Drug manufacturing and pricing vaulted into the news several years ago when a privately held company raised the price of a drug used for infections from US$13.50 to $750 for one pill.
After an outcry from hospitals, the company later relented, dropping its price by a small margin. Still, this single dramatic increase shed light on the once obscure arena of older generic drugs that continue to be in short supply and whose prices occasionally skyrocket.
Frustrated with these shortages and alarmed by the potential for price gouging, a coalition of hospitals has recently struck back. Four not-for-profit, religiously affiliated hospital systems and the U.S. Veterans’ Administration announced their intent to form a company that would manufacture generic drugs, thereby helping to mitigate or eliminate shortages and prevent future massive price spikes for rarely used generic drugs.
I’m an economist who has studied the health care industry, including the U.S. generic industry, and I see a few regulatory and business hurdles to this approach.
Like any large company, a modern hospital has hundreds – even thousands – of workers using countless computers, smartphones and other electronic devices that are vulnerable to security breaches, data thefts and ransomware attacks. But hospitals are unlike other companies in two important ways. They keep medical records, which are among the most sensitive data about people. And many hospital electronics help keep patients alive, monitoring vital signs, administering medications, and even breathing and pumping blood for those in the most dire conditions.
A 2013 data breach at the University of Washington Medicine medical group compromised about 90,000 patients’ records and resulted in a US$750,000 fine from federal regulators. In 2015, the UCLA Health system, which includes a number of hospitals, revealed that attackers accessed a part of its network that handled information for 4.5 million patients. Cyberattacks can interrupt medical devices, close emergency rooms and cancel surgeries. The WannaCry attack, for instance, disrupted a third of the UK’s National Health Service organizations, resulting in canceled appointments and operations. These sorts of problems are a growing threat in the health care industry.
Protecting hospitals’ computer networks is crucial to preserving patient privacy – and even life itself. Yet recent research shows that the health care industry lags behind other industries in securing its data.
I’m a systems scientist at MIT Sloan School of Management, interested in understanding complex socio-technical systems such as cybersecurity in health care. A former student, Jessica Kaiser, and I interviewed hospital officials in charge of cybersecurity and industry experts, to identify how hospitals manage cybersecurity issues. We found that despite widespread concern about lack of funding for cybersecurity, two surprising factors more directly determine whether a hospital is well protected against a cyberattack: the number and varied range of electronic devices in use and how employees’ roles line up with cybersecurity efforts. Read More »
They say timing is everything. And in sub-Saharan Africa, where roughly a third of untreated HIV infected babies die before they reach the age of one, a timely diagnosis is everything.
According to the latest UNAIDS data, 150, 000 children are infected with HIV in sub-Saharan Africa, annually. Due to the high number of children dying, diagnosing babies with HIV as early as possible is critical.
Public health officials have been grappling with this for many years. How can they reduce the time it takes to get newborns’ blood samples to the diagnostic lab and the test results back? This matters because it determines how soon babies can start medical treatment. The average turnaround time in sub-Saharan Africa often range from one to three months.
In general, shorter turnaround times can be achieved by improving the clinic-to-lab supply chain. This can happen through increasing the number of vehicles equipped to transport samples, hiring enough drivers, training enough medical personnel, buying the right type of diagnostic equipment, and improving communication systems.
African countries like Malawi and Nigeria have done this, with impressive results. Read More »
Imagine this scenario: A patient named John has waited 5.5 years for a much-needed kidney transplant. One day, he learns that a deceased donor kidney is available and that he is the 153rd patient to whom this kidney was offered.
Clearly, this is not a “high-quality” organ if it was declined by 152 patients or the clinicians treating them. But because John has been waiting a long time for a new kidney, should he accept or decline the kidney? And can analytics and machine learning help make that decision easier?
Currently, that decision is usually made by John’s doctor based on a variety of factors, such as John’s current overall health status on dialysis and a gut instinct about whether (and when) John will get a better offer for a healthier kidney.
If John is young and relatively healthy, the risk of prematurely accepting a lower-quality kidney is future organ failure and more surgeries. If John’s health status is critical and he rejects the kidney, he could be underestimating how long it will take until a higher-quality organ is available. The decision could be a matter of life or death.
John’s dilemma isn’t unique in the world of kidney transplantation, where current demand outpaces supply. Since 2002, the number of candidates on the waiting list has nearly doubled, from slightly more than 50,000 to more than 96,000 in 2013. During the same time, live donation rates have decreased. Complicating this problem of supply and demand is an unacceptably high deceased donor organ discard rate, as much as 50 percent in some instances.
MIT Sloan Research Associate and Lecturer, Tage Rai
From Behavioral Science
“A sick, demented man.” That was Donald Trump’s assessment of Stephen Paddock, who shot nearly 600 people, leaving 58 dead, during a concert in Las Vegas on Sunday. Echoing Trump’s rhetoric, House Speaker Paul Ryan said that “one of the things we’ve learned from these shootings is often underneath this is a diagnosis of mental illness.” Most Americans agree that there is a strong link between mental illness and mass shooting, and shifting the national conversation to mental health reform carries the advantage of avoiding the more politically divisive gun-control debate. But what if Stephen Paddock had no diagnosable mental illness? And what if his mental state was the rule, not the exception?
In the aftermath of a mass shooting, we naturally seek to understand the killer’s motives. Our first instinct is to assume that the killer must be mentally deranged somehow. He must be a sadist who takes pleasure in the suffering of innocents, or a psychopath who feels no empathy for his victims, or a schizophrenic haunted by paranoid delusions. How else could someone commit such an awful atrocity? Yet, there is no evidence that Stephen Paddock was any of those things. He had no history of mental illness. He had no criminal record. He was a successful businessman. Relatives and people who know him are in disbelief. Paddock’s father was a notorious bank robber, but the two men never met, and if Paddock inherited violent tendencies from his father genetically, they never manifested until now. Read More »