In this course, you’ll learn the foundational economic theories behind health care innovation and how to optimize your own health care practice or organization. Designed to help you gain a practical understanding of the theoretical frameworks of behavioral economics and operations management in the health care setting, this course will help you apply these frameworks to assess health care practices and apply innovation while managing risk. You’ll also explore the best practices for evaluating one’s innovative practices, using real-life examples of success to see the concepts in action. By the end of this course, you’ll have honed your skills in optimizing health care operations, and be able to develop the right set of evaluations and questions to achieve best innovative practices within your organization.
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Module 4
This module was designed to highlight the importance of behavioral economics in health care practice and how to utilize behavioral economic theory to optimize the operations of your health care organization. You’ll explore Expected Utility Theory and the different types of Prospect theory such as Reference Dependence, Diminished Sensitivity, and Loss Aversion in the context of health care. By studying real-life examples of Default Bias and Increased Cost Sharing case studies, you’ll establish a framework for health care interventions that will be effective and successful after implementation. By the end of this module, you’ll be better able to determine the theoretical basis for certain health care practices such as nudge units and whether such practices would benefit your organization.
Andrew M. Heller Professor at the Wharton School, Senior Fellow Leonard Davis Institute for Health Economics Co-Director, Mack Institute of Innovation Management The Wharton School
Amol S. Navathe, MD, PhD
Assistant Professor of Medical Ethics and Health Policy Department of Medical Ethics and Health Policy
David A. Asch, MD, MBA
Professor of Medicine and Professor of Medical Ethics and Health Policy Department of Medicine
Roy Rosin, MBA
Chief Innovation Officer Penn Medicine
Kevin Volpp, MD, PhD
Professor of Medicine, Division of Health Policy / Professor of Health Care Management Perelman School of Medicine / The Wharton School
In this session, we'll describe the interesting phenomenon of choice architecture,
and how the way in which choices are presented to people
has an outsized impact on what they actually choose.
Often people will stick with the choice they had previously made even when
better choices exist or a default choice that is picked by a choice architect for them.
The tendency to stick with the status quo,
specifically a status quo bias,
underlies the asymmetry between how people view
losses and gains relative to a particular starting or reference point.
We should not assume people will carefully search and find the best alternative in
the market as they will often overweight
the perceived losses and giving up their current choice.
I will show you some examples to illustrate the power of
choice architecture before describing some issues to consider.
So let's start with this example.
Imagine that our goal is to get people to be
more physically active and to use the stairs.
In the picture on the left,
we have an example of people taking the escalator to go to the fitness center.
This is obviously not what we're trying to achieve,
but you can see how in this case the path of
least resistance is taking the escalator even if I'm on my way to work out.
If we had situated the escalators somewhere else and just have the stairs there,
it's pretty likely these people would have taken the stairs.
In the picture on the right,
we have a picture of a gleaming beautiful staircase in an office building,
and you can see how in this case the stairs
practically beg you to walk up the stairs as opposed to taking an elevator.
The path of least resistance in this case would be to take the stairs.
In every day, we face choice environments like this,
where we can either choose a healthier choice or a less healthy choice.
What we decide to do often is heavily based on what seems to be easiest at the moment,
in other words, what is the path of least resistance.
So let's consider a different kind of example.
Imagine that you are in a scenario as a consumer where
lower cost auto insurance confers a limited right to sue,
and has the advantage of being lower cost.
Imagine you're a driver and you can choose
either the lower cost or higher cost option with much greater rights to sue.
Which would you choose?
This is a real life example and it turns out
what you would choose would have depended a lot on the default.
So around the same time,
New Jersey and Pennsylvania who were two adjacent states in
the United States took a different approach to offering these plans.
In New Jersey, the default was the limited right to sue.
In Pennsylvania, the default was the full right to sue.
Drivers in either state could choose either option.
What happened was that in New Jersey,
people chose the limited right to sue about 80 percent of the time.
In Pennsylvania, drivers chose the limited right to sue about 30 percent of that time,
meaning that in both cases people chose the default 70 to 80 percent of their time.
Here you can see this example where we have roughly equivalent choices in both states,
and just by dint of what was chosen at the default,
we end up with very different outcomes.
Here's another famous example.
This graph depicts organ donor registration rates in different European countries.
On the left in yellow are countries where you have
to opt in to be listed on the donor registry.
On the right in grey are countries where you are assumed to be on
the donor registry unless you explicitly say you would like to opt out.
You can see there are enormous differences in these rates.
The opt in countries are ranges from 4 to 27 percent.
The opt out country is between 86 and 100 percent.
The one country which reportedly put a lot of effort into educating people,
Chinese social norms to get the donor registration rate up was the Netherlands,
and you can see that that got them to 27 percent but that's
much lower than what we observe in the opt out countries.
There are very few interventions where you can have this kind of
difference in participation rates by dint of flipping a switch.
Here's another common example.
We know that foods that are more easily accessible tend to be chosen more often.
So in a grocery store this is food at eye level,
in a cafeteria it's typically food
that's in the middle of the cafeteria near the cashier.
There's no escaping that somebody has to decide how to lay out the food in the cafeteria.
You can put the unhealthy food in the prime position, the healthy food,
the most profitable food,
or you could do it at random and not think about this at all.
But this is a very nice example of libertarian paternalism,
because people can be steered towards healthier choices without
limiting options for those who want to eat unhealthy food who have strong preferences.
If I want the supersize burger,
supersized fries, I can still have them.
Nobody's told me I can't.
Nobody has taken them off the menu.
Nobody has changed the differential prices.
The choice architect in this case has just made it subtlely a
little bit more difficult to get those unhealthy foods,
but the choice is still mine to make and nobody is telling me what to do.
Here's another very common example.
When people sign up for a health plan,
they're typically asked to choose a doctor.
The typical way these lists are sorted is alphabetical.
So we have a long list of names ranging from A to Z,
and there's very little information inherent in this list that gives me
guidance on who might actually be a good doctor or who might provide high value care,
in other words a good ratio of quality to price.
So imagine we thought about doing better than just
simply providing the list in alphabetical order.
We could come up with some kind of smart default,
where we would rank the doctors in descending order of quality.
We could rank them in order of increasing cost.
There are many better alternatives and alphabetical,
and these all provide opportunities to try to help people
make decisions that might be better for them.
In summary, we all make many choices every day.
What we may not have fully appreciated is the degree to
which our choices are influenced by how somebody,
a choice architect, presents our choices to us.
It can be that default insurance plan,
the layout of the food in a cafeteria grocery store,
the ordering of a list from which we choose.
As program designers, we should consider how
we might want to design these choice environments.
How do we optimally structure them so as to help people make better choices?
A tricky question of course is who gets to define what is better,
and on what basis do they make that determination?
In cafeteria type settings,
there less likely to be significant objections to putting
the healthy food in a more easily accessible location,
but you can imagine many other scenarios where this is considerably more complicated.
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Christian TerwieschAndrew M. Heller Professor at the Wharton School, Senior Fellow Leonard Davis Institute for Health Economics Co-Director, Mack Institute of Innovation Management
Amol S. Navathe, MD, PhDAssistant Professor of Medical Ethics and Health Policy
David A. Asch, MD, MBAProfessor of Medicine and Professor of Medical Ethics and Health Policy
Roy Rosin, MBAChief Innovation Officer
Kevin Volpp, MD, PhDProfessor of Medicine, Division of Health Policy / Professor of Health Care Management
