Picture yourself going to the doctor. You arrive by car, park nearby, and when you enter a receptionist greets you and checks your information on a computer. You’re led into a comfortable, well-lit office; the cabinets are fully stocked. Your records are on hand. The nurses and doctors are well educated and knowledgeable, their equipment at the ready. If they can’t help you, they refer you to someone who can.
Now try to picture the same scene in sub-Saharan Africa. If you’re wealthy, your experience may be similar. But if you’re not, it’s altogether different. The roads are unpaved and riddled with potholes; it might take all day to get to the clinic by public transport. The queue to see the doctor is long–an eight-hour wait is not unusual–and there’s nowhere to sit. You might have to bribe someone to be seen. The electricity is unreliable; the clinic’s supplies are running low. Your medical records are incomplete, perhaps even non-existent. The doctors and nurses, while trained and dedicated, are not up-to-date on current treatments, and lack access to the tools they need.
As our healthcare system moves from compensating providers on the basis of quantity of care to quality of care, it’s very important to measure hospital performance. But a key limitation for that measurement is patient selection.
A large body of research suggests that it doesn’t matter where patients go for treatment. Teaching hospitals, for example, have been found to achieve modestly better health outcomes. Unfortunately, patients in worse health tend to choose or are referred to hospitals based on the facilities’ capabilities. So hospitals with higher levels of treatment intensity – meaning teaching hospitals or hospitals that perform the latest procedures – could appear to have poorer grades on healthcare report cards because they are treating the sickest patients.
Our community was wrestling with the exorbitant and still rising cost of educating our students. Technology, salary and other expenses are factors of course, but those generally grow in line with the economy overall. The big problem is the cost of medical coverage for faculty and staff, growing faster than other revenues or expenses, thereby crowding out other important needs.
In this regard, health care is an extreme outlier. With everything else – cars, computers, entertainment, transportation– we assume that availability and quality will go up while unit costs keep going down, and goods and services become more plentiful. But not with health care. Appropriately enough for someone who works at MIT, an answer can be found by considering this nation’s health care system as a system engineering problem. Read More »
As the debate about health care costs swirls, I’ve published an article that challenges the common view that higher healthcare spending is not correlated with better health outcomes. To the contrary, I found that tourists who become ill and receive emergency care at “high-spending” hospitals have significantly lower mortality rates compared to tourists who end up in “lower-spending” hospitals.
Because hospitals in general tend to spend more on sicker patients, I knew how difficult it is to estimate returns to healthcare spending. My goal was to compare apples to apples. It’s not possible to conduct a randomized experiment where some patients go to a high-spending hospital system and others are sent to a low-spending one. Since most people don’t choose their vacation destinations based on the budgets of local hospitals, tourists come close to mimicking this type of random assignment: some are exposed to high-spending hospital systems while others are exposed to low-spending ones.
Pointing out that 16 cents of every dollar of the GDP in the U.S. goes to health care, Associate Professor of Operations Management Retsef Levisays that the United States spends more on health care than any other developed nation. Yet long patient wait times are a problem that continues to plague the system.
By approaching health care delivery as a management problem, Levi is collaborating with a major Boston hospital to reduce patient wait times by improving patient, information and resource flow through the perioperative care process. By getting rid of paper transactions, moving to a team-based model, and changing the process for assigning surgeries and doctors to operating rooms, the hospital was able to halve the average patient wait time from 88 minutes to 43 minutes.
During an Alumni Weekend presentation, Levi advocated for a research center at MIT Sloan that would bring together academics and all the participants in the health care system to address these types of systemic challenges within the U.S. health care system.