hi08 comparing payor performace

Comparing payor performance to enhance health outcomes

A new McKinsey tool enables payors to identify where

their performance is weak, what they can do to

improve it, and which peer organizations they can learn from.

4949

In many countries, health outcomes vary

markedly across regions, often in ways

that do not correlate with health care spending.

The use of evidence-based interventions

also varies markedly. These variations in out-

comes and care delivery are coming under

increasing scrutiny from a range of stakeholders,

including payors, government officials, and

the general public.1 These stakeholders share the

concern that some patients are being given

substandard care. Many are also troubled about

the economic impact of substandard care,

since high-quality care, particularly for chronic

diseases, often lowers future health costs.

For payors, substandard care is particularly

worrisome, because their core mission

is to fund care delivery and thereby improve

public health.

But reducing variations in outcomes and care

delivery is no simple task. Payors have little

or no direct control over some of the factors that

strongly influence health outcomesespecially

social determinants, such as education level

and employment status. Payors can influence the

extent to which providers use evidence-based

interventions, which have been proven to enhance

health outcomes. But few payors take a rigor-

ous approach to identifying which results they

most need to improve, which interventions

they should promote, or how they can find exam-

ples of best practices to emulate.

We have developed a proprietary analytic

framework to help payors take on this challenge.

This framework, McKinseys Payor Health

Index, enables payors to determine which health

outcomes to focus on first, as well as which

interventions and other actions would have the

greatest impact on those outcomes. In addi-

tion, the index allows payors to identify other

organizations in similar circumstances so that

they can ascertain which ones have lessons

to teach them.

The Payor Health Index was originally devel-

oped in England but has since been adapted for

use in several other countries, including the

Netherlands, Saudi Arabia, and the United States.

More recently, McKinsey has been developing

a similar tool to help health systems determine

what their priorities should be.

In this article, we will describe how and why

the Payor Health Index was developed,

what results it provides, and how payors can

use the results to improve health outcomes.

Variations in care delivery

As the focus on health care quality has increased

around the world, even highly developed

countries have discovered that they experience

wide regional variations in health outcomes.

In France, for example, infant mortality

ranges from a low of 1.4 per 1,000 live births in

Corse to a high of 4.5 per 1,000 in Alsace.2

Similar differences are seen in many other

countries (Exhibit 1).

Regional variations have also been noted in

other health outcomes, including cancer survivor

rates, cardiovascular death rates, and years

of life lost to chronic conditions. In England, for

example, we identified an almost ninefold

difference among the regions in the number of

life years lost to diabetes (Exhibit 2). The dif-

ference cannot be fully explained by how much

the regions spend on health care (Exhibit 3).

What does help explain the variations in out-

comes are differences in underlying risk

factors and the use of evidence-based inter-

ventions. For example, both teenage pregnancy

and smoking during pregnancy are known

Paul Betts; Farhad

Riahi, MD; and

Siddharth Shahani

1 For another look at how increased transparency into health outcomes and care delivery is affecting health systems, see How hospitals can respond to increased quality transparency, p. 58.

2 National Institute for Statistics and Economic Studies, France, 2007.

50 Health International 2009 Number 8

risk factors for neonatal mortality. In Australia,

the teenage pregnancy rate is six times higher

in some regions than in others.3 In parts

of England, only 4 percent of pregnant women

smoke; in other areas, one-third of pregnant

women do.4 Likewise, early detection with

mammography can improve survival for women

with breast cancer. In Italy, the percentage of

eligible women who regularly undergo mammog-

raphy is twice as high in some regions than in

others.5 Similar variations in risk-factor

prevalence and evidence-based intervention use

can be found in countries around the globe.

How the Health Index was built

We began work on the Payor Health Index after

noticing the differences in health outcomes

in the various regions of England. We wanted

to find a way to analyze and compare the

performance of Englands primary care trusts

(PCTs)the payors responsible for health

care in the regionsso that we could help them

improve their results.

We focused on Englands payors because

they have been tasked explicitly with

improving health outcomes in their covered

3 Australian Institute of Health and Welfare, 2007.

4 Healthcare Commission: Acute Trusts 2007/08 National StandardsSmoking During Pregnancy.

5 D. Giorgi et al., Mammography screening in Italy: 2005 data and 2006 preliminary data, Epidemiologia e Prevenzione, 2008, Volume 32, Number 2, Supplement 1, pp. 722.

Exhibit 1

Variations in infant mortality

Within France, Sweden, and Italy, infant mortality rates vary by region.

Infant mortality under age 1 (deaths per 1,000 live births by region)

Average = 3.6

Health International 2009PHIExhibit 1 of 6Glance: Within France, Sweden and Italy, levels of infant mortality vary by region. Exhibit title: Variations in infant mortality

Alsace

France

Lorraine

Champagne-Ardenne

Haute-Normandie

Ile-de-France

Nord-Pas-de-Calais

Source: National Institute for Statistics and Economic Studies, France, 2007; Central Bureau of Statistics, Sweden, 2007; Health for All Database, Italy, 2005

4.5

Poitou-Charentes 2.9

4.5

4.4

Aquitaine

Picardie

Franche-Comt

3.8

3.8

3.6

4.2

3.9

3.8

Languedoc-Roussillon

Pays de la Loire

Provence-Alpes-Cte dAzur

Midi-Pyrnes

Centre

Rhne-Alpes

3.5

3.5

3.4

Basse-Normandie

Bourgogne

Auvergne

3.2

3.0

3.0

Corse 1.4

Bretagne

Limousin

2.6

2.5

3.4

3.4

3.3

Gotland

Sweden

Kalmar

Norrbotten

Vsternorrland

Blekinge

Dalarna

4.8

Kronoberg 2.3

4.4

3.7

Jnkping

Srmland

Vstmanland

3.3

3.2

3.2

3.7

3.3

3.3

rebro

Halland

Gvleborg

Jmtland

Vstra Gtaland

stergtland

2.9

2.8

2.8

Skne

Vrmland

Stockholm

2.6

2.6

2.5

Uppsala

Vsterbotten

2.3

2.2

2.8

2.7

2.7

Calabria

Italy

Sicilia

Basilicata

Puglia

Campania

Lazio

5.4

Molise 2.0

5.1

4.7

Valle dAosta

Friuli VG

Emilia Romagna

3.9

3.7

3.5

4.6

4.3

4.2

Abruzzo

Lombardia

Trentino AA

Marche

Piemonte

Veneto

3.4

3.3

3.1

Liguria

Toscana

Sardegna

2.6

2.6

2.6

3.1

2.9

2.8

Average = 2.8 Average = 3.7

Above country average

51

Exhibit 2

No standard outcomes

In England, as in many other countries, differences in health outcomes are not easily explained.

Primary-care-trust (PCT) outcomesOutcome metricDisease area

Average Distribution across 152 PCTs

Health International 2009PHIExhibit 2 of 6Glance: In England, as in many other countries, differences in health outcomes arenot easily explained. Exhibit title: No standard outcomes

1 International Classification of Diseases (ICD) 10 codes I61-I64: intracerebral hemorrhage, other nontraumatic intracranial hemorrhage, cerebral infarction, stroke not specied as hemorrhagic or infarction.

Source: Payor Health Index

Coronary heart disease

Standardized mortality ratio

147Highest11980th percentile8920th percentile58Lowest

104

Cervical cancer

5-year relative survival %

74Highest6880th percentile

6020th percentile56Lowest

64

Infant mortality

Deaths in first year per 1,000 live births

10.8Highest

6.180th percentile4.020th percentile2.3Lowest

5.1

Diabetes Years of life lost per 10,000 people

14Highest

6.280th percentile3.120th percentile1.6Lowest

4.8

Stroke1 % deaths within 30 days of admission

42Highest

3080th percentile2320th percentile

8Lowest

26

Comparing payor performance to enhance health outcomes

populations. Furthermore, theylike payors in

other publicly funded health systemsmust

bear the long-term costs of health care provision.

For such payors, it can often be less expensive

to provide high-quality care than to address

the complications that eventually arise when such

care is not offered.

In developing the Payor Health Index, our first

step was to answer two questions: what were

the most important disease areas to study, and

what publicly available data sources could

we use to investigate those disease areas? We

defined important disease areas as those that

are highly prevalent and have a strong impact on

overall public health. (Colds, for example,

are very common but have little impact. AIDS

significantly affects patients but has a fairly

low prevalence in most economically advanced

countries.) We used publicly available data

because we wanted to study all of Englands

payors using the same source information.

In total, we used almost a dozen different demo-

graphic and clinical databases.


The result was a list of 11 disease areas:

cardiovascular disease, diabetes, cancer, asthma,

stroke, chronic obstructive pulmonary disease,

pediatric health, sexual health, geriatric

health, mental health, and alcohol/drug abuse.

For each of these areas, we then defined the

most important health outcomes to study, using

published clinical studies as a guide. Some

of these outcomes reflected patients current

health status (for example, hypertension

and asthma prevalence). Others indicated how

often preventive services were being deliv-

ered (the percentages of women whose breast

cancers were detected at an early stage, for

instance, or of elderly patients who were given

pneumococcal vaccination). In still other

cases, the outcomes reflected the relative success

of treatment (for example, years of life lost,

survival without permanent disability, percentage

of low-birth-weight deliveries).

Our next step was to identify the factors that

could explain the regional variations in

these outcomes, such as the use of evidence-based

interventions, the available resources (for

example, the number of physicians), and under-

lying population risk factors (such as smoking

rates and eating habits). Because we were

using publicly available data, we could not always

analyze the factors likely to have the strongest

impact on outcome variations (see sidebar,

Understanding current data limitations, p. 55).

Nevertheless, we were able to quantify a dozen or

more metrics for each disease area. We then

used this information to create, for each PCT, an

individual index for each disease area.

What the Health Index tells a payor

The index for each disease area is presented in

a simple visual format designed to convey

a great deal of information quickly (Exhibit 4).

Exhibit 3

Health versus spending

The number of deaths from diabetes does not correlate with per-patient spending.

Relationship between primary-care-trust (PCT) spending on diabetes and deaths from diabetes in each PCT

6

5

Deat

hs fr

om d

iabe

tes

mel

litus

as

a %

of

all d

iabe

tic p

atie

nts

in a

PCT

r2 = 0.0024

Health International 2009PHIExhibit 3 of 6Glance: The number of deaths from diabetes does not correlate with spending.Exhibit title: Health versus spending

r2 is the proportion of variance explained by a regression.


4

3

200

2

1

00 400 600 800 1,000 1,200

PCT spending on diabetes per diabetic patient,

53Comparing payor performance to enhance health outcomes

Exhibit 4

Cancer health index

Metrics on cancer health can be grouped into four major categories.

Wait for treatment

Highest performer

Average, all PCTs

PCT1 figureUnitsMetricsScores, 15A

C

BD

73.5102102.7SMR3 1. Cancer mortality2A. Outcomes 2.62

5-year survival

Detected early

8.06.36.4 2. Lung

55.047.846.6 3. Colon

82.378.676.8% 4. Breast

73.764.363.8 5. Cervical

75.667.158.0 6. Prostate

74.762.169.0% 7. Breast

88.680.081.0% 8. Cervical

Screening coverage

B. Interventions 4.59

81.169.676.1% 9. Cervical

84.173.382.3%10. Breast

C. Resources 1.98

100.099.098.811. 31-day

100.099.799.612. 2-week

100.091.688.0%13. 62-day

1.219.935.614. MRI long waiters2

9.16.46.515. Proportion spent on cancer

5.31.00.216. Medical oncologists

D. Risk factors 2.79

% 17.626.627.117. Estimated smoking prevalence

Health International 2009PHIExhibit 4 of 6Glance: Metrics on cancer health can be grouped into four major categories. Exhibit title: Cancer health index

1 Primary care trust.2Low gure = good performance.3Standard mortality ratio.4Full-time equivalent.


FTE4 per 100,000 population

First, it tells a PCT how well it is doing on each

metric studied. In addition, it provides national

averages for each metric, as well as the results

achieved by the highest-performing payor on each

metric. This format enables a PCT to ascertain

how its cancer mortality rate, for example, com-

pares with the national average, as well as

by how much it could lower that rate if it were

to achieve results comparable to those of

the highest-performing PCT.

The metrics are grouped into four major

categories: outcomes, interventions, resources,

and risk factors. In addition, the individual

scores for each metric in a category are combined

into a global score for that category, which

enables a PCT to compare its performance

at a glance with that of other payors. The

global scores range from 1 (low performance)

to 5 (high performance).


For example, a PCT that has comparatively low

mortality from cardiovascular disease and

coronary heart disease, as well as a low prevalence

of diabetes, hypertension, and obesity, would

be given a high global score for cardiovascular

outcomes. To receive a high global score for

cardiovascular interventions, the PCT would have

to show, among other things, that it is doing

a good job controlling blood pressure and choles-

terol levels in its population. High scores for

risk factors and resources would indicate that the

PCT has minimized the risk factors in its

population (by reducing smoking rates, for

example) and has sufficient resources to provide

high-quality care.

Finally, the global scores are graphed into

a diamond. A PCT performing ideally

would have a diamond that is almost entirely

blue. In our experience, however, no payors

perform ideally, and thus some white

always appears. The proportion of the diamond

that is white, and the areas where the white

appears, indicate how much of a problem a payor

may have. For example, if the only large area

of white appears at the bottom of the diamond,

the payor may not have a problem at all; the

white simply indicates that its resource allocation

in that disease area is small. This could be

appropriate if the prevalence of risk factors in

its population is low, if it is using evidence-

based interventions appropriately, and if it is

achieving good results (the payor could devote its

resources to disease areas with greater needs).

Conversely, a payor with a lot of white only at the

top of the diamond has a major problem,

because it is achieving poor outcomes while

expending significant resources.

Exhibit 5

Mixed performance

Payor performance often varies by disease area.

PCT B PCT CPCT1 A

Outcome score (1 = poor, 5 = good)

Cardiovascular

Cancer

Mental health

Sexual health

Drugs/alcohol

Child health

Diabetes

Stroke

Elderly health

Asthma

COPD2

Health International 2009PHIExhibit 5 of 6Glance: Payor performance typically varies by disease area. Exhibit title: Mixed performance

1Primary care trust.2Chronic obstructive pulmonary disease.


1 2 3 4 5 51 2 3 4 1 2 3 4 5


We have found that the performance of most

payors is mixed; they tend to do well

in some disease areas and poorly in others

(Exhibit 5). Very few payors score well

across the board. This is an important point to

remember, given the paucity of outcomes

data available in most countries. The fact that

a payor achieves good results in one area

(for example, cardiovascular disease manage-

ment) does not guarantee that it will

achieve similarly good results in other areas.

How payors can use the results

The wealth of data the Payor Health Index

provides is often invaluable to payors. The index

enables them to determine which disease areas

and which outcomes to focus on, as well as

which underlying factors they should attempt to

change first. In addition, it helps payors identify

peers that are achieving better outcomes,

which then allows them to learn from their peers

and improve their own performance.

Pinpointing what to focus on

The experience of three PCTs, all of which had

scored poorly on breast cancer outcomes,

illustrates the kind of help the Payor Health Index

can provide. The first PCT discovered that its

screening rate (the percentage of women

who regularly receive mammograms) was only

half the national average; it was therefore not

surprising that the PCTs early detection and

five-year survival rates were low. For this payor,

the challenge was to determine how it could

better reach out to the women in its community

to persuade them to undergo mammography.

Understanding current data limitations

At present, both the health data gathered directly by payors

and the information that is publicly available have two

important limitations that must be kept in mind when payor

performance is being compared: availability and reliability.

These limitations do not prevent us from comparing

performance, but the comparisons would be stronger if the

available data were more robust.

Availability. Throughout the world, efforts to monitor the quality of care are in their infancy, and thus the data

that providers are required to report to payors are incomplete

and somewhat arbitrary. In some cases, the metrics

were selected simply because they are easy to measure,

rather than because they are the best predictors

of outcome.

An even greater problem with data availability is that many

types of information are not being systematically collected.

Breast cancer provides a good illustration of this problem.

Currently, few, if any, payors or government agencies are

collecting basic demographic information from women about

age at first period, total number of pregnancies, or age at

menopause. They are also failing to collect more sophisticated

information, such as family history of breast cancer

or the presence of genetic markers. In most communities,

information about smoking rates in women is available,

but smoking is a much, much weaker risk factor for breast

cancer than family history or genetic markers.

Reliability. Publicly available information is not always as reliable as we would like. For example, it may have

been derived from relatively small surveys conducted over

a brief period of time, and errors may have crept in as

the numbers were extrapolated upward. Alternatively, the data

may have come from a large-scale (for example, national)

survey, but the local population may not be a representative

sample of the national population.

Because of these limitations, the risk factors and interventions

included in the Payor Health Index are not always the ones

that would have been best to use; some were simply the best

available. We strongly believe that payors should demand to

be given better information. This may be easier to achieve with

providers than with local governments, but it is something

payors should strive for if they want to improve health

outcomes.


The second PCT had a higher-than-average

screening rate but a lower-than-average

early detection rate. The cause of this anomaly

was not immediately evident. It is unlikely

that the wrong women were being screened,

because the guidelines for patient selection

are very clear. The PCT is currently investigating

whether its providers equipment may be

malfunctioning and whether radiologists may be

reading the mammograms inaccurately.

The third PCT had a different problem: both

its screening rate and its early detection

rate were markedly above the national average,

but its five-year breast cancer survival rate

was below average. When the payors chief exec-

utive looked into the problem, she discovered

that it most likely resulted from the regions

fragmented network of oncology providers. Best-

practice payors use a high-volume provider

typically a regional cancer centerthat offers

high-quality services, including breast surgery,

radiotherapy, and chemotherapy.

We have seen similar results in other disease

areas. Occasionally, the Payor Health

Index highlights underlying factors that a payor

has little or no control over (the number of

single-parent families in a community, for exam-

ple). Even in these cases, however, it provides

helpful information because it enables the payor

to make better decisions about how to combat

certain conditions. A payor that wanted to

improve its asthma outcomes, for instance, could

realize that it needed to enlist the help of other

organizations, such as housing authorities

and environmental agencies, in order to reduce

some of the risk factors for that disease.

Identifying peers to learn from

Many payors claim that their circumstances are

too unusual to permit easy comparisons:

their population is too rural (or too urban), its

average age is too young (or too old), and so

forth. In actuality, this is rarely the case.

There are many meaningful ways to categorize

payors into peer groups.

For example, we segmented all of Englands

PCTs into nine sets, based on each communitys

deprivation level and its risk factors for diabetes.

We then looked closely at the three sets

of PCTs that had a high number of diabetes

risk factors to see how their performance

compared (Exhibit 6). As expected, performance

within each set varied widely. However, the

relative level of deprivation (low, moderate, or

high) had very little impact on performance; in

fact, the PCT with the highest performance was in

a highly deprived community. These findings

support our contention that payors can learn

from their peers even their peers in more

deprived communities.

Using the Health Index in other settings

Since we first developed the Payor Health Index,

we have adapted it for use in other countries.

In each case, the purpose and approach remained

the same, but we tailored the index either to

better reflect locally available data or to provide

greater focus on, and a more extensive set

of metrics for, disease areas of particular concern

(for instance, cancer and diabetes).

More recently, we have begun to develop a similar

tool, which we have dubbed Health Insights,

for use in health systems. The aim of the Health

Insights is to provide health system executives

with the intelligence required to make the most

effective use of their health care budgets. Health

Insights is designed to permit comparisons

both within and across health systems; it allows

executives to measure how well the regions

within their own systems are doing, as well as


how well their systems compare with the health

systems in other countries. This tool assesses

the resources spent, outcomes achieved, and key

drivers of performance, and it identifies the

areas most in need of improvement. Our hope is

that the executives who opt to use Health

Insights will develop into a peer community

of regional health leaders who share experiences

with and learn from one another.

Around the world, increasing attention is being

paid to both the cost of health care and the

quality of care delivered. As a result, it is doubtful

that many countries will continue to tolerate

the wide regional variations in health outcomes

that are seen today. The Payor Health Index

and its offshoot, Health Insights, can help

payors and health systems identify where their

performance is weak, what they can do

to improve it, and which peer organizations they

can learn from.

Paul Betts, a senior research analyst with McKinseys

health care practice in London, works extensively with

Englands National Health System. Farhad Riahi, MD,

a principal in the London office, leads McKinseys work

on clinical health economics. Siddharth Shahani, a

research analyst at McKinseys Health Systems Institute,

currently focuses on making health information compa-

rable across different health systems.

Exhibit 6

Performance varies

Even within peer groups, payor performance varies.

Diab

etes

mor

talit

y, y

ears

of

life

lost

per

10,

000

peop

le

16

14

12

10

8

6

4

2

Health International 2009PHIExhibit 6 of 6Glance: Exhibit title: Payor performance varies even within peer groups


High risk factorsHigh deprivation

High risk factorsSome deprivation

High risk factorsLow deprivation

Each dot represents a different payor

hi08 comparing payor performace

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