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.
De la lección
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
Let's start our discussion of health incentives by examining a couple of
programs that do not heavily leverage behavioral economics.
We know that incentives work simply by changing the effective ratio of
benefit to cost.
While most of our discussion will focus on health behavior,
we'll start with a well known example of this,
in the context of health service utilization, to illustrate that as we
change the effective prices that a consumer faces utilization will change.
Then we'll go through some examples of standard economic incentives,
in which we provide a reward to participants who complete certain tasks.
Let's start with this example here, of what happens when an annual drug benefits
cap was put in place of $1,000 among some Medicare Advantage beneficiaries.
As you can see from the graph in the left, fairly quickly,
there was a significant percent decrease in medication utilization.
This was between 30 and 40%.
This significantly reduced total spending on pharmacy by about 28%.
But it unfortunately also led to an increase in emergency room
visits of about 9%, hospitalizations of about 13%.
What that meant was that on net, this program didn't actually save money.
More concerning, mortality rates were actually higher
once this drug benefits cap was put in place compared to when it was not.
This is an example of how as you change somebody's benefits,
think about a health insurance plan as a giant incentive plan.
You can make some services less attractive, more costly, and
therefore less likely to be consumed.
Lots of evidence of this in the context of health benefits.
Let's turn now to talking about smoking,
the leading cause of preventable mortality among Americans.
More than 400,000 Americans die each year of tobacco-related illnesses, but
only 3% per year quit smoking.
Smoking cessation programs have been shown to be highly effective, and
cost effective.
Yet their utilization tends to be very low.
We did a study to examine whether we could use financial incentives to increase
the rate at which people sign up for a smoking cessation program and complete it.
We enrolled 179 veterans at the Philadelphia VA Medical Center
who were interested in quitting smoking.
We randomized them to either a free program with free pharmacologic aids,
such as nicotine patches for quitting smoking.
Or the same program plus incentives of $20 for
attending each of five classes that were part of the smoking cessation program, and
an additional $100 if they quit smoking 30 days after program completion.
Self-reported smoking was then confirmed with cotinine tests.
Let's look at what happened.
The incentive program doubled the rate of the smoking cessation program enrollment
from about 20% to 43%.
It also significantly increased the program completion rates from 12% to 25%.
There was an important behavioral element to this, which is that we gave
the $20 incentive to each participant immediately at the time of the class.
So they were getting frequent feedback without any delay.
We then found that after program completion, smoking cessation rates were
actually significantly higher in the short-term, 16.3 versus 4.6%.
However, we did see relapses between the 75 day mark and
the sixth month mark at which we next checked for smoking cessation.
Such that the intervention difference in smoking sensation rates was no
longer significant, it was a difference of about 6.5% versus 4.5%.
This led us to think that what we needed to do in designing our next study was
really focusing more on longer term cessation because that is of course
where the health and economic benefits are.
We got support from the Centers for Disease Control to do a larger term study
among 878 participants from 85 General Electric work sites across the US.
We conducted a two-arm randomized controlled trial in which we randomized
participants to either information about smoking cessation programs versus
the same information plus a package of incentives that were worth $100 for
completing a smoking cessation program, $250 for cessation within 6 months.
$400 if they were then able stay smoke-free for
an additional 12 months, and we could confirm that biochemically.
Eligibility was tied to quitting within the first six months, and
after 12 months the incentives were discontinued.
At 12 months, we found significant differences in quit rates.
5% in the control group.
14.7% in the incentive group.
A ratio of about 2.9.
We then observed what happened in the subsequent six months and
measured quit rates at 18 months.
We found their quit rates of 3.6% in the control group,
9.4% in the incentive group, a ratio of 2.6.
So there are some relapses, but
they happen at roughly the same rate, in the control and the incentive group.
Based on this GE implemented a plan for all 152,000 employees in the US in 2010.
There are two behavioral elements to this program,
there's a deadline which helps to reduce procrastination.
And the incentives also are awarded outside of premium structure
because of mental accounting, which makes them more salient and more visible.
The Corcoran Collaborative has reviewed all of the smoking cessation incentive
studies in workplace settings and they did what's called a meta analysis.
A meta analysis is a structured review of the data that in essence takes all
the studies that meet certain criteria, in this case they were all randomized trials.
And takes advantage of the fact that,
although many of these studies were too small to really have enough statistical
power to test outcomes, pooled together we can say something much more
meaningful than we could by looking at many of these studies alone.
The study I just described to you was a well powered study, but
many of the studies that were reviewed were much smaller.
Instead of having 878 participants, many of them had 30, 40, 50 participants.
So the confidence intervals around their findings were very wide.
Pooled together, what we find is that at six months there's an odds
ratio of programs that offered incentives for
smoking cessation compared to programs that did not of 1.72.
Meaning that there's an increase in the odds of a successful
cessation confirmed biochemically of about 70% from these programs.
There are also a number of studies that have been done,
around what's called contingency management,
the use of incentives to help substance abusers stop using various drugs.
These have been done in a lot of different contexts.
And here's another example of a meta analysis
that looked at the factors that predicted the greatest success in these programs.
Here we're looking at effect size, and the farther you are on the graph to the right,
the more effective the intervention.
We see the point estimate as a dot, the 95% confidence interval as a line.
Two things stand out in looking at the results from this meta analysis.
One is that the larger the reward, the bigger the effectiveness of the program.
The other is that the immediacy of the reward is a significant predictor
of the effectiveness of the program.
As these studies show, incentives can be very effective in changing behavior.
The designs do not have to be particularly complicated.
But two keys highlighted here are, to give feedback close in time to the desired
action, and make sure the reward is salient and visible to the beneficiary.
Another key element is to make sure the incentive is large enough to
get the attention and
increase engagement amongst those whose behavior you are trying to influence.
Explora nuestro catálogo
Inscríbete de manera gratuita y obtén recomendaciones personalizadas, actualizaciones y ofertas.
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
