A Retrospective Analysis of Health Systems in Denmark and Kaiser Permanente.

ral
ssBioMed CentBMC Health Services Research
Open AcceResearch article
A retrospective analysis of health systems in Denmark and Kaiser
Permanente
Anne Frølich*1, Michaela L Schiøtz2, Martin Strandberg-Larsen2, John Hsu3,
Allan Krasnik2, Finn Diderichsen4, Jim Bellows5, Jes Søgaard6 and
Karen White7
Address: 1Copenhagen Hospital Corporation, Bispebjerg Bakke 23, Bispebjerg Hospital, 2400 Copenhagen NV, Denmark, 2Institute of Public
Health, University of Copenhagen, Øster Farimagsgade 5, Building 15, 1014 Copenhagen K, Denmark, 3Center for Health Policy Studies, Kaiser
Permanente, 2000 Broadway, Oakland, CA 94612, USA, 4Institute of Public Health, University of Copenhagen Øster Farimagsgade 5, Building 9,
1014 Copenhagen K, Denmark, 5Care Management Institute, Kaiser Permanente, One Kaiser Plaza, 16th Floor, Oakland, CA 94612, USA, 6Danish
Institute for Health Services Research Dampfærgevej 27–29, 2100 Copenhagen Ø, Denmark and 7Institute for Global Health, University of
California/San Francisco, 50 Beale Street, San Francisco, CA 94105, USA
Email: Anne Frølich* - anne.frolich@dadlnet.dk; Michaela L Schiøtz - m.schiotz@pubhealth.ku.dk; Martin Strandberg-Larsen - m.strandberg-
larsen@pubhealth.ku.dk; John Hsu - John.T.Hsu@kp.org; Allan Krasnik - krasnik@pubhealth.ku.dk;
Finn Diderichsen - f.diderichsen@socmed.ku.dk; Jim Bellows - jim.bellows@kp.org; Jes Søgaard - jes@dsi.dk;
Karen White - kwhite@psg.ucsf.edu
* Corresponding author
Abstract
Background: To inform Danish health care reform efforts, we compared health care system inputs and performance
and assessed the usefulness of these comparisons for informing policy.
Methods: Retrospective analysis of secondary data in the Danish Health Care System (DHS) with 5.3 million citizens
and the Kaiser Permanente integrated delivery system (KP) with 6.1 million members in California. We used secondary
data to compare population characteristics, professional staff, delivery structure, utilisation and quality measures, and
direct costs. We adjusted the cost data to increase comparability.
Results: A higher percentage of KP patients had chronic conditions than did patients in the DHS: 6.3% vs. 2.8% (diabetes)
and 19% vs. 8.5% (hypertension), respectively. KP had fewer total physicians and staff compared to DHS, with134
physicians/100,000 individuals versus 311 physicians/100,000 individuals. KP physicians are salaried employees; in
contrast, DHS primary care physicians own and run their practices, remunerated by a mixture of capitation and fee-for-
service payments, while most specialists are employed at largely public hospitals. Hospitalisation rates and lengths of stay
(LOS) were lower in KP, with mean acute admission LOS of 3.9 days versus 6.0 days in the DHS, and, for stroke
admissions, 4.2 days versus 23 days. Screening rates also differed: 93% of KP members with diabetes received retinal
screening; only 46% of patients in the DHS with diabetes did. Per capita operating expenditures were PPP$1,951 (KP)
and PPP $1,845 (DHS).
Conclusion: Compared to the DHS, KP had a population with more documented disease and higher operating costs,
while employing fewer physicians and resources like hospital beds. Observed quality measures also appear higher in KP.
However, simple comparisons between health care systems may have limited value without detailed information on
mechanisms underlying differences or identifying translatable care improvement strategies. We suggest items for more
Published: 11 December 2008
BMC Health Services Research 2008, 8:252 doi:10.1186/1472-6963-8-252
Received: 30 May 2008
Accepted: 11 December 2008
This article is available from: http://www.biomedcentral.com/1472-6963/8/252
© 2008 Frølich et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 8
(page number not for citation purposes)
in-depth analyses that could improve the interpretability of findings and help identify lessons that can be transferred.

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Background
While promoting effective, affordable health care is a uni-
versal goal, health care systems vary considerably in their
approach. Comparisons of health systems could help
identify successful strategies and models for achieving this
goal and provide useful benchmarks for change [1].
A structural reform of the Danish healthcare system
(DHS) was undertaken in 2007 with the goal of perform-
ance improvement and increasing effectiveness of care.
We looked to other health systems for transferable prac-
tices, and Kaiser Permanente (KP) had been described as
providing effective care at costs comparable to those of the
UK National Health Service [2]. Of particular interest was
KP's experience with developing care for chronic condi-
tions, for which prevalence rates are high and increasing
in Denmark.
Many existing studies and reports generate a 'landscape'
view of health systems, presenting data in various catego-
ries from four to eight or more nations [3-8]. For health
policymakers, these broad comparisons highlight trends
and identify high performers, but leave many questions
unanswered about the interpretability and utility of the
information. For instance, determinating how much of
the observed variation reflects delivery and financing
approaches, rather than unmeasured variations in the
underlying population and medical practices, can be chal-
lenging and often missing. When modifiable health sys-
tem structures do explain outcome differences, the
transferability into other contexts and specific implemen-
tation strategies often are unknown [9,10].
Only a few comparisons attempt to examine two systems
along several dimensions [11]. In this paper, we compare
two systems along six dimensions–population, profes-
sional staff, delivery system, utilisation patterns, quality
measures, and medical costs–using typically available sec-
ondary data. By using data available in most health care
system settings, our comparison attempts to develop a
widely applicable health policy tool. We use a national
health care system and a sub-national system as examples,
examining the extent to which our comparison helps us
understand their differences and similarities. We also sug-
gest a framework for health systems comparisons that
supports more robust knowledge.
We compared data from the Kaiser Permanente integrated
care delivery system in California (KP), and the Danish
Health Care System (DHS). The populations are approxi-
mately the same size at 6.1 million and 5.3 million,
respectively; however, the enrolled KP population and the
geographically-bounded Danish population differ. The
under Medicare or Medicaid. The two systems have com-
parable benefits and entitlements [12], with a few excep-
tions. Kaiser Permanente members are only partially
covered for inpatient and emergency room admittance,
mental health inpatient services, and substance abuse
inpatient services.
Methods
To compare the two health care systems, we focused on
aspects of the health care system derived from both the
Chronic Care Model and Donabedian's model of struc-
ture, process, and outcome [13,14]. We further refined
our categories according to the availability of data.
We focused on identifying secondary data sources that
were as comparable as possible. KP data came from auto-
mated data systems, the national Healthcare Effectiveness
Data Information Set [15], published reports [16], and an
internal member survey [17]. DHS data came from gov-
ernment ministry reports [18-24], national registries and
professional organisations [25,26], published reports [27-
29], and Organisation for Economic Co-operation and
Development and World Health Organization reports
[30-32].
Additionally, to increase comparability, we adjusted the cost
data in several ways. First, we converted Danish gross expen-
ditures in Danish krone (DKK) to USD using 2000 purchas-
ing power parities. We then subtracted capital depreciation
and profit from gross expenditures to obtain operating
expenditures for each system. Dental benefits vary between
the systems, so we excluded these costs. We also excluded
long-term nursing care expenses from DHS costs, because,
while the figures reported to the Organisation for Economic
Co-operation and Development include these costs, the care
is provided and funded by the municipal social service sys-
tem. Long-term nursing care for KP was not included, since
individuals, supplemental long-term care insurance, or gov-
ernmental agencies pay for it.
We adjusted the Danish per capita expenditures for differ-
ences between the populations in age, education, and
income. Danish income data was converted to US dollars
using purchasing power parity (PPP) conversion rates. We
then stratified Danish health care costs into age, educa-
tion, and household income categories. By applying the
characteristics of the KP population to these stratified
costs, we adjusted the per capita Danish costs for differ-
ences between the populations.
Results
Population
The KP population was younger, better educated, andPage 2 of 8
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KP population generally does not include unemployed
individuals and under represents individuals who are eld-
erly, low-income, or handicapped, as they are covered
wealthier on average, compared to the DHS population. A
lower percentage of KP members were 65+ years (10.2%)
than in the DHS (15.1%) (Table 1). Nearly 95% of KP

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members had a high school diploma, while less than two
thirds did in the DHS. In US dollars, 6.1% of KP members
reported annual household incomes below $15,000,
compared with 16% in the DHS. Conversely, 18% of KP
members reported household incomes higher than US
$100,000 per year, compared to only 5% of the Danish
population.
More KP members reported having chronic conditions
than did Danish citizens: 6.3% reported having diabetes
mellitus in KP vs. 2.8% in DHS; 19% reported having
hypertension in KP vs. 8.5% in DHS; and 1.0% reported
having a stroke in KP vs. 0.2% in DHS.
The rates for individual risky behaviours such as excess
weight and smoking also varied between the populations
(Table 2). Fewer KP members reported smoking on a daily
basis than did Danish citizens. While the percentages who
were overweight, defined as having a BMI from 25–30, were
similar in the two populations, a higher percentage of KP
members met the definition of obesity; i.e., BMI > 30.
Professional Staff
KP had fewer physicians and total health professionals
than did the DHS: 134 physicians and 1,125 health pro-
fessionals per 100,000 members versus 311 physicians
and 2,025 health professionals per 100,000 citizens. Phy-
sicians include all types of physicians: residents, physi-
cians, specialists, and general practitioners. Health
professionals cover all health professionals except physi-
cians.
Delivery system
Both systems rely on contractual relationships between
individual physicians and the health care delivery system.
However, the delivery systems for primary care are quite
different. All KP physicians are salaried members of multi-
specialty physician groups. In the DHS, specialists are pri-
marily salaried hospital employees, but all primary care
physicians (PCPs) are self-employed and receive a combi-
nation of capitation and fee-for-service compensation. In
addition, 38% of DHS PCPs have solo practices.
Utilisation patterns
Hospital beds in KP were occupied 270 days per 1,000
persons per year, compared to 814 days per 1,000 persons
per year in the DHS. Acute care admission rates showed a
similar spread: seven per 1,000 persons per year in KP and
18 per 1,000 persons per year in Denmark.
Table 1: Population characteristics
Kaiser Permanente (%) Danish Population (%)
Age in years
0–4 6.0 6.4
5–15 15.0 13.0
16–44 43.1 40.2
45–64 25.7 25.6
65–74 6.3 8.1
75–84 3.2 5.2
≥ 85 0.7 1.8
Educational level
Less than high school 5.3 37.4
High school or higher 54.9 42.3
Bachelors degree or higher 39.8 20.3
Household income in USD (thousands)
< 15 6.1 16.0
15–25 9.2 14.6
25–35 11.1 13.8
35–50 17.5 15.6
50–65 12.9 17.9
65–80 13.3 11.1
80–100 12.1 6.1
> 100 17.9 4.9
Data on educational level of KP membership is from 2002. Page 3 of 8
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Danish utilisation index is from 2001; index adjusted for age, sex and income where all inhabitants older than 15 years = 100.
Data on household income levels of Kaiser Permanente membership is from 1998.

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The length of stay for acute admissions averaged 3.9 days
at KP and 6.0 days in Danish hospitals (Table 3). Stroke
patients displayed the most remarkable difference in aver-
age length of stay. They remained hospitalised an average
of 4.26 days at KP, compared to 23 days in Denmark.
At KP, cardiovascular angioplasty rates were 25% higher
and the rate of coronary bypass grafts was twice that of the
DHS. KP also had higher kidney transplantation rates (4.8
per 100,000 population compared to 2.9 per 100,000).
Quality Processes
KP had higher rates for breast cancer screening (78% vs.
10%), retinal screening among patients with diabetes
(93% vs. 46% in the only reporting Danish county), and
beta-blocker use among patients with acute myocardial
infarction (93% vs. 69%). Screening rates for cervical can-
cer were roughly comparable at 80% and 75%.
Medical Costs
Operating expenditures for KP and the DHS were similar
at PPP $12,975 million and $12,535 million (Table 4).
Per capita expenditures were higher for KP at PPP $1,951,
compared to PPP $1,845 for the DHS. Adjusting for differ-
ent distributions of age, education and income yielded
Danish per capita expenditures of PPP $1,480, 24% less
costly than at KP.
Discussion
Our comparison revealed intriguing differences between
health systems in all the dimensions we explored. How-
ever, we were interested not only in the differences them-
selves, but also in how they could inform policy
decisions.
The interpretability of health outcome and efficiency dif-
ferences was limited by substantial variations between the
populations. These variations were both in principle, i.e.
an enrolled and a geographic population in two different
countries, and empirical. The structures and system out-
puts also appear to differ between the systems, but not
always with consistent patterns. Observed cross-sectional
differences between the systems might reflect differences
in the timing or pace of similar trends, rather than struc-
tural differences. For instance, cross-sectional observation
fails to reveal that the trend in the DHS follows the move-
ment within KP from inpatient to outpatient delivery set-
tings. The average length of stay in the DHS decreased
from 6.0 in 1993 to 3.8 in 2000 and, over the same time
period, the number of hospital beds in the DHS per1000
members of the population decreased from 5.0 to 4.3.
Populations, behaviours, and disease detection
The two populations have different reported prevalence
rates of selected chronic diseases and risk factors. The
higher reported disease prevalence in KP could be attrib-
utable to several factors, including more aggressive case
finding, high rates of obesity and inactivity throughout
the US, and, perhaps, differing emphasis on disease pre-
vention between the two systems. While we cannot be cer-
tain of the relative contribution of these factors, we
believe that the higher prevalence rates in KP cannot be
attributed solely to case finding and that the higher real
disease prevalence in the KP population is driven largely
by social and cultural differences between the US and
Denmark.
Unfortunately, variable rates of case finding make it
impossible for us to quantify the real differences in dis-
Table 2: Smoking and obesity rates
Kaiser Permanente 2002
Age ≥ 20 years
DHS population 2000
Age ≥ 16 years
DHS population 2005
Age ≥ 16 years
Risk factors Men Women Men Women Men Women
Smoking rate (%) 14 11 39 35 32 28
Overweight (%) (BMI between 25 to 30) 43.4 26.0 40 26 41 26
Obese (%) (BMI > 30) 21.9 23.3 10 9 12 11
Data on risk factor prevalence is from the Northern California region only.
Table 3: Mean hospital lengths of stay by diagnosis for patients age 65 and over
Diagnoses KP Days (mean) DHS Days (mean)
Stroke 4.3 23.0
COPD 3.8 5.1
Coronary bypass 9.8 N/A
AMI 4.4 7.2
Angina pectoris 2.2 4.5
Hip replacement 4.5 9.5Page 4 of 8
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Hip fracture 4.9 12.1
Kidney or urinary bladder infection 3.8 5.0

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ease prevalence or to apply case-mix adjustment to our
utilization and cost statistics. Case-mix adjustment would
lower KP's costs relative to DHS and further lower its uti-
lization. Drawing conclusions about the differences
between populations requires more data on disease pat-
terns and risk factors and also requires data that is more
comprehensive, detailed, standardised, and objective
(e.g., administrative or derived from electronic health
records or population studies that include health exami-
nations). One great challenge to a more detailed compar-
ison is that there is no diagnosis recording in primary
healthcare in Denmark.
Staffing and structure
One of our most striking findings is the more than two-fold
difference in the numbers of physicians in the two systems.
Several interpretations are possible. KP physicians tend to
work more hours, from 40–70 hours per week compared to
about 40 hours in the DHS [32,33]. But Denmark may also
be lagging behind in transfer of clinical and other tasks
from physicians to other health professionals.
The relationship between primary and specialty care is
also unclear from these figures and warrants a closer look.
Are generalists expected and able to provide some spe-
cialty care, for newly diagnosed diabetes, for instance? Or
all patients referred to another setting for care? At what
point does that referral take place?
The differences in hospital utilisation are also striking.
or favourable reimbursement approaches, including use-
based payments. Indeed, the total number of hospital
beds in each system is important information that sup-
ports understanding absolute and excess capacity. While
the hospital reimbursement practices are comparable in
the two systems, existing utilisation data fail to capture
variations in structure and cultural patterns of care. In gen-
eral, acute care lengths of stay in US hospitals have
dropped over the last decade [35]. Additionally, in Den-
mark, stroke patients receive rehabilitation services in the
hospital, while in KP and the United States in general,
more of these patients receive some of their care outside
of the hospital. The number of beds in alternative care set-
tings and patterns of hospital discharge practices would
provide much-needed context for the differing lengths of
stay in the two systems.
Higher specialty procedure rates at KP may reflect discrep-
ancies in chronic disease prevalence. If more members do
indeed have heart disease, then the higher procedure rates
follow. However, supply can also contribute to higher pro-
cedure rates, as do prevailing practice patterns [36,37].
Additional data on availability of specialists and specialty
care facilities, practice norms, and reimbursement differ-
ences are needed. To develop a complete picture of the rel-
ative utilisation of levels of care, we would also need
indicators of use and access, like primary and specialty care
visit rates and wait times for primary and specialty care.
Modifiable and transferable practices
Table 4: Health care expenditures
Category Kaiser Permanente (2000)
US Dollars
Danish Healthcare System (2000)
US Dollars
Gross expenditures/revenue adjusted for: $14 200 m $12 791 m
Less capital depreciation - $557 m - $256 m
Less profit - $668 m - 0
Operating expenditures: $12 975 m $12 535 m
Operating expenditures corrected for different expenditures: $12 975 m $12 535 m
Dental care - $10 m -$473 m
Special circumstances - $ 1 065 m -$278 m
Long-term nursing care - $ 2 283 m
Net expenditures after corrections: $11 900 m $ 9 779 m
Standardised per capita expenditures
(6.1 million people for Kaiser; 5.3 million people for DHS)
$1 951 $1 845
Adjustments for age differences $1 951 $1 639
Final adjusted per capita expenditure $1 951 $1 480
‘Special circumstances’ refers to sales, marketing, and malpractice insurance (Kaiser Permanente) and research and development and state-covered
malpractice insurance (Danish Healthcare System). For the DHS net expenditures ($9 799m) equals operating expenditures less (dental costs plus
long term care, less supplementary private health insurance).Page 5 of 8
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Higher hospital utilisation can reflect failures of primary
care [33], greater supply such as more available beds [34],
Readily available data only allowed us to paint a very cur-
sory picture of the two systems. Transferring successful