1. No category

Going beyond diagnosis-based case-mix systems:
How adding pharmacy information to your
decision support systems can improve the
efficient delivery of health care.
A study on the national Swedish drug register
Karen Kinder Siemens, Ph.D., MBA
Health Services R&D Center
The Johns Hopkins University
Presented at the Bättre beskrivning av vården bidrar till en
effektivare sjukvård!
May 14 2009 Luleå Sweden
Copyright Notice
This presentation is copyrighted by the
Johns Hopkins University (© 2008), all
rights reserved. You may distribute this
presentation in its unaltered entirety
within your organization in either printed
or electronic form. It may not be
distributed in other manner or
incorporated into other presentations
without permission of the author.
Copyright 2005, Johns Hopkins University,02/07/2006
2
Goals for this Presentation
• Convey the benefits of pharmacy-based risk predictions for
applications in financial planning and care management
• Introduce the Rx-PM model - part of the Johns Hopkins ACG
System
• Present to results from the project with data from the
Swedish national drug register
Copyright 2005, Johns Hopkins University,02/07/2006
3
Conceptual Basis
Initial Motivation for an
Rx-Based Case-Mix Model
“I have loads of patients with drug codes,
but no diagnostic information. How can I
use the ACG system to identify the high
risk patients?”
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5
Conceptual basis for an
Rx-Based Case-Mix Model
• Pharmaceutical utilization is a proxy for an
underlying morbidity.
• The therapeutic goal of pharmacotherapy
adds a new dimension to the Johns Hopkins
ACG Case-Mix System.
• Risk assessment accounts for the severity of
the underlying morbidity, the therapeutic goal
of medication use, and the duration of
treatment.
Copyright 2005, Johns Hopkins University,02/07/2006
6
Some of the Advantages of
Rx-based Models
• Some health care systems do not collect outpatient
diagnostic data
• There can be a long lag to obtain complete diagnostic data.
• Most pharmacy data is easily accessible and is automated.
• Prescriptions are linked to specific clinical course of action.
• Pharmacy data may be more appropriate when chronic
conditions are associated with clear pharma-cotherapies.
• Diagnostic data from automated databases may be
imprecise and may capture rule-outs.
• Automated databases may not capture 4th or 5th digits.
• Pharmacy-based risk model may capture health risk for
persons with stable well-managed chronic disease.
• Diagnosis-based risk models may miss some well managed
but expensive patients.
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7
Difficulties in Working with
Rx Data
• Drug use is NOT synonymous with presence of
specific diseases
– Multiple indications for same drug
• Approved uses
• Off-label uses
– There are no definitive drug therapies for some conditions
• Patterns of practice can directly influence risk scores
• Complexities of working with numerous coding
systems
– Actual product dispensed may be different from drug code
recorded
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8
Capturing Dx alone misses some
ENDOCRINE
Frequency
NONE
1156471
Percent
Cum.
Frequency
Cum.
Percent
80.43
1156471
80.43
Dx
34971
2.43
1191442
82.86
Rx
132735
9.23
1324177
92.09
BOTH
113754
7.91
1437931
100.00
Copyright 2005, Johns Hopkins University,02/07/2006
9
Introducing Rx-MGs
ATC Introduction
11
http://www.whocc.no/atcddd/
• ATC: Anatomical Therapeutic Chemical Classification System.
• Since 1982, the ATC system has been maintained by the
WHO Collaborating Centre for Drug Statistics Methodology in
Oslo, Norway.
• The system provides a global standard for classifying medical
substances and serves as a tool for drug utilization research.
• Since 1996, WHO Headquarters recommend the ATC system
for global drug utilization studies.
Copyright 2005, Johns Hopkins University,02/07/2006
ATC Example
12
• WHO Anatomical Therapeutic Chemical classification system that
groups drugs in 5 levels according to:
– Organ or System on which they act
– Properties:
• Therapeutic
• Pharmacological
• Chemical
– Example: A10BA02
Level
1st
2nd
3rd
4th
5th
Description
Anatomical main group
Therapeutic subgroup
Pharmacological Subgroup
Chemical Subgroup
Chemical Substance
Copyright 2005, Johns Hopkins University,02/07/2006
Count
12
94
266
861
4,174
1st
A
A
A
A
A
2nd
3rd
4th
5th
10
10
10
10
B
B
B
A
A
02
Description
Alimentary tract and metabolism
Drugs used in diabetes
Blood glucose lowering drugs, excl. insulins.
Biguanide
Metformin
13
From NDCs/ATCs to MGs
NDC
110,000
Generic - Route
2,700
ATC
4,100
Copyright 2005, Johns Hopkins University,02/07/2006
Rx-MG
60
Clinical Criteria for Rx-MG
Assignment
1)
Morbidity-type
- symptom v disease
2)
Duration of morbidity
3)
4)
5)
- chronic v time-limited
Stability of morbidity
- stable v unstable
Route of administration
- oral, inhaled, topical, intramuscular, intravenous
Therapeutic goal
- curative, palliative, preventive
Copyright 2005, Johns Hopkins University,02/07/2006
14
Example of Medication
Classification
HCTZ*
Hypertension
Common
Morbidity
Slow disease
process
Therapeutic
Goal
Chronic Stable
Duration
& Severity
Cardiovascular/
Hypertension
*HCTZ - hydrochlorothiazide
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15
Chemical Structure:
Thiazide
Mechanism of Action:
Diuretic
Therapeutic Class:
Antihypertensive
Oral
Route of
Administration
NDC Rx-MG Example:
16
Corticosteroids
Active Ingredient
Route of
Administration
Rx-MG
Methylprednisoloneneomycin
topical
Skin / Acute and Recurrent
Prednisolone
compounding
Allergy / Immunology / Immune
Disorders
Prednisolone
injectable
Musculoskeletal / Inflammatory
Conditions
Prednisolone
oral
Allergy / Immunology / Chronic
Inflammatory
Prednisolone
ophthalmic
Eye / Acute Minor: Palliative
Prednisolone-sodium
sulfacetamide
ophthalmic
Eye / Acute Minor: Curative
Copyright 2005, Johns Hopkins University,02/07/2006
The Major Rx-MG Categories
•
•
•
•
•
•
•
•
•
•
Allergy/Immunology
Cardiovascular
Ears, Nose, Throat
Endocrine
Eye
Female Reproductive
Gastrointestinal/Hepatic
General Signs & Symptoms
Genito-urinary
Hematologic
Copyright 2005, Johns Hopkins University,02/07/2006
Infections
Malignancies
Musculoskeletal
Neurologic
Psychosocial
Respiratory
Skin
Toxic Effects/ Adverse
Reactions
• Others / non-specific
medications
•
•
•
•
•
•
•
•
17
Patient Clinical Profile
18
Time Period: One Year (Slide 1 of 2)
Patient #1
3 Rx-MGs
Patient #2
4 Rx-MGs
Patient #3
11 Rx MGs
Genito-Urinary /
Acute Minor: Palliative
Cardiovascular /
High Blood Pressure
Allergy/Immunology / Acute Minor: Palliative
Allergy/Immunology / Asthma
Respiratory /
Acute Minor: Palliative
Cardiovascular /
Hyperlipidemia
Endocrine / Chronic Medical
Skin /
Acute and Recurrent
Genito Urinary /
Acute Minor: Palliative
Gastrointestinal/Hepatic / Acute Minor: Palliative
Infections /
Acute Minor: Curative
General Signs and Symptoms / Nausea and
Vomiting
General Signs and Symptoms / Pain
Infections / Acute Minor: Curative
Psychosocial / Anxiety
Psychosocial / Depression
Respiratory / Acute Minor: Palliative
Respiratory / Chronic Medical
Copyright 2005, Johns Hopkins University,02/07/2006
Patient Clinical Profile Time Period:
One Year(Slide 2 of 2)
Patient #1
3 Rx-MGs
Patient #2
4 Rx-MGs
Patient #3
11 Rx MGs
$2,754
$4,151
$7,900
80%
90%
98%
Age
59
57
39
Sex
M
M
F
Rx-ACG –Risk Score Predicted Cost
Percentile Rank
Data Source: PharMetrics, a unit of IMS, Watertown, MA
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19
Applications
Potential Applications for
Rx-based case-mix models
• Disease/Case Management
– High risk case identification for case management
– Chronic disease “tiering” for disease management
– Quick case finding before ICD data are available
• Profiling – Population and Provider
– Rx practice patterns, overall and by disease group
– Disease patterns within Rx-defined morbidity groups
• Forecasting Rx and Total costs for large groups
– NOT a tool for provider payment
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21
Disease/Case Management
• Excellent classification statistics
– Total $: Area Under the ROC = 0.83
– Rx $:
Area Under the ROC = 0.93
• Could begin assigning scores as soon as medication list is
obtained
• Case Finding
– Rx information can be used to augment ICD information to
identify certain types of patients
• For example: Depression
– ICD codes for depressive disorder (3% of population)
– Depression Rx-MG: SSRIs and Tricyclics (13% of
population)
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22
Performance of models for case
identification: outcome is top 1% most
at risk individuals
Area Under the Curve
(1% Cut Point)
Risk Model
Total $
Rx $
Age / Gender
.76
.69
Chronic Disease Score
.81
.85
ACG-PM
.86
.88
.83
.93
.86
.93
Rx-PM
Rx-PM w/ ACG-PM Score
Source: PharMetrics, a unit of IMS, Watertown, MA Validation Dataset, n=904,007, 2001-02;
total costs truncated at $50K, and Rx costs truncated at $50K.
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23
Using Rx-PM Risk Scores to Target
24
Disease Management Program
Participants
% Enrollees in Rx-MG Risk
Category
Condition
Below
90%
Diabetes
Resource Use of Cohort Relative
to Total Population
90-95%
Above
95%
Below
90%
90-95%
Above
95%
40.5
38.0
10.7
1.34
4.90
7.44
Ischemic Heart
Disease
38.4
43.1
13.5
1.26
4.99
7.22
Congestive
Heart Failure
22.9
66.2
33.1
1.14
6.02
7.93
Copyright 2005, Johns Hopkins University,02/07/2006
How Well Does Rx Data Perform for
High Risk Case Identification
Percent True Positives by Source
25%
36%
39%
46%
29%
25%
1 Month Rx
Prior Cost
Both
12 Month Rx
Prior Cost
Both
Calculations for a commercial health plan with 400,000 members
Comparing Rx-PM to predict total medical expenditures to 12 months total prior cost
Copyright 2005, Johns Hopkins University,02/07/2006
25
How Much Rx Data Do You Need?
• If time is not an issue, waiting for a full year of claims is best
• For new datastreams, Rx-PM is a viable alternative with less than a
full year
1
Month
Rx
3
Months
Rx
6
Months
Rx
12
Months
Rx
12
Months
Rx+Dx+
Prior Cost
12
Months
Prior
Cost
No
truncation
6.91
8.57
8.86
8.86
15.81
14.83
$50,000
truncation
14.55
16.87
17.26
17.38
23.79
19.04
R-squared calculations for a commercial health plan with 400,000 members
Comparing data time limits in predicting total medical expenditures
Copyright 2005, Johns Hopkins University,02/07/2006
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Making the Most of Your Data,
Combining Rx and Dx Data
Risk Factors in The Johns Hopkins
DxRx-PM (diagnosis + pharmacy)
Complicated
Pregnancy Marker
Rx-Defined
Morbidity
Groups
Age
Gender
DxRx-PM
Risk Score
Frailty
Hospital Dominant
Conditions
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27
Overall Disease Burden
Selected Medical
Conditions
Summary of Johns Hopkins Rxbased Case-Mix System
28
1)
The ACG-Rx system, based on the unique Rx-MG categories,
is an Rx-based risk adjustment tool (NDC, ATC, Read code)
that can be used as a predictive model and to understand
patterns of medication use.
2)
Rx-MGs were developed using medical and pharmacological
frameworks.
3)
The statistical performance of the ACG-Rx model is excellent,
and superior to prior costs and existing Rx-based models.
Copyright 2005, Johns Hopkins University,02/07/2006
Benefits for Financial Planning
• Risk adjustment useful for
–More timely and efficient financial planning
–Differentiating high, average and low-risk small groups
–Identifying the underlying morbidity profile of group and thus
what programs might benefit their population
–Useful for explaining increasing/decreasing costs over time
and how these are linked to underlying changes in the
morbidity of the population
Copyright 2005, Johns Hopkins University,02/07/2006
29
Summary of Johns Hopkins Rxbased Case-Mix System
30
1)
The ACG-Rx system, based on the unique Rx-MG categories,
is an Rx-based risk adjustment tool (NDC, ATC, Read code)
that can be used as a predictive model and to understand
patterns of medication use.
2)
Rx-MGs were developed using medical and pharmacological
frameworks.
3)
The statistical performance of the ACG-Rx model is excellent,
and superior to prior costs and existing Rx-based models.
Copyright 2005, Johns Hopkins University,02/07/2006
31
Applying the Rx-system
on the Swedish National
Drug Register
Copyright 2005, Johns Hopkins University,02/07/2006
Background
• Development of patient´s choice model in Sweden
”Vårdval”
• Need of instruments to measure morbidity
• Focus on cost of pharmaceuticals
• Pharmacy data is collected nationally but at this
stage not diagnosis set in Primary Care
• Need of describe performance in Primary Care
Copyright 2005, Johns Hopkins University,02/07/2006
32
Scope
• Apply the JHU Rx-model on the Swedish National
Drug Register (period 2006-2008)
• Analyse and compare results between different
county councils
• Analyse if the drug use in the population can be
used as an approximation for the need of care and
as a tool to adjust the capitation payment system
in the county councils
Copyright 2005, Johns Hopkins University,02/07/2006
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Project participants
•
•
•
•
•
•
•
Mona Heurgren, SoS
Lisbeth Serdén, SoS
Örjan Ericsson, SoS
Andreas Johansson, Ensolution AB
Fredrik Berns, Ensolution AB
Karen Kinder, JHU
Patricio Muñiz, JHU
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34
Grouping results
• 6,2 Mill. unique patients, 29 Mill. combinations of
patients and used ATC-codes for each year
• Periods 2006, 2007, 2008
• Annually 24-25 Bill. SEK in total cost
• The grouping went well in practice
Copyright 2005, Johns Hopkins University,02/07/2006
35
36
Analysis model
Step 1
Actual pharmacy cost and predicted
pharmacy cost per county council
What is the
cost level?
Step 2
Actual costs per inhabitant and predicted cost
per inhabitant per county council/municipality
How is the difference
in consumption?
Step 3
Proportion of risk patients per municipality
Does specific outliers
influence the results?
Step 4
SMRs for Major Rx-MGs per county council
Does specific groups
and practices
influence the results?
Step 5
Comparisons of specific Rx-MGs
per county council
Detailed comparison
on practices and costs
Copyright 2005, Johns Hopkins University,02/07/2006
Actual pharmacy cost and predicted
pharmacy cost per county council
Step 1
Actual 2008
2008Pharmacy Cost Variation in
Pharmacy
Prediction Using
predicted costs and
County council Expense
Unscaled PRI
actual for 2008
5 303 647 178
Stockholm
5 139 988 335
1,03
847 118 520
Uppsala
864 249 195
0,98
705 655 420
Södermanland
703 511 857
1,00
1 027 450 358
Östergötland
1 139 305 741
0,90
898 536 388
Jönköping
895 076 767
1,00
504 075 321
Kronoberg
487 266 166
1,03
618 074 215
Kalmar
637 789 791
0,97
152 748 364
Gotland
150 180 671
1,02
393 225 987
Blekinge
398 831 013
0,99
3
538
233
088
Skåne
3 267 557 141
1,08
772 191 846
Halland
781 341 882
0,99
4 049 344 382
Västra Götaland
4 209 239 410
0,96
800 328 490
Värmland
749 962 928
1,07
701 933 901
Örebro
758 137 722
0,93
707 962 041
Västmanland
672 419 683
1,05
773 043 612
Dalarna
739 503 341
1,05
744 218 130
Gävleborg
745 912 033
1,00
696 730 564
Västernorrland
651 852 324
1,07
318 765 227
Jämtland
352 880 219
0,90
737 689 835
Västerbotten
692 121 750
1,07
756 503 275
Norrbotten
678 160 685
1,12
25 105 554 100
24 820 166 508
1,01
PRI = Prognostiserad Resurs Index
Unscaled PRI, PRI i absoluta tal
Copyright 2005, Johns Hopkins University,02/07/2006
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Actual pharmacy cost and predicted
pharmacy cost per county council
Step 2
Actual costs per inhabitant and predicted cost
per inhabitant per county council/municipality
Step 3
Proportion of risk patients per municipality
Step 4
SMRs for Major Rx-MGs per county council
Step 5
Comparisons of specific Rx-MGs
per county council
Actual pharmacy cost per inhabitant
per inhabitant and county council
Step 1
Actual 2006 Actual 2007 Actual 2008
Pharmacy Pharmacy Pharmacy
County council Expense
Expense
Expense
Stockholm
2481
2594
2761
Uppsala
2397
2516
2627
Södermanland
2438
2569
2652
Östergötland
2266
2358
2443
Jönköping
2514
2614
2688
Kronoberg
2551
2666
2782
Kalmar
2469
2516
2609
Gotland
2411
2487
2642
Blekinge
2312
2449
2571
Skåne
2743
2823
2966
Halland
2431
2540
2648
Västra Götaland
2481
2536
2613
Värmland
2768
2842
2879
Örebro
2334
2400
2523
Västmanland
2551
2657
2815
Dalarna
2513
2646
2761
Gävleborg
2538
2616
2667
Västernorrland
2631
2753
2827
Jämtland
2509
2485
2470
Västerbotten
2670
2793
2828
Norrbotten
2806
2918
2954
Copyright 2005, Johns Hopkins University,02/07/2006
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Actual pharmacy cost and predicted
pharmacy cost per county council
Step 2
Actual costs per inhabitant and predicted cost
per inhabitant per county council/municipality
Step 3
Proportion of risk patients per municipality
Step 4
SMRs for Major Rx-MGs per county council
Step 5
Comparisons of specific Rx-MGs
per county council
Comparison in predictive resource
usage per county council
Step 1
Rescaled
Rescaled
Rescaled
Pharmacy PRI Pharmacy PRI Pharmacy PRI
County council (2006)
(2007)
(2008)
Stockholm
0,97
0,98
0,99
Uppsala
0,94
0,95
0,95
Södermanland
1,00
1,01
1,00
Östergötland
0,94
0,94
0,93
Jönköping
0,99
0,99
0,98
Kronoberg
1,05
1,05
1,05
Kalmar
0,99
0,99
0,98
Gotland
0,97
0,98
0,99
Blekinge
0,97
0,99
1,00
Skåne
1,05
1,04
1,04
Halland
0,99
0,99
0,98
Västra Götaland
1,00
1,00
0,99
Värmland
1,09
1,08
1,07
Örebro
0,99
0,98
0,99
Västmanland
1,01
1,01
1,02
Dalarna
1,02
1,03
1,02
Gävleborg
1,01
1,02
1,00
Västernorrland
1,02
1,03
1,02
Jämtland
1,00
0,98
0,96
Västerbotten
1,02
1,03
1,00
Norrbotten
1,05
1,06
1,04
PRI = Prognostiserad Resurs Index
Rescaled PRI, PRI i relativa tal
Copyright 2005, Johns Hopkins University,02/07/2006
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Actual pharmacy cost and predicted
pharmacy cost per county council
Step 2
Actual costs per inhabitant and predicted cost
per inhabitant per county council/municipality
Step 3
Proportion of risk patients per municipality
Step 4
SMRs for Major Rx-MGs per county council
Step 5
Comparisons of specific Rx-MGs
per county council
Pharmacy cost per inhabitant as a
comparison between the
municipalities
2006
Copyright 2005, Johns Hopkins University,02/07/2006
2007
Actual pharmacy cost and predicted
pharmacy cost per county council
Step 1
Step 2
Actual costs per inhabitant and predicted cost
per inhabitant per county council/municipality
Step 3
Proportion of risk patients per municipality
Step 4
SMRs for Major Rx-MGs per county council
Step 5
Comparisons of specific Rx-MGs
per county council
2008
40
Predicted change in pharmacy cost
2006-2008 as a comparison between
the municipalities
Step 1
Actual pharmacy cost and predicted
pharmacy cost per county council
Step 2
Actual costs per inhabitant and predicted cost
per inhabitant per county council/municipality
Step 3
Proportion of risk patients per municipality
Step 4
SMRs for Major Rx-MGs per county council
Step 5
Comparisons of specific Rx-MGs
per county council
41
0,00 – 0,90
0,90 – 1,65
1,65 – 2,00
Verklig kostnad – kostnadsökning / minskning
mellan 2007 och 2008
Copyright 2005, Johns Hopkins University,02/07/2006
Beräknad kostnad – baserad på predicerad
kostnadsökning på 2007 års Unscaled PRI
Difference between actual change in
cost and calculated change in cost
– 0,2
- 0,2 till +0,2
+0,2
Copyright 2005, Johns Hopkins University,02/07/2006
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Step 1
Analysing risk patients
Actual pharmacy cost and predicted
pharmacy cost per county council
Step 2
Actual costs per inhabitant and predicted cost
per inhabitant per county council/municipality
Step 3
Proportion of risk patients per municipality
Step 4
SMRs for Major Rx-MGs per county council
Step 5
Comparisons of specific Rx-MGs
per county council
43
Comparsion of municipalities for % of patients with a Probability to Have High
Pharmacy > 0,8 (Period 2008)
0,07
0,06
0,05
0,04
0,03
0,02
0,01
0
1
13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241 253 265 277 289
Probability > 0,8 = % av patienten som har en sannolikhet över 80%
att vara riskpatienter för läkemedelskostnader under kommande period
Copyright 2005, Johns Hopkins University,02/07/2006
Analysing risk patients on
municipality level. Example from
Kronoberg and Jönköping
Step 1
ANEBY
GNOSJÖ
MULLSJÖ
HABO
GISLAVED
VAGGERYD
JÖNKÖPING
NÄSSJÖ
VÄRNAMO
SÄVSJÖ
VETLANDA
EKSJÖ
TRANÅS
UPPVIDINGE
LESSEBO
TINGSRYD
ALVESTA
ÄLMHULT
MARKARYD
VÄXJÖ
LJUNGBY
HÖGSBY
% with a Probability to Have
% with a Probability to
High Total Expense > 0,8
Have High Pharmacy > 0,8
(2008)
(2008)
5,18%
4,60%
4,75%
4,04%
4,91%
4,47%
4,43%
3,89%
4,97%
4,69%
4,79%
4,20%
4,97%
4,78%
5,49%
5,50%
5,04%
4,70%
5,18%
4,75%
5,22%
4,88%
5,46%
5,26%
5,49%
5,38%
5,85%
5,83%
5,95%
6,16%
6,04%
5,96%
5,38%
5,34%
5,17%
4,90%
5,45%
5,28%
5,11%
5,04%
5,41%
5,09%
5,36%
4,75%
Actual pharmacy cost and predicted
pharmacy cost per county council
Step 2
Actual costs per inhabitant and predicted cost
per inhabitant per county council/municipality
Step 3
Proportion of risk patients per municipality
Step 4
SMRs for Major Rx-MGs per county council
Step 5
Comparisons of specific Rx-MGs
per county council
Probability > 0,8 = % av patienten som har en sannolikhet över 80% att vara
riskpatienter för läkemedelskostnader respektive totalkostnad under kommande period
Copyright 2005, Johns Hopkins University,02/07/2006
44
Step 1
SMR by Major Rx-MG per
county council
2008
Major Rx-MG
ALL
CAR
EAR
END
EYE
FRE
GAS
GSI
GUR
HEM
INF
MAL
MUS
NUR
PSY
RES
SKN
TOX
ZZZ
Age/Sex Expected/1000
Major Rx-MG Name
Allergy/Immunology
Cardiovascular
Ears, Nose, Throat
Endocrine
Eye
Female Reproductive
Gastrointestinal/Hepatic
General Signs and Symptoms
Genito-Urinary
Hematologic
Infections
Malignancies
Musculoskeletal
Neurologic
Psychosocial
Respiratory
Skin
Toxic Effects/Adverse Effects
Other and Non-Specific Medications
Actual pharmacy cost and predicted
pharmacy cost per county council
Step 2
Actual costs per inhabitant and predicted cost
per inhabitant per county council/municipality
Step 3
Proportion of risk patients per municipality
Step 4
SMRs for Major Rx-MGs per county council
Step 5
Comparisons of specific Rx-MGs
per county council
45
19 Västmanland 20 Dalarna 21 Gävleborg 22 Västernorrland 23 Jämtland 24 Västerbotten 25 Norrbotten
132,18
133,56
133,62
133,69
133,38
130,98
133,03
220,84
234,94
233,43
235,31
233,92
213,09
227,93
5,67
5,78
5,78
5,79
5,75
5,57
5,77
116,87
122,58
122,25
122,95
121,43
112,56
119,84
74,82
77,18
76,79
77,24
77,31
73,40
74,98
70,38
66,21
67,49
66,64
68,30
75,13
67,00
127,89
133,63
133,01
133,64
133,61
124,66
129,86
216,12
222,77
222,49
222,96
222,66
212,03
219,41
32,90
35,17
34,93
35,26
35,12
32,01
34,54
3,65
3,70
3,70
3,68
3,72
3,62
3,69
266,66
267,56
267,74
268,35
267,92
265,30
266,09
13,56
14,33
14,25
14,34
14,30
13,19
13,96
10,42
11,18
11,07
11,16
11,21
10,10
10,72
39,77
41,16
41,06
41,13
41,17
38,95
40,35
148,21
154,02
153,50
153,87
154,01
144,73
150,02
159,62
161,16
161,11
161,76
160,94
157,36
160,05
108,45
110,16
110,08
110,33
110,23
107,76
109,17
0,05
0,05
0,05
0,05
0,05
0,04
0,05
157,86
164,08
163,12
163,84
164,19
154,53
158,87
SMR, Standard Morbidity Rate = Antal förväntade patienter
per Major Rx-MG grupp per 1000 invånare
Major Rx-MG grupp = Sjukdomsgrupper baserade på läkemedelskonsumtion
Copyright 2005, Johns Hopkins University,02/07/2006
Step 1
SMR by Major Rx-MG per
county council
2008
Major Rx-MG
ALL
CAR
EAR
END
EYE
FRE
GAS
GSI
GUR
HEM
INF
MAL
MUS
NUR
PSY
RES
SKN
TOX
ZZZ
Age/Sex Expected/1000
Major Rx-MG Name
Allergy/Immunology
Cardiovascular
Ears, Nose, Throat
Endocrine
Eye
Female Reproductive
Gastrointestinal/Hepatic
General Signs and Symptoms
Genito-Urinary
Hematologic
Infections
Malignancies
Musculoskeletal
Neurologic
Psychosocial
Respiratory
Skin
Toxic Effects/Adverse Effects
Other and Non-Specific Medications
Actual pharmacy cost and predicted
pharmacy cost per county council
Step 2
Actual costs per inhabitant and predicted cost
per inhabitant per county council/municipality
Step 3
Proportion of risk patients per municipality
Step 4
SMRs for Major Rx-MGs per county council
Step 5
Comparisons of specific Rx-MGs
per county council
46
01 Stockholm 03 Uppsala 04 Södermanland 05 Östergötland 06 Jönköping 07 Kronoberg
128,61
129,29
132,62
130,87
131,02
131,69
184,31
193,95
225,06
212,25
216,12
221,09
5,36
5,44
5,71
5,56
5,58
5,62
101,27
104,78
118,73
112,41
113,78
115,43
69,17
70,19
75,46
73,75
74,95
75,42
81,33
79,92
68,41
73,12
70,83
71,04
113,95
117,19
129,40
124,75
126,32
128,33
200,20
203,46
217,73
211,90
212,71
215,69
26,79
28,84
33,62
31,53
32,09
33,24
3,64
3,63
3,67
3,62
3,61
3,64
264,55
264,42
267,32
265,46
266,25
266,64
11,56
12,12
13,76
13,10
13,34
13,65
8,52
9,07
10,61
10,07
10,31
10,61
36,63
37,27
40,11
38,97
39,24
39,79
134,83
137,50
149,55
145,11
146,27
148,37
155,85
155,94
160,73
157,80
159,11
159,27
104,07
105,33
108,92
107,44
107,80
108,59
0,04
0,04
0,05
0,04
0,04
0,05
142,71
146,36
159,47
154,85
157,37
158,81
SMR, Standard Morbidity Rate = Antal förväntade patienter
per Major Rx-MG grupp per 1000 invånare
Major Rx-MG grupp = Sjukdomsgrupper baserade på läkemedelskonsumtion
Copyright 2005, Johns Hopkins University,02/07/2006
Step 1
SMR by Major Rx-MG per
county council
2008
Major Rx-MG
ALL
CAR
EAR
END
EYE
FRE
GAS
GSI
GUR
HEM
INF
MAL
MUS
NUR
PSY
RES
SKN
TOX
ZZZ
Age/Sex Expected/1000
Major Rx-MG Name
Allergy/Immunology
Cardiovascular
Ears, Nose, Throat
Endocrine
Eye
Female Reproductive
Gastrointestinal/Hepatic
General Signs and Symptoms
Genito-Urinary
Hematologic
Infections
Malignancies
Musculoskeletal
Neurologic
Psychosocial
Respiratory
Skin
Toxic Effects/Adverse Effects
Other and Non-Specific Medications
Actual pharmacy cost and predicted
pharmacy cost per county council
Step 2
Actual costs per inhabitant and predicted cost
per inhabitant per county council/municipality
Step 3
Proportion of risk patients per municipality
Step 4
SMRs for Major Rx-MGs per county council
Step 5
Comparisons of specific Rx-MGs
per county council
47
08 Kalmar 09 Gotland 10 Blekinge 12 Skåne 13 Halland 14 Västra Götaland 17 Värmland 18 Örebro
133,93
133,02
133,12
131,09
131,44
130,52
133,64
132,03
237,50
225,66
231,88
210,46
216,07
206,79
234,32
220,34
5,80
5,74
5,74
5,55
5,62
5,52
5,77
5,64
123,72
119,24
121,09
112,06
114,09
109,94
122,56
116,43
77,72
75,15
76,77
73,60
74,42
72,89
77,26
75,14
67,10
69,56
66,76
75,98
70,32
75,72
67,81
72,22
134,88
129,93
132,29
124,37
125,92
122,75
133,63
128,18
224,22
219,22
221,04
211,75
212,72
209,80
222,89
216,02
35,56
33,58
34,89
31,11
32,40
30,72
34,96
32,64
3,71
3,77
3,64
3,66
3,67
3,66
3,70
3,65
268,17
266,30
267,69
266,64
266,84
265,47
267,91
267,19
14,49
13,82
14,19
13,02
13,38
12,84
14,35
13,53
11,33
10,63
11,04
9,94
10,25
9,80
11,16
10,44
41,42
40,46
40,70
38,94
39,24
38,60
41,20
39,80
155,25
150,74
152,33
144,86
145,69
143,22
154,15
148,62
161,35
159,65
160,97
158,28
160,08
157,44
160,96
159,39
110,67
109,03
109,91
107,49
107,78
106,84
110,25
108,57
0,05
0,05
0,05
0,04
0,04
0,04
0,05
0,05
165,55
159,87
162,32
154,43
156,30
152,63
164,09
158,55
SMR, Standard Morbidity Rate = Antal förväntade patienter
per Major Rx-MG grupp per 1000 invånare
Major Rx-MG grupp = Sjukdomsgrupper baserade på läkemedelskonsumtion
Copyright 2005, Johns Hopkins University,02/07/2006
Step 1
Comparsion between Rx-MGs
Number of cases per 1000 inhabitants for two selected Rx-MG groups (2007)
Norrbotten
Västerbotten
Jämtland
Västernorrland
Gävleborg
Dalarna
Västmanland
Örebro
Värmland
Västra Götaland
Halland
Skåne
Blekinge
Gotland
Kalmar
Kronoberg
Jönköping
Östergötland
Södermanland
Uppsala
Stockholm
0,0
50,0
100,0
150,0
Cardiovascular / High Blood Pressure
Copyright 2005, Johns Hopkins University,02/07/2006
200,0
250,0
Endocrine / Diabetes With Insulin
300,0
Actual pharmacy cost and predicted
pharmacy cost per county council
Step 2
Actual costs per inhabitant and predicted cost
per inhabitant per county council/municipality
Step 3
Proportion of risk patients per municipality
Step 4
SMRs for Major Rx-MGs per county council
Step 5
Comparisons of specific Rx-MGs
per county council
48
Conclusions
49
• The Rx-model works well for Swedish data
• The model provides an large amount of data for analysis and usage in
practice
• The model provides functionality for also predicting change in total cost
• Specific analysis for measuring costs for high risk patients
• Measures generated from the system could i.e. be used in open
comparisions (öppna jämförelser)
• More analysis with diagnosis and cost data on county council level still
needed to prove if Rx-MG can be a useful tool for resource allocation in
a capitation model
• The combined models (Rx-PM + Dx-PM) with diagnoses and pharmacy
data is recommended to use
• Pharmacy data alone has an higher explanatory value than age and
gender but still low in comparsion with combined models
Copyright 2005, Johns Hopkins University,02/07/2006
Opportunities for Learning and
Interaction Regarding ACGs
•
Web Site:
– www.acg.jhsph.edu
– www.ensolution.se
•
Contact:
Dr. Karen Kinder Siemens –
Director, International ACG
kkinder@jhsph.edu
Andreas Johansson, Ensolution AB
andreas.johansson@ensolution.se, Mbl 0709-900030
•
More information in the Ensolution stand
Copyright 2005, Johns Hopkins University,02/07/2006
50