Outline
Comparative Analysis of
Referral Costs in GP Practices
Maria C. Portela
Emmnuel Thanassoulis
Sofia Nogueira Silva
Ano lectivo 2008/2009
Maria Conceição Silva Portela
•
•
•
•
•
•
Objectives of the study
Data and Variables
Weight restrictions
DEA models
Results on Aggregate total referral cost model
Results on Disaggregate total referral cost
model
• Conclusion
Ano lectivo 2008/2009
Objectives
• In the UK GP practices are responsible for the health
assurance of the individuals that are registered within the
practice.
• Referral costs depend crucially on the decisions made
by the GP practice, including the choice of inpatient or
outpatient referral and the choice of hospital for the
referral, as treatment costs are largely standardised
through
• Most studies have focused on analysing the efficiency of
GP practices providing the health service (i.e. translate
resources into outcomes (or intermediate outputs));
• Here we focus on the efficiency of GP practices in
purchasing the hospital treatment for their patients
Ano lectivo 2008/2009
Maria Conceição Silva Portela
Maria Conceição Silva Portela
Data and Variables
Inputs
Outputs
Model 1
Total costs (inpatient
referral costs)
+
outpatient
List of population enrolled in the practice,
divided into homogeneous groups
Model 2
Cost of referrals for inpatient care
List of population enrolled in the practice,
divided into homogeneous groups
Cost of referrals for outpatient care
Volume of referrals
Price of referrals
- Data on 75 GP practices
Ano lectivo 2008/2009
Maria Conceição Silva Portela
Data and Variables
Data and Variables
• The population characteristics were
measured through:
– Number of people in certain age bands;
– Index of multiple deprivation (IMD), where we
considered the number of people in each age band
that lived in an area with na IMD above a certain
percentile (50%, 60%, 70%, 80%, and 90%)
– Several specifications of DEA models and regression
analysis were tried to arrive at the final specification
of outputs
Ano lectivo 2008/2009
Maria Conceição Silva Portela
Band code
Age/gender
Band 10
age < 3
3.09%
Band 20
age >= 3 and age < 18
17.58%
Band 30
age >= 18 and age < 35 and sex = Male
10.23%
Band 35
age >= 18 and age < 35 and sex = Female
9.84%
Band 40
age >= 35 and age < 48 and sex = Male
10.48%
Band 45
age >= 35 and age < 48 and sex = Female
9.92%
Band 50
age >= 48 and age < 70
27.11%
Band 60
age >= 70
11.74%
Number of people in these age bands below and above the 80%
Percentile were considered as the outputs (16 in total)
Ano lectivo 2008/2009
Weight Restrictions
- Costs for people above the percentile (more deprived) are higher;
- Some age bands can be considered as having similar costs and other have
clearly higher costs (looking at values below the percentile)
Ano lectivo 2008/2009
Weighte
d average
inpatient
cost per
person
Weighted
Average
inpatient
cost (
below 80%
percentile)
Weighted
Average
inpatient
cost
(above 80%
percentile)
Band
Age/gender
Band 10
age < 3
173.57
174.81
157.12
Band 20
Age [3, 18[
72.02
71.88
74.26
Band 30
age [ 18, 35[ Male
71.92
71.09
85.38
Band 35
age [ 18, 35[ Female
235.58
231.035
303.78
Band 40
age [ 35, 48[ Male
107.99
103.36
198.15
Band 45
age [ 35, 48[ Female
209.30
205.55
287.66
Band 50
Age [ 48, 70[
278.29
276.16
331.95
Band 60
age >= 70
742.56
739.72
817.19
Maria Conceição Silva Portela
Maria Conceição Silva Portela
DEA Models
Weight restictions were imposed to reflect that:
Similar costs:
Band 30, 20, 40;
Bands 10, 45, 35
Average percentage
in this band in the
GP practices
• Aggregate total referral cost model
s
m
 s
Max ∑ u r y ro | ∑ u r y rj − ∑ vi xij ≥ 0,
r =1
i =1
 r =1
m
∑v x
i
io
i =1

= 1, Au ≥ 0, u r , vi ≥ 0

• Disaggregate total referral cost model
n
n
m


Min c = ∑ pio xi | ∑ λ j xij ≤ xi , ∑ λ j y rj − A −1 w ≥ y ro , λ j , xi ≥ 0
j =1
j =1
i =1


Technical efficiency
Ano lectivo 2008/2009
Allocative efficiency
Maria Conceição Silva Portela
DEA Models
Results
• Price efficiency models (Tone and Tsutsui,
2007)
n

Min  ρ | ∑ λ j CijT ≤ ρ CioT ,
 j =1
Price efficiency
n
∑λ
j =1
n

Min ∑i Ci | ∑ λ j CijT ≤ Ci ,
j =1

Allocative efficiency
∑C
∑C
i
T
ij
i
ij
Ano lectivo 2008/2009
∑ρ C
∑C
*
×
i
i
T
ij
T
ij
×
∑C
∑ρ C
*
i

y rj ≥ y ro , λ j ≥ 0

n
∑λ
j
j =1
*
ij
i
j
=
T
ij

y rj ≥ y ro , λ j ≥ 0

∑C
∑C
i
*
ij
i
ij
Maria Conceição Silva Portela
Ano lectivo 2008/2009
Results – aggregated model
average
Number eff
minimum
•
Cost CRS
Efficiency
Cost VRS
Efficiency
Scale
Efficiency
78.29%
85.41%
91.69%
6
16
6
61.36%
63.00%
71.06%
• Returns to scale
Maria Conceição Silva Portela
Results – Aggregate model
• Benchmarking practices were identifed
Returns to scale were constant or increasing up to a list size of 2808, and
after 4687 people, only DRS were identified.
List size
Average
CRS
St dev
CRS
<3000
83.64%
[3000,5000[
76.23%
[5000,7000[
[7000,10000[
>10000
Ano lectivo 2008/2009
Average
VRS
St dev
VRS
Average
scale eff
St dev
scale
0.129
88.18%
0.113
79.78%
75.89%
0.103
79.15%
0.117
75.75%
0.074
0.125
95.14%
0.084
16
0.102
95.38%
0.032
17
83.63%
0.100
90.65%
0.033
10
86.93%
0.090
90.73%
0.053
16
88.20%
0.082
85.93%
0.036
16
Maria Conceição Silva Portela
N
Ano lectivo 2008/2009
Maria Conceição Silva Portela
Results – disaggregate model
• Average efficiency scores
• Cost benchmark GP practices
Cost eff
CRS
Tech
eff CRS
Alloc
eff CRS
Scale
eff
Cost eff
average
79.00%
84.95%
92.95%
94.05%
min
58.33%
62.65%
77.67%
69.36%
Number Eff
5
10
5
10
Tech
eff VRS
Alloc
eff VRS
86.48%
90.35%
95.61%
59.71%
63.11%
79.24%
VRS
16
VI
21
16
• Scale efficiency results from aggregate model
were confirmed;
• Benchmark practices for cost and technical
efficiency measures were identified
Ano lectivo 2008/2009
Results – disaggregate model
Maria Conceição Silva Portela
G5
G13
G40
G44
Average
G45
G9
402
1463
2437
359
1028.2
858
789
VO
2079
6968
11958
2245
5062.2
4275
2996
VI/VO
0.193
0.210
0.204
0.160
0.203
0.201
0.263
PI
1241.97
1307.53
1244.66
1203.02
1261.62
1011.15
1034.38
88.91
86.84
87.33
97.12
89.36
96.77
93.24
13.97
15.06
14.25
12.39
14.12
10.45
11.09
2780
9004
14696
2786
6411
3519
3484
PI/PO
List Pop
Ano lectivo 2008/2009
Results- Disaggregate model
Maria Conceição Silva Portela
Results – Disagregate model
• Cases of volume increase or decrease to
achieve cost efficiency (after technical
efficient targets)
% TVI/VI
>1
# cases
average
<=1
# cases
average
=1
# cases
average
Ano lectivo 2008/2009
% TVO/VO
13
43
1.006
1.033
46
16
0.906
0.904
16
16
1.000
1.000
Maria Conceição Silva Portela
Price efficiency results:
average
# Eff
Ano lectivo 2008/2009
Tech Eff
VRS
Price Eff VRS
Mix Eff VRS
Cost Eff VRS
90.35%
97.32%
96.02%
84.54%
21
35
15
15
Maria Conceição Silva Portela
Results – disaggregated model
Results
II
Price efficient benchmarks of GP 43
Ano lectivo 2008/2009
Maria Conceição Silva Portela
Conclusion
• Some GP practices were found cost inefficient, i.e.
facing excessive costs in relation to the other
practices that serve similar populations:
– (i) scale issues - smaller GP practices appear more efficient than
larger practices but at the same time have more variability in cost
efficiency, which may be a result of less stable case-mixes over time;
– (ii) technical efficiency- most GP practices presented excessive
volume of inpatient and outpatient referrals given their population
characteristics;
– (iii) price efficiency- some GP practices face much lower average
prices and therefore achieve lower costs by means of lower prices;
– (iv) mix efficiency- some GP practices may reduce their costs by
adjusting the volume mix to closely match the relationship between
their prices .
Ano lectivo 2008/2009
Maria Conceição Silva Portela
Ano lectivo 2008/2009
Maria Conceição Silva Portela