Nudge Units and Some of Their Successes

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Del curso dictado por University of Pennsylvania

Health Care Innovation

35 calificaciones

University of Pennsylvania

Health Care Innovation

35 calificaciones

Curso 4 de 4 en SpecializationThe Business of Health Care

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.

Why Studying Behavioral Economics is Important5:20

Economics, Rationality, and Prospect Theory9:36

Behavioral Biases and Asymmetric Paternalism8:32

Introduction to Choice Architecture7:31

Standard Incentives8:29

Introduction: Framework for Intervention3:04

Nudge Units and Some of Their Successes6:09

Conoce a los instructores

Christian Terwiesch
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

Today we're going to talk about the UK Behavioral Insights Team and

some of its successes today.

But let me remind you about Nudge units and libertarian paternalism, and

the conceptual appeal.

The hope is to provide low cost unobtrusive solutions to big problems.

Regulatory purchase in many cases, taxes, and subsidies are difficult to impose.

And they're most easily justified if individual's actions harm other people,

such as smoking.

By recognizing less than perfectly rational behavior, behavioral

economics point some broader set of options to help people help themselves.

The UK Behavioral Insights Team was the first Nudge unit to get created.

Founded as a cabinet level position by UK Prime Minister David Cameron,

this unit got a lot of visibility.

Let's take a look at a couple examples of programs they ran, and

talk about some of what was learned.

One interesting challenge they took on was increasing organ donation rates.

In many countries there are tens of thousands of people who die each year

because there aren't sufficient organs available.

And you can imagine a group of people in the traditional way,

in which letters might get sent out, sitting around a table.

And debating at lengths, what is the most effective way to communicate with people.

And they might come up with a list like this, there's a standard way of doing it,

the way we've always done it, or we could appeal to social norms.

Everyday thousands of people see this page decide to register.

We could give, in addition, to the social norms a picture of people.

Norms in the picture.

We could add the NHS logo.

We could appeal to people's sense of, wow,

bad things really happen if we don't do this.

Three people die each day because there are not enough organ donors.

You could save or transform up to nine lives as an organ donor.

Norms of reciprocity; if you needed an organ transplant, would you have one?

If so, please help others.

If you support organ donation, please turn your support into action.

Any of these might be a reasonable choice for what these letters might look like.

And the key thing to note is that in a typical organization,

a group of smart people or an individual would make a decision

without really having necessarily any data.

They picked the one approach that everyone believes will be most effective.

That would get sent to everybody without anybody really knowing whether

the approach that is picked is actually more effective than the alternatives.

So what the UK Behavioral Insights Team did in this

context is they ran an experiment.

And they randomly assigned people,

more than a million people over a month to get one of these letters.

In the control group, 2.3% of people register as organ donors.

In the action group, 2.8% registered.

In the social norm group, 2.9%, adding the logo didn't matter,

it was also 2.9% for the norm plus the logo.

Telling people that their donation might affect nine lives also got 2.9%.

Telling people three people would die, I got 3.1%.

Highlighting the norms of reciprocity, 3.2%.

The norm in the picture, 2.2%.

Now it's important to note that this 3.2% translated into

96,000 additional completed donor registrations a year.

So we could look at this data, these results, in a few different ways.

One is there's a 0.9 percentage point difference between 2.3% in

the control group, 3.2% in the reciprocity group.

0.9 of percentage point doesn't sound like that much.

But this is also improvement to a 39.1% increase which is a lot.

It also translates into 96,000 extra people registering at no additional

expense.

So keep in mind that any of these alternatives could have been chosen

by a group of people sitting around a table.

And sending out the letters using reciprocity doesn't cost any more

than the less effective alternatives.

And that seems like a big win.

Another example of an approach tested by the UK Behavioral Insights Team in health

was related to social norm feedback and

the prescribing of antibiotics in general practice.

Again, a big problem in the UK as it is in the US.

They provided feedback to the 20% highest prescribers

on the fact that they prescribed more than the other 80%.

This feedback alone led to

a 3.3 percentage point decrease in the rate of antibiotic prescribing,

translating to about 75,000 fewer antibiotic prescriptions per year.

So again, it doesn't appear like a huge effects size on the surface,

but translated over a large population, and given the low cost,

it's a pretty good effect, 75,000 fewer unnecessary antibiotics prescriptions.

A common approach that the UK behavioral insights team takes is testing and

trialling ideas before they are scaled up.

This is important because it enables understanding both of what works, and

what does not.

Others successes have included efforts to improve tax collections,

loan payback rates, reducing consumption of high calorie sugar beverages, and

the rate of no shows of outpatient appointments.

The UK Behavioral Insights Team is now a central part

of how the government in the UK goes about its business.

And its services are purchased by many organizations and

governments throughout the world.

The US is following suit and

the US Social Behavioral Science Team has now conducted more than 30 pilots

with members representing more than 25 organizations across the government.

The goal of this in some sense, is captured by one of their memos,

in which they say, quote, effective and efficient government should reflect our

best understanding of human behavior in designing policies and programs.

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