americans may dislike socialized medicine, ultimately all healthcare where somebody else pays for it, and therefore all health insurance, is by definition “social” and therefore all medicine is social medicine. In his opinion physicians have a responsibility to be stewards and spend resources wisely. But as I said earlier, there’s more to this than meets the eye. Notice anything missing in this entire discussion?

The patient. Both gentlemen are so focused on fixing society that they forget about the patient that they’re supposed to be helping and begin treating him or her as just another input into the medical industrial complex. Gauze, saline, Plavix, patient. The patient no longer has needs, preferences, goals or capacity, they are now merely the vehicle of a disease to be solved efficiently by the system. Social medicine therefore infantilizes the patient in two key ways. The first is that  it strips the patient of agency. Like children that have to do what their parents tell them, the patient now has to follow the decision made between the physician and insurer. What if my insurance covers the more expensive Brilinta, but I would like to make the socially responsible choice and go with Plavix? There are strange people like that out there after all; some even voluntarily pay more in taxes than they have to. What if my insurance only covers the cheaper Plavix, but I want to pay for Brilinta out of pocket? What do I work for after all? Some people work for a bigger house, others for a car, and perhaps I work for better care, even if it’s only 2% better. I don’t get to express my preferences because the system has decided for me. The second way that social medicine infantilizes the patient is by assuming that they are incapable of taking care of themselves. Children don’t have jobs and need to be financially supported by their parents. Similarly social medicine assumes that patients are financially helpless and need to be taken care of by the insurer. Perhaps that’s true for the $4,680 per year Brilanta, but why is that an assumption for the $180 per year Plavix? It’s insulting that the automatic assumption of the healthcare system is that the patient needs insurance to afford this generic. Frankly most people don’t need insurance for a $180 per year prescription, they need it for that $16,891 cardiovascular event we mentioned earlier. Mukherjee mentions the Ashish Jha study that I critiqued in my previous post to discuss the causes of high healthcare expenditure in the US: administrative waste, drug prices, procedure prices. Maybe expenditure wouldn’t be so high if we stopped using health insurance as a healthcare subscription and stopped hiding every single cost, including $180 per year generics, in the premiums?

Drs. Mukherjee’s and Duvefelt’s ask “what is a doctor supposed to do?” How about stop treating their patients like helpless children. Don’t withhold information from them, don’t make decisions for them, don’t assume their resources and circumstances. The patient doesn’t need you to be their parent and they certainly don’t need you to be an efficient steward of economic resources. There is no profound ethical dilemma to be resolved. You work neither for the government nor the insurer, as a physician you work solely for the patient. Explain to them all of their options, give them your recommendation, and let them decide what to do. That’s what a doctor is supposed to do; nothing more, nothing less.

On Comparing Healthcare Systems

Ashish Jha et al published a paper in this month’s JAMA comparing the US healthcare system to other countries. It’s a fairly robust effort comparing several system features, so the paper made its rounds through medical twitter, although I wouldn’t say that it’s any different from the Commonwealth Fund reports that get published in Health Affairs every year and from which the Jha paper draws heavily (more on this later). I’m not sure how familiar the medical community is with Health Affairs literature, since that’s a journal designed for administrative types that are the mortal nemesis of clinicians, so there may be some utility in publishing this in JAMA. The paper’s basic argument is that the US does not appear to be meaningfully different from the mean of a basket of comparable OECD countries on measures of utilization or social spending and therefore the cause of the high healthcare spending in the US is prices. This is an analysis that we have seen before, notably Uwe Reinhardt’s 2003 paper which reached the same conclusion. Although I do not disagree with the Jha conclusion that the U.S. prices for goods and services are elevated and need reform, the proposition that the U.S. isn’t structurally different from other systems is incorrect and needs to be put into context.

When I started my degree in health policy, it immediately struck me how the U.S. healthcare system does not function as a free market. Without prices set by competitive interaction between patients and providers the system suffers from the chronic information problem Hayek highlighted in The Fatal Conceit where any innovations are stuck in the hospital where they were developed and diffuse through slow channels like academic journals and conferences rather than the much faster market pressures. Even if a large system like Kaiser Permanente is doing something very unique and effective, the rest of the healthcare system may never find out about it because of the lack of signal mechanisms. My intuition was therefore to focus on comparative healthcare research and to look for international perspectives of how a healthcare system could be organized and coordinated. Perhaps we can identify best practices by figuring out what some countries are doing drastically differently to achieve drastically different results? The good news is that there is indeed a rich area to explore, especially in the developing world where healthcare systems can’t afford to waste money like we do (my quick look into Iran’s community health worker system is just one example). The bad news is that that comparative healthcare research is a very new area and the research methodology and data are often questionable. Although comparisons of the US healthcare system to other countries have been made many times in the past, such as this talk from the 1980s by Milton Friedman at the Mayo Clinic in which he compares us to the NHS, the first major thrust for comparative healthcare system research was made by the WHO with their 2000 health system report  in which they attempted to rank 192 countries in a variety of areas, ultimately nominating France as the best healthcare system. Although this pleased the French, the report was deeply criticzed by academic and politicians.

Data Problems

The major problem with the WHO report, and much of comparative healthcare literature in general, is that the data was unreliable. The report provided an 80% uncertainty interval for each country, which obviously leaves a lot of room for error that would be unacceptable in other healthcare literature. These intervals made it difficult to state with a large degree of certainty that any system really was better than another and resulted in large potential swings, with the US potentially ranking anywhere between 7th and 24th. In order to improve the data available and expand our understanding, the Commonwealth Fund began compiling comparative reports on the performance of several English-speaking countries, eventually expanding to include several other Western European and Nordic countries. The Jha paper relies on the Commonwealth Fund surveys for perception and access metrics. Does the Commonwealth data achieve their goal of providing high quality data? I respect the Commonwealth Fund tremendously and appreciate the difficulty of their task, but having worked with their survey I don’t believe that this goal is achieved for the US. The Commonwealth surveys have sample sizes of 1000-2000 per country, with the exception of Canada which generally has samples of 4000-5000 because the Canadian Ministry of Health contributed funds to expand their survey sample size. A sample of size of 1000 may be sufficient for small countries with homogeneous systems like the UK, Switzerland or Sweden, but a basic sanity check tells us that it would be inadequate for competently describing a diverse federal system like the United States, especially when you consider that New York is not Alaska and the 2000 sample size is spread out evenly across the 50 states. 40 responders don’t tell me much about healthcare in NY, especially considering how the differences between NYC and upstate NY are like night and day. In addition to the Commonwealth surveys, the Jha paper also relies on the 2016 OECD Health panel for utilization and spending data. I haven’t personally worked in depth with the OECD data, but my general attitude can be summarized by a gaffe from a former Ukrainian state economist, “the Ministry hasn’t published the report yet because we haven’t decided yet what we want the numbers to say.” The Jha paper itself admits that there’s problems with the OECD data: “When OECD data were not available for a given country or more accurate country-level estimates were available, country-specific data sources were used.” I propose that it’s irresponsible to make authoritative claims like “utilization rates in the United States were largely similar to those in other nations” when the data is compiled from a diverse set of sources of clearly varying reliability.

Analysis Problems

Jha et al suggest that the US system is unremarkable because US utilization for various services is similar to the average utilization of the eleven countries analyzed. For example, US discharges for mental and behavioral conditions is 679 per 100,000 population so the US is unremarkable because it’s close to the average discharge rate of 736. This analytical strategy is fundamentally flawed for two reasons.

The first is that the average behavior of a basket of very different systems effectively has no meaning because you cannot infer any conclusions on the behavior of any single system based on its relationship to the average. If you take the mental health discharges example, if I wanted to design an average healthcare system from scratch, the average of 736 per 100,000 population tells me nothing about how much staff I should hire, what facilities I should construct and which patients I should admit. Furthermore it would be incorrect to describe the US protocols as “typical” because these systems are clearly very different. The mental health discharge rate for Netherlands is 119 per 100,000 population, while the rate for Germany is 1719 per 100,000. These are not systems that are doing the same thing and marginally varying around a mean, there’s a difference by a factor of 14! The US can’t be a “typical” system because a “typical” system clearly can’t exist with such large variability. Either the Germans or the Dutch are doing something very wrong, because I highly doubt that they’re both right. And if they are somehow both right then the entire concept of the average becomes irrelevant because countries deliver care that is appropriate for their population and any nation’s position with respect to the average is irrelevant since the average is not representative of their proper patient population. Another example of this problem is hospital discharges per 1000, where Japan and US have similar rates of ~125 hospital discharges per 1000. Can we safely conclude that Japan and US have similar admission and discharge practices? That would be an impossible conclusion if you knew that Japanese hospitals are simply closed on evenings and weekends. These two systems will clearly behave very differently as a result of this hospital schedule even though they may appear to be similar when only looking at them on a graph.

The second problem with the use of the average of eleven countries as a benchmark to compare US performance to is that the average can be manipulated to serve your needs by adding or removing countries. Why does Jha use these specific countries? They state that it’s to use the wealthiest countries, but that’s a dubious proposition because the US is MUCH wealthier than the rest of the world. If Germany was a US state it would be the 40th poorest state in the union, France would be the 48th poorest. Are these really comparable? We have to therefore admit that we’re taking some liberties in how strict we are in our selection, and if we are going to do that then maybe the analysis should be expanded to the full 34 countries of the OECD? Alternatively, maybe we should be more strict with the selection criteria. Culture obviously has a large impact on population health, and Japan is one included country that’s clearly very different from the rest as reflected by Japan being the lowest on just about every utilization metric. But if we’re removing Japan, are the Germanic countries really that similar to the US? Perhaps it would be best to focus on the four English-speaking countries which will be the most similar culturally? But doing that changes all the averages. It shifts the COPD discharges per 100,000 population from 206 to 252 and suddenly the US utilization rate of 230 goes from the middle of the pack to the lowest utilizer! The point is that there is no such thing as objective data, all data has an opinion, and all comparative analysis of many countries is vulnerable to manipulation by adding or removing countries until you make the US look the way you want it to look. Using an average as a benchmark does not fix this critical flaw.

Knowledge Problems

Finally, a major challenge that I encountered while working on comparative system research data is simply figuring out the truth of what I’m looking at and how the data maps onto reality. Nassim Taleb calls it the pseudo expert problem, where you may have a lot of knowledge about a topic but still have no idea what’s really going on because you don’t have direct contact with the situation. In comparative system research the problem comes from trying to reduce complex systems to simple metrics that can be analyzed and compared, which unfortunately strips out critical qualitative information.

For example, Jha puts forward data suggesting that social spending in the US is similar to other countries, but doesn’t comment that this is only true after adding up public and private contributions to the programs that constitute “social spending”. That’s not exactly what most people would consider as “social” spending and after taking away private contributions the US quickly falls behind. Is it appropriate to combine private and public social spending because Americans are generally wealthier than Europeans and have more disposable income? I don’t know, but the issue is too complicated to be casually glanced over. One more example is hospital beds per 1000, which Jha reports as 2.8 in the US and 2.7 in Canada. These figures may lead a policy analyst to conclude that these systems have similar levels of hospital bed supply and similar problems, but this would be incorrect. Canada has a chronic shortage of hospital beds that does not exist in the US because Canadian law requires patients to be discharged directly to appropriate post-acute care. However, there is a shortage of post-acute care facilities in Canada, resulting in as many as 13% of Canadian hospital beds at any time being occupied by patients who no longer need acute care and are simply waiting to be discharged, sometimes for months. These beds are effectively virtual, resulting in blockages in other areas, such as EDs where it is not uncommon for Canadians to wait over 24 hours just to be admitted. Another example is primary care physician consultations for which Jha reports 4 visits per person per year in the US compared to an average of 6.6 visits per person per year. An analyst may even be impressed with Germany which reports just over 10 visits per person per year. This analysis, however, makes the error of assuming that all consultations are born equal and ignores the fact that American consultations are on average ~22 minutes long compared to only ~8 minutes in Germany. To make matters worse, we don’t even know if the American consultations are of higher quality, because the American doctor may be fiddling with the EHR for 20 minutes with only 2 minutes dedicated to the patient, while the German doctor may be able to dedicate the full 8 minutes to the patient. Baicker and Chandhra raise similar concerns regarding intensity, quality and measurement  in their response to Jha. We almost never know what the reality is!


This rant isn’t meant to discredit the work done by Jha et al, or the effort of the organizations responsible for the data they use in this paper. A lot of work obviously went into this report and it has definitely expanded people’s knowledge of the area, as evidenced by the fanfare it continues to receive on social media. I’m only seeking to express my frustration over the significant analytical errors that are present in most literature comparing healthcare systems. Healthcare is 18% of the economy and any errors have massive ramifications. My own view, to echo Hayek, is that these systems are far too complex to be properly analyzed en masse and instead it is best to focus on very specific topics ie rural nursing home care in Japan and United States. Anything more broad than that is almost certainly going to be wrong and I encourage researchers to hesitate before making firm statements like “it’s the prices stupid”. If we don’t respect the complexities of these systems, how could we possibly expect the New York Times to understand them?

Bezos, Buffett and Dimon walk into a bar

That must be how the healthcare story of the week must have started. Amidst the drama about the State of the Union address and the FISA memo release came a tidal wave of coverage about the announcement made by the leaders of Amazon, Berkshire Hathaway and JP Morgan Chase that they will be collaborating on a new healthcare venture seeking to reduce the burden of healthcare costs. The subsequent media coverage was torn between promises of major disruption and foreboding of incontinent failure. What struck the most was not the announcement itself, but the absolute conviction with which these predictions were made despite there being so much unknown. Here’s what I mean.

The first thing that struck me is the simple question “why these guys?” Nobody seems to be asking this, but it’s critically important. It’s especially strange to have a venture between three different partners. Usually it’s a single company entering into the market, such as with Oscar insurance which also promised disruption. Otherwise it’s two firms cooperating, such as Apple’s venture into the data space while partnering with various healthcare provider networks around the country or the CVS/Aetna merger. But three firms getting together? That’s bizarre. Why not four? It would make perfect sense to throw in a pharmaceutical company into the mix. Why not two? It’s not immediately clear what critical skillset JP Morgan is bringing to the mix. Three? When was the last time you heard of a joint venture between three leading partners? It’s so strange that one has to wonder if they came up with the idea while drinking in a bar or playing golf. It’s probably the golf course, Warren Buffett is on record saying that he doesn’t drink.

The bizarre partner mix brings us to the strategy, which also doesn’t make much sense. Despite the deep conviction of the media analysis, the actual announcement didn’t include ANY specifics. The official statement says that the mission was “to address healthcare for their U.S. employees, with the aim of improving employee satisfaction and reducing costs.” This roughly means nothing, so all we’re left is looking at the core competencies of the companies involved. Much of the subsequent coverage focused on Amazon’s technological innovation and ability to disrupt markets. Is this even true? Does Amazon disrupt? Their Fire OS phones were an abysmal failure, which died off so fast that the media seems to have forgotten they even existed. Similarly their Anime Strike service was shuttered after only a year, while less notorious businesses like Crunchyroll and Funimation are operating just fine. Would people even pay for the Amazon Video service if it was unbundled from Amazon Prime? Amazon acquired Whole Foods but the results so far are mixed. The only business that Amazon ever truly disrupted was the retail market which was done through aggressive supply chain management and competitive pricing. But healthcare isn’t a retail market, it’s a services industry which is an entirely different game (read Baumol’s cost disease). The other business that Amazon has been successful with is Amazon Web Services, which provides an on-demand cloud computing service to individuals and businesses. Amazon could try to leverage this IT expertise, along with their AI projects, to better assess patient data, but this runs into the dual problem of legal restrictions on the usage of patient data and the fact that most patient data is junk (there’s a very good reason why MedPAC is rolling back on MIPS). Amazon also has a relationship with the Express Scripts PBM, but it’s hard to call that a core competence. Berkshire Hathaway is a conglomerate that runs a variety of businesses of which there are too many to describe individually, but my understanding is that they’re also involved with healthcare only tangentially. They don’t run any hospital networks, nor any biotech or pharmaceutical companies. Warren Buffett basically said fairly recently that pharma investment is too difficult to discern and Berkshire Hathaway at the time accordingly only had exposure to positions in vanilla players like Johnson & Johnson and GlaxoSmithKlein. They do provide health re-insurance through the General Re business, but that’s a somewhat different expertise compared to what a health insurer does. Finally we have JP Morgan Chase, which is a bank, they do bank things. Could they be replaced with any other large bank? Probably. So why JP Morgan Chase? Who knows, but I cite my golf course theory. Presumably they’re involved in the project to capitalize this new venture, but the announcement stated that this is going to be a non-profit, which is a hard sell to both shareholders and potential investors.

This brings me to the final thing that I noticed, which was the assumption by the media that this was some benevolent project that seeks to save American healthcare from persistent cost inflation. The joint statement clearly said that the goal was to provide solutions for “their employees”, which indicates that they would start locally before even considering expanding to America as a whole. This isn’t a charitable action, despite calling it a non-profit, but intended to benefit these companies and their shareholders. The question is what that benefit is. The thing that struck me most was the statement of intent to focus on providing “simplified, high-quality and transparent healthcare at a reasonable cost.” That could be a plausible benefit, but is this really something that people at these companies don’t have access to right now and are complaining about? All of these are big brands, one would assume that employees there have access to cadillac plans with low deductibles and broad access. This bares out in the Glassdoor pages, where Amazon’s insurance benefits’ rating of 4.1/5 is supported by comments like “Amazon has the best health insurance benefits I have ever seen.” The same is true for JP Morgan and Berkshire Hathaway. Employees at these companies seem satisfied with their benefits, so something is not right about this announcement. If the employees are not going to benefit, maybe the companies can? It could make sense to establish a non-profit insurer and then transfer the savings that would normally be the insurers markup to the shareholders of the three companies, therefore effectively transferring wealth from the insurer shareholders to parent company shareholders (note, not a charitable action), but this assumes that the new non-profit can operate more efficiently than the existing businesses. Contrary to popular belief insurers aren’t making enormous profits, publicly traded health insurers have a profit margin of only 3% on their revenue. Can Amazon et al negotiate more aggressively with pharmaceuticals and hospitals than their current insurer? It’s doubtful, they don’t have much market power. Although much of the media tried to position the 1 million US employees who work for these companies as a competitive advantage, this isn’t actually a large number. To put things into context the largest US insurer, United, has 70 million subscribers and the 5th largest, Cigna, has 15 million. Amazon+Berkshire+JP Morgan Health just doesn’t bring much weight to the table.

So what’s going on and what’s going to happen? The problem I have with this announcement is the amount that we don’t know and the amount that doesn’t make sense. There’s no company name. There’s no business plan. There’s no competitive advantages. There’s little relevant expertise. The bizarre partner mix makes no sense. All we have is a vague mission statement about improving quality and reducing cost for their employees, but even this doesn’t square out with what employees are saying on Glassdoor. If this announcement was brought in front of a venture capitalist by no-name Jeff, Jim, and John instead of Bezos, Buffett and Dimon they would have been thrown out of the room for wasting people’s time. By their own admission the business is in “early stages” but if you’re just starting out, why even make an an announcement? This is the one question I can’t reasonably answer, why even make an announcement? When nothing makes sense, your assumptions are wrong. This leads me to believe that this isn’t about making a business but all about sending a message. Obviously there’s no proof, just as none of the other theories have proof, but it seems the least incoherent answer. They’re signaling to somebody, most likely insurers but also possibly the government or phama, that this is something they consider to be a problem and are willing to exercise a nuclear option on (ie starting a dubious business) if the rest of the industry doesn’t shape up. It’s about having a strategic bargaining chip on the negotiating table, however credible or real it may (not) be.


Turns Out Celebs Are Bad Health Policy Analysts

After Jimmy Kimmel’s impassioned critique of the Graham-Cassidy repeal bill and the canonization of the Jimmy Kimmel Test for evaluating health policy,  celebrities are becoming increasingly active in the nation’s policy debates. I really wish they would stop, because they don’t know what they’re talking about, especially when it comes to healthcare, and the attention that they receive with their celebrity status makes it incredibly irresponsible for them to misinform the public. The newest entrant into the policy arena is Julia Louis-Dreyfus, famous for her roles on Seinfeld and Veep, who announced on Twitter that she has been diagnosed with breast cancer (get well, Julia).


The problem is that she then took the opportunity to plug universal health care as the solution to cancer mortality. Why is this a problem? Let’s take a quick look at how poorly the US compares to other OECD countries in breast cancer survival rates:

source: OECD

Oh. Well. That’s awkward. Despite not having universal health coverage, the United States is the best healthcare system for actually treating women with breast cancer and keeping them alive. That’s because contrary to popular belief, we do actually receive higher quality care than other countries. It’s important to point out that the US is a lot better than some key countries. The Commonwealth Fund’s darling “Best Healthcare System” in the United Kingdom is almost 9% behind the US in 5 year breast cancer survival rates. This is actually a massive problem, because as Julia Louis-Dreyfus correctly points out, 1 in 8 women will be diagnosed with breast cancer. That’s a full 1% of all British women who die because the NHS is failing them on this single important disease. Julia Louis-Dreyfus should study the facts before pushing such a system on American women as well.

By the way, since we’re sharing personal stories, my mother was diagnosed with breast cancer and as an uninsured poor immigrant went through chemo paying for treatment with loans. She lived. It’s certainly tough financially, but it’s not impossible, and the idea that being sick while uninsured is an automatic death sentence is yet another irresponsible falsehood.