HCM 320 Milestone Two Guidelines and Rubric
Overview: For Milestone Two, you will describe for your audience the nature of your chosen public health issue, so that they will be able to understand and
appreciate your presentation. Building upon your Milestone One worksheet submission, your analysis will include the economic principles and impacts of the
principles involved with your public health issue, related socioeconomic factors, and the healthcare organizations impacted. Use the feedback you received on
Milestone One to assist you in developing your introduction.
Submit your analysis as a short paper that you may use to develop speaker’s notes for your final presentation.
Prompt: Describe for your audience the nature of your chosen public health issue, including the economic considerations involved.
Specifically, the following critical elements must be addressed:
I.
Analysis of the Health Issue:
A. Outline the underlying economic principles and indicators at play using specific examples. To what extent do those principles and indicators
apply in understanding your chosen public health issue?
B. Demonstrate the economic impacts of your public health issue. Provide specific examples of each impact.
C. Analyze the larger context within which your chosen public health issue exists. To what extent is the issue a product of larger socioeconomic
factors?
D. Examine the major healthcare organizations impacted by the public health issue. How are they currently acting and reacting to the issue?
Rubric
Guidelines for Submission: Your paper must be submitted as a 2- to 3-page Microsoft Word document with double spacing, 12-point Times New Roman font,
one-inch margins, and at least three sources cited in APA format.
Critical Elements
Analysis of the Health Issue:
Economic Principles and
Indicators
Proficient (100%)
Outlines the underlying economic
principles and indicators at play, using
specific examples
Analysis of the Health Issue:
Economic Impacts
Demonstrates the economic impacts of
the public health issue and provides
specific examples of each impact
Needs Improvement (75%)
Outlines the underlying economic
principles and indicators at play, but
there are inaccuracies or the outline
lacks specific examples
Demonstrates the economic impacts of
the public health issue, but there are
inaccuracies or the demonstration fails
to provide specific examples of each
impact
Not Evident (0%)
Does not outline the underlying
economic principles and indicators at
play
Value
23
Does not demonstrate the economic
impacts of the public health issue
23
Analysis of the Health Issue:
Socioeconomic Factors
Analysis of the Health Issue:
Healthcare Organizations
Articulation of Response
Analyzes the larger context within
which the public health issue exists by
qualifying the extent to which the issue
is a product of larger socioeconomic
factors
Examines the major healthcare
organizations impacted by the public
health issue, including their actions and
reactions to the issue
Submission has no major errors related
to citations, grammar, spelling, syntax,
or organization
Analyzes the larger context within
which the public health issue exists, but
fails to fully or accurately qualify the
extent to which the issue is a product of
larger socioeconomic factors
Examines the major healthcare
organizations impacted by the issue,
but fails to fully or accurately explain
their actions and reactions to the issue
Submission has major errors related to
citations, grammar, spelling, syntax, or
organization that negatively impact
readability and articulation of main
ideas
Does not analyze the larger context
within which the public health issue
exists
23
Does not examine the major healthcare
organizations impacted by the issue
23
Submission has critical errors related to
citations, grammar, spelling, syntax, or
organization that prevent
understanding of ideas
8
Total
100%
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CHAPTER
SUPPLY AND DEMAND ANALYSIS
10
Learning Objectives
After reading this chapter, students will be able to
•
•
•
•
define demand and supply curves,
interpret demand and supply curves,
use demand and supply analysis to make simple forecasts, and
identify factors that shift demand and supply curves.
Key Concepts
• A supply curve describes how much producers are willing to sell at
different prices.
• A demand curve describes how much consumers are willing to buy at
different prices.
• A demand curve describes how much consumers are willing to pay at
different levels of output.
• At the equilibrium price, producers want to sell the amount that
consumers want to buy.
• Markets generally move toward equilibrium outcomes.
• Expansion of insurance usually makes the equilibrium price and
quantity rise.
• Insurance and professional advice influence the demand for medical
goods and services.
• Regulation and technology influence the supply of medical goods and
services.
• Demand and supply curves shift when a factor other than the product
price changes.
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
10.1 Introduction
Healthcare markets are in a constant state of flux. Prices rise and fall. Volumes rise and fall. New products succeed at first and then fall by the wayside.
Familiar products falter and revive. Economics teaches us that, underneath
the seemingly random fluctuations of healthcare markets, systematic patterns
can be detected. Understanding these systematic patterns requires an understanding of supply and demand. Even though healthcare managers need to
focus on the details of day-to-day operations, they also need an appreciation
of the overview that supply and demand analysis can give them.
The basics of supply and demand illustrate the usefulness of economics. Even with little data, managers can forecast the effects of changes in
policy or demographics using a supply and demand analysis. For example, the
impact of added taxes on hospitals’ prices, the impact of increased insurance
coverage on the output mix of physicians, and the impact of higher electricity
prices on pharmacies’ prices can be analyzed. Supply and demand analysis is
a powerful tool that managers can use to make broad strategic decisions or
detailed pricing decisions.
10.1.1 Supply Curves
Exhibit 10.1 is a basic supply and demand diagram. The vertical axis shows
the price of the good or service. In this simple case, the price sellers receive
is the same price buyers pay. (Insurance and taxes complicate matters because
the price the buyer pays is different from the price the seller receives.) The
horizontal axis shows the quantity customers bought and producers sold.
EXHIBIT 10.1
Equilibrium
$350
$300
S
$250
Price
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156
$200
$150
$100
D
$50
$0
0
20
40
60
80
100
120
Quantity
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C hap ter 10 : Sup p ly and D em and A naly sis
The supply curve (labeled S) describes how much producers are willing to sell at different prices. From another perspective, it describes what the
price must be to induce producers to sell different quantities. The supply
curve in Exhibit 10.1 slopes up, as do most supply curves. This upward slope
means that, when the price is higher, producers are willing to sell more of a
good or service or more producers are willing to sell a good or service. When
the price is higher, producers are more willing to add workers, equipment,
and other resources to sell more. In addition, higher prices allow firms to
enter a market they could not enter at lower prices. When prices are low, only
the most efficient firms can profitably participate in a market. When prices are
higher, firms with higher costs can also earn acceptable profits.
157
Supply curve
A graphic
depiction of how
much producers
are willing to sell
at different prices
10.1.2 Demand Curves
The demand curve (labeled D) describes how much consumers are willing
to buy at different prices. From another perspective, it describes how much
the marginal consumer (the one who would not make a purchase at a higher
price) is willing to pay at different levels of output. The demand curve in
Exhibit 10.1 slopes down, meaning that, for producers to sell more of a
product, its price must be cut. Such a sales increase might be the result of an
increase in the share of the population that buys a good or service, an increase
in consumption per purchaser, or some mix of the two.
Demand curve
A graphic
depiction of how
much consumers
are willing to buy
at different prices
10.1.3 Equilibrium
The demand and supply curves intersect at the equilibrium price and quantity. At the equilibrium price, the amount producers want to sell equals the
amount consumers want to buy. In Exhibit 10.1, consumers want to buy 60
units and producers want to sell 60 units when the price is $100.
Markets tend to move toward equilibrium points. If the price is above
the equilibrium price, producers will not meet their sales forecasts. Sometimes producers cut prices to sell more. Sometimes producers cut production.
Either strategy tends to equate supply and demand. Alternatively, if the price
is below the equilibrium price, consumers will quickly buy up the available
stock. To meet this shortage, producers may raise prices or produce more.
Either strategy tends to equate supply and demand.
Markets will not always be in equilibrium, especially if conditions
change quickly, but the incentive to move toward equilibrium is strong. Producers typically can change prices faster than they can increase or decrease
production. A high price today does not mean a high price tomorrow. Prices
are likely to fall as additional capacity becomes available. Likewise, a low price
today does not mean a low price tomorrow. Prices are likely to rise as capacity
decreases. We will explore this concept in more detail in our examination of
the effects of managed care on the incomes of primary care physicians.
Equilibrium price
Price at which
the quantity
demanded equals
the quantity
supplied (There
is no shortage or
surplus.)
Shortage
Situation in which
the quantity
demanded at
the prevailing
price exceeds
the quantity
supplied (The
best indication of
a shortage is that
prices are rising.)
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
10.1.4 Professional Advice and Imperfect Competition
Healthcare markets are complex. The influence of professional advice on consumer choices is a complication of particular concern. The assumption that
changes in supply will not affect consumers’ choices (i.e., demand) can be
misleading. If changes in factors that ought not to affect consumers’ choices
(such as providers’ financial arrangements with insurers) influence providers’
recommendations, a supply and demand analysis that does not take this effect
into account could be equally misleading. Even more important, few healthcare markets fit the model of a competitive market (i.e., a market with many
competitors who perceive they have little influence on the market price). We
must condition any analysis on the judgment that healthcare markets are competitive enough that conventional supply curves are useful guides. In markets
that are not competitive enough, producers’ responses to changes in market
conditions are likely to be more complex than supply curves suggest. This text
focuses on applications of demand and supply analysis in which neither providers’ influence on demand nor imperfect competition is likely to be a problem.
10.2 Demand and Supply Shifts
A movement along a demand curve is called a change in the quantity
demanded. In other words, a movement along a demand curve traces the link
between the price consumers are willing to pay and the quantity they demand.
Demand and supply analysis is most useful to healthcare managers in understanding how the equilibrium price and quantity will change in response to
shifts in demand or supply. With limited information, a working manager can
sketch the impact of a change in policy on the markets of most concern.
What factors might cause the demand curve to shift to the right (greater
demand at every price or higher prices for every quantity)? We need detailed
empirical work to verify the responses of demand to market conditions, but the
list of standard responses is short. Typically, a shift to the right results from an
increase in income, an increase in the price of a substitute (a good or service used
instead of the product in question), a decrease in the price of a complement (a
good or service used along with the product in question), or a change in tastes.
Economists often use mathematical notation to describe demand. Q =
D(P,Y ) is an example of this notation. It says that the quantity demanded
varies with prices (represented by P) and income (represented by Y ), which
means that quantity, the relevant prices, and income are systematically
related. A demand curve traces this relationship when income and all prices
other than the price of the product itself do not change.
What factors might cause the supply curve to shift to the right (greater
supply at every price or lower prices at every quantity)? Typically, a shift to
the right results from a reduction in the price of an input, an improvement
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in technology, or an easing of regulations. In mathematical notation, we can
describe supply as Q = S(P,W). Here, W represents the prices of inputs (the
factors such as labor, land, equipment, buildings, and supplies that a business
uses to produce its product). Unless technology or regulations are the focus
of an analysis, we do not make their role explicit.
Case 10.1
Worrying About Demand Shifts
“You know, this business is changing,” said Terry,
the business manager. “It used to be that an
administrator like me had to worry only about running a good nursing home and keeping an eye on the other nursing homes in town,
but these days we have more competitors than I can shake a stick
at. Some of the folks at Sunshine Assisted Living would have been
residents in our nursing home a few years ago. Not today, though. We
admit their residents only when they are getting close to needing total
care. Without changing offices, I feel like I switched from running a
nursing home to running a hospice. The thing that has me spooked,
though, is this new home health agency. It has billboards out on the
interstate with a picture of a senior citizen and a slogan that says,
‘Stay healthy. Stay active. Stay home.’ I’m worried that it will siphon off
a significant part of our residents. It’s just supply and demand.”
With that Terry jumped up, went over to the whiteboard, and drew
a simple graph (Exhibit 10.2). “Here’s where we are today. We have a
EXHIBIT 10.2
Terry’s Supply
and Demand
Graph
Demand
Price
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C hap ter 10 : Sup p ly and D em and A naly sis
Supply
Quantity
(continued)
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
Case 10.1
census of about 150, and we’re doing fine. But when
those home health folks are done with us, we’ll be
lucky to have a census of 100. I’m worried.”
“Whoa, partner,” interrupted Tracey, who handled marketing. “I
like the graph, but I don’t think a home health agency is going to have
that sort of impact on us. A 2007 study out of Brown University by Gruneir and colleagues did not find the impact you are describing. I just
don’t think many of our residents are candidates for home health services. By the time we see them, they need more care than most home
health agencies can offer.”
(continued)
Discussion questions:
• Exhibit 10.2 shows the current situation. What did Terry think the
graph would look like after the home health care agency entered
the market? What did Tracey think the new graph would look like?
• Over the next few years, what demographic changes seem likely to
shift the demand for nursing home care?
• What changes in the local market might cause the sort of shift in
demand that Terry is concerned about?
10.2.1 A Shift in Demand
Demand shift
A shift that
occurs when a
factor other than
the price of the
product itself
(e.g., consumer
incomes) changes
Supply shift
Shift that occurs
when a factor
(e.g., an input
price) other than
the price of the
product changes
We begin our demand and supply analyses by looking at a classical problem in
health economics: What will happen to the equilibrium price and quantity of
a product used by consumers if insurance expands? Insurance expands when
the insurance plan agrees to pay a larger share of the bill or the proportion
of the population with insurance increases. This sort of change in insurance
causes a demand shift (or shift in demand). As shown in Exhibit 10.3, the
entire demand curve rotates. As a result of this insurance expansion, the
equilibrium price rises from P1 to P2 and the equilibrium quantity rises from
Q 1 to Q 2. For example, as coverage for pharmaceuticals has become a part
of more Americans’ insurance, the prices and sales of prescription pharmaceuticals have risen.
10.2.2 A Shift in Supply
Exhibit 10.4 depicts a supply shift (or shift in supply). The supply curve
has contracted from S1 to S2. This shift means that at every price, producers
want to supply a smaller volume. Alternatively, it means that to produce each
volume, producers require a higher price. A change in regulations might
result in a shift like the one from S1 to S2. For example, suppose that state
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Price
P2
161
EXHIBIT 10.3
An Expansion of
Insurance
D2
S
D1
P1
Q2
Q1
Quantity
regulations mandated improved care planning and record keeping for nursing homes. Some nursing homes might close down, but the majority would
raise prices for private-pay patients to cover the increased cost of care. The
net effect would be an increase in the equilibrium price from P1 to P2 and
a reduction in the equilibrium quantity from Q1 to Q2. A manager should
be able to forecast this effect with no information other than the realization
that the demand for nursing home care is relatively inelastic (meaning that
the slope of the demand curve is steep) and that the regulation would shift
the supply curve inward.
EXHIBIT 10.4
A Supply Shift
S2
D
Price
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C hap ter 10 : Sup p ly and D em and A naly sis
S1
P2
P1
Q2 Q1
Quantity
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162
Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
The Supply of Physicians’ Services
Empirical analyses of supply usually find that an increase in earnings
results in higher volume (i.e., supply curves usually slope up). Rizzo
and Blumenthal (1994) found that young, male, self-employed physicians fit this pattern. A 1 percent increase in hourly earnings increased
annual practice hours by 0.23 percent. One reason that the response
was so muted is that an increase in hourly earnings also increases
total income, and having a higher income usually leads to a reduction
in hours. Confirming this supposition, these authors found that a 1
percent increase in income from all sources reduced annual hours by
0.26 percent. Many young physicians have spouses with high earning
potential, which also tends to reduce hours. A 1 percent increase in a
physician’s spouse’s income reduced annual hours by 0.02 percent. In
other words, the study found that change in either nonpractice income
or in a spouse’s earnings shifts the supply curve. So, both practice and
nonpractice earnings affected annual hours of work. As is usually the
case in labor supply analyses, both effects were relatively small.
The implication for managers is that financial incentives may have
modest effects on the decisions of higher-income workers. Managers
may need to emphasize the intrinsic rewards of work (or pay a lot to
change behavior).
Responses to changing market conditions depend on how much time
passes. A change in technology, such as the development of a new surgical
technique, initially will have little effect on supply. Over time, however, as
more surgeons become familiar with the technique, its impact on supply will
grow. Short-term supply and demand curves generally look different from
long-term supply and demand curves. The more time consumers and producers have to respond, the more their behavior changes.
10.3 Shortage and Surplus
A shortage exists when the quantity demanded at the prevailing price exceeds
the quantity supplied. In markets that are free to adjust, the price should rise
so that equilibrium is restored. At a higher price, less will be demanded, leaving a greater supply.
In some markets, though, prices cannot adjust, often because a public
or private insurer sets prices too low and consumers demand more than producers are willing to supply. Exhibit 10.5 depicts a shortage situation. The
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EXHIBIT 10.5
A Shortage
D
S
Price
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C hap ter 10 : Sup p ly and D em and A naly sis
P*
P2
Q S Q*
QD
Quantity
equilibrium price is P* and the equilibrium quantity is Q*, but the insurer
has set a price of P2, so consumers demand Q D and producers supply Q S.
Because the price cannot adjust, a shortage equal to Q D − Q S exists.
A surplus exists when the quantity supplied at the prevailing price
exceeds the quantity demanded. In markets that are free to adjust, the price
should fall so that equilibrium is restored. In some markets, prices are free to
fall but do so slowly. For example, in the 1990s, many hospitals had unfilled
hospital beds because the combination of managed care and new technology
reduced the demand for inpatient care. Over time, insurance companies used
this excess capacity to secure much lower rates (even though Medicare and
Medicaid rates remained unchanged), and enough hospitals closed or downsized to eliminate the excess capacity.
Case 10.2
Surplus
Situation in which
the quantity
supplied at
the prevailing
price exceeds
the quantity
demanded (The
best indication of
a surplus is that
prices are falling.)
How Large Will the Shortage of
Primary Care Physicians Be?
The Affordable Care Act (ACA) has increased the share of the population with health insurance. Most of the newly insured were reasonably
healthy and viewed health insurance as too expensive, given its likely
benefits. As a result, the ACA will primarily affect the demand for primary care services, and many anticipate a shortage of primary care
physicians (Robeznieks 2013).
(continued)
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
Case 10.2
Some other observers suggest that this concern is overblown (Auerbach et al. 2013). The production of primary care is changing in ways that
shift its supply. One change is the rapid expansion of patient-centered
medical homes, which emphasize a greater role for technology, physician assistants, and nurse practitioners. Another change is the growth
of nurse-managed clinics (of which MinuteClinic, discussed in Case 7.1,
is an example). Both of these innovations reduce the number of physicians needed to provide primary care for a population.
(continued)
Discussion questions:
• Set up a model of the demand and supply for primary care
physicians. (It should have salary on the vertical axis and number
of primary care physicians on the horizontal axis.) Assuming that
the production of primary care does not change (i.e., the supply
curve does not shift), how do you expect the market equilibrium to
change?
• How have the incomes of primary care physicians changed in the
last few years? Are these changes consistent with your prediction?
(You can get income data from Medscape Physician Compensation
Reports.)
• Are the changes in the incomes of primary care physicians
consistent with the prediction of a shortage? That is, have they
risen rapidly?
• If retail clinics and patient-centered medical homes continue to
expand, how will they affect the market equilibrium? Which curve
would shift as a result—demand or supply?
10.4 Analyses of Multiple Markets
Demand and supply models can also be helpful in forecasting the effects
of shifts in one market on the equilibrium in another. Such forecasts can
be made only if the markets are related—that is, the products need to be
complements or substitutes.
Spetz and colleagues (2006) provided an example of this effect. They
showed that the demand for licensed practical nurses (LPNs) increased as the
wages of registered nurses (RNs) rose (as a result of the shortage of RNs)
(see Exhibit 10.6). Increases in the wages of RNs shifted the demand curve
for LPNs from D1 to D2. As a result, employment of LPNs rose from Q 1 to
Q 2 and wages rose from W1 to W2.
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S2
D2
Price
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C hap ter 10 : Sup p ly and D em and A naly sis
165
EXHIBIT 10.6
Higher RN
Wages Shift
Demand for
LPNs
D1
W2
W1
Q1 Q2
Quantity
Source: Data from Spetz et al. (2006).
10.5 Conclusion
Supply and demand analysis can help managers anticipate the effects of
changes in policy, technology, or prices. Supply and demand analysis is a
valuable tool that managers can use to quickly anticipate the effects of shifts
in demand or supply curves. Short-term shifts in demand are likely to result
from one of two factors: changes in insurance or shifts in the prices or characteristics of substitutes or complements. Short-term shifts in supply are likely
to result from one of three factors: changes in regulations, shifts in the prices
or characteristics of inputs, or changes in technology.
Most demand curves slope down, which means that consumers will buy
more if prices are lower. It also means that consumers who are willing to purchase
a product only at a low price do not place a high value on it. In contrast, most
supply curves slope up, which means that higher prices will motivate producers
to sell additional output (or motivate more producers to sell the same output).
Exercises
10.1 Physicians’ offices supply some urgent care services (i.e., services
patients seek for prompt attention but not for preservation of life or
limb).
a. Name three other providers of urgent care services.
b. What sort of shift in supply or demand would result in a market
equilibrium with higher prices and sales volume?
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
c. What might cause such a shift?
d. What sort of shift in supply or demand would result in a market
equilibrium with higher prices but lower sales volume?
e. What might cause such a shift?
10.2 Suppose the market equilibrium price for immunizations is $40 and
the volume is 25,000.
a. Identify three providers of immunization services.
b. What sort of shift in supply or demand would reduce both prices
and sales volume?
c. What might cause such a shift?
d. What sort of shift in supply or demand would result in a market
equilibrium with a price above $40 and a volume below 25,000?
e. What might cause such a shift?
10.3 The table contains data on the number of doses of an antihistamine
sold per month in a small town.
Price
Demand
Supply
$10
185
208
$9
187
205
$8
188
202
$7
190
199
$6
191
196
$5
193
193
$4
194
190
$3
196
187
$2
197
184
$1
199
181
a. To sell 196 doses to customers, what will the price need to be?
b. For stores to be willing to sell 196 doses, what will the price
need to be?
c. How many doses will customers want to buy if the price is $2?
d. How many doses will suppliers want to sell if the price is $2?
e. Is there excess supply or excess demand at $2?
f. What is the equilibrium price? How can you tell?
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167
10.4 The table contains demand and supply data for eyeglasses in a local
market.
Price
Demand
Supply
$300
7,400
8,320
$290
7,480
8,200
$280
7,520
8,080
$270
7,600
7,960
$260
7,640
7,840
$250
7,720
7,720
$240
7,760
7,600
$230
7,840
7,480
$220
7,880
7,360
a. At $280, how many pairs will consumers want to buy?
b. How many pairs will consumers want to buy if the price is $290?
c. How many pairs will stores want to sell at $290?
d. Is $290 the equilibrium price?
e. Is there excess supply or excess demand at $290?
f. What is the equilibrium price? How can you tell?
10.5 The exhibit shows a basic demand and supply graph for home care
services. Identify the equilibrium price and quantity. Label them P*
and Q*.
Price
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C hap ter 10 : Sup p ly and D em and A naly sis
Supply
Demand
Quantity
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168
Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
a. Retirements drive up the wages of home care workers. How
would the graph change? How would P* and Q* change?
b. Improved technology lets home care workers monitor use of
medications without going to clients’ homes. How would the
graph change? How would P* and Q* change?
c. The number of people needing home care services increases.
How would the graph change? How would P* and Q* change?
d. A change in Medicare rules expands coverage for home care services.
How would the graph change? How would P* and Q* change?
10.6 The demand function is Q = 600 − P, with P being the price paid by
consumers. Put a list of prices ranging from $400 to $0 in a column
labeled P. (Use intervals of $50.)
a. Consumers have insurance with 40 percent coinsurance. For each
price, calculate the amount that consumers pay. (Put this figure
in a column labeled PNet.)
b. Calculate the quantity demanded when there is insurance. (Put
this figure in a column labeled DI.)
c. Plot the demand curve, putting P (not PNet) on the vertical axis.
d. The quantity supplied equals 2 × P. Put these values in a column
labeled S.
e. What is the equilibrium price?
f. How much do consumers spend?
g. How much does the insurer spend?
10.7 The demand function is Q = 1,000 − (0.5 × P ). P is the price paid
by consumers. Calculate the quantity demanded when there is no
insurance. (Put these values in column DU of the table.)
P
DU
PNet
DI
S
560
$176
912
952
$1,000
$960
$920
$880
$840
$800
$760
$720
$680
$640
$600
$560
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C hap ter 10 : Sup p ly and D em and A naly sis
10.8
10.9
10.10
10.11
169
a. The state mandates coverage with 20 percent coinsurance, meaning
that the demand function becomes 1,000 − (0.5 × 0.2 × P).
b. For each price, calculate the amount consumers pay. (Put this
figure in column PNet.)
c. Calculate the quantity demanded when there is insurance. (Put
this figure in column DI.)
d. Plot the two demand curves, putting P (not PNet) on the vertical
axis.
e. How do DU and DI differ? Which is more elastic?
The supply function for the product in Exercise 10.7 is 160 + (0.9 ×
P). P is the price received by the seller. At the equilibrium price, the
quantity demanded will equal the quantity supplied.
f. What was the equilibrium price before coverage? After?
g. After coverage begins, how much will the product cost insurers?
How much will the product cost patients? How much did
patients pay for the product before coverage started?
Consumers who can buy health insurance through an employer get a
tax subsidy. Use demand and supply analysis to assess how this subsidy
affects consumers who cannot buy insurance through an employer.
Why are price controls unlikely to make consumers better off if a
market is reasonably competitive?
Make the business case why healthcare providers should advocate for
expansion of insurance coverage for the poor.
References
Auerbach, D. I., P. G. Chen, M. W. Friedberg, R. Reid, C. Lau, P. I. Buerhaus, and
A. Mehrotra. 2013. “Nurse-Managed Health Centers and Patient-Centered
Medical Homes Could Mitigate Expected Primary Care Physician Shortage.”
Health Affairs 32 (11): 1933–41.
Gruneir, A., K. L. Lapane, S. C. Miller, and V. Mor. 2007. “Long-Term Care Market Competition and Nursing Home Dementia Special Care Units.” Medical
Care 45 (8): 739–45.
Rizzo, J. A., and D. Blumenthal. 1994. “Physician Labor Supply: Do Income Effects
Matter?” Journal of Health Economics 13 (4): 433–53.
Robeznieks, A. 2013. “What Doctor Shortage? Some Experts Say Changes in
Delivery Will Erase Need for More Physicians.” Modern Healthcare 43 (45):
14–15.
Spetz, J., W. T. Dyer, S. Chapman, and J. A. Seago. 2006. “Hospital Demand for
Licensed Practical Nurses.” Western Journal of Nursing Research 28 (6):
726–39.
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CHAPTER
FORECASTING
9
Learning Objectives
After reading this chapter, students will be able to
•
•
•
•
articulate the importance of a good sales forecast,
describe the attributes of a good sales forecast,
apply demand theory to forecasts, and
use simple forecasting tools appropriately.
Key Concepts
•
•
•
•
•
Making and interpreting forecasts are important jobs for managers.
Forecasts are planning tools, not rigid goals.
Sales and revenue forecasts are applications of demand theory.
Changes in demand conditions usually change forecasts.
Good forecasts should be easy to understand, easy to modify, accurate,
transparent, and precise.
• Forecasts combine history and judgment.
• Percentage adjustment, moving averages, and seasonalized regression
analysis are common forecasting methods.
• Assessing external factors is vital to forecasting.
9.1 Introduction
Making and interpreting forecasts are important jobs for managers. Sales
forecasts are especially important because many decisions hinge on what the
organization expects to sell. Pricing decisions, staffing decisions, product
launch decisions, and other crucial decisions are based on the organization’s
revenue and sales forecasts.
Inaccurate or misunderstood forecasts can hurt businesses. The organization can hire too many workers or too few. It can set prices too high or
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140
Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
too low. It can add too much equipment or too little. At best, these sorts of
forecasting problems will cut into profits; at worst, they may drive an organization out of business.
The consequences of bad or misapplied forecasts are particularly serious in healthcare. For example, underestimating the level of demand in the
short term may result in stock shortages at a pharmacy or too few nurses
on duty at a hospital. In both cases, the healthcare organization will suffer
financially and, more important, put patients at risk. It will suffer because the
costs of meeting unexpected demand are high and because the long-term
consequences of failing to meet patients’ needs are significant. The best outcome in this case will be unhappy patients; the worst outcome will be that
physicians stop referring patients to the organization.
Overestimating sales can also have serious long-term effects. A hospital
may add too many beds because its census forecast was too high. This surplus
will depress profits for some time because the facility will have hired staff and
added equipment to meet its overestimated forecast, and the costs of hiring and paying new employees and buying new equipment will substantially
exceed actual sales profits. In extreme cases, bad forecasts may drive a firm
out of business. A facility that borrows heavily in anticipation of higher sales
that do not materialize may be unable to repay those debts. Bankruptcy may
be the only option.
Sales and revenue forecasts are applications of demand theory. The factors that change sales and revenues also change demand. The most important
influences on demand are the price of the product, rivals’ prices for the product, prices for complements and substitutes, and demographics. Recognizing
these influences can simplify forecasting considerably because it focuses our
attention on tracking what has changed.
9.2 What Is a Sales Forecast?
A sales forecast is a projection of the number of units (e.g., bed days, visits,
doses) an organization expects to sell. The forecast must specify the time
frame, marketing plan, and expected market conditions for which it is valid.
A forecast is a planning tool, not a rigid goal. Conditions may change.
If they do, the organization’s plan needs to be reassessed. Good management
usually involves responding effectively to changes in the environment, not
forging ahead as though nothing has shifted. In addition, fixed sales goals
create incentives to behave opportunistically (that is, for employees to try
to meet their goals instead of the organization’s goals). For example, sales
staff may harm the organization by making overblown claims of a product’s
effectiveness to meet their sales goals, even though their actions will harm the
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C hap ter 9 : Forec asting
141
company in the long run. Alternatively, sales managers may bid on unprofitable managed care contracts just to meet goals.
Whenever possible, a sales forecast should estimate the number of
units expected to be sold, not revenues. The number of units to be sold
determines staffing, materials, working capital, and other needs. In addition,
costs often vary unevenly with volume. A small reduction in sales volume may
save an entire shift’s worth of wages (thereby avoiding considerable cost), or
an increase in sales may incur an insignificant cost increase if it requires no
additional staff or equipment.
Case 9.1
Building a New Urgent Care Center
“It’s a slam dunk,” said Kim, the marketing analyst. “The volumes we’ve forecasted ensure that
the new urgent care center will be profitable within six months.”
“Great,” replied Angel, vice president of strategic management,
“but I think it would be useful to walk through those numbers to put
everyone at ease.”
“OK, here’s how we forecasted visits,” said Kim. “There are 40,000
people living in our primary market area. National rates suggest that
a population of this size will make 6,000 urgent care visits each year.
Right now, our emergency department sees 2,500 urgent—but not
emergent—visits each year. We believe that 1,500 of them will come
to the urgent care center. Our seat-of-the-pants estimate is that Providence, the other hospital serving our primary market area, sees 2,000
urgent care patients per year in its emergency department. We expect
to get half of those visits. We also expect that the added convenience
of the urgent care center will bring in an additional 500 visits each
year. So, 3,000 visits per year, each yielding revenue of $125, give us
$375,000 in total revenue. We have fixed costs of $200,000. Our best
estimate is that each visit has variable costs of $20, so we’re talking
profits of $115,000, for a margin on sales of 30 percent.”
“That’s nice and clear,” said Angel, “but I’d like to take a closer
look. About half of the patients Providence sees would have to drive
past Providence to get to our urgent care center. Do we have any indication that those folks will do that? My second concern is that our
emergency department is open 24/7. The urgent care center will be
open 82 hours per week. Can we really hope to capture 60 percent of
the emergency department’s urgent care patients?”
(continued)
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142
Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
Case 9.1
Discussion questions:
• What happens to profits if the urgent care
center has only 2,000 visits?
• To what extent does Kim’s forecast rely on judgment rather than
data? Would additional information help resolve Angel’s concerns?
What sort of data would you suggest gathering?
• Does building the urgent care center seem risky? Could you do
anything to reduce the amount of risk?
• Would there be any advantages to planning for a small patient
volume and letting your customers surprise you? Suppose you plan
for 2,000 visits but volume turns out to be 3,000. What happens?
Would underestimating the volume be better or worse than
planning for 3,000 and getting only 2,000?
(continued)
The dollar volume of sales can vary in response to factors that do not
affect the resources needed to produce, market, or service sales. Discounts
and price increases are examples of such factors. Revenues can vary even
though neither volume nor costs change. Finally, managers can easily forecast
revenue given a volume forecast. In general, managers should build their
revenue estimates on sales volume estimates.
Good forecasts have five attributes. They should be
1.
2.
3.
4.
5.
easy to understand,
easy to modify,
accurate (i.e., they contain the most probable actual values),
transparent about how variable they are, and
precise (i.e., they give the analyst as little wiggle room as possible).
These attributes often conflict. Managers may need to underplay how
imprecise simple forecasts are because their audience is not prepared to consider variation. As Aven (2013) points out, many decision makers are more
comfortable working with a single, very precise estimate, even though it may
be inaccurate. Precision and accuracy always conflict because a more precise
forecast (80 to 85 visits per day) will always be less accurate than a less precise
forecast (70 to 95 visits per day). Offering decision makers several precise
scenarios is usually a good compromise. For example, busy decision makers
generally can use a forecast such as “Our baseline forecast is 82 visits per day
for the next three months; our low forecast is 75 visits per day, and our high
forecast is 89 visits per day.”
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C hap ter 9 : Forec asting
143
9.3 Forecasting
All forecasts combine history and judgment. History is the only real source
of data. For example, sales can be forecasted only on the basis of data on past
sales of a product, past sales of similar products, past sales by rivals, or past
sales in other markets. History is an imperfect guide to the future, but it is
an essential starting point.
Judgment is also essential. It provides a basis for deciding what data
to use, how to use the data, and what statistical techniques, if any, to use.
In many cases (such as introductions of new products or new competitive
situations), managers who have insufficient data have to base their forecasts
mainly on judgment.
As mentioned in Section 9.2, a forecast must specify the time frame,
marketing plan, and expected market conditions for which it is valid. Changes
in any of these factors will change the forecast.
A forecast applies to a given period. Extrapolating to a longer or
shorter period is risky; conditions may change. The time frame varies according to the forecast’s use. For example, a staffing plan may need a forecast
for only the next few weeks. Additional staff can be hired over a longer time
horizon. In contrast, budget plans usually need a forecast for the coming
year. Organizations usually set their budgets a year in advance on the basis
of projected sales. Strategic plans usually need a forecast for the next several
years. Longer forecasts are generally less detailed and less reliable, but managers know to take these factors into account when they develop and use them.
Forecasts should be as short term as possible. A forecast for next
month’s sales will usually be more accurate than forecasts for the distant
future, which are likely to be less accurate because important facts will have
changed. Your competitors today are likely to be your competitors in a month.
Your competitors in two years are likely to be different from your competitors
today, so a forecast based on current market conditions will be poor.
Marketing plan changes will influence the forecast. A clinic that
increases its advertising expects visits to increase. A forecast that does not
consider this increase will usually be inaccurate. Increasing discounts to pharmacy benefits managers should result in increased sales for a pharmaceutical
firm. Again, a forecast that does not account for additional discounts will usually be deficient. Any major changes in an organization’s marketing efforts
should change forecasts. If they do not, the organization should reassess the
usefulness of its marketing initiatives.
Changes in market conditions also influence forecasts. For example, a
major plant closing would probably reduce a local plastic surgeon’s volume.
Plant employees who had intended to undergo plastic surgery may opt to
delay this elective procedure, and prospective patients who work for similar
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144
Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
Percentage
adjustment
Percentage
adjustment of the
past n periods of
historic demand
(The adjustment
is essentially a
best guess of what
is expected to
happen in the next
year.)
Moving average
The unweighted
mean of the
previous n data
points
Seasonalized
regression
analysis
A least squares
regression that
includes variables
that identify
subperiods (e.g.,
weeks) that
historically have
had above- or
below-trend sales
plants may defer discretionary spending in fear that they too may lose their
jobs. Alternatively, a hospital closure will probably cause a competing hospital
to forecast more inpatient days. Historical data have limited value in projecting such an effect if a similar closure has not occurred in the past. Approval
of a new drug by the Food and Drug Administration should cause a pharmaceutical firm to forecast a decrease in sales for its competing product. This
sort of change in market conditions is familiar, and the firm’s marketing staff
will probably draw on experience to predict the loss.
Analysts routinely use three forecasting methods: percentage adjustment, moving averages, and seasonalized regression analysis. If the
data are adequate and the market has not changed too much, seasonalized
regression analysis is the preferred method. However, whether the data are
adequate and whether the market has changed too much are judgment calls.
Percentage adjustment increases or decreases the last period’s sales volume by a percentage the analyst deems sensible. For example, if a hospital had
an average daily census of 100 the previous quarter, and an analyst expects the
census to fall an average of 1 percent per quarter, a reasonable forecast would
be a census of 99. Because of its simplicity, managers often use percentage
adjustment; however, this simplicity is also a shortcoming. In principle, a manager can use any percentage adjustment that he or she wants to. Without some
requirement that percentage adjustments be well justified, this approach may
not yield accurate forecasts. For example, a manager might justify a request
for a new position based on a forecast that average daily census will increase
by 5 percent, even though the average daily census had been falling for the last
14 quarters. In addition, percentage adjustment does not allow for seasonal
effects. (Seasonal effects are systematic tendencies for particular days, weeks,
months, or quarters to have above- or below-average volume.)
Demand theory can be used to add rigor to percentage adjustment.
For example, if the price of a product has changed, an estimate of the percentage change in sales can be calculated by multiplying the percentage
change in price by the price elasticity of demand. So, if an organization has
chosen to raise prices by 3 percent and faces a price elasticity of demand of
−4, sales will drop by 12 percent. Similar calculations can be used if the price
of a substitute, the price of a complement, or consumer income has changed.
The moving-average method uses the average of data from recent
periods to forecast sales. This method works well for short-term forecasts,
although it tends to hide emerging trends and seasonal effects. Exhibit 9.1
shows census data and a one-year moving average for a sample hospital.
Exhibit 9.1 also illustrates the calculation of a seasonalized regression
format. Excel was used to estimate a regression model with a trend (a variable that increases in value as time passes) and three quarter indicators. The
variable Q 1 has a value of one if the data are from the first quarter; otherwise,
its value is zero. Q 2 equals one if the data are from the second quarter, and
Q 3 equals one if the data are from the third quarter. For technical reasons,
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C hap ter 9 : Forec asting
Quarter
Census
Moving Average
First
Second
Third
Trend
1
99
1
0
0
1
2
109
0
1
0
2
3
101
0
0
1
3
4
107
0
0
0
4
5
104
104.0
1
0
0
5
6
116
105.3
0
1
0
6
7
100
107.0
0
0
1
7
8
106
106.8
0
0
0
8
9
103
106.5
1
0
0
9
10
107
106.3
0
1
0
10
11
90
104.0
0
0
1
11
12
105
101.5
0
0
0
12
13
102
101.3
1
0
0
13
14
94
101.0
0
1
0
14
15
98
97.8
0
0
1
15
16
104
99.8
0
0
0
16
17
99
99.5
1
0
0
17
18
105
98.8
0
1
0
18
19
94
101.5
0
0
1
19
20
102
100.5
0
0
0
20
21
100
100.0
1
0
0
21
22
145
EXHIBIT 9.1
Census Data
for a Sample
Hospital
100.3
Seasonalized Regression Model
Coefficient
t-statistic
108.811
40.90
R2 = 0.55
−3.968
−1.53
F(4,20) = 4.98
0.732
0.27
p = 0.01
Third quarter
−8.534
−3.16
Trend
−0.334
−2.16
Intercept
First quarter
Second quarter
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146
Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
Mean absolute
deviation
The average
absolute
difference between
a forecast and the
actual value (It is
absolute because
it converts both 9
and −9 to 9. The
Excel function
ABS( ) performs
this conversion.)
EXHIBIT 9.2
An Overview of
the Forecasting
Process
the average response in the fourth quarter is represented by the constant.
A negative regression coefficient for trend indicates that the census is in a
downward trend. The results also show that the typical third-quarter census
is smaller than average because the coefficient for Q 3 is large, negative, and
statistically significant.
The forecast based on seasonalized regression analysis is calculated as
follows: 108.811 + (−0.334 × 22) + 0.732. Here, 108.811 is the estimate of
the constant, −0.334 is the estimate of the trend coefficient, 22 is the quarter
to which the forecast applies, and 0.732 is the estimate of the Q 2 coefficient.
Therefore, the seasonalized forecast is 102.2, slightly higher than the forecast
based on the moving average. Overall the seasonalized forecast is a little more
accurate than the one-year moving average. The mean absolute deviation
for the regression is 2.3 for periods 5 through 21, and the mean absolute
deviation for the moving average is 4.0.
Exhibit 9.2 shows an overview of the forecasting process. The main
message of this exhibit is that a forecast is one part of the overall product
Assess internal and external factors.
Develop an initial forecast.
Develop an initial marketing strategy and then modify the forecast
and marketing strategy until they are consistent.
Monitor sales, internal factors, external factors,
and the marketing strategy.
Modify the forecast and marketing strategies as needed.
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C hap ter 9 : Forec asting
147
management process. In addition, the forecast will change as managers’
assessments of relevant internal factors (e.g., cost and quality), external factors (e.g., the competitive environment and reimbursement levels), and the
marketing plan change.
Simple Forecasting Techniques
A naïve forecast uses the value for the last period as the forecast for
the next period—in other words, a 0 percent adjustment forecast.
Exhibit 9.3 shows an example of a naïve forecast. A moving-average
forecast uses the average of the last n values, where n is the number
of preceding values used in the forecast. For example, the first entry
in the Two-Period Moving Average column in Exhibit 9.3 equals (189 +
217) ÷ 2, or 203.
Month
Sales
Naïve
Forecast
Two-Period MovingAverage Forecast
February
189
March
217
189
April
211
217
203
May
239
211
214
June
234
239
225
July
243
234
236.5
EXHIBIT 9.3
Naïve and
Moving-Average
Forecasting
Techniques
To compare forecasting techniques, analysts sometimes use the
mean absolute deviation, which is the average of the forecast’s absolute deviations from the actual value. (When using the absolute deviation, it doesn’t matter if a value is higher or lower than the actual value;
all the deviations are positive numbers.) For April through July, the
naïve forecast above has a mean absolute deviation of 12.0, and the
two-period moving-average forecast has a mean absolute deviation of
12.1. From this perspective, the naïve forecast performs a little better.
These (and other) mechanistic forecasting methods do not allow
managers to explore how changes in the environment are likely to
affect sales. How would changes in insurance coverage change sales?
Naïve forecasts and moving-average forecasts are little help in such
situations.
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
9.4 What Matters?
Assessment of external factors (i.e., factors beyond the organization’s control) is vital to forecasting. General economic conditions are a prime example.
Expected inflation and interest rates are good indicators of the state of the
economy. Local market conditions, such as business rents and local wages,
also play an important role.
Government actions also can have a major impact on healthcare firms.
For example, changes in Medicare rates affect most healthcare firms. Alternatively, regulations can have a significant effect on costs. Expansion of Medicaid eligibility can have major effects on some hospitals and minor effects
on others. These sorts of changes will also affect most of your competitors,
but forecasters would be ill advised to ignore changes in government policy.
The plans of key competitors must also be considered. Closure of a
competing clinic or hospital can increase volume significantly and quickly.
Introduction of a generic drug can have a dramatic effect on a pharmaceutical manufacturer. Changes in competitors’ pricing policies can have a major
impact on sales.
Technological change is always an important issue. If a rival gains a
technological advantage, your sales can drop sharply. For example, if a rival
introduces minimally invasive coronary artery bypass graft surgery, admissions to your cardiac unit will probably drop significantly until you adopt
similar technology. In other cases, your own advances may affect sales of
substitute products. For example, introduction of highly reliable MRI may
sharply reduce the demand for conventional colonoscopy. Keep in mind,
however, that if you don’t introduce technologies that add value for your customers, someone else will. A decision not to introduce an attractive product
because it will cannibalize sales is usually a mistake.
Finally, although markets usually change slowly, differences in general
market characteristics (e.g., median income and percentage with insurance
coverage) may be important in forecasting sales of a new product.
Developing a Five-Year Forecast
Beech (2001) shows how to develop a five-year forecast for a hospital’s
strategic financial plan. He begins the analysis by defining the hospital’s service area and then estimating how the population of the service area will change during the next five years in terms of gender and
(continued)
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C hap ter 9 : Forec asting
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(continued)
age. Next, Beech uses several data sources to forecast admission rates
and length of stay for the services used by these population groups.
(The hospital characterizes these services as medical and surgical,
obstetrics and gynecology, pediatric, and psychiatric.) He then uses
the hospital’s own data and his estimates of overall admission rates
and length of stay to calculate the hospital’s market share for medical
and surgical services, obstetric and gynecological services, pediatric
services, and psychiatric services.
On the basis of these market share estimates, Beech develops four
demand forecasts. His baseline forecast predicts that the hospital’s
market share and overall utilization will not change. His decreased utilization forecast predicts that overall days of care will drop by 10 percent but that the hospital will maintain its market share. His decreased
market share forecast predicts that the hospital’s market share will
fall by 2 percent, and his increased market share forecast predicts
that the hospital’s market share will rise by 2 percent. (The decreased
market share and increased market share forecasts predict that utilization rates will not change.) History suggests that Beech’s decreased
utilization forecast is most likely to occur. To the relief of the hospital’s
management, the area’s population growth will offset most of the drop
in days of care per thousand residents, so overall days of care will drop
by less than 1 percent. However, this scenario implies that pediatric
days will drop by more than 7 percent, so the hospital will need to look
carefully at costs in this service line.
Assessment of internal factors (i.e., factors within an organization’s
control) is also vital to forecasting. For example, existing production may
limit sales, or may have limited sales in the past. If so, changes in capacity or
productivity need to be considered. Changes in the availability of resources
and personnel can also have a powerful effect on sales. For many healthcare
organizations, the entry or exit of a key physician can dramatically shape volume. In addition, changes in the size, support, composition, and organization of the sales staff can affect sales dramatically. For instance, a small drug
firm may experience a large increase in sales if one of its products is marketed
by a larger firm’s sales staff.
Failures or improvements in key systems can also have dramatic effects
on sales. Breakdowns in a clinic’s phone or scheduling system may drive away
potential customers. Fixing the phone system, in contrast, might be the most
effective marketing campaign the clinic ever launched.
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
Case 9.2
Forecasting the Demand for
Transfusions
“Our blood inventories are shrinking,” said Drew, the director of transfusion services, “and I’m worried that we might not be able to supply
our customers if there’s a surge in demand. We need to do something.
And it needs to be smart, because the shelf life of blood is only six
weeks. Simply collecting more may not solve our problem.”
At this point, Kim, the marketing analyst, chimed in, “We need
to do more than just predict our annual volume. We need a monthly
forecast. That way we can target our drives so that we collect enough
blood shortly before we expect to use it. Less blood will expire, and we
will only need to address unexpected spikes in use.”
“The good news,” said Taylor, the assistant director of transfusion services, “is that we have monthly data for the last six years. We
should be able to use them. I know that in 2004 Dr. Pereira tested a
number of time-series models and suggested several that work reasonably well. I think he found some clear seasonal effects.”
Discussion questions:
• What sort of model would you recommend to predict the demand
for blood? What would you do with your predictions?
• Why would the presence of seasonal effects be important?
• Taylor suggested using a statistical model to forecast demand.
What judgment does a statistical model require?
• What sorts of changes in the environment would you need to
account for in your forecasting model?
9.5 Conclusion
Making and interpreting forecasts are important tasks for healthcare managers. Not only are most crucial decisions based on sales forecasts, but also the
consequences of overestimating or underestimating demand can be catastrophic. Overestimating demand can put the financial future of an organization at risk, whereas underestimating demand can compromise the care of
patients and harm the organization’s reputation.
Analysts should apply demand theory to their sales forecasts to better
recognize changes. Demand theory limits what analysts need to consider: the
price of the product, the price of substitutes, and the price of complements.
The key idea of demand theory is that the out-of-pocket price drives most
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C hap ter 9 : Forec asting
151
consumer demand. The amount the consumer has to pay depends largely on
the terms of his or her insurance contract. Is the product covered? What is
the required copayment? Changes in the answers to these two questions can
shift sales sharply. The same concerns affect the prices of substitutes. The
most important substitutes are similar products offered by rivals, but other
products that meet some of the same needs should also be considered.
Demographic factors are important. Population size, income per
capita, the age distribution of the population, the ethnic makeup of the
population, and the insurance coverage of the population are some examples.
Although vital, demographic factors tend to be stable in the short term.
Demographics are much more important in long-range forecasts.
“Forecasting is hard, especially when it involves the future.” This old
saying reveals a core truth about forecasting: You often will be wrong. Knowing that, a shrewd manager will make decisions that can be modified as conditions change. The shrewd manager will also know which data are likely to be
the most problematic or most variable and will monitor those data carefully.
Management decisions require sales forecasts. Off-the-cuff forecasts
often fail to consider key factors and can lead to risky decisions. Imperfect forecasts can be used to make decisions as long as you recognize that
your predictions will sometimes be wrong and you structure your decisions
accordingly.
Exercises
9.1 The table lists visits for the four clinics operated by your system. You
anticipate that volumes will increase by 4 percent next year. Forecast
the number of visits for each clinic, and explain what assumptions
underlie your forecasts. For example, are you sure that all of the
clinics can serve additional clients?
Period
Clinic 1
Clinic 2
Clinic 3
Clinic 4
Total
This year
16,640
41,600
24,960
33,280
116,480
Next year
?
?
?
?
121,139
9.2 Your data for the clinics in Exercise 9.1 suggest that Clinic 2 is
operating at capacity and is highly efficient. Its output is unlikely to
increase. Furthermore, Clinic 4 has unused capacity but is unlikely
to attract additional patients. How would these facts change your
answer to Exercise 9.1? Continue to assume that overall volume will
rise to 121,139.
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152
Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
9.3 You estimate that the price elasticity of demand for clinic visits
is −0.25. You anticipate that a major insurer will increase the
copayment from $20 to $25. This insurer covers 40,000 of your
patients, and those patients average 2.5 visits per year. What is your
forecast of the change in the number of visits?
9.4 A major employer has just added health insurance coverage for its
employees. Consequently, 5,000 of your patients will pay a $30
copayment rather than the list price of $100 per visit. These patients
average 2.2 visits per year. You believe the price elasticity of demand
is between −0.15 and −0.35. What is your forecast of the change in
the number of visits?
9.5 The table shows data on asthma-related visits. Is there evidence that
these visits vary by quarter? Can you detect a trend? A powerful test
would be to run a multiple regression in Excel. If the function is
already loaded, you will find it in Data > Data Analysis > Regression.
If not, get help in adding the Analysis Tool Pak. To test for
quarterly differences, create a variable called Q1 that equals 1 if the
data are for the first quarter and 0 otherwise, a variable called Q2
that equals 1 if the data are for the second quarter and 0 otherwise,
and a variable called Q4 that equals 1 if the data are for the fourth
quarter and 0 otherwise. (Because you will accept the default, which
is to have a constant term in your regression equation, do not
include an indicator variable for quarter 3.) Also create a variable
called Trend that increases by 1 each quarter.
Year
Q1
Q2
2001
Q3
Q4
1,513
1,060
2002
1,431
1,123
994
679
2003
1,485
886
1,256
975
2004
1,256
1,156
1,163
1,062
2005
1,200
1,072
1,563
531
2006
1,022
1,169
9.6 Your marketing department estimates that Medicare urology visits
equal 5 − (1.0 × C) + (−6.5 × TO) + (5 × TR) + (0.01 × Y ). Here,
C denotes the Medicare copayment (now $20), TO is waiting
time in your clinic (now 30 minutes), TR is waiting time in your
competitor’s clinic (now 40 minutes), and Y is per capita income
(now $40,000).
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C hap ter 9 : Forec asting
153
a. How many visits do you anticipate?
b. Medicare’s allowed fee is $120. What revenue do you anticipate?
c. What might change your forecast of visits and revenue?
9.7 Because of fluctuations in insurance coverage, the average price paid
out of pocket (P) by patients of an urgent care center varied, as the
table shows. The number of visits per month (Q ) also varied, and
an analyst believes the two are related. The analyst also thinks the
data show a trend. Run a regression of Q on P and Period to test
these hypotheses. Then use the estimated parameters a, b, and c
and the values of Month and P to predict Q (number of visits). The
prediction equation is Q = a + (b × Month) + (c × P).
Month
1
2
3
P
$21 $18
Q
193 197 256
4
5
$15 $24 $18
179
231
6
7
$21 $18
8
9
10
11
$15 $20 $19 $24
214 247 273 223 225
198
12
$20
211
9.8 Use the data in Exercise 9.7 to answer these questions:
a. Calculate the naïve estimator, which is Q t = Q t − 1.
b. Calculate the two-period moving-average forecast.
c. Calculate the mean absolute deviation for the regression forecast,
the naïve forecast, and the two-period moving-average forecast.
d. Which forecast seems to perform the best? Why?
9.9 Sales data are displayed in the table.
Month
Sales
Month
Sales
February
224
January
260
March
217
February
284
April
211
March
280
May
239
April
271
June
234
May
302
July
243
June
286
August
238
July
297
September
243
August
301
October
251
September
309
November
259
October
314
December
270
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154
Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
a. Calculate the naïve estimator, which is Salest = Salest − 1.
b. Calculate the two-period and three-period moving averages.
c. Calculate the mean absolute deviation for each of the forecasting
methods.
9.10 A pharmaceutical company produces a sinus medicine. Monthly sales
(in thousands of doses) for the past three years are shown in the
table.
Jan
Feb
Mar
Apr
May
June
July
586 2,260 2,232
Aug
Sept
8,018
9,384
6,788 8,020
1,848
410
9,136
7,420
3,350
1,998
1,972
3,572 4,506 10,474 13,358
9,628
7,826
3,528
2,126
2,070
3,762
4,754
Oct
Nov
6,916 5,698
8,232
Dec
6,940
8,218 10,248
11,010 14,040 8,646 8,634 10,782
a. Develop a regression model that allows for trend and seasonal
components. Obtain the Excel output for this model.
b. Calculate a two-period moving-average forecast.
c. Compare the mean absolute deviations for these approaches.
d. Use one of these models to forecast sales for each month of year 3.
References
Aven, T. 2013. “On How to Deal with Deep Uncertainties in a Risk Assessment and
Management Context.” Risk Analysis 33 (12): 2082–91.
Beech, A. J. 2001. “Market-Based Demand Forecasting Promotes Informed Strategic
Financial Planning.” Healthcare Financial Management 55 (11): 46–56.
Pereira, A. 2004. “Performance of Time-Series Methods in Forecasting the Demand
for Red Blood Cell Transfusion.” Transfusion 44 (5): 739–46.
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CHAPTER
THE DEMAND FOR HEALTHCARE PRODUCTS
7
Learning Objectives
After reading this chapter, students will be able to
calculate sales and revenue using simple models,
discuss the importance of demand in management decision making,
articulate why consumer demand is an important topic in healthcare,
apply demand theory to anticipate the effects of a policy change,
use standard terminology to describe the demand for healthcare
products, and
• discuss the factors that influence demand.
•
•
•
•
•
Key Concepts
• The quantity demanded is the amount of a good or service purchased
at a specific price when all other factors are held constant.
• When a product’s price rises, the quantity demanded usually falls.
• Demand (a demand curve) describes the amounts of a good or service
that will be purchased at different prices when all other factors are held
constant.
• An increase or decrease in demand reflects a shift in the entire list of
amounts purchased at different prices. An increase or a decrease in
demand results when another factor that influences consumer decisions
changes.
• Other factors that influence demand for healthcare products include
consumer income, insurance coverage, perceptions of health status,
perceptions of the productivity of goods and services, prices of other
goods and services, and tastes.
• The amount of money a consumer pays for a good or service is called
the out-of-pocket price of that good or service.
• Because of insurance, the total price and the out-of-pocket price can
differ quite a bit.
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110
Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
• A complement is a good or service that is used in conjunction with
another good or service. Demand for a good falls if the price of a
complement increases.
• A substitute is a good or service used instead of another good or
service. Demand for a good rises if a substitute increases in price.
7.1 Introduction
Demand
The amounts
of a good or
service that will
be purchased at
different prices
when all other
factors are held
constant
Demand is one of the central ideas of economics. It underpins many of the
contributions of economics to public and private decision making. Analyses
of demand tell us that human wants are seldom absolute. More often they
are conditioned by questions: “Is it really worth it?” “Is its value greater than
its cost?” These questions are central to understanding healthcare economics.
Demand forecasts are essential to management. Most managerial decisions are based on revenue projections. Revenue projections in turn depend on
estimates of sales volume, given prices that managers set. A volume estimate is
an application of demand theory. An understanding of the relationship between
price and quantity must be part of every manager’s tool kit. On an even more
fundamental level, demand forecasts help managers decide whether to produce
a certain product at all and how much to charge. Suppose you conclude that
the direct costs of providing therapeutic massage are $48 and that you will need
to charge at least $75 to cover other costs and offer an attractive profit margin.
Will you have enough customers to make this service a sensible addition to your
product line? Demand analyses are designed to answer such questions.
7.1.1 Rationing
Market system
A system that uses
prices to ration
goods and services
Quantity
demanded
The amount of a
good or service
that will be
purchased at a
specific price when
all other factors
are held constant
On an abstract level, we need to ration goods and services (including medical
goods and services) somehow. Human wants are infinite, or nearly so. Our
capacity to satisfy those wants is finite. We must develop a system for determining which wants will be satisfied and which will not. Market systems use
prices to ration goods and services. A price system costs relatively little to
operate, is usually self-correcting (e.g., prices fall when the quantity supplied
exceeds the quantity demanded, which tends to restore balance), and allows
individuals with different wants to make different choices. These advantages
are important. The problem is that markets work by limiting the choices of
some consumers. As a result, even if the market process is fair, the market
outcome may seem unfair. Wealthy societies typically view exclusion of some
consumers from valuable medical services, perhaps because of low income or
perhaps because of previous catastrophic medical expenses, as unacceptable.
The implications of demand are not limited to market-oriented systems. Demand theory predicts that if care is not rationed by price, it will be
rationed by other means, such as waiting times, which are often inconvenient
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C h a p ter 7 : The D em and for H ealthc are Produc ts
111
for consumers. In addition, careful analyses of consumer use of services have
convinced most analysts that medical goods and services should not be free.
If care were truly costless for consumers, they would use it until it offered
them no additional value. Today this understanding is reflected in the public
and private insurance plans of most nations.
Value-Based Insurance
Consumers buy more when prices drop. Insurance plans have long
used this principle to keep spending down. Unfortunately, consumers
are as likely to reduce use of highly effective products as they are to
reduce use of fairly ineffective products. This result is even more apt
to be true if the benefits of a service are subtle or accrue over time. For
example, taking a drug called a statin can lower your cholesterol and
may reduce plaque in arteries, which can lower the risk of heart attack.
However, you do not feel any better if you take a statin, and you do not
feel any worse if you stop. Adherence, which involves getting people to
fill their prescriptions for statins and take their medicine, is not easy.
From an insurance perspective, however, improving adherence can
reduce the odds of a very expensive heart attack.
Value-based insurance sharply reduces the prices that consumers pay for effective treatments, hoping to improve adherence. At Pitney Bowes, statin adherence for a group of high-risk employees had
steadily declined to about 71 percent. To try to increase adherence,
Pitney Bowes reduced copayments from more than $24 per month to
less than $1 (Choudhry et al. 2010). It worked.
This study and others suggest that insurers can use copayments to
steer consumers to high-value services. It seems fairly clear that doing
so can improve health outcomes. It is less clear whether value-based
insurance can reduce spending.
Care cannot really be free. Someone must pay, somehow. Modern
healthcare requires the services of highly skilled professionals, complex and
elaborate equipment, and specialized supplies. Even the resources for which
there is no charge represent a cost to someone.
7.1.2 Indirect Payments and Insurance
Because the burden of healthcare costs falls primarily on an unfortunate few,
health insurance is common. Insurance creates another use for demand analyses. To design sensible insurance plans, we need to understand the public’s valuation of services. Insurance plans seek to identify benefits the public is willing
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112
Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
Out-of-pocket
price
The amount of
money a consumer
pays for a good or
service
to pay for. The public may pay directly (through the out-of-pocket price) or
indirectly. Indirect payments can take the form of health insurance premiums,
taxes, wage reductions, or higher prices for other products. Understanding
the public’s valuation is especially important in the healthcare sector because
indirect payments are so common. When consumers pay directly, valuation is
not very important (except for making revenue forecasts). Right or wrong, a
consumer who refuses to buy a $7.50 bottle of aspirin from an airport vendor
because it is “too expensive” is making a clear statement about value. In contrast, a Medicare patient who thinks coronary artery bypass graft surgery is a
good buy at a cost of $1,000 is not providing us with useful information. The
surgery costs more than $30,000, but the patient and taxpayers pay most of
the bill indirectly. Because consumers purchase so much medical care indirectly,
with the assistance of public or private insurance, assessing whether the values
of goods and services are as large as their costs is often difficult.
7.2 Why Demand for Healthcare Is Complex
The demand for medical care is more complex than the demand for many
other goods for four reasons.
1. The price of care often depends on insurance coverage. Insurance has
powerful effects on demand and makes analysis more complex.
2. Healthcare decisions are typically quite perplexing. Consumers would
prefer to be healthy and use no medical services. Medical services have
value largely because of their impact on health. The links between
medical care and health outcomes are often difficult to ascertain at the
population level (where the average impact of care is what matters)
and stunningly complex at the individual level (where what happens to
oneself is what matters). Forced to make hard choices, consumers may
make bad choices.
3. This complexity contributes to consumers’ poor information about
costs and benefits of care. Such “rational ignorance” is natural. Because
most consumers will not have to make most healthcare choices, it
makes no sense for them to be prepared to do so.
4. The net effect of complexity and consumer ignorance is that producers
have significant influence on demand. Consumers naturally turn to
healthcare professionals for advice. Unfortunately, because they are
human, professionals’ choices are likely to reflect their values and
incentives as well as those of their patients.
Demand is complicated by itself. To keep things simple, we will first
examine the demand for medical goods and services in cases where insurance
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C h a p ter 7 : The D em and for H ealthc are Produc ts
113
and the guidance of healthcare professionals play no role. The demand for
over-the-counter pharmaceuticals, such as aspirin, is an example. We will then
add insurance to the mix but keep professional advice out. The demand for
dental prophylaxis will be the example in this case. Finally, we will add the
role of professional advice.
7.3 Demand Without Insurance and Healthcare
Professionals
In principle, a consumer’s decision to buy a particular good or service reflects
a maddening array of considerations. For example, a consumer with a headache who is considering buying a bottle of aspirin must compare its benefits,
as he or she perceives them, to those of the other available choices. Those
choices might include taking a nap, going for a walk, taking another nonprescription analgesic, and consulting a physician.
Economic models of demand radically simplify descriptions of consumer
choices by stressing three key relationships that affect the amounts purchased:
1. The impact of changes in the price of a product
2. The impact of changes in the prices of related products
3. The impact of changes in consumer incomes
This simplification is valuable to firms and policymakers, who cannot change much besides prices and incomes. This focus can be misleading,
however, if it obscures the potential impact of public information campaigns
(including advertising).
7.3.1 Changes in Price
The fundamental prediction of demand theory is that the quantity demanded
will decrease when the price of a good or service rises. The quantity
demanded may decrease because some consumers buy smaller amounts of
a product (as might be the case with analgesics) or because a smaller proportion of the population chooses to buy a product (as might be the case
with dental prophylaxis). Exhibit 7.1 illustrates this sort of relationship. On
demand curve D1 a price reduction from P1 to P2 increases the quantity
demanded from Q1 to Q2.
Exhibit 7.1 also illustrates a demand shift (or shift in demand). At
each price, demand curve D2 indicates a lower quantity demanded than
demand curve D1. (Alternatively, at each volume, willingness to pay will be
smaller with D2.) This shift might be due to a drop in income, a drop in the
price of a substitute, an increase in the price of a complement, a change in
demographics or consumer information, or other factors.
Demand curve
A graphic
depiction of how
much consumers
are willing to buy
at different prices
Demand shift
A shift that
occurs when a
factor other than
the price of the
product itself
(e.g., consumer
incomes) changes
Substitute
A product used
instead of another
product
Complement
A product used in
conjunction with
another product
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Ec o n o m ic s f o r H e a l th c a re M a n a g e r s
EXHIBIT 7.1
Linear Demand
Price
D1
P1
P2
D2
Q2
Q1
Quantity
Demand curves can also be interpreted to mean that prices will have
to be cut to increase the sales volume. Consumers who are not willing to pay
what the product now costs may enter the market at a lower price, or current
consumers may use more of the product at a lower price. Demand curves are
important economic tools. Analysts use statistical techniques to estimate how
much the quantity demanded will change if the price of the product, income,
or other factors change.
Like Exhibit 7.1, Exhibit 7.2 illustrates a shift in demand. In Exhibit
7.2, however, the demand curves are not straight lines.
EXHIBIT 7.2
Nonlinear
Demand
D1
Price
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D2
P1
Q1
Q2
Quantity
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Substitution explains why demand curves generally slope down, that
is, why consumption of a product usually falls if its price rises. Substitutes
exist for most goods and services. When the price of a product is higher
than that of its substitute, more people choose the substitute. Substitutes for
aspirin include taking a nap, going for a walk, taking another nonprescription analgesic, and consulting a physician. If close substitutes are available,
changes in a product’s price could lead to large changes in consumption. If
none of the alternatives are close substitutes, changes in a product’s price
will lead to smaller changes in consumption. Taking another nonprescription
analgesic is a close substitute for taking aspirin, so we would anticipate that
consumers would be sensitive to changes in the price of aspirin.
Substitution is not the only result of a change in price. When the
price of a good or service falls, the consumer has more money to spend on
all goods and services. Most of the time this income effect reinforces the
substitution effect, so we can predict with confidence that a price reduction
will cause consumers to buy more of that good. In a few cases, things get
murkier. A rise in the wage rate, for example, increases the income you would
forgo by reducing your work week. At first blush, you might expect that a
higher wage rate would reduce your demand for time off. At the same time,
though, a higher wage rate increases your income, which may mean more
money for travel and leisure activities, increasing the amount of time you
want off. In these cases empirical work is necessary to predict the impact of
a change in prices.
Two points about price sensitivity need to be made here. First, a general perception that use of most goods and services will fall if prices rise is
a useful notion to keep tucked away. Second, managers need more precise
guidance. How much will sales increase if I reduce prices by 10 percent?
Will my total revenue rise or fall as a result? To answer these questions takes
empirical analysis. Fleshing out general ...
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