Public private partnerships in healthcare. Evaluation of 10 years´ experience in Spain.

Ph.D. Program in Business Administration
27th October 2014
Public-Private Partnerships in
Healthcare. Evaluation of
10 years’ experience
in Spain.
Doctoral Thesis
Antonio Clemente
Directors:
David Vivas
Maria CaballerISBN: 978-84-606-8865-5
DoctoralThesis:Public-PrivatePartnershipsinHealthcare.Evaluationof10years’experienceinSpain.AntonioClemente

VALENCIA POLYTECHNIC UNIVERSITY
PhD in Business Administration
Public-Private Partnerships in Healthcare.
Evaluation of 10 years’ experience in Spain
Author: Antonio Clemente
Directors: PhD. María Caballer
PhD. David Vivas
October, 2014
1

To my parents and brother for their constant encouragement and unconditional
support to all my projects.
To my uncle Jose María for his charisma and particular way of seeing life.
To my Coach for his advice and, above all, for his trust in me.
To Jorge and my colleagues at Marina Salud because they gave me
the opportunity to develop the necessary tools to conduct this study.
Dénia has always been, is, and will, be my school in healthcare management.
To my directors María and David for their patience and devotion to this study.

2

INDEX
INDEX 3
ABSTRACT 13
1. INTRODUCTION AND OBJECTIVES 18
1. Introduction 19
1.2 Research objectives 23
1.2.1 General objective 23
1.2.2 Specific objectives 23
2. BACKGROUND 25
2.1 Spanish healthcare context 26
2.2 Healthcare context in the Valencia region 30
2.2.1 The Alzira model 33
2.2.1.1 Basic concepts of the Alzira model 38
2.2.1.2 The beginnings of the Alzira model: La Ribera Hospital 43
2.3 A review of the literature on healthcare service assessments 45
2.3.1 Concept and measurement techniques of efficiency in the hospital
industry 47
2.3.2 Efficiency analysis method 49
2.3.2.1 Multivariate methods 50
2.3.2.2 Non-stochastic methods 52
2.3.3 Hierarchical analysis. Clusters 57
2.3.4 Diagnosis related groups (DRGs) 59
2.3.4.1 Origin of the DRGs 60
4

INDEX
2.3.4.2 Development of the DRGs 61
2.3.4.3 The weightings of the DRG 61
2.3.4.4 The "product" that a hospital provides 64
2.3.5 Public-private collaboration experience in healthcare 66
3. ASSUMPTIONS AND INFORMATION SOURCES 73
3.1 Assumptions for the study 74
3.2 Information sources 74
3.2.1 Sources of economic information 77
3.2.2 Quality information sources 82
3.2.3 Information sources for the healthcare production 84
3.3 Indicators and variables used 87
3.3.1 Economic or cost variables 87
3.2.2 Quality variables 91
3.2.2.1 Quality indicators 92
3.2.2.2 Delay indicators 93
3.2.2.3 Qualitative economic indicators 97
3.2.2.4 Healthcare process indicators 97
3.2.2.5 Public health indicators 99
3.2.2.6 Safety indicators 100
3.2.3 Structural variables 102
3.2.4 Variables in healthcare activity 106
5

INDEX
3.3. Cost breakdown method 110
4. RESULTS 113
4.1 Cost analysis 114
4.1.1 Main healthcare indicators 114
4.1.2 Cluster analysis 118
4.1.3 Overall hospital costs in the Valencia region 120
4.1.4 Cost per equivalent patient and area 122
4.1.5 Healthcare production in equivalent patients by area 130
4.1.6 Assessment of the activity through an adjusted cost-production
analysis 137
4.2 Analysis of the healthcare quality 143
4.2.1 Quality Analysis in Emergency Department 145
4.2.2 Quality analysis in the surgical area 148
4.2.3 Quality Analysis in the outpatient area. 151
4.3 Analysis of the healthcare activity 154
4.3.1 Overall healthcare production 155
4.3.2 Healthcare production in the medical area 159
4.3.3 Healthcare production in the surgical area 162
4.3.4 Healthcare production in outpatient services 166
4.3.5 Healthcare production in the emergency department 169
4.4 Study of the effect of the management model 172
6

INDEX
4.4.1 Total differences 172
4.5 Assessment of the efficiency between the PPP and the directly
managed hospitals 177
4.5.1 Overall efficiency 177
4.5.2 Efficiency in the medical area 182
4.5.3 Efficiency in the surgical area 186
4.5.4 Efficiency in the outpatient service area 190
4.5.5 Efficiency in the emergency department area 194
5. DISCUSSION 199
5.1 Contribution to knowledge and new lines of research 221
6. CONCLUSIONS 224
7. BIBLIOGRAPHY 234
8. ANNEXES 249
7

TABLE INDEX
Table 1. Global competitiveness index 27
Table 2. Total budget and spending in Spain (million euros) 28
Table 3. Healthcare budget per capita in 2003-2012 31
Table 4. Population in the Valencia region 32
Table 5. Overall budget for the Valencia region and budget for its Health
Department. 34
Table 6. Main direct expense items for H10 90
Table 7. Equivalency of the processes to calculate the equivalent patients
121
Table 8. Main hospital activity indicators in 2010 115
Table 9. Main indicators of ambulatory production in 2010 117
Table 10. Relative position of the Management Agreements in 2010 144
Table 11. Results of the overall analysis using the general linear regression
model. 158
Table 12. Results of the general linear regression analysis in the medical
area 161
Table 13. Results of the general linear regression analysis in the surgical
area 165
Table 14. Results of the general linear regression analysis in the outpatient
service area 168
Table 15. Results of the general linear regression analysis in the emergency
department area 171
Table 16. Statistics for the mean difference 173
Table 17. Results of the Mann-Whitney test 174
Table 18. Variables selected for the overall efficiency analysis 178
Table 19. Score overall efficiency 180
Table 20. Overall efficiency score for cluster 1 181
Table 21. Overall efficiency score for cluster 2 182
8

TABLE INDEX
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
Table 22. The variables selected for analyzing the medical area
Table 23. Inpatient efficiency score
Table 24. Efficiency score in the inpatient area for Cluster 1
Table 25. Efficiency score in the inpatient area for cluster 2
Table 26. The variables selected for analyzing the surgical area
Table 27. Efficiency score in the surgical area
Table 28. Efficiency score in the surgical area for cluster 1
Table 29. Efficiency score in the surgical area for cluster 2
Table 30. The variables selected for analyzing the outpatient area
Table 31. Efficiency score in the outpatient service area
Table 32. Efficiency score in the outpatient area for cluster 1
Table 33. Efficiency score in the outpatient service area for cluster 2
Table 34. The variables selected for analyzing the emergency department
area
Table 35. Efficiency score in the emergency department area
Table 36. Efficiency score in the emergency area for cluster 1
Table 37. Efficiency score in the emergency department area for cluster 2
9
198

GRAPHIC INDEX
Graphic 1. Healthcare spending as a percentage of GDP in 2007 22
Graphic 2. Healthcare spending by source and funding in 2013 29
Graphic 3. Public healthcare spending breakdown in 2012 30
Graphic 4. Population pyramid in the Valencian region in 2010 33
Graphic 5. Population breakdown by healthcare district 36
Graphic 6 Healthcare concessions in Spain 37
Graphic 7. Main principles of the Alzira Model 39
Graphic 8. Characteristics and correction factors of the per capita funding
system 41
Graphic 9. Dendogram showing the clusters 119
Graphic 10. Overall direct cost per hospital in 2010 (in millions of euros)
121
Graphic 11. Cost per equivalent patient in inpatient care area in 2010
123
Graphic 12. Cost per equivalent patient in the surgical area in 2010
126
Graphic 13. Cost per equivalent patient in the outpatient service area in 2010
127
Graphic 14. Cost per equivalent patient in the emergency department area
in 2010 129
Graphic 15. Equivalent patients in the medical and surgical area en el área
in Cluster 1 131
Graphic 16. Equivalent patients in the outpatient service area in cluster 1
132
Graphic 17. Equivalent patients in the Emergency department in Cluster 1
133
Graphic 18. Equivalent patients in the medical and surgical area in Cluster 2
134
10

GRAPHIC INDEX
Graphic 19. Equivalent patients in the outpatient service area in Cluster 2
135
Graphic 20. Equivalent patients in the Emergency department in Cluster 2
136
Graphic 21. Cost of the surgical and medical area compared to the
equivalent patients 138
Graphic 22. Inpatient cost compared with the equivalent patients 140
Graphic 23. Cost of operating rooms compared with the equivalent patients
142
Graphic 24. Quality analysis in the emergency department 146
Graphic 25. Quality analysis in the surgical area 149
Graphic 26. Quality analysis in the outpatient area 152
Graphic 27. Linear regression analysis of the cost and overall equivalent
patients 156
Graphic 28. Linear regression of the cost and equivalent patients in the
medical area 160
surgical area 163
outpatients area 167
emergency department area 170
Graphic 32. Adjusted cost per capita in the Valencia region 211
11

ABSTRACT
Public- Private Partnerships in Healthcare. Evaluation of 10 years’
experience in Spain
Health is one of the fundamental human rights, which is included in
the World Health Organization's Constitution of July 1946:
• The enjoyment of the highest attainable standard of health is one
of the fundamental rights of every human being.
• The right to health includes access to timely, acceptable, and
affordable health care of appropriate quality.
• The right to health means that every country must generate
conditions in which everyone can be as healthy as possible.
Healthcare is also one of the fundamental mainstays of the welfare
state in developed countries. Citizens' health is an essential objective
of each country, although it requires special attention and analysis
from an economic standpoint to ensure universal access and
sustainability.
In the last decade, healthcare management options different to the
conventional ones have been developed with the aim of ensuring
good healthcare quality and optimizing public resource utilization.
Among these options, one of the models that has attracted greater
interest in Spain is the administrative concession or Public Private
Partnership (from now on PPP) .
13

ABSTRACT
The first hospital in Spain to operate under the administrative
concession was in Alzira (Valencia), after the Valencia regional
government approved Law 15/1997 of 25 April, which enabled new
forms of management. Therefore, it is a public hospital that is
managed by a private company and provides specialist and primary
healthcare to a reference population through an economic
agreement, that establishes a fixed fee for each allocated patient,
undertaking to make the necessary investments in infrastructure and
equipment.
Control methods were jointly established with the Administration in
terms of compensation payments regarding the patients treated
outside the concession and those who are cared for without belonging
to it.
This model was later extended to a total of five districts in the
Valencia region and implemented in other regions under the name of
the "Alzira Model”.
Objec&ve

This PhD dissertation is aimed at analyzing the influence of the
health management model (direct management or PPP) in terms of
economic efficiency and healthcare quality.
14

ABSTRACT
Methodology

The data for the analyses were obtained from the information sources
of the Valencia Health Department for public hospitals. The
economic data for the PPPs (Alzira, Dénia, Torrevieja, Elche and
Manises) were provided directly by the hospitals themselves. In both
cases, the data refers to 2009 and 2010.
The variables included in the analysis are as follows: costs per
procedure, quality indicators, activity indicators and structural
indicators.
To compare efficiency and the factors influencing it, we performed
multivariate and non-stochastic analyses.
We also performed a hierarchical cluster analysis to group and
classify the hospitals in the Valencia region in a standardized way.
We used the data envelopment analysis (DEA) to divide hospitals into
efficient and inefficient in terms of management (direct or PPP),
overall and by specific unit in the surgical, inpatient, outpatient and
emergency department areas as well as by cluster. This was combined
with the quality and activity indicators and outputs.
15

ABSTRACT
Results

We obtained the following results in the study:
- The fact of being a hospital run by a PPP implies that they have a
lower cost than the rest of the other hospitals in the sample.
- The cost analysis by patient, adjusted for the case mix, shows that
the PPPs have lower than average costs in the surgical and outpatient
service areas. In the inpatient area, the PPPs have higher than
average costs, but all of them were considered efficient in the DEA
when quality indicators where included.
- In the emergency department area, one of the analyzed PPPs has
higher than average costs. These results were significant in the
regression analysis.
- The PPPs scored better in the quality indicators that were analyzed.
- In the overall DEA, two of the three PPPs obtained maximum
efficiency. Nine of the nineteen directly managed hospitals that were
analyzed achieved this level.
Conclusions

The performance and efficiency analyses show that the group of
PPPs outperformed the average for the directly managed hospitals,
but they were not always better.
16

ABSTRACT
The results of this dissertation will provide a sound basis for the
future research of economic assessments for healthcare management.
Nevertheless, new studies should be conducted that include a larger
number of hospitals which use the public-private collaboration
model.
17

1. INTRODUCTION
AND OBJECTIVES
18

CHAPTER 1 INTRODUCTION AND OBJECTIVES
1. Introduction
Healthcare is one of the most complex and dynamic industries in our
society. Its function is to look after individuals' health in an
increasingly complex situation as a result of users' growing demands,
social pressure, high-cost technology and scientific advances, plus
highly qualified professionals who constantly refresh and update
themselves. Healthcare accounts for 8% of the world GDP (Spanish
Health Ministry, 2008).
In the European Union, the public healthcare systems are
characterized by the following:
- Universal coverage for the population through social security
systems.
- Funding through the taxes accrued based on income.
- Coverage of hospital and pharmacy benefits through prescriptions.
- Control tools to maintain the system's sustainability.
Healthcare is one of the most complex and dynamic industries in our
society since it looks after our most valuable possession: our health.
Two forms of funding coexist in Europe: the Bismarck model (funded
with the social security system) and the Beveridge models funded by
taxes, as in the case of the Spanish National Health Service (Freire,
2006).
19

As a result of ﬁnancial austerity and an ever-increasing demand for
resources, the world's healthcare organizations and systems are
currently facing a major challenge (Walshe and Smith, 2011).
Evans (2005) drafted three basic fundamental questions which should
lead to healthcare reform and, therefore, to a possible change in the
management model.
1. Who funds healthcare and how much does it cost?
2. Who receives healthcare, what type of healthcare is received, when
should it be received and who is responsible for providing it?
3. Who gets paid for providing the service and how much?
Evans suggests that the conﬂict between the healthcare stakeholders
is usually because of their different views on how to answer such
questions.
Healthcare users, regardless of how they are called (patients,
consumers or customers), cannot be compared to consumers of other
public services or to clients of a service provided by a private
company:
Firstly, because the existing information between patients and
healthcare providers is asymmetrical; very few patients can contradict
20

a doctor's recommendation or treatment, no matter how qualiﬁed or
informed they are.
Secondly, patients are generally emotionally vulnerable, so they are
unlikely to act independently or assertively, which is usually the case
in other public or private services.
Therefore, the healthcare organizations and their managers have an
additional responsibility to offset the unequal situation of the patients
being cared for in the health system since, in the end, all the
hospitals, whether they are public, private or administrative
concessions, share the same concern about their wellbeing.
These are clearly changing times in the world healthcare scenario,
where it will be necessary to innovate and implement new resource
management methods (Drucker, 2006). In some countries, the
governments have recently increased their control, as in the case of
Mexico and Colombia, which have implemented a social security
system (Guerrero et al., 2011).
21

Graphic 1. Healthcare spending as a percentage of GDP in 2007
Source:
Spanish
Healthcare
Ministry,
2008

Graphic 1 shows the breakdown of healthcare spending as a
percentage of GDP in the OECD countries in 2007, based on
whether it is funded by the public or private system. The main
feature is the weighting of public funding in all the countries, even in
the United States.
22

1.2 Research objectives
1.2.1 General objective
The main objective of this PhD dissertation is to analyze and
compare the influence of the public or direct healthcare
management model with the public-private partnerships from the
standpoint of healthcare quality and economic efficiency.
1.2.2 Specific objectives
1. Analyze the existing literature in Spain and abroad with the aim of
identifying the main specific variables for benchmarking.
2. Select the most significant variables for constructing indicators
with the aim of measuring the efficiency and quality of healthcare
organizations.
3. Allocate a standard measurement with the aim of comparing the
hospitals and the cost breakdown.
4. Group the hospitals based on their structural resources and
healthcare production capacity.
5. Find the variables to explain the cost for each hospital area by
using a regression analysis.
23

6. Rank the relative efﬁciency of the hospitals and management
models based on this study.
24

2. BACKGROUND 
25

CHAPTER 2 BACKGROUND
2.1 Spanish healthcare context
Spain is currently in a critical economic context due to record
unemployment rates. As a result of this, plus an increase in life
expectancy (boosting the number of pensioners) and the excessively
large amount of civil servants (inherited from past economic booms),
the Administrations' current expenses are substantially higher than
their revenues (Arenas, 2013).
These factors have created a considerable budgetary tension and, as a
result, the regional policies prioritize the spending allocated to
maintaining the services rather than investing in infrastructure. As a
direct consequence, Spain has fallen considerably in the global
competitiveness index drafted by the World Economic Forum (2013),
as shown in table 1.
This chapter reviews the healthcare's economic aggregates within the
nationwide and regional context in Spain. It also summarizes the main
characteristics of the public-private partnership agreements. Lastly, it shows the
main Spanish and foreign contributions to healthcare service assessments.
26

Table 1. Global competitiveness index
Source:
Schwab
in
the
World
Economic
Forum,
2013.

As can be seen, Spain dropped 20 places in this index. In 2002, it was
ranked 22nd, higher than countries like France, while in 2010, it fell to
42nd.
This loss of competitiveness could jeopardize some of the
cornerstones of the welfare state: pensions, education, social services
and healthcare (Ochando, 2009).
Therefore, it is logical that one of the main debates right now is how
to cope with the growing healthcare spending (Table 2) while
establishing the necessary measures to control resource utilization,
which is somehow not being materialized but cannot be deferred any
longer.
Country/
Year
2002 2007 2009 2010
USA 1 1 2 4
UK 11 12 12 12
Germany 14 7 7 5
Spain 22 29 29 42
France 30 16 16 15
Italy 39 49 49 48
27

Table 2.Total budget and spending in Spain (million euros)
Source:
the
author,
based
on
Arenas
et
al.
(2013)
and
Informe
Nacional
de
Salud

2012
Nevertheless, despite the economic situation, the Spanish healthcare
system is still considered to be one of the best in the world. This is
evidenced in the studies by Gay et al. (2011), which analyze avoidable
mortality, where Spain is ranked among the top. It is considered to be
the reference indicator for healthcare quality (Nolte and McKee,
2008).
Therefore, we believe that it would be interesting to quantify (broadly
speaking) healthcare in Spain firstly and then in the Valencia region,
so that we are aware of the magnitude and need to implement
measures to improve the system's management and efficiency.
Year Budget Total

spending
Absolute

deficit
Deficit
(%)
2007 52,383 64,339 11,956 22.82
2008 56,559 71,170 14,611 25.74
2009 58,960 75,395 16,435 27.87
2010 59,738 74,732 14,956 25.10
Total 227,640 285,636 57,996 25.48
28

In 2010, the Spanish Federation of Associations for the Defense of
Public Health highlighted the difﬁculty in maintaining annual
increases of 10% in the government healthcare budgets (Federation
of Associations for the Defense of Public Healthcare, 2010).
According to García et al. (2010), healthcare spending is funded
mainly by government taxes, as can be seen in the healthcare
spending breakdown by source in 2013 (Graphic 2), where 71% is
used mainly for the headings stated in the preceding paragraph and
for medicines.
Graphic 2. Healthcare spending by source and funding in 2013
Source:
Spanish
Ministry
of
Health,
Equality
and
Social
Aﬀairs,2013.

.

Other
9%
Pharmacy
20%
Primary Care
16%
Specialized care
55%
Copayment
23%
Insurance
6%
Taxes
72%
29

2.2 Healthcare context in the Valencia
region
Graphic 3 shows the healthcare spending (5.49 billion euros)
breakdown by source in 2012, where the main headings are staff and
pharmacy expenses, in terms of both hospitals and prescriptions.
Graphic 3. Public healthcare spending breakdown in 2012
Source: the author, based on theValencia Health Department's budget for 2012..
8%
8%
7%
15%
4%
11%
47%
Staff PPP Prosthetics
Hospital pharmacy Pharmacy prescriptions Healthcare materials
General expenses
30

As can be seen in table 3, all the regions increased their healthcare
budget per capita in the 2003-2012 period, although there are
differences among them.
In 2012, the Valencia region had the lowest healthcare budget per
capita (1,110 euros) in Spain, i.e. 52% lower than Extremadura, the
region with the highest (1,692 euros).
Table 3. Healthcare budget per capita in 2003-2012
Source:
the
author,
based
on
the
Spanish
Ministry
of
Healthcare,
Equality
and
Social

Services,
2012
Some factors had a strong impact on the increase in healthcare
spending in the Valencia region:
• The 25.52% growth in the registered population between
1999 and 2013 (Table 4).
31

Table 4. Population in theValencia region
Source:
the
author,
based
on
the
NaKonal
StaKsKcs
InsKtute,
2013.

• A change in the demand structure: the ageing of the Spanish
and registered foreign population (graphic 4) plus an increase
in chronic diseases have had a direct impact on healthcare
spending. Also, as a result of the access to healthcare
information, patients now demand more from their doctors
than ever before. 
32

Graphic 4. Population pyramid in the Valencia region in 2010

Source:
Valencia
Region
Healthcare
Plan
through
the
PopulaKon
InformaKon
System

(SIP)
data,
2013
In 2010, healthcare spending amounted to 5.72 billion euros in the
Valencia regional government's budget (39.7% of the total). Despite
the overall reduction in the regional government's budget, the
percentage for healthcare has remained stable at around 40% since
2007 (Table 5).
Spanish males Spanish females Foreign femalesForeign males
33

Table 5. Overall budget for theValencia region and budget for its
Health Department.
Source:
Valencia
Regional
Budget
Act,
2012

The current funding system has not yet implemented any changes to
offset this situation: users maintain their status without contributing
their part to the service provided, and demand is higher than if it
were regulated by market forces. As a result of the imbalance
between supply and demand, there is a delay in the medical
treatment given to users.
Most of the public healthcare services are supplied by the institutions,
whose political responsibility depends on the elections, so the
decisions are usually very biased.
With the aim of improving the healthcare services' economic
efﬁciency without jeopardizing their quality, an innovative public-
2007 2008 2009 2010 2011 2012
Valencian
Regional
Budget
12.893 13.828 14.286 14.392 13.713 12.784
Healthcare
Budget
5.089 5.454 5.659 5.720 5.515 5.492
Healthcare
as a % of
the total
regional
budget
39,4% 39,5% 39,6% 39,7% 40,2% 39,9%
34

private partnership plan was implemented in 1997 both in the
Valencia region and in the rest of Spain.
2.2.1 The Alzira model
The Abril Report (Abril Martorell, 1991) analyzes the National
Health System's challenges and proposes ways in which to make the
system more viable and efficient in the future. The report provides a
novel concept: it separates healthcare funding from the service
provision.
The Valencia regional government culminated the legal reform
process that began with Law 15/1997 of 25 April on new forms of
management, which "opens up the healthcare services with any legal
form allowed by law". As a result of this law, the first hospital was
created under an administrative concession in Spain in 1999: La
Ribera Hospital.
At present, five healthcare districts are partially operated by private
insurance companies under the so-called "administrative concession",
which handles approximately 20% of the Valencia region's
population, as can be seen in graphic 5, which shows the number of
reference patients by healthcare district.
35

Graphic 5. Population breakdown by healthcare district
Source:
Healthcare
Plan
through
the
PopulaKon
InformaKon
System
(SIP),
2013

In the administrative concession agreements, most of the district has
public funding but is managed by a private company. The
agreements are awarded via government tenders (Caballer et al.,
2009).
The public-private partnership model, which includes managing the
healthcare staff, is present in both the Valencia and Madrid regions.
Graphic 6 shows a breakdown of the hospital bed numbers and the
year in which the hospitals were opened.
36

Graphic 6. Healthcare concessions in Spain
Source:
Spanish
InsKtute
for
Healthcare
Development
and
IntegraKon,
2013
Therefore, the ﬁve healthcare districts operated under an
administrative concession in the Valencia region are: Alzira,
Torrevieja, Dénia, Manises and Elche-Crevillente.
An administrative concession is an agreement that manages the
healthcare service of the reference population. Its purpose is to
provide comprehensive primary and specialist healthcare to the
population and it is funded by a premium per capita; the movements
of the protected population are invoiced, as well as the possible
patients from outside the district (graphics 7 and 8). The agreement
Number beds
Number beds
Number beds
Inauguration
Inauguration
Inauguration
Hospitals Others
Inauguration
37

lasts for 15 years and can be extended to 20 years. The activity is
supervised by the Administration through the Valencia Health
Department's Commissioner (De Rosa and Marín, 2007).
This management model includes the basic principle of separating
the funding made by the public sector from the service provided by
the private sector, as set out in the Abril Report. In this case, the joint
venture that was awarded the concession agreement is responsible for
providing the service. The public sector owns, funds and controls the
healthcare service while the private sector provides the service itself,
respecting the principles that it must be a free, quality, efﬁcient and
equitable service. One of the advantages of the model for the
regional administration is that it provides the healthcare network with
a quality public service without having to make any initial
investments and where the future costs are known and can be
planned (Tarazona et al., 2005).
2.2.1.1 Basic concepts of the Alzira model
The model is named after the Valencian town of Alzira in the La
Ribera area, where the ﬁrst hospital of this type was built.
This health management model is based on the following principles
(graphic 7):
38

Graphic 7. Main principles of the Alzira model
Source:
the
author,
based
on
De
Rosa
and
Marín,
2007

The point of view of the stakeholders in the public-private
partnership agreements (the Public Administration and the awarded
company) is summarized as follows:
The Public Administration
The following main points are deduced from the agreements'
speciﬁcations::
q The investment cost is borne by the awarded company.
39

q If there is staff that belongs to the public administration
(statutory civil servants), their cost must be borne by the
concession company. Such services are compensated between
the concession company and the public administration,
including the social security costs.
q If the healthcare services required by the citizens allocated to
the district are not available, so they need to be taken
somewhere else or rerouted, the Law on Rates (Ley de Tasas)
are applied since it indicates the cost of such treatments, as in
the case of transplants, which are very complex treatments that
must be applied at the reference hospitals.
In both cases, the statutory civil servants and the rerouting costs form
part of the services that must be compensated between the
concession company and the Administration during the concession
period.
The awarded company
a) Funding
q Per capita: this is the amount that the company receives for
each citizen allocated to the district. This premium is updated
annually and the increase cannot exceed the average increase
for the other regions and it must be at least the consumer price
40

index (CPI). In 2014, the per capita funding was 660 euros per
citizen in each district.
Graphic 8. Characteristics and correction factors of the per capita
funding system
Source:
the
author

q Census: the PPP shares the population information system
(SIP) with the Administration; this system determines the
number of people and their basic contact data. Thanks to this
system, the district and the Health Department can monitor
the patients and invoice the account of the patients treated at
other hospitals. It applies the number of people at September
30, even if this ﬁgure ﬂuctuates during the year.
41

q Other revenue sources: the concession company can invoice
the services provided to patients outside the protected
population. The price for providing the service has a discount
with respect to the Law on Rates: 20% in the case of Alzira
and 15% in the other concessions (see graphic 8).
The concession company can provide services to patients who belong
to insurance companies and work-related mutual societies,
establishing the rates with the company in question or applying the
rates for traffic accidents.
The specifications state that the concession company will not be
compensated for the patients covered by the Valencia Health Agency
dealt with in the primary healthcare centers who do not form part of
the reference population.
b) Investments
At the end of the PPP agreement, the concession company
undertakes to deliver all the used assets to the Administration.
c) Maximum profitability
The PPP cannot earn more than a 7.5% profitability. If this occurs,
the concession company is obligated to return that surplus to the
Administration by investing it in healthcare.
42

2.2.1.2 The beginnings of the Alzira model: La Ribera
Hospital
The first hospital under an administrative concession began at the
start of 1999. This significant event took place in La Ribera, the
former healthcare district number 10.
The model was faced with specific determining factors in terms of
both the social and economic conditions, including the following:
1. The additional problem of being the first hospital.
2. The establishment of a stable link between the Health
Department and the PPP.
3. The controversy in the public health system of a new
healthcare management formula.
4. The pressure from the media, political establishment and
trade unions.
The preceding factors were present in the social context and can be
classified as threats.
On the other hand, the model was faced with the following
challenges:
1. It would be difficult to manage only specialist healthcare.
43

2. Since it was the latest user to enter the system, it was
expected to reach optimal quality levels right from the start.
3. The economic variables were adjusted for the premium per
capita and for a short concession period (10 years) in
principle.
4. The industrial relations needed to be particularly analyzed
because of their importance in each concession agreement
under the Alzira model. Specifically, La Ribera Hospital had
to face the following factors:
✓ For the first time in the Spanish health system, the concession
company's staff had to work with the statutory civil servants at
the same hospital.
✓ A variable economic supplement was established based on
objectives.
The initial objectives of La Ribera Hospital were conditioned by
some "needs": firstly, the need to meet the population's demands; and
secondly, the need to prove that the new healthcare management
system was viable.
Therefore, La Ribera Hospital's strategy had to include concepts like
implementing competitive differentiation factors, providing added
44

value to its patients, creating the smallest possible conflict and taking
advantage of the private management tools.
2.3 A review of the literature on
healthcare service assessments
To make more reliable decisions, tools should also be used in the
healthcare industry that facilitate management and, in turn, provide
greater knowledge of the process efficiency.
Nevertheless, there are impressive studies such as the one conducted
by Holmberg and Rothstein (2011) which conclude that, after
analyzing the data in 120 countries, efficiency takes place when
public resources are well managed, i.e. when commissions in
developed countries and bribes in poor countries are eradicated and
when there is transparency in information and management based
on rational and not arbitrary decisions.
It has also been shown that the healthcare indicators do not have a
direct correlation to healthcare spending. In other words, higher
spending does not necessarily mean better results in health (a higher
life expectancy or a lower death rate) based on a threshold (The
National Academies, 2013).
45

One of the usual ways to assess performance is using an indicator-
based instrument, i.e. a Balanced Scorecard (BSC), and a non-
stochastic method to assess efficiency, i.e. the data envelopment
analysis (DEA) (Amado et al., 2012). The DEA has been widely used
in the healthcare sector, where Hollingsworth's (2008) review of the
literature stands out. Another very interesting contribution is the
assessment using the methodology proposed by Ballestero and
Maldonado (2004).
We also researched the multiple objective programming methods to
determine the efficient frontiers that combine the achievements in
quality and costs, as proposed by Romero (2004) in other fields.
It was not until 1988, with the work of López-Casasnovas and
Wagstaff, that the efficiency of Spanish hospitals began to be
measured, although it was not until three years later, with the work of
Ley (1991), that the DEA was first applied to assessing a sample of
Spanish hospitals. For example, at regional level, the efficiency of the
hospitals in Galicia was analyzed by Seijas and Iglesias (2009), who
analyzed the hospitals belonging to the Galician Health Service
between 2001 and 2006.
Outside Spain, the first ones to apply the DEA to the hospital
industry were in the United States: Sherman (1984); Banker et al.
(1986); and Grosskopf and Valdmanis, (1987).
46

The work by Puig Junoy and Dalmau (2000) and Cabasés et al. (2003
and 2007) provided evidence in this area since they made a thorough
review of the literature on the efficiency of hospital organizations in
Spain. Rodríguez-López and Sánchez Macías (2004) also made their
contribution by assessing the efficiency of the specialist healthcare
system in Spain.
The reference work regarding the stochastic efficiency frontier in
healthcare organizations was conducted by O'Neill et al. (2008), who
reviewed 79 studies using this technique, and Hollingsworth (2008),
who completed the study by reviewing 317 articles based on
measuring productivity and efficiency at hospitals, while also using
the frontier techniques.
2.3.1 Concept and measurement techniques of
efficiency in the hospital industry
"Hospitals or hospital areas must be oriented towards reaching
optimal results with a determined resource level". The first author to
introduce this concept in the literature was Debreu (1951).
Authors of economic theory take into consideration different options
when focusing on the hospitals to be assessed that will be used for
measuring efficiency. The two most usual functions are healthcare
costs and production, and these two variables are the usual ones that
47

are used to determine and define efficiency. The production frontier
determines the maximum output that can be made based on a
certain input level. In terms of costs, it represents the minimum
economic cost with which a certain output can be produced.
When measuring efficiency in economic and objective terms, we are
referring to the overall or economic efficiency which, in turn, are
divided into overall and allocative technical efficiency. In the former,
it measures the relationship between optimal inputs and outputs; in
the case of allocative efficiency, they are the output combinations at
the price level.
The starting point for measuring overall efficiency is the methodology
presented by Debreu and Farrell (1951), which is still used at present
to assess the efficiency of hospitals and other production units.
Broadly speaking, we can make a distinction between non-frontier
methods, econometric models and other models where an optimal
reference needs to be established, and frontier methods where non-
parametric and parametric models need to be differentiated.
One of the most common methods used at healthcare organizations
is the Data Envelopment Analysis (DEA), which is the reference non-
parametric technique. The calculations are made using linear
programming since it is not necessary to establish a reference unit in
the frontier that determines the optimal level; instead, this frontier
48

will be determined by the behavior of the other units in the sample.
One of the main features of the DEA is its deterministic nature, so
the deviations between the assessed units and the optimal frontier are
considered to be a technical inefficiency.
In the group of parametric techniques based on econometric
methodologies, the random ones based on a certain form of
production stand out (stochastic frontier). The difference with the
preceding ones (deterministic) is that, in this case, the deviations
include, apart from the technical inefficiency, external factors that do
not depend on the company management but on the context.
The methodology used in this dissertation is the Data Envelopment
Analysis (DEA) which, given its flexibility with respect to the initial
assumptions and to the lower demand in the observations, will enable
us to assess the efficiency of hospital organizations based on their
type of management.
2.3.2 Efficiency analysis method
To analyze efficiency, we will use two types of models: the
generalized linear regression models and the non-stochastic methods
using the Data Envelopment Analysis (DEA).
49

2.3.2.1 Multivariate methods
Regression analysis (or econometric models): this is the usual
type of analysis when assessing hospital efficiency. By using the data
from all the hospitals or a particular healthcare service, we establish a
production function with several inputs as independent variables that
influence the result and a single dependent variable that determines
the performance, the procedural effectiveness or the cost efficiency.
We then estimate different regressions, assessing and determining
how the independent variables influence the dependent or
performance variable individually.
Therefore, each regression becomes the prediction of a situation. For
example, for a number of operating rooms or doctors (input), we can
determine the result, i.e. the number of hospital stays or the average
complexity in the process (output). Such predictions are obtained
using the average for the other hospitals' results. Therefore, the
difference between a hospital's performance results and the sample
average will be determined by the regression remainder.
The regression remainder will be positive in the hospitals that obtain
better results than expected. The best result for a specific hospital will
be the one that obtains the largest remainder.
50

Our study also includes the generalized linear regression model, to
identify the variables that have a greater effect on the composition of
the overall costs, and the costs by area.
To see the effect of the management model on the several variables
addressed by our study, we analyzed the difference in the averages by
applying the T-test to the variables that meet the normality
assumptions and the Mann–Whitney U test (1947) to the variables
that do not meet Kolmogorov's normality test.
The regression analysis has certain limits when identifying the best
praxis since the efficiency information that it provides is limited. A
method such as the stochastic frontier regression (SFR) enables us to
model the error term in two parts: the first one shows the deviations
with respect to an optimal frontier, and the second one determines
the conventional statistical noise (Chirikos et al., 2000). The
stochastic frontier regression breaks down the error term and
determines the overall efficiency level based on the sample of possible
suppliers, and it subsequently calculates their deviations based on
their distance with respect to the efficiency frontier. The
aforementioned authors state that there is a need to conduct more
comparative studies of the results obtained using the DEA and SFR,
whose main characteristics are detailed in the next section.
51

2.3.2.2 Non-stochastic methods
Data Envelopment Analysis (DEA): the DEA has become a very
valuable tool for making comparative efficiency analyses, especially in
the public sector. Efficiency studies are now conducted on hospitals to
assess their behavior based on the basic principles of microeconomic
theory, such as maximizing profits.
Using the DEA technique, efficiency is calculated by solving the
multiple linear programming problems for each hospital, calling them
decision making units (DMU), with the aim of determining their
overall efficiency level so their inputs are weighted to maximize the
weighted results between both, taking into account the restriction that
all hospitals using this weighting obtain the maximum result,
represented by the value 1, or lower than this value if they are
inefficient with respect to the others.
In this way, different ratios are obtained with the most beneficial
weightings for each hospital; such ratios and the radial efficiency
concept were established for the first time by Farrell in 1957, which is
why they are also called efficiency indexes.
The frontier is established by the healthcare units considered to be
efficient since they have an optimal reference index (1) and any linear
combination thereof; in this way, one point in the frontier dominates
or equals, in production terms, the maximum vector of outputs given
52

certain inputs or the minimum inputs given a vector output, or any
other feasible production place or unit observed. If we want to assess
a group of "N" production units and each unit consumes "K" inputs
(x1,….,xk) and produces "M" outputs (y1,….,ym), the efﬁciency of
DMU 1 will be assessed by solving the following problem:
subject to:
where:
ys0 = quantity of output s per DMU,
us = weighting corresponding to output s,
xm0 = quantity of input m per DMU,
vm = weighting corresponding to input m, and where n is the
observation of the various decision making units (j = 1, 2,..., n) that
use p inputs (inputs = 1, 2,..., p) to produce v outputs (v = 1, 2,..., s),
where the variables to be weighted of both the inputs and outputs are
vi and ur, respectively, and the inputs and outputs observed are those
of the assessed unit xij0 and yrj0.
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The DEA model is also known as CCR because of the surnames of
its authors, Charnes, et al. (1978). They developed it with three
restrictions on technology: specifically, they referred to constant
returns to scale, convexity of the set of feasible input-output
combinations and strong disposability of inputs and outputs.
Banker et al. (1984) also contributed to the CCR model, developing it
from the original, by taking into account the fact that underlying
technology could provide a different and variable performance. This
model is called BCC and it is the methodology that we applied to
determine the efficiency of the hospitals in the Valencia region based
on their management model. The hospitals are compared with other
similar sized ones, which is why we grouped the hospitals into clusters
in our sample, so that the analyses have significant conclusions and
the efficiency indexes can determine the result of a hospital with
respect to the one with the highest productivity and efficiency, which
we will use in chapter four below.
Both the BCC and CRR models were used in their input-oriented
versions, relating the necessary inputs to reach the efficiency frontier
in a certain output. One characteristic when making the analyses is
that the hospital cannot influence the output level since they have to
care for the patients who go to the hospitals randomly and
exogenously. This is why we believe that it is more appropriate to
analyze their behavior from the point of view of the minimum use of
54

resources to meet the healthcare demand and not the other way
around.
The efficiency results of the CCR and BCC models show the inputs'
maximum proportional reduction to reach the efficiency frontier.
Since the hospitals use different production factors (inputs) at the
same time to produce different outputs, it is necessary to use tools
that analyze both factors (input and output). In other words, it would
be interesting to know not only if the hospitals have chosen the
production level that maximizes profit but also if that production
level has been achieved with the lowest quantity of inputs or by
minimizing the production cost.
The main advantage of using the DEA is the flexibility that it gives
when analyzing the information. The inputs can be continuous
variables, ordinals or categories grouped into variables. They can also
be represented in different measurement units depending on the
analysis to be made (case mix, beds in use, delays, etc.). In the same
way, the output term can be analyzed from a much broader
perspective, including quality and performance results.
The most widely accepted advantages of using the DEA when
establishing comparative analyses in the service sector are as follows:
‣ The DEA mathematically establishes the optimal weighting for
each input and product considered. Since the DEA is a non-
55

parametric technique, there is no need to allocate a weighting to each
variable; the DEA's methodology itself allocates a weighting to each
input and output.
‣ The DEA can make simultaneous comparative analyses of multiple
dependent performance variables (cost efficiency and results, quality
and results) and provide a scale based on the best practices. In this
way, each hospital can be compared with a similar sample and
measured from two standpoints: allocative and technical efficiency.
‣ Once the suppliers that form the efficiency frontier are determined,
the DEA can estimate the quantity of idle resources or the additional
quantity of results, quality or production that can be made by an
inefficient DMU, or hospital in our case.
The main limitations of the DEA are as follows:
‣ Since it is a non-parametric technique, it does not have any
statistical indicators to measure the error term (noise) as in the case
of regressions. This is why it is not the best technique for making
assumptions.
‣ Another technical consideration which could limit the scope of the
analysis depending on each case is the number of DMUs to be
considered. Although there are no studies or fixed rules, many
authors suggest that the number of variables should be between 4
56

and 15 observations for each independent variable included in a
regression analysis.
‣ The same occurs with the number of input and output variables to
be included. Too many variables are considered to be
methodologically wrong, so our study does not include more than
four.
‣ When performing the DEA, the result determines the suppliers
considered to be efﬁcient; nevertheless, the DEA does not
discriminate the relative differences between the various DMUs.
As a result of such considerations, most authors use at least two of
the preceding tools in a supplementary way with the aim of obtaining
different perspectives of the relative efﬁciency results, particularly in
the case of healthcare service suppliers.
2.3.3 Hierarchical analysis. Clusters
To see the different performance of the hospitals, it is fundamental to
take into account the structural and activity characteristics of each
one.
The need to compare hospitals as a way of sharing improvement was
a concept implemented in the public sector by Marshall et al. (2000),
in which standard groups or clusters were used.
57

The cluster analysis is a group of techniques used for classifying
objects or cases into standard groups called clusters with respect to a
predetermined selection criterion (Anderberg, 1973).
The purpose of the cluster analysis is to group the observations so
that the data are very similar within the same groups (minimum
variance) and these groups are as different as possible between them
(maximum variance). In other words, if the classiﬁcation is optimal,
the objects within each cluster will be similar to each other and the
different clusters will be very different from each other. In this way,
we obtain the classiﬁcation of the multivariate data with the aim of
having a better understanding and the population to where they
belong. We can make a cluster analysis of cases or variables or by
blocks if variables and cases are grouped.
After selecting the variables and calculating the similarities, we began
the grouping process. Firstly, we selected the grouping's algorithm to
form the groups (clusters) and, subsequently, we determined the
number of groups to be formed. These two procedures will depend
on the results obtained and on the interpretation arising therefrom.
There are two types of grouping procedures: hierarchical and non-
hierarchical. The hierarchical cluster is characterized by a tree
hierarchy or structure (dendrogram). In that way, clusters are formed
only by the union of existing groups; therefore, any member in a
58

cluster can trace its relationship in an unbreakable path that starts
with a simple connection.
2.3.4 Diagnosis related groups (DRGs)
In the healthcare context, we remember that no two episodes are the
same, even if the same disease is treated. Nevertheless, thanks to the
DRGs, we can group patients with a similar resource utilization level
to obtain the hospital case mix (Guadalajara, 1994).
These cases are the episodes that are treated, i.e. the patients cared
for at the hospital; therefore, when referring to the case mix, we mean
the different types of patients treated.
For professional doctors, the case mix complexity entails a clinical
complexity; in this case, a greater complexity will entail a worse
situation for the patient. Therefore, a higher case mix indicates a
worse prognosis and greater need for healthcare resources. For the
hospital managers and in the role played by the heads of department
of the administrations, a higher case mix implies greater resource
utilization which, therefore, entails higher costs.
However, the purpose of the DRGs is to relate the hospital case mix
to the demand for resources and the costs incurred by the hospital so,
from the standpoint of the DRGs, a greater case mix complexity
means that patients will need more hospital resources.
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As a result of using the DRG system to measure hospital complexity
in the last few years, the isolated management based on the clinical
service has evolved into a cross-sectional process management based
on the product. This has led to a style of hospital, complexity or case
mix management in which the organization's management is based
on the hospital processes.
2.3.4.1 Origin of the DRGs
The DRGs were designed and developed at the end of the 1960s at
Yale University (United States). The initial reason for developing
them was to analyze healthcare quality and the use of the hospital
services. The work, which was commissioned by the Health Care
Financing Administration, lasted just over a decade. The research
was carried out by a multidisciplinary technical team directed by
professor Robert Fetter. Specifically, the initial study focused on Yale-
New Haven Hospital (Fetter and Freeman, 1986).
The system was first implemented at a large scale at the end of the
1970s in New Jersey (US). In this case, the DRGs were used for a
specific fixed payment system based on each patient treated in
accordance with his own DRG, which determined the average cost of
treating this disease (Hsiao et al., 1986).
60

2.3.4.2 Development of the DRGs
During the DRG development process, it was considered that the
patient classification system should have the following characteristics
so that it would be as practical and logical as possible:
‣ The information about the patient characteristics used for defining
the DRGs would had to be usually summarized in the hospital
reports. The DRGs had to be based on easily available information.
‣ The grouping had to include all the hospital patients with a
manageable number of DRGs, limiting the amount of groups to
ensure their practical use.
‣ The patients within each DRG had to have a similar resource
utilization level, implying a similar treatment cost. Even though there
could be variations in the resource volume used by the patients of a
certain group, they would be known and predictable.
‣ The patients within each DRG had to be similar from a clinical
standpoint, i.e. there had to be a clinical coherence.
2.3.4.3 The weightings of the DRG
The concept of weighting refers to the resource level that may be
needed to treat a case in a specific DRG. The weighting is calculated
based on relativizing the average cost of each patient group, i.e. it is
61

obtained by comparing the individual costs of the various DRGs with
the average cost per patient. Therefore, the relative weighting
associated with each DRG represents the foreseeable cost of that
patient type with respect to the average cost of all the patients.
If the relative weighting of the DRG is equal to 1, this means that the
cost of treating such patients is equivalent to the average cost of the
inpatient (standard). However, if this value is higher or lower than 1,
this means that the speciﬁc cost of this DRG is higher or lower,
respectively, than the cost of the standard patient.
In Spain, the weighting of the DRGs based on the calculations made
in the United States was used until 1997 since there were no speciﬁc
studies. In 1997, the Spanish Ministry of Health and Consumption
began a study called "Analysis and development of the DRGs in the
National Health System", which was coordinated by Rivero (1997).
The various Spanish regions participated in that work since the
healthcare powers had been devolved to them. As a result, national
weightings were implemented and it provided a way of standardizing
the Spanish system of allocating costs at hospitals. The study marked
a turning point in this area and, since then, the national DRG
weightings are reviewed annually. They are calculated by using the
hospital cost information, obtained by the analytical accounting
systems, of a sample of patient discharges representing all the
62

Spanish hospitals. At present, of all the different versions, the system
uses version 23 of the AP-GRD, which includes a total of 676 DRGs.
The combination of the DRG categories with the Spanish weightings
is a very important instrument thanks to their multiple uses. The
DRGs are mainly used as the basis for the healthcare funding,
internal management and quality improvement systems.
In the healthcare industry, the DRG weightings and costs are widely
used as a budget tool. In Spain, most of the hospitals partially or fully
fund their activity based on the DRGs.
In the Valencia region, the funding of the districts under concession
is per capita and the invoicing between hospitals is calculated based
on DRG-assessed processes.
The DRGs relate the patient type in a hospital to the costs that
should be incurred by the hospital for treating such patients;
therefore, from the management standpoint, estimating the average
costs can be used for controlling the service use and facilitating
hospital management in relation to the resource utilization.
Moreover, this provides a double reading: ﬁrstly, the hospitals have a
nationwide reference; and secondly, using the standardized
measurement, they can be compared with each other thanks to the
63

DRGs by detecting areas for improvement at the hospitals themselves
and facilitating the decision-making of the management teams.
In the context of quality improvement in healthcare, hospitals can
use them as standard indicators, using the DRGs as a healthcare
quality management tool.
Healthcare quality improvement is based on analyzing the deviation
from the rule. When the deviation is signiﬁcant, the managers must
determine the reason for this. A usual example is to use the average
stay of patients per DRG with the aim of detecting possible
complications in the procedures with hospital admission.
Therefore, a DRG is a group of patients with a certain illness that
need similar treatments and use similar resource levels (Fetter et al.,
1980). The cases that belong to the same category have similar costs,
so we know the average cost of treating the patients within the same
DRG and, therefore, the average total cost of that clinical service
area.
2.3.4.4 The "product" that a hospital provides
Healthcare organizations are currently considered to be service
companies as part of the business network. They are companies that
combine human and physical factors (real estate or supplies) in
clinical processes with the aim of optimizing the health and wellbeing
64

of their patients. Having defined this concept, we will know talk
about the "product" that a hospital provides and how it is measured.
In 2000, a study by Brignall and Modell divided the "product" and its
performance into three sections: financial results, quality indicators
based on the performance of the organization's professionals and
resource utilization. The importance of each group is determined by
its setting and by the hospital strategy.
The hospital organizations have very diverse procedures due mainly
to the unique characteristics of the patients they care for. Therefore,
hospitals provide both tangible products (blood test or X-ray results
or prosthetics) and intangible results (the perceived service, the
clinical diagnosis, etc.). However, the "product" is defined as the care
of the patient to whom the doctor has applied a clinical treatment
(Fresneda, 1998). Even if two patients have the same disease, there
may be different underlying factors that determine the procedure and
make them different.
Therefore, a healthcare organization has both the "products" and the
patients that they care for and it is difficult to standardize them as in
other industrialized sectors. This difficulty is determined by the
differences in, and amounts of, patients cared for and the hospital
procedures.
65

2.3.5 Public-private collaboration experience in
healthcare
Regarding the public-private collaboration level, the amount of
infrastructure for providing public services with private funding has
quadrupled in recent years (Abadie, 2008), especially in Europe (La
Forgia and Harding, 2009).
A widely used public-private collaboration model is when a private
company rebuilds a hospital whose infrastructure has become
obsolete (Gomez-Ibañez, 2003). This model is called PFI (Project
Finance Initiative) and was developed in the United Kingdom in the
early 1990s by the Labour and Conservative governments, becoming
a reference and test bed for the other European countries (Nieto,
2004).
In the United Kingdom alone, there are more than 100 projects of
this nature, with an estimated value of 25.8 trillion dollars, which
vary from hospitals for isolated communities, with a budget of
around 15 million dollars, to more than 2 trillion dollars such as the
refurbishment of the Royal London and St. Bartholomew's hospitals
in London (Barlow et al., 2013).
Regarding previous experience in comparing the models, a study was
conducted in Brazil in 2009 (La Forgia et al.), where a PPP (Public
66

Private Partnership) was implemented to care for the low-income
population living in the periphery of São Paulo state.
That study compared 12 directly managed hospitals with 12 under
concession. Both groups were considered to be standard in terms of
size, cost per bed and complexity of the population cared for.
The increase in the use of the public-private collaboration models in
healthcare is conditioned by the current economic recession and the
tax restrictions. We will see greater development in post-Soviet
Europe, where the hospitals do not meet the patients' service demand
and manage their resources inefficiently (Coelho et al., 2009).
The European Commission has recently published an assessment
study on the public-private partnerships in Europe (EXPH, 2014).
One of the main conclusions is that there is insufficient information
to assess the PPP model compared with direct management, so this
dissertation undoubtedly provides scientific value and knowledge to
this area. In the same line, in 2012 the Spanish Society of Public
Health and Healthcare Administration (SESPAS) stated that there
was no evidence of there being any advantages in implementing
PPPs or needing to conduct studies to demonstrate this (Palomo et
al., 2012).
The study by the Commission's expert panel compares the most
frequently used PFI models in Europe. Nevertheless, hospitals in
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Western Europe, with more modern facilities, should be redeﬁned by
the change in the hospital model trend towards more ambulatory
processes and management of chronic cases, thus reducing the need
for beds for acute processes (Rechel et al., 2009).
There are 19 public-private collaboration projects in the healthcare
industry in Spain (James et al., 2010), which are worth 2.3 trillion US
dollars, considerably below other countries, where these types of
contracts are used much more widely, as in the case of the United
Kingdom (stated above) and Italy, where there are 71 projects worth
5.7 trillion dollars; although considerably higher than countries such
as France, which has 16 projects worth 1.6 trillion dollars.
As stated at the start of this chapter, the PFI collaborations, apart
from funding the construction work, also have a service provision
contract to maintain the building or the central non-medical services,
such as restaurants and coffee shops, laboratories, sterilization
services, waste collection and surveillance services.
A limit to the model with respect to the market is that it does not
enable free competition due to the transaction costs for both the
establishment and maintenance.
In Spain, there is a benchmark study in the healthcare industry
conducted by Catalan company IASIST, which issued a report in
2011 based on the Minimum Basic Data Set (MBDS), whose
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database is one of the sources we used for our dissertation. IASIST
compares the directly managed hospitals with other forms of
management, such as foundations, consortiums, PFI and PPPs.
Arenas (2013) conducted a comparative study in the Valencia region.
The analysis compared the cost of the reference patients (per capita)
of the concession districts with those of the directly managed ones:
the cost was 31.77% lower in the case of the PPPs.
When comparing the models, there are also unfavorable opinions
about the use of the public-private collaboration model. The
SESPAS report (Sanchez-Martínez et al., 2014) states that the public
or private ownership of hospitals does not determine their results.
Likewise, it states that the discussion should be abandoned since there
are no factors that can assess the performance of both options.
In the healthcare area, there is a study which shows that quality does
not differ depending on the legal form, but it does acknowledge that
there are better results in ambulatory major surgery rates and greater
clinical effectiveness due to the concession companies' technological
equipment (Coduras et al., 2008).
A study by Salvador Peiró in 2012 comparing the efﬁciency of the
hospitals under concession and those directly managed shows that the
lower cost of admissions at the PPPs seems to be related to a larger
69

number of admissions at them, so the ﬁxed costs are spread out, thus
reducing this value.
Peiró (2013) added to the preceding study that private management
does not guarantee higher healthcare quality than direct
management or vice versa.
Outside Spain, Masson et al. (2010) state that the hospitals managed
under public-private collaboration treat less complex patients than
the other hospitals belonging to the United Kingdom's National
Health Service. Likewise, those private hospitals have a lower coding
level than the public ones.
Another comparative study of the PFI models was conducted in Italy
(Vecchi et al., 2010), which concludes that the return obtained on this
model by the investors is considerably larger than that expected in a
competitive environment.
In Germany, Herr (2008) compared the healthcare results obtained at
the hospitals using the PFI model with those directly managed, where
the average stay at public hospitals was 3.52 days less than at the PFI
ones. Likewise, the study states that only 59% of the PFI hospitals
have an ambulatory unit which, therefore, leads to greater pressure
on hospital admissions.
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In France, Dormon and Milcent (2012) studied a sample of 1,604
hospitals, i.e. 95% of the specialist healthcare supply in the country,
for a period of 5 years (1998-2003). To make the comparisons, they
divided those hospitals into three groups based on the number of
discharges and they weighted the stays of the DRGs based on the
equivalence tables called ISAs (Indice synthétique dáctivité). Using
this indicator per hospital bed, the private hospitals are 70.6% more
efficient. This is because there are more unoccupied beds at the
public hospitals. In the same way, the French public hospitals focus
more on longer stays, while the private hospitals have a larger rate of
operations. This is particularly significant when analyzing the small
hospitals where the stay is 9.3 days at public ones and 3.8 at private
ones. Therefore, it is not surprising that there is more healthcare staff
(7.6 vs. 1.7 and 3.7 vs. 1.9) at small and medium hospitals,
respectively.
Since there is a lack of studies in Spain, we need to review the
literature from other countries that have traditionally used public-
private collaboration (PPC) but with PFI models (Barlow et al., 2013).
Based on the comparison of such PFI models in the United
Kingdom, the data show that there may be higher costs with respect
to public sector borrowing due to the higher financial costs of the
private operators and their economic margins (Hellowell and Pollock,
2007 and 2009).
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When comparing the results from the PFI models with those of direct
management, some authors state that the public-private collaboration
in building a hospital implies higher costs. Likewise, the authors state
that this could also mean a lower quality service in managing the
general services (McKee et al., 2006). Such authors also highlight the
studies which show the complex nature of managing a long-term
healthcare concession.
Another controversial point is the need to have control mechanisms
between the parties involved (Brown and Potosky, 2004).
There are also studies that warn about the underlying risk in public-
private collaboration beyond the PFI model.
72

3. ASSUMPTIONS AND
INFORMATION
SOURCES
73

CHAPTER 3 ASSUMPTIONS AND INFORMATION SOURCES
3.1 Assumptions for the study
In the preceding chapter, we provided the various contributions made
to the comparisons between the public-private collaboration and the
traditional public healthcare or direct management model.
The main assumption for this PhD dissertation, which analyzes the
healthcare quality and efficiency of the economic model based on the
type of management, is that the PPP model is more efficient in terms
of healthcare quality and economics than direct management (public
sector), although this efficiency is nuanced or influenced by the
hospital area in question and will have a different result depending on
the analyzed indicators.
3.2 Information sources
We can group the various information sources used for this
dissertation based on the nature of their indicators:
Having reviewed the scientific contributions, this section will now detail the
main assumptions used for the study and show the information sources used
with their corresponding indicators, which will determine the field study and its
conclusions.
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-Economic information/costs: we obtained this data from the
Valencia Health Department's Economic Information system (SIE)
regarding the hospitals that are directly managed. Since the
concession companies are not included in that information system,
we asked them to complete a form with the costs based on their
source or cost pool. Once that information was collected, we adapted
the data to the SIE to standardize this.
- Quality: we obtained the quality indicator results based on the
assessment carried out by the Valencia Health Department (2010) in
the Management Agreements, where the hospitals that are directly
managed and those under concession have been assessed.
- Healthcare production: to obtain the hospital revenues, we used
the Minimum Basic Data Set (MBDS) for all the hospital discharges.
Nevertheless, to have an overall view of the specialist healthcare, we
obtained the information from the SISAL (Healthcare Activity
Information System) for the outpatient and emergency areas. Both
databases were provided by the Valencia Health Department.
- Structure: to obtain the structural aggregates of the hospitals
managed directly and those under concession, we used the
information from the SISAL system.
- Period: all the assessed data refer to 2009 and 2010.
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- Hospitals analyzed:
✓ Directly managed hospitals:
La Plana Hospital (Castellón)
University General Hospital (Valencia)
Castellón General Hospital
Castellón Provincial Hospital
Arnau de Vilanova Hospital (Valencia)
Requena Hospital (Valencia)
Sagunto Hospital
Vega Baja Hospital (Orihuela)
Vilajoyosa Hospital (Alicante)
San Juan Hospital (Alicante)
Clinical University Hospital (Valencia)
Malvarrosa Hospital (Valencia)
Alicante General Hospital
Verge dels Liris Hospital (Alcoi)
San Francisco de Borja Hospital (Gandía)
Dr Peset Hospital (Valencia)
Onteniente Hospital
Lluís Alcañiz Hospital (Xátiva)
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Elda General Hospital
Elche General Hospital
Vinaroz Hospital
La Fe Hospital
✓ Hospitals under an administrative concession (Valencia):
La Ribera Hospital (Alzira)
Elche-Vinalopó Hospital
Dénia Hospital
Torrevieja Hospital
Manises Hospital
3.2.1 Sources of economic information
"SIE is an information system that collects and analyzes the data on
activity and costs in hospitals and provides standardized indicators for
costs, activities and cost per activity which provide a comparison
between hospitals, making it a tool to help decision-making. It is used
as a healthcare management instrument, helping to improve the
effective and efﬁcient use of the public healthcare system's
resources" (Valencia Health Department, 2002).
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It is an analytical accounting system based on an the activity-based
costing (ABC) model. The system comprises two large subsystems of
data collection and analysis: one for the total costs incurred by the
hospitals and the other for the activities generated by the resource
utilization. It shows the cost of each intermediate product identified
in the healthcare process, based on the actual cost of the pool and on
the activity performed in the same period.
The data with which the SIE works is obtained from other
information systems on utilization and activity that form part of the
healthcare system, such as salaries, supplies and storage, specialist
healthcare management indicators, pharmacy, prosthetics, concerted
agreements and procedure catalogues. The SIE is a flexible system,
i.e. it can adapt to the different information availabilities of each
hospital and its specific organizational features.
SIE was implemented in 1992 to improve the economic information
of the Health Department's healthcare districts, helping to make
better decisions by combining the two aspects of healthcare activity:
production and resource utilization.
Since the beginning, SIE has evolved with the aim of adapting as far
as possible to the complex healthcare structure. As a result of
standardizing the activity that is measured at hospitals through the
SIE, part of the results are used for drafting the healthcare tariffs
78

included in the Valencia regional government's Law on Rates,
through which the compensation between districts is made.
SIE has been implemented at all the hospitals in the Valencia region's
public network and, at present, it has a single database for all the
centers.
Nuances to the economic data:
As stated previously, the data included in the SIE cover the hospitals
that are directly managed by the public sector. This means that it
does not have information about the districts managed under PPPs
(Manises, La Ribera, Dénia, Elx-Crevillent and Torrevieja). Since the
economic efﬁciency analysis was made for 2010, we have included
only three PPPs, Alcira (La Ribera Hospital), Dénia and Torrevieja,
as we did not have the costs for the others.
Likewise, we must remember that our data collection date is very
close to the date on which Dénia hospital was opened (2009), so there
may be inefﬁciencies due to the opening itself, aggravated by the fact
that it was a move and not a new opening.
Also, we did not include information about medium- and long-stay
hospitals since they do not have a Minimum Basic Data Set (MBDS/
CMBD in Spanish) and the cost per DRG cannot be calculated.
79

We did not include the DRGs classiﬁed as indeterminate in the
MBDS (type 0) since we cannot classify such processes as either
medical or surgical.
The scope of the information is at hospital level in both cases, i.e.
direct management and under concession. This means that we did
not include the activity carried out at the outpatient services
(specialist centers, integrated hospitals, etc.) or in the primary care
units.
We grouped the information recorded at the cost and cost pools
(hereinafter, CACs) into the four large healthcare activity areas of a
hospital: outpatient, emergency, inpatient and surgical; and we
allocated their direct costs.
We did not take into account the allocation of the structural and
logistics costs such as the support units (admissions, patient care, etc.)
and secondary structural costs, or the primary structural costs (water,
electricity, maintenance, etc.).
Another limit to the information is that we did not include the costs
from the central healthcare CACs (the diagnosis and treatment
CACs: the hematology, biochemical, microbiology, radiology,
rehabilitation, lithotripsy, electrophysiology and cardio stimulation,
hemodynamics and interventional cardiology laboratories, etc.) or the
surgical area. In fact, the costs of those CACs should be passed on to
80

the end CACs but SIE does not allocate such costs; that is the
criterion we used when allocating the information about the
concession costs.
Therefore, the costs of the end inpatient or ambulatory CACs do not
include the cost of the diagnosis and therapeutic tests made on the
inpatients or the patients dealt with in the outpatient services and
emergency units. They do include the cost of the specialization's
diagnosis and therapeutic procedures (neurology, digestive medicine
or the clinical service in question) carried out in the inpatient rooms
during the admission period.
We excluded the following ambulatory activities: the day hospital and
the home care programs.
Regarding the surgical area, we included the costs of the surgical
group and of the associated, local and general anesthesia acts which
are included separately in the SIE. Major ambulatory surgery is
considered to be a different CAC in the SIE but we included it to
cover all the surgical group's expenses.
We also excluded the CACs that are not allocated from the CAC list.
Such costs are those borne by the hospital but do not correspond to
their own activity. The pharmaceutical products dispensed to
ambulatory patients stand out since they account for a large volume
81

of the cost, and include dispensing pharmaceutical products for
hospital use to ambulatory patients, medicines to dialysis clubs, etc.
3.2.2 Quality information sources
Management Agreements (Acuerdos de Gestión)
The Management Agreements were implemented in the Valencia
Health Agency hospitals through an agreement with the Valencia
Government's Council in 2004, which establishes a variable bonus to
motivate and differentiate the remuneration of the healthcare staff.
Meeting such targets depends on the results obtained in the quality
and efﬁciency indicators, which are previously agreed by the Valencia
Health Department and the management of each healthcare district.
Because of the importance of human resources in the healthcare
industry, the Strategic Plan conﬁrms the need to develop them so that
the system can work correctly. This means that there is a need to
implement mechanisms that acknowledge and compensate the
professionals' performance, based on their participation in the Health
Department's targets.
Therefore, the Management Agreements show the strategy which
must be carried out by the hospitals that belong to the Valencia
Health Department: the healthcare districts, the public hospitals, the
chronic and long-term care hospitals, the inspection bodies, etc.
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Each hospital has KPI (Key Performance Indicators) with a goal and
a weighting. Through the Strategic Plan Ofﬁce, the Health
Department monitors the indicators every month and makes a ﬁnal
assessment at December 31, and subsequently publishes the results,
comparing the hospitals by standardized group.
The main characteristics of the Valencia Health Department's
Management Agreements are as follows:
a) There is a link between the public hospitals and all their
professionals with the strategy previously set out by the Health
Department.
b) Because of the possible differences between the hospitals and the
professionals, it is necessary to be impartial when establishing the
targets, so the requirements must be associated with each
organization's resources.
c) Teamwork will be necessary for assessing the professionals'
individual actions in their own service. Regardless of their category,
all the professionals are assessed for their individual contribution to
the results of the unit where they work.
d) The professionals and their representatives must participate in
establishing the targets.
83

e) The economic compensation must be in proportion to meeting the
targets.
f) The process must be transparent, through publicity and control
throughout the year.
g) All the process of establishing and assessing the indicators must be
objective.
The fundamental objective of the Management Agreements is to
achieve greater efﬁciency in the public healthcare service provided by
the healthcare district regarding both the population allocated to it
and that from the other hospitals requiring its services.
Through the Management Agreements, the Valencia Health
Department analyzes and ranks the hospitals; from 2013, this was
carried out by dividing the hospitals into standardized groups
regarding to the management model (direct or PPP).
3.2.3 Information sources for the healthcare
production
The Minimum Basic Data Set (MBDS/CMBD in
Spanish)
84

The basic data necessary for allocating a patient to a certain DRG
comes from the hospital discharge report. Such data is included in
the MBDS, deﬁned as the Minimum Basic Data Set for hospital
discharges. The MBDS is a source of standardized data that contains
21 variables with administrative, demographic and clinical-
epidemiological information about the morbidity treated of the
inpatients and of the patients at the ambulatory surgical services
(Fusté et al., 2002).
After its approval by the Interregional Council, the MBDS entered
into force in Spain in 1987. Nevertheless, it was not until 1992 when
it became mandatory to include in the MBDS the data of the
patients cared for and those who have had at least one hospital stay
(see section 3.2.4) in the Valencia region.
The variables used (included in the MBDS) to classify the patients
through the DRGs are as follows:
1. Main diagnosis
2. Procedures
3. Age
4. Situation upon discharge
5. Secondary diagnoses
85

The main diagnosis is defined as the diagnosis which, after the
patient is examined by the doctor, is established as the reason for
being admitted to a hospital. The secondary diagnoses are the
diagnoses which refer to complications or comorbidities (Nanda,
2005) which increases the complexity of the care and, therefore,
influence the duration of the patient's stay at the hospital or of the
administered treatment.
The complications are the disease processes arising during the
hospital stay, while the comorbidities are the patients' health
problems or illnesses before being admitted to hospital.
The diagnoses and procedures are coded through the International
Classification of Diseases, Ninth Revision, Clinical Modification
(ICD-9-CM). The ICD-9-CM is divided into two groups:
✓ The diseases are classified in 17 chapters.
✓ The procedures are classified in 16 chapters.
Since the MBDS does not have any information about the
ambulatory healthcare production (outpatient services and the
emergency department), we obtained this from SISAL information
system. Although the ambulatory surgical processes are not counted
as a inpatient admission, they are included in the MBDS.
86

3.3 Indicators and variables used
3.3.1 Economic or cost variables
For this PhD dissertation, and as stated in the preceding section, we
will analyze the costs that are directly allocated, divided as follows:
A. Human resources expenses
‣ Medical staff: this group includes specialist doctors, regardless of
their activity center, pharmacist.
‣ Non-medical healthcare staff: this group includes the staff that
carries out healthcare functions but does not have a degree. They
have a nursing diploma or are midwives, assistants, optometrists,
physiotherapists or specialists.
‣ Non-healthcare staff: this group includes the other administrative
staff and the management team, regardless of their training (medical,
care and non-care personnel).
The amount that will be used refers to all the concept included in
chapter I of the budget:
- The employee salary plus the corresponding social security
contribution.
87

- The contribution for professional training or unemployment.
- The recovery for temporary disability.
The percentage of dedication by the hospital staff, based on the
corresponding activity center, must be updated with the aim of
processing this together with the data sent by the calculation center. It
is fundamental to maintain this since the staff makes up most of the
total cost of a CAC and, furthermore, the conversion of the staff
dedication into the full-time equivalent (FTE) is used as a breakdown
criterion for many secondary structural costs and, in some cases, for
the ambulatory logistics costs.
The staff costs will be allocated to their corresponding CACs every
month.
For the medical healthcare staff, we will take the following into
account:
The cost of the continued care by the medical healthcare staff will be
allocated to the various hospital areas: inpatient, outpatient, surgical
and emergency services.
In the ordinary activity, the medical healthcare staff provides its
services to the inpatient services, ambulatory care, operating rooms
and emergency departments or in laboratories, so its cost must be
divided based on their dedication to each CAC, in accordance with
88

the chief of the service. The cost of the non-medical healthcare staff
and non-healthcare staff will be allocated to the CAC where they
provide their services, and their cost must be allocated, if they carry
out activities in different CACs, in proportion to the time devoted to
each one.
The pension bonuses for the non-medical healthcare staff and the
service commissions (for the staff remunerated by the specialist care
center but which carries out its activities in another center) will be
included in group 9 of the CAC which is not allocated. The cost of
the residents will be allocated in the following way:
- The cost of the residents in family and community medicine is not
allocated to the SIE in specialist care since this is included in the
budget of the primary care cost pools. If they provide continuous
care at the hospital, the cost of the shifts will be allocated to the
corresponding CAC.
- The cost of residents in other specialties will be included in group 9
of the CAC which is not allocated, in the ﬁrst three years, unless the
chief of the unit believes that they are carrying out activities for that
service, in which case the head of department must determine the
percentage of dedication to the corresponding CAC. If they provide
continuous care at the hospital, their cost will be allocated to the
corresponding CAC.
89

B. Supply utilization
✓ Utilization of healthcare material
✓ Utilization of pharmaceutical products
The cost of the pharmaceutical products include those acquired from
third parties and those produced by the hospital's pharmacy service.
The CACs of which we know the product utilization with accuracy
will be allocated directly. The logistics centers corresponding to each
product line will be used for allocating the utilization costs that
cannot be differentiated and, subsequently, for breaking them down
among the corresponding inpatient area and ambulatory care centers
using an objective criterion.
As a result, we have the information per hospital with a similar
structure to that shown in table 6, where we see the cost breakdown
by area for Hospital H10.
Table 6. Main direct expense items for H10
Source: the author
90

3.2.2 Quality variables
Based on the results of the Management Agreements, twelve
indicators were chosen for this study out of a total of 95, grouped
into six variables and provided by the Valencia Health Department:
a) Quality indicators
b) Delay indicators
c) Economic indicators
d) Healthcare process indicators
e) Public health indicators
f) Safety indicators
Likewise, we included the overall score indicator in the Management
Agreements through which the Health Department makes its annual
rankings for the hospitals. Those 13 indicators were also present in
the previous year (2009), so that we could study their evolution.
On one hand, as we said, we included the overall score of the
Management Agreements as the overall indicator, which groups both
the variables chosen for this dissertation and the others, maintaining
the targets for each indicator established by the Health Department
to obtain the hospital ranking.
91

We will now provide details of each of the 12 indicators chosen and
of the overall score indicator which determines the ﬁnal rankings, as
well as the formula used to calculate this, arranged per perspective.
3.2.2.1 Quality indicators
Synthetic satisfaction index
OBJECTIVE: To improve the satisfaction perceived by the patients
in relation to how the healthcare service works.
DEFINITION: The synthetic index obtained in the patient
satisfaction surveys conducted by the Directorate General for Quality
and Patient Care.
This is measured by combining ﬁve indexes of the quality perceived
by the patients, obtained from the results of the patient opinion
surveys. The perceived quality indexes are calculated for both
ambulatory care and hospital care, and are as follows:
1. "Satisfaction assessment" index
2. "Perceived improvement" index
3. "Information quality" index
4. "Accessibility" index
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5. "Comfort" index
We added the assessment of the compliance with the objectives
defined for each district and with the actions for improving patient
satisfaction to those indicators.
SOURCE: Directorate General for Quality and Patient Care.
PURPOSE: To maximize this.
NOT APPLICABLE TO: Healthcare districts or hospitals with a
critical mass of insufficient surveys.
3.2.2.2 Delay indicators
Delay in the first visit to a specialist doctor
OBJECTIVE: To reduce the patients' waiting period to visit the
specialist doctor for the first time.
DEFINITION: Average waiting time to visit the specialist doctor.
where:
D: The delay stated in days.
SOURCE: The Healthcare Information System Analysis Service.
DCE
=
D
93

PURPOSE: To minimize this.
NOTE: Since this indicator depends greatly on the healthcare
districts transferring the information in the appropriate format and
characteristics, if the necessary ﬁles for assessing this indicator are not
received within an acceptable time period and in due form so that the
healthcare district can assess it, this is considered to be total non-
compliance (0 points).
Percentage of patients with surgery delayed longer than 180
days
OBJECTIVE: To ensure operating compliance with the surgery
guarantee deadlines, even when the choice is not legally required.
DEFINITION: Number of patients with surgery delayed longer than
180 days, divided by the total number of patients on the waiting list
obtained from the LEQ (surgery waiting list) system in the June and
December cutoffs.
The LEQ system.
The average surgery delay (DMI)
94

OBJECTIVE: To reduce the average time, stated in days, that
patients have to wait until surgery. The waiting time is deﬁned as the
difference in days between the registered date and the cutoff date.
DEFINITION:
where:
FC: the cutoff date or the date on which the indicator is obtained.
FRSi: the date on which patient i is included in the waiting list.
N: the number of patients waiting for surgery on the cutoff date.
The sum includes the N patients who are waiting for surgery on the
cutoff date.
The LEQ system.
Emergency department waiting times
OBJECTIVE: To reduce the patients' wait times in the emergency
department until receiving medical treatment.
95

DEFINITION: The average waiting time (in hours) for medical
treatment in terms of a standardized emergency department (ED)
triage scale.
SOURCE: SASIS. The SIDO22 emergency information system.
NOT APPLICABLE TO: The healthcare districts in which the
hospitals have not implemented the SIDO22 emergency information
system.
Weeks elapsed until the start of treatment once breast
cancer is suspected after a mammogram
OBJECTIVE: To guarantee effective treatment after a breast cancer
screening.
DEFINITION: The 75th percentile of the distribution of the
number of weeks elapsed until the start of treatment.
SOURCE: The Directorate General for Public Health. The Cancer
Ofﬁce.
NOTE: At least 75% of the women should start the treatment within
8 weeks.
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3.2.2.3 Qualitative economic indicators
Absenteeism index due to non-work-related temporary
disability (IT)
OBJECTIVE: To reduce the impact of non-work-related
absenteeism.
DEFINITION:
where:
ITi: the number of days with temporary disability due to a common
illness or non-work-related accident of worker i in one year.
Ti: the number of days in the contract of worker i in one year.
SOURCE: The Strategic Plan Ofﬁce.
3.2.2.4 Healthcare process indicators
Ambulatory replacement rates
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OBJECTIVE: To know about the major ambulatory surgery
performed at the hospitals with respect to those that could potentially
be made ambulatory.
DEFINITION: The percentage of operations that could potentially
be part of the major ambulatory surgery.
where:
TSA: the ambulatory replacement rate
Icma: the number of operations made as part of the major
ambulatory surgery of the DRGs that could potentially be made
ambulatory.
NIPA: the total number of operations made of the DRGs that could
potentially be made ambulatory.
SOURCE: The Minimum Basic Data Set (MBDS) and SASIS.
Vaginal delivery rates with epidural anesthesia
DEFINITION:
PAE/V
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where:
PAE: the number of vaginal deliveries with epidural anesthesia
during the period calculated for the indicator.
V: the total number of vaginal deliveries during the period calculated
for the indicator.
The Minimum Basic Data Set (MBDS).
NOT APPLICABLE TO: Healthcare district 6.
3.2.2.5 Public health indicators
Diabetes screening indicator
OBJECTIVE: To increase the diabetes diagnoses with the aim of
reducing known diabetes.
DEFINITION:
where:
99

DR: the number of patients aged over 45 with blood glucose
recorded in the previous three years.
DT: the number of patients aged over 45 allocated to consultation.
SOURCE: The Abucasis Ofﬁce. The SIA.
NOTE: The hospitals that have implemented Abucasis II less than 12
months are excluded.
3.2.2.6 Safety indicators
Rate of hip fracture surgery with a delay longer than 2 days
(DQFC)
OBJECTIVE: To reduce the delay in hip fracture surgery since this is
associated with poorer treatment results and greater complications.
DEFINITION:
where:
FC48: the number of operations made over 48 hours after the
emergency admission during the period calculated for the indicator.
100

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