Comparison of Different Implementations of a Process Limiting

Pharmaceutical Expenditures Required by German Law

Timo Emcke

, Thomas Ostermann

, Marc Heidbreder

and Reinhard Schuster

Chair of Department of Prescription Analysis, Association of Statutory Health Insurance Physicians,

Bismarckallee 1-6, 23812 Bad Segeberg, Germany

Chair of Research Methodology and Statistics in Psychology, Witten/Herdecke University,

Alfred-Herrhausen-Straße 50, 58448 Witten, Germany

Medical Advisory board of Statutory Health Insurance in Northern Germany, Katharinenstraße 11,

23554 L

ubeck, Germany

Chair of Department of Health Economics, Epidemiology and Medical Informatics, Medical Advisory Board of Statutory

Health Insurance in Northern Germany, Katharinenstraße 11, 23554 L

ubeck, Germany

Keywords:

Controlling Measures, Outpatient Care, Morbidity Adjusted Benchmark, Prescription Data, MRG, Big Data.

Abstract:

German legislation demands controlling measures for outpatient drug costs. As of 2017 the health insurance

actors rose to a challenge to reform the benchmark system on the federal state level. We look at the previous

system applied until 2015, the improvements in 2016 and the method the regional parties agree on for 2017.

After discussing hard- and software systems and the underlying data brieﬂy we describe the ﬂaws of the old

approach and develop a general model for controlling measures in the outpatient ﬁeld. Finally we present the

ﬁrst real world applications of the new model: a patient type classiﬁcation system leading to target costs and a

derived distance structure of physicians regarding their prescription behaviour.

1 INTRODUCTION

In Europe and especially in Germany rising pharma-

ceutical expenditures put the health service at risk.

Every modern health care system has to ensure the

quality and equity of care while keeping the cost

down. Therefore controlling measures were establis-

hed by the German legislation as early as in 1993.

Since 1995 this is subject to regional negotiations bet-

ween Statutory Health Insurances (SHI) and SHI as-

sociated physicians. This type of regulation aims to

limit expenditures per patient without restricting the

necessary treatment.

Of the exiting two types of instruments, the ﬁrst

one puts German patients/cases in certain more or less

morbidity related cost groups, the other promotes or

restricts drug classes with different economic charac-

teristics but same curative effects. We will look at tho-

se using health insurance data of the German Federal

State Schleswig-Holstein in 2015.

In the years from 1995 till 2015 physician groups

got three different treatment case budgets for each

insurance status deﬁned by statutory health insuran-

ce (member [M], dependent coverage [D] and retired

[R]). Some regions merged status [M] and [D]. Se-

veral expensive drug substances and pharmaceuticals

regulated by treatment regimen are excluded resulting

in internal inconsistencies and uncertainties regarding

all participating players.

Budgets are calculated using expenditure shares

for the mentioned case groups per physician group in

a reference period (last year) and the negotiated target

volume of expenditure for the resent year.

In December 2013 the social welfare court

of Dresden passed the sentence that guide va-

lues/budgets have to be based on age groups. Addi-

tionally the Federal Social Court judged that authori-

ties have an obligation to regulate atypical drug pres-

criptions. As an immediate consequence regarding the

budget calculation for 2016 four age groups superse-

ded insurance status: 0-15, 16-49, 50-64 and 65 and

above. Those groups, utilized in all statutory health

insurances, have a very poor age resolution for this

ﬁeld of application in general.

From 2017 on, the federal legislator made re-

gional negotiated far-reaching reforms of control-

ling measures possible (Versorgungsst

arkungsgesetz

= Supply Support Act). A new system developed in

this context is expenditure controlling by Morbidi-

ty Related Groups (MRG). MRG is an adaption of

Emcke T., Ostermann T., Heidbreder M. and Schuster R.

Comparison of Different Implementations of a Process Limiting Pharmaceutical Expenditures Required by German Law.

DOI: 10.5220/0006114800350040

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 35-40

ISBN: 978-989-758-213-4

the Diagnosis-Related-Group-System (DRG) used for

classiﬁcation and compensation of hospital cases and

put into effect in 2003 by German legislation. It is ba-

sed on similar systems elsewhere: Since the ﬁrst use

for hospital payment in the United States in the ear-

ly 1980s, DRG-type systems have become the main

method of hospital payment in the majority of OECD

countries. The German version (G-DRG) is based on

the Australian DRG-system (AR-DRG).

Hereinafter we will compare the systems based on

insurance status, age groups and MRG, including so-

me new results for MRG.

2 MATERIAL AND METHODS

For comparing the previous and the new controlling

measures we analyze detailed prescription data of the

Statutory Health Insurance in Schleswig-Holstein of

quarter two in 2015. There’s no beneﬁt using annu-

al data due to the stable prescription framework. The

data on the prescription level are combined with ma-

ster data containing drug classes (ATC [anatomic-

therapeutic-chemical] with some additions for pro-

ducts not classiﬁed), physician groups and drugs to

be excluded. Treatment cases of the Association of

Statutory Health Insurance Physicians are also ad-

ded. Obtaining results required the processing of large

amounts of data.

The hardware used is a dedicated Debian

GNU/Linux Server administered by the Medical Ad-

visory board of Statutory Health Insurance in Nor-

thern Germany also used to generate consultation ma-

terials from the same data.

It runs a LAMP conﬁguration (Debian

GNU/Linux, Apache 2.4, MYSQL Community

Edition 5.7 [extensive use of partitioning] and PHP 7

[with PEAR framework esp. for spreadsheet output]).

The inexpensive open source/free software setup

makes the cooperation of different administrative

bodies possible. The coding was done using the Perl

programming language.

The previous model used till 2016 applies pres-

cription data, treatment cases and status deﬁned by

statutory health insurance/age groups. The implemen-

tation is straight forward. Treatment cases in a certain

age/status group get their share of the negotiated vo-

lume of expenditure based on the development of last

years‘expenditures and treatment cases.

The new MRG-model requires prescription data,

the ATC classiﬁcation and physician group informa-

tion depending on the model conﬁguration. It can be

deﬁned as follows:

B = set of physicians/practices

F = set of physician groups

There is a transformation mapping physi-

cians/practices to groups: f = f (b) while splitting up

practices containing different physician groups.

P(b) = patients of b ∈ B, b = b(p) is the mapping

of patients and physicians whereas the transformation

D = D(p) maps patients p ∈ P(b) to the prescribed

drugs. Multiple prescriptions of one drug are coun-

ted repeatedly. o(d) is a quantity factor for d ∈ D

representing the ration of package size of the pres-

cription drug in relation to the biggest one available.

A pharmaceutical classiﬁcation system (e.g. ATC4)

as transformation: a = a(p),a ∈ A used identiﬁcati-

on of similar medicinal products. The drugs d ∈ D

are linked to costs by the cost function: k = k(d),k ∈

R,k > 0.The age of the patient is deﬁned by: t = t(p)

in ﬁve-yearly stages. A MRG is a pair d = (c, s)

[c:basic MRG, s:degree of severity] with c ∈ A,s ∈

Z,0 ≤s ≤ 9.

Cost per ATC =

k(p,a

∗

) =

∑

d∈D(p),a(p)=a

∗

k(d)

ATC with the highest costs = basic MRG is characte-

rized by

k(p,c) ≥

k(p,a) for all a ∈ A. In case of the

occurrence of several c

the lexicographically domina-

ting element is chosen. c = c(p) is the transformation

to determine patients basic MRG. Number of ATC4

groups per patient (multimedication) is deﬁned as:

v(p) = #{a ∈ A :

k(p,a) > 0}.

The number of prescriptions for patient p ∈ P assi-

gned to basic MRG c(p) is represented by:

¯o(p) = w



∑

d∈D(p):a(d)=c(p)

o(d)



with

w(x) =



x, if x ∈ Z

bxc, if x /∈ Z.

We deﬁne threshold values for subgroups:

,··· ,v

) = (0.5, 0.75,1.25,1.5, 2.0,2.5, 5,10)

i(v) = i is true if v

≤ v < v

+ 1. m(X) shall be the

mean of x ∈ X. The costs of basic MRG c ∈ A in the

physician group are deﬁned as:

∗

, f

∗

) = m

p∈P: f (b(p))= f

∗

,c(p)=c

∗



k(p,c

∗

)



and adding the age dimension the term changes to:

∗

, f

∗

) = m

p∈P: f (b(p))= f

∗

,c(p)=c

∗

,t(p)=t

∗



k(p, c

∗

)



whereby the age related severity is given by:

(c, f ,t) = i



∗

(c, f ,t)/k

∗

(c, f )



Costs differentiated by multimorbidity are expressed

by the formula:

HEALTHINF 2017 - 10th International Conference on Health Informatics

∗

, f

∗

, j

∗

) = m

p∈P: f (b(p))= f

∗

,c(p)=c

∗

,v(p)= j

∗



k(p, c

∗

)



with the corresponding degree of severity:

(c, f , j) = i



∗

(c, f , j)/k

∗

(c, f )



The same can be done by looking at prescription in-

tensity:

∗

, f

∗

, j

∗

) = m

p∈P: f (b(p))= f

∗

,c(p)=c

∗

, ¯o(p)= j

∗



k(p, c

∗

)



(c, f , j) = i



∗

(c, f , j)/k

∗

(c, f )



Total degree of severity is given by:

i(p) = max



(c(p), f (b(p),t(p)),i

(c(p)), f (b(p)), v(p)),

(c(p)), f (b(p), ¯o(p))



The MRG including severity levels is recalculated

with respect to physician groups:

∗

, f

∗

, j

∗

) = m

p∈P: f (b(p))= f

∗

,c(p)=c

∗

,i(p)= j

∗



k(p, c

∗

)



Thereby we get the target cost for benchmarking the

physician:

∑

p=P(b)

∗



c(p), f (b(p)),i(p)



In our setting we look for the group with the hig-

hest drug costs within a quarter for each consulted

physician for a certain patient. This group should

strongly be related to the morbidity of the patient

and we will call it therefore Morbidity Related Group

(MRG). One considers the costs as a proxy for the

severity of drug treatment and could also take other

weight functions instead of cost. The following is an

example regarding a diabetes patient who belongs to

the basic group A10A (Insulins and analogues) with

total patient cost of 1,536.75 e:

Table 1: Example of (basic) MRG determination.

cost nr ATC substance drug amount

320.74 1 B01AF01 Rivaroxaban XARELTO 15 mg 98

272.61 1 N06AX21 Duloxetine CYMBALTA 60 mg 98

248.02 2 A10AD04 Insulin Lispro LIPROLOG Mix 25 10X3

208.25 7 V04CA02 Glucose CONTOUR Test-

streifen

159.35 1 N02AA55 Oxycodone TARGIN 10 mg/5

100

124.01 1 A10AD04 Insulin Lispro LIPROLOG Mix 50 10X3

112.35 1 N02AA55 Oxycodone TARGIN 5 mg/2.5

100

23.97 1 C10AA01 Simvastatin SIMVA ARISTO 40

100

19.22 1 C03CA04 Torasemide TORASEMID AL 20

100

16.27 1 N02BB02 Metamizole

Sodium

NOVAMINSULFON

100

15.41 1 H03AA01 Levothyroxine

Sodium

L-THYROX HE-

XAL 125

100

13.98 1 C07AB12 Nebivolol NEBIVOLOL Glen-

mark 5 mg

100

As an initial adjustment factor the age of patients

can be applied. In each 5 year group of patients the

ratio of costs per patient in the subgroup compared

to the whole MRG was considered. If the ratio lies in

certain intervals (0-0.5, 0.5-0.75, 0.75-1.25,..., 10) the

age severity level 0,1,...,9 were assigned. The same

can be conducted with respect to other factors corre-

lated with morbidity. By using subgroup structures a

risk adjustment can be accomplished. All of this has

not to be precise on the level of the patient but on

the physicians level. Regarding the considered MRG

A10A (Insulins and analogues) seven degrees of se-

verity in the range of 101.27 e up to 1,385.61 e re-

sulted:

Table 2: Example of severity levels of MRG A10A.

degree cost in Euro number of patients

2 101.27 21

3 273.68 60,634

4 517.87 16,840

5 707.95 20,904

6 995.74 2,085

7 1,385.61 1,954

We divide the (basic) MRG into several severi-

ty levels that will be analysed by Lorenz curves and

the corresponding Gini coefﬁcients: Additionally the

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

costs in %

patients in %

Lorenz curve for MRG AL04A (immunosuppressants) - general practicioners

Figure 1: Lorenz curve of MRG AL04A.

Shannons entropy (−

∑

log(p

)) can be applied to

the patient type structure in each physicians group

with respect to the MRG basic groups:

Table 3: Shannon entropy per physician group (1).

enthropy number of patients physician group

3.9985 994,220 general practitioners

3.8156 95,054 paediatricians

3.5635 3,548 non-specialised internists

3.4234 4,991 nephrologists

3.1437 1,955 haematologist/oncologists

3.1149 3,285 cardiologists

2.9632 2,961 gastroenterologists

Comparison of Different Implementations of a Process Limiting Pharmaceutical Expenditures Required by German Law

Table 4: Shannon entropy per physician group (2).

enthropy number of patients physician group

2.7608 84,660 dermatologists

2.7552 16,155 surgeons

2.7327 40,045 neck nose ear physician

2.6253 91,868 gynaecologists

2.4939 37,199 urologists

2.2263 49,653 neurologists

2.1716 1,325 endocrinologists

2.1210 3,851 rheumatologists

2.0282 40,149 orthopaedic

1.6552 2,123 anaesthetists

1.4179 51,819 ophthalmologists

1.3517 26,392 pulmonologists

1.1722 7,003 psychiatrists

3 RESULTS

The application of the treatment case oriented approa-

ches over the last decades showed that these systems

are incapable of considering age and progress related

increase of prescription costs. Recent analysis of the

age distribution of treatment cases in each Statutory

Health Insurance status group shows that the applied

age groups might be too coarse and unsuited as the

insurance status for the morbidity related depiction of

prescription costs per patient:

50,000

100,000

150,000

200,000

250,000

300,000

0 10 20 30 40 50 60 70 80 90 100

treatment cases

age

age dependent treatment cases per insurance status

Figure 2: Age dependent number of treatment cases per ins-

urance status/age groups.

The high correlation of the results of the two

methods applied until 2016 conﬁrms that shifting to

age groups on the physicians level had practically no

beneﬁt:

y = 0.9505x + 0.0514

R² = 0.9784

50%

100%

150%

200%

250%

0% 50% 100% 150% 200% 250%

benchmarking result age groups

benchmarking result health insurance status

benchmarking practices using health insurance status/age groups

Figure 3: Correlation of benchmarks using health insurance

status vs. age groups.

Hence, a new system based on MRG is introduced

in 2017. There is little correlation between the results

obtained by the previous and the new results on the

practitioners level. That‘s due to the fact that many

factors were disregarded in the past and inconsisten-

cies were compensated by “manual intervention“:

y = 1,74x - 0,7687

R² = 0,2571

25%

50%

75%

100%

125%

150%

175%

200%

0% 25% 50% 75% 100% 125% 150% 175% 200%

benchmarking results of practices using the

appoach applied until 2016

benchmarking result of practices using the MRG approach used in 2017

correlation of benchmarking results at the practitioners' level

Figure 4: Correlation of benchmarks applying the previous

(until 2016) and the new approach (2017).

Sorting the practices in ascending order for all af-

fected groups due to their MRG benchmarking result

and comparing those to the outcomes of the older

system demonstrates the progress in model adaption

made:

Figure 5: Results of MRG vs. system based on treatment

cases in 2016 (each line one practice).

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In those new MRG models all patients of a cer-

tain practice are classiﬁed and a speciﬁc structure for

each practice is the result. As an example, we consider

a physician with 14.0 % of his patients in the MRG

A10A (Insulins and analogues) and 11.8 % patients in

the MRG V04C (other diagnostic agents = test strips

measuring glucose). In this group of general practi-

tioners (GP) patients in those groups only account for

3.8 % in these two groups. The physician can thereby

be identiﬁed as a diabetologist:

Table 5: Patient structure of a diabetologist.

nr MRG nr. cost per prop. prop. drug droup

pat. patient doc. group

1 A10A 193 463.12 14.0% 2.4% Insulins and analo-

gues

2 V04C 162 307.76 11.8% 1.4% Other diagnostic

agents

3 H03A 86 22.28 6.3% 4.5% Thyroid preparations

4 A10B 82 185.78 6.0% 2.7% Oral blood glucose

lowering drugs

5 A02B 73 60.91 5.3% 7.2% Drugs for peptic

ulcer and gastro-

oesophageal reﬂux

disease (gord)

6 B01A 53 366.21 3.9% 4.0% Antithrombotic

agents

1 J01D 22 31.70 1.6% 2.4% Other beta-lactam

antibacterials

19 C10A 17 58.30 1.2% 3.0% Cholesterol and tri-

glyceride reducer

20 N03A 16 275.63 1.2% 1.4% Antiepileptics

After the formation of groups for all practi-

ces/physicians one can compare the MRG distributi-

ons and values in each group:

Table 6: MRG patient shares of orthopaedics.

frac. cost MRG drug group

pat. p. pat.

42.9% 19.95 M01A Antiinﬂammatory and antirheumatic products,

non-steroids

13.6% 26.26 H02A Corticosteroids for systemic use, plain

12.4% 23.81 N02B Other analgesics and antipyretics

8.2% 134.66 M05B Drugs affecting bone structure and mineraliza-

tion

6.1% 123.78 N02A Opioids

3.6% 92.18 B01A Antithrombotic agents

3.3% 40.71 M03B Muscle relaxants, centrally acting agents

2.5% 33.75 A02B Drugs for peptic ulcer and gastro-oesophageal

reﬂux disease (gord)

1.1% 2,515.02 L04A Immunosuppressive agents

1.0% 304.63 L01B Antimetabolites

Regarding orthopedics we observe a patient type

structure, in which 42.9 % of all patients belong to

the MRG M01A (antiinﬂammatory and antirheuma-

tic products, non-steroids). The 10 leading positions

cover 94.6 % of the costs. Costs again depend mainly

on the medical discipline. In oncology average costs

per patient are 15,288.17 e in the MRG L04A (im-

munosuppressive agents including all the other drugs

for the patient) versus 2,515.02 e for orthopedics. In

urology the top ten positions with respect to the num-

ber of patients cover 83.6 % of the costs. In the case

of GP these costs are only 44.2 %:

Table 7: MRG patient shares of urologists.

frac. cost MRG drug group

pat. p. pat.

34.8% 44.67 G04C Drugs used in benign prostatic hypertrophy

16.7% 136.12 G04B Other urologicals, incl. antispasmodics

9.9% 19.84 J01M Quinolone antibacterials

6.8% 618.61 L02A Hormones and related agents

4.9% 30.42 J01X Other antibacterials

3.1% 33.47 J01D Other beta-lactam antibacterials

2.0% 154.56 G03B Androgens

1.9% 22.13 J01E Sulfonamides and trimethoprim

1.7% 27.71 D01A Antifungals for topical use

1.7% 4,122.10 L02B Antimetabolites

Table 8: MRG patient shares of general practitioners.

frac. cost MRG drug group

pat. p. pat.

7.3% 62.55 A02B Drugs for peptic ulcer and gastro-oesophageal

reﬂux disease (gord)

5.4% 42.04 C07A Beta blocking agents

5.1% 34.67 M01A Antiinﬂammatory and antirheumatic products,

non-steroids

4.7% 24.25 H03A Thyroid preparations

4.2% 185.14 R03A Adrenergics, inhalants

3.8% 316.40 B01A Antithrombotic agents

3.8% 124.59 C09D Angiotensin II antagonists, combinations

3.4% 85.10 C09C Angiotensin II antagonists, plain

3.4% 30.68 C09A Ace inhibitors, plain

3.2% 88.42 N06A Antidepressants

The MRG patient shares can be utilized to gene-

rate distance measures for the clustering of all practi-

ces/physicians. Let p

be the fraction of patients with

MRG m (m ∈ M) for the physician k (k ∈ P). With

respect to the medical discipline s (s ∈ S) and let q

be the respective fraction. Let r and s be such fracti-

ons for physicians or medical disciplines we can use

a Manhattan distance:

∑

i∈M

−s

Alternatively we can apply spherical distances on the

n-dimensional sphere where n is the number of MRG

classes with respect to the points:

∑

j∈M

and

∑

j∈M

√

and

√

The spherical distances are differentiable with respect

to the components of r and s and thereby is more sui-

table for optimization procedures.

Comparison of Different Implementations of a Process Limiting Pharmaceutical Expenditures Required by German Law

We can deﬁne the discipline t ∈ S of a physician

k ∈ P by the value s ∈ S for which:

∑

m∈M

−q

has a minimal value. The distance of a physician to a

group measures to which extent he is typical or not.

Extreme values may be a hint for the need for spe-

cial considerations. One can use cluster methods in

order to receive a classiﬁcation of physicians without

the use of their medical discipline which is primarily

determined by admission law.

4 CONCLUSIONS

The 2016 switch from health insurance status to age

groups did not eliminate the ﬂaws of the old bench-

marking/budget approach. New promising ideas on

the regional level like MRG have a huge potential still

to be researched and utilized. The necessary data is

provided, hard-/software and knowledge are availa-

ble. Steady change and especially new form of he-

alth care require adapting benchmarking systems on

a sound data and legal foundation. Therefore MRG

seems to be a highly suitable approach meeting the

criteria.

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HEALTHINF 2017 - 10th International Conference on Health Informatics