Benefits and Limitations of Age Group-Adjusted Average in the

Profitability Audit for Pharmocotherapy

Reinhard Schuster

1,*

, Thomas Ostermann

2,†

, Timo Emcke

3,‡

and Fabian Schuster

1,4,§

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

Insurance in Northern Germany (MD Nord), 23554 Lübeck, Germany

Department of Research Methodology and Statistics in Psychology, Witten/Herdecke University, 58448 Witten, Germany

Department of Prescription Analysis, Association of Statutory Health Insurance Physicians, 23812 Bad Segeberg,

Germany

Sigmund-Freud-PrivatUniversity SFU Vienna, Faculty of Law, 1020 Vienna

Keywords: Drug Prescriptions in Outpatient Treatment, Statutory Health Insurance in Germany, Sickness (Health

Insurance) SHI Funds in Germany, Morbidity Related Groups (MRG), Risk Structure Compensation between

the Health Insurance Companies (RSC), Gini Coefficients, Lorenz Curves.

Abstract: Benchmarks for pharmaceuticals have been used for over 25 years to limit the cost increase in the second

largest cost block in statutory health insurance in Germany with financial punishments for the physicians. The

Regional Social Court of Dresden declares such a payback practice to be inadmissible if no age reference is

used. In 2016, in most regions of the statutory health insurance associations, the division into status groups

members, family members and pensioners has been changed into four age groups. The Supply Strengthening

Act has opened up the possibility of drafting regional agreements. In Schleswig-Holstein, Morbidity Related

Groups (MRG) were introduced for morbidity-related considerations. A number of other regions are currently

using retrospective average cost limitations, which have the same problems as the benchmark restrictions.

The aim of this paper is to investigate the influence of the type of health insurance (sickness) fund on the

benchmark result with status and with age groups. Different morbidity structures between the health insurance

funds are the subject of the risk structure compensation. For doctors, this aspect is not given sufficient

consideration with respect to patient-specific morbidity characteristics till now.

1 INTRODUCTION

Drug prescriptions in outpatient treatment of SHI

(statutory health insurance) physicians are subject to

the economic efficiency requirement according to

German Law with respect to §§ 2 and 12 SGB V.

This means that all drug treatments that are provided

in the statutory medical practice must be sufficient,

appropriate and economical and that what is

necessary must not be exceeded. A control instrument

that has been used for a long time was the so called

“Richtgrößen” benchmark, cf. (Busse et. al., 2015),

(Bratzke et. al., 2012). Based on the average actual

situation in the SHI-insured groups M (members), F

https://md-nord.de

†

https://uni-wh.de

‡

https://kvsh.de

https://sfu.ac.at

(family members) and pensioners (R) as well as the

expenditure volume determined in negotiations

between the contracting parties (health insurance

companies and the Association of Statutory Health

Insurance Physicians), benchmarks were set for

assessing the drug expenditure. Depending on the

negotiation region, drugs were removed from the

restrictions if it should be assumed that their

prescription was not inefficient („Anlage-Präparate”,

“drug contract annex”). In December 2013, the

Dresden Social Court considered exams to be

unlawful because there were no age-related patient

groups. The Federal Social Court ruled in June 2013

that the review committees have a duty to investigate

atypical prescription cases. Since they had data on the

390

Schuster, R., Ostermann, T., Emcke, T. and Schuster, F.

Beneﬁts and Limitations of Age Group-Adjusted Average in the Proﬁtability Audit for Pharmocotherapy.

DOI: 10.5220/0010807500003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 390-396

ISBN: 978-989-758-552-4; ISSN: 2184-4305

doctor to be examined and the comparison group,

they would have to investigate deviations and discuss

the reasons with the doctor. These judgments are a

step towards morbidity-related considerations, cf.

(Urt. LSG Sachsen-Anhalt, 2014), (Urt. BSG Juni,

2013), (Urt. SG Dresden, 2013), (GKV-Versorgungs-

stärkungsgesetz, 2015), (Gottwald, 2015), (Korzilius,

2015), (Sinowatz, 2015), (Wersborg, 2006). As will

be described in more detail below, there are data in

the big data environment, which results opportunities

and difficulties for doctors, negotiators and

examination boards.

In (Schuster et. al., 2016) the influence of the

changeover from the status groups to the age groups

0-15 years, 16-49 years, 50-64 years and 65 years of

the patients and over on the database 3/2014 - 2/2015

as a first calculation and a second calculation for the

first two quarters of 2018 for Schleswig was

examined. One way of assessing the fairness of the

changeover is to compare the subdivision only

according to status groups or only according to age

groups with a combined view. Morbidity-related

misjudgments that occur in the combined groups are

not recorded.

The combination of patient members and age

group 16-49 years, with 30.7% of patients, which is

the strongest combination among general

practitioners, is assessed by the age assessment

almost as in the combination of status group and age,

so that the age groups appear fair from a combination

perspective. But they lose 37% of their benchmark

drug volume according to status groups. In the third

largest combination group of members and ages 50-

64, this group was underrated by 29% according to

status group and 12% overrated according to age

group.

The comparison of the benchmark fulfillment

according to status and age groups results in a

correlation coefficient R^2 between 0.9724 for

pediatricians and 0.9999 for hematologists/

oncologists and rheumatologists, for the largest group

of general practitioners it is 0.9784. The rather rough

age division into only four groups had a much smaller

effect in terms of a fairer assessment than was

expected from the court judgments examined. The

main points of criticism in the grounds for the

judgment therefore remain.that papers in a technically

unsuitable form will be returned for retyping. After

returned the manuscript must be appropriately

modified.

In Schleswig-Holstein, the pharmaceutical

partnership agreement between the SHI physician

organization and the SHI funds for 2017 was changed

to Morbidity Related Groups (MRG), cf. Emcke

et.al., 2017), (Prüfvereinbarung, 2016), (Schuster et.

Al., 2017), (Schuster et. al. ,2016). The relationship

to morbidity is established on the basis of the

prescribed drugs and the active substance

classification (international ATC classification) and

not on the basis of the diagnostic data (international

ICD classification). The relationship between MRG

and ICD diagnoses is examined in (Schuster et. al.,

2017), (Schuster et. al. 2017). Diagnostic data are

only available on a quarterly basis, prescription data

according to Section 300 SGB V have a prescription

date. The diagnostic data differentiate between acute

illness and permanent diagnosis, but there is no main

diagnosis like in the inpatient area.

The active ingredient classification is based on the

international ATC (anatomical-therapeutic-chemical)

classification with national characteristics on the

basis of drug approval law. The MRG classification

has analogies to the Diagnoses Related Groups

(DRG) in the inpatient area, but does not serve to

reimburse medical services. For MRG, the patient and

his entire morbidity are the focus of considerations.

For each quarter and medical specialty, the drug

group at the level of the ATC four-digit code with the

highest costs is selected as dominating for the patient.

For all patients in this base group, it is considered how

age in 5-year steps, multimorbidity in the sense of

multi-medication and prescription intensity affect the

average costs in a comparison of specialist groups. On

this basis, a prospective guaranteed value for its

pharmaceutical expenditure is first determined for

each doctor. If the morbidity of the patients or their

number increases in the prescription year, the

guaranteed value will increase. With this approach,

particularities in practice are generally well captured.

A fair assessment is achieved if all doctors and all

patients are included in the evaluations. The MRG

concept for general assessment is supplemented by a

drug agreement in which specific control effects are

to be achieved in selected areas.

In the following, it will be examined to what

extent the morbidity differences occurring in the

types of health insurance funds influence the results

of profitability analyzes. This also includes the

question of the extent to which a doctor bears an

increased or decreased risk of exams due to the

different structure of the insurance type of the patients

with regard to the health insurance companies. In

order to counteract the different morbidity of the

insured persons, there is a risk structure compensation

between the health insurance companies (RSC). In

relation to a doctor, this has not been considered for

benchmarks and average values. In the MRG system,

such differences are taken into account in the case

Beneﬁts and Limitations of Age Group-Adjusted Average in the Proﬁtability Audit for Pharmocotherapy

391

groups. In contrast, morbidity differences are

essentially not taken into account in drug agreements.

Differences in the distribution are to be examined

with Gini coefficients for Lorenz curves.

2 MATERIAL AND METHODS

We analyze all treatments and prescriptions of

physicians for patients of the statutory health

insurance (SHI) by SHI physicians in Schleswig-

Holstein in the first and second quarter of 2018. The

datasets of all treatments and prescriptions of all

physicians with respect to a patient are used. The

dataset of the second quarter of 2018 covers around

1,700,000 patients with diagnoses and around

1,400,000 patients with drug prescriptions using a

pseudonymized patient identity with age, gender and

insurance fund information. We utilize the

International Statistical Classification of Diseases

and Related Health Problems [ICD]. The knowledge

of the total number of patients is a side-effect of the

diagnose statistic. The same diagnoses for the same

patient by different physicians are counted

repeatedly, if the analysis is aimed for drug economy

with respect to physicians, with respect to

epidemiologic analysis it can be more adequate to

analyze all diagnoses and treatments for a patient with

respect to all physicians. For prescription analysis the

International Anatomic Therapeutic Chemical (ATC)

classification system with German specifications

provided by the German Institute of Medical

Documentation and Information (DIMDI) is used.

Statutory health insurance (SHI) in Germany

through sickness funds is compulsory for workers

whose gross income does not exceed a certain level,

for unemployed and retired people, and for certain

other population groups (such as farmers, artists, and

students). Employees with incomes above the

threshold may be voluntary sickness fund members if

they have been members before. Around 88 percent

of the total population in Germany is covered by the

SHI (74 percent obligatorily and 14 percent

voluntarily).

Sickness (health insurance) SHI funds in

Germany are differentiated into the following types:

- general regional funds (Ortskrankenkassen

AOK)

- substitute funds (Ersatzkassen, VdEK)

- company-based funds (Betriebskranken-

kassen, BKK)

- guild funds (Innungskrankenkassen, IKK)

- farmers funds (Landwirtschaftliche

Krankenkassen, LKK)

- miners fund (Bundesknappschaft, BKN)

- sailors fund (See-Krankenkasse, SEE).

All funds have a not-for-profit status and are

based on the principle of self-government. Miners

fund and sailors fund are merged now.

To avoid having all insured people choose funds

with a low contribution rate because of a historically

good risk profile a risk structure compensation (RSC)

scheme was introduced on the level of funds. But a

risk compensation on the level of physicians with

respect to drug economic considerations which can

lead to paying-back regulations for the doctors was

not established.

The health funds and each regional association of

SHI physicians set target volumes for physicians in

each medical specialty in annual negotiations. These

correspond to the average prescription volume per

calendar quarter for each specialty which may be

done with prospective and with retrospective

considerations. SHI physicians who exceed their

individual target limit by more than 15% with

regional exceptions related to medicines and patients

may be advised in writing to critically reconsider their

prescription behaviour to the joint examination office

of the SHI association and the health insurance funds.

The SHI-limit for overprescribing and paying-back

has been set at 125% of the individual target. Those

physicians who exceed the target by 25% are asked to

justify the overprescription although this would

actually already be possible to a considerable extent

on the basis of the data records available in the

examination office. If their arguments are rejected,

they are subject to recourse and usually pay back the

difference between the overprescribed amount and

115% of the target.

In the discussion between the doctors and the

examining body, the doctors have a lack of

information because they know their own

prescriptions but not those of their specialist group.

Ideally, this is balanced out by joint advisory services

by the SHI association and the health insurance funds.

With regard to the doctors, this justification

procedure sometimes delivers very different results,

depending on whether one adjusts for status groups or

age groups. If you look at the more than half a million

different drugs according to the product designation

(pharmaceutical central number, PZN) and the widely

spread diagnosis according to ICD, in connection

with the specified number of patients, one can see that

it situated in the big data environment.

HEALTHINF 2022 - 15th International Conference on Health Informatics

392

The KM6 statistics of the statutory health

insurance companies describe the number of insured

persons at the annual mean. Population statistics are

based on the status at the end of the year. From the

difference between the two, one can deduce the

proportion of persons SHI-insured with a certain

degree of accuracy.

Costs per patients are used for benchmark

calculations and average value checks. The point of

reference here are the medical treatment cases, this

also includes those patients without medication

prescriptions, which can be calculated by the ICD

statistic. Alternatively, prescription patients (i.e. only

patients with drug prescriptions) or all SHI-insured

persons can be used, with advantages and

disadvantages. The choice of all patients as a

reference point is intended to support the limitation of

minor prescriptions. In the MRG system, the

prescription patients are the reference point. Since

comparisons are made based on morbidity, the

“thinner problem” (patents with few cheap drugs) is

not an obstacle due to regulations. In the case of

regional morbidity analyzes, the insured person's

reference (in relation to statutory health insurance,

GKV) is relevant. This is different from the

population reference. The information provided by

insured persons is available with the KM6 statistics

differentiated according to federal state and type of

insurance fund (insurance members in month 6,

middle of the year). Unfortunately, the reference

point is the middle of the year, as opposed to the

majority of population statistics which refer to the end

of the year.

The script languages gawk and perl were used for

the calculations. These were carried out for a joint

working group of the health insurance companies and

the Association of Statutory Health Insurance

Physicians with their drug billing data for the second

quarter of 2018.

3 COSTS PER CASE AND COST

SHARES ACCORDING TO

TYPE OF INSURANCE

Age information is only available for the age group 0-

14 and then in 5-year steps up to 89 years and a group

from 90. This means that the age and gender-related

share of statutory health insurance insured persons

can only be estimated with a higher resolution using

interpolation methods, cf. Figure 1.

Figure 1: Age and gender-dependent proportion of

GKV insured persons in Schleswig-Holstein (man = m,

woman = w).

There was a particularly high proportion of privately

insured persons in the low-birth year 1945, at the

height of the baby boom a (locally) minimal and then

at the point of the pill break again a (locally) maximal

proportion of privately insured persons. In connection

with the higher average social status of privately

insured persons compared with those with statutory

health insurance, a lower risk of morbidity is to be

expected. The gender-specific proportion of people

insured with statutory health insurance can also result

in differences in morbidity when looking at the

insured person or the number of cases. Population

models over long periods of time are considered in

(Schuster et. al., 2017).

Figure 2 gives an overview of the gender-

differentiated influence of age in annual resolution on

the average case costs for general medicine

depending on the types of insurance.

Figure 2: Case costs according to type of insurance.

70%

75%

80%

85%

90%

95%

100%

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

fraction

age

Statutory funds fraction

m w

0 €

50 €

100 €

150 €

200 €

250 €

300 €

350 €

400 €

450 €

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

cost

age

case costs

AOK BKK BKN

IKK LKK VdEK

Beneﬁts and Limitations of Age Group-Adjusted Average in the Proﬁtability Audit for Pharmocotherapy

393

Figure 3: Number of cases according to health.

Figure 3 shows the number of cases that are

specific to the health insurance scheme, which

represent the age structures of the patients relevant to

treatment. The importance of the risk structure

compensation for the health insurance funds is clearly

evident here, but also that certain areas are of

considerable importance within age groups.

4 LORENZ CURVES AND GINI

COEFFICIENTS

As an example, we consider in Table 1 the mean case

costs in the age group 0-14 years, depending on the

type of health insurance, as well as the associated

cumulative patient and cost shares.

Table 1: Case costs, cumulative patient and cost shares

according to insurance fund.

Fund

type

Case

costs €)

pat.-frac.cum.

(%)

Cost-part.cum.

(%)

2 34,35 17,99 13,58

5 36,93 26,01 20,10

6 40,93 71,45 60,98

3 55,10 72,87 62,70

4 61,47 99,19 98,27

1 97,40 100,00 100,00

This gives us a corresponding Lorenz curve in

Figure 4 with the Gini coefficient 0.12 (area between

the curve determined by patient and cost shares and

the diagonal; alternatively, a normalization with a

factor of 2 between the curves is used in the

literature).

Figure 4: Lorenz curve for patient and cost shares in the age

group 0-14 years with the proportion of patients on the

vertical axis and the proportion of costs on the vertical axis.

For the Gini coefficients of the specialist groups,

there are clear differences with regard to the types of

health insurance fund, cf. Table 2.

Table 2: Gini coefficients for the type of health insurance

for medical specialty group.

Gini

coefficent (%) medical specialty group

1,02 Oral, maxillofacial and facial surgeons

1,42 Internists (pulmonology)

3,17 Pediatricians

3,41 Child / adolescent psychiatrist

3,94 Dermatologists

4,10 Internists (hematology / oncology)

4,49 Ophthalmologists

4,67 Gynecologists

4,97 Internists (rheumatology)

5,02 Surgeons

5,41 Urologist

5,61 ENT doctors

5,72 Internists (cardiology)

5,94 Orthopedists

6,19 Neurology / Neurology

7,45 Anesthetists

8,94 General practitioners

8,94 Radiologists

9,08 Psychiatry and psychotherapy / neurology

10,02 Internists (nephrology)

10,62 Internists (specialists)

10,64 Medical psychotherapists

11,32 Internists (gastroenterology)

13,50 Internists (endocrinology)

24,14 Radiation therapists

For The smallest differences in costs with regard

to the types of insurance are next to the small

specialist group of oral and maxillofacial surgeons for

pulmonologists and paediatricians. Gastroentero-

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

fraction

age

number of cases

AOK BKK BKN

IKK LKK VdEK

0.2 0.4 0.6 0.8 1.0

0.2

0.4

0.6

0.8

1.0

HEALTHINF 2022 - 15th International Conference on Health Informatics

394

logists and radiation therapists have the greatest cost

differentiation with regard to the type of health

insurance. The General practitioners are in the

middle.

5 DISCUSSION AND

CONCLUSION

The considerable influence of the type of insurance

funds on the cost structure makes it clear that case

costs specific to age groups should be used depending

on the type of insurance fund. This approach

describes a morbidity-related risk structure

compensation for the physician. The application of

the risk structure compensation (RSC, Morbi-RSA),

which is common for health funds, to doctors appears

to be a necessary consequence. As an example,

consider in Table 3 the effects at the upper and lower

limit points for general practitioners.

Table 3: Profitability evaluation with and without risk

structure compensation (RSC) for the doctor.

Physician

with

Compensation

for morbidity

(€)

without

Compensation

for morbidity

(€)

Over- /

under-

payment

(%)

result

(%)

1 1.534,91 1.346,52 87,73 114

2 533.128,00 473.200,02 88,76 113

3 55.261,27 49.869,03 90,24 111

4 715.327,48 656.349,11 91,76 109

5 631.481,05 579.615,71 91,79 109

6 138.328,83 127.563,42 92,22 108

7 353.775,03 326.719,76 92,35 108

8 254.136,29 236.049,46 92,88 108

9 356.234,76 331.451,26 93,04 107

10 259.817,72 241.763,38 93,05 107

… … … … ..

1.203 369.268,96 398.390,82 107,89 93

1.204 266.444,41 287.501,12 107,90 93

1.205 160.722,60 173.465,69 107,93 93

1.206 215.969,65 234.045,34 108,37 92

1.207 35.858,68 38.910,51 108,51 92

1.208 227.612,77 247.060,47 108,54 92

1.209 103.938,17 113.090,71 108,81 92

1.210 104.004,85 114.433,94 110,03 91

1.211 3.410,21 3.766,44 110,45 91

1.212 52.584,56 58.213,02 110,70 90

Failure to take account of the type of health fund

with a view to compensating for morbidity ranges

from a disadvantage of 14% to an advantage of 10%

for general practitioners, whereby the drug costs

involved are also very different. Large amounts play

a role in both the upward and downward deviations.

In the case of benchmark and average restrictions,

an excess of 25% can trigger a pay-back procedure (if

the physician is included permanent in the

contractually agreed sample). If 14% of these cases it

can already be due to the insurance fund

(disadvantage due to the patient structure), it seems

advisable to take insurance fund into account. Table

4 shows the budget overrun with and without

insurance dependent compensation for morbidity.

Table 4: Deviances in the profitability evaluation with and

without morbidity risk structure compensation (RSC) for

the doctor.

medical

speciality

group

Physician

Drug costs

(€)

Budget

overrun with

Compensation

for morbidity

(%)

Budget

overrun

without

Compensation

for morbidity

(%)

GP 1 532.498,04 123,7 131,4

GP 2 713.782,57 123,4 125,4

GP 3 715.327,48 118,5 129,2

GP 4 652.004,87 123,4 125,9

GP 5 350.017,51 124,9 127,1

GP 6 189.596,78 122,6 125,0

GP 7 17.059,82 119,3 126,2

GP 8 610.218,21 122,8 126,3

GP 9 854.378,71 122,6 125,7

Anesthetists 10 937.725,13 124,5 126,4

Gynecologists

11 1.040.364,55 124,6 125,5

Urologist 12 295.275,67 123,1 125,3

Internists 13 289.871,33 122,0 131,0

Internists 14 341.535,80 118,5 126,4

Depending on the starting point in the calculation

that is not adjusted for the type of health insurance

fund, the 25% range will then be exceeded. In the

period under review (first calculation), there were 14

physicians that would not come into the area of pay-

back with morbidity adjustment with regard to the

health fund using the age group adjustment, but

would fall into the anomaly area without the

additional adjustment with respect to the health

insurance fund.

The problems presented do not occur when the

MRG model is used. The problems presented do not

occur when the MRG model is used. If, however,

economic feasibility analyzes are carried out without

prescription-related morbidity evaluations, an

additional adjustment of the type of health insurance

Beneﬁts and Limitations of Age Group-Adjusted Average in the Proﬁtability Audit for Pharmocotherapy

395

provider is required in addition to the age group

adjustment in order to meet the conditions required in

the social court judgments cited above.

Till 2020 in the majority of the federal states in

Germany (more precisely: in the regions of the

Association of Statutory Health Insurance Physicians

SHI), the economic feasibility studies with regard to

the drug prescriptions were carried out with the help

of the discussed problematic benchmarks and average

calculations. The service providers (doctors) are

largely satisfied with this because the limits of the

problematic sanctions (pay-back) have been

significantly increased. This evaluation largely (with

the exception of the exceptions discussed) contains all

drug prescriptions, which leads to a fairness between

the doctors. The necessary increase in equity for

doctors with regard to the membership of their

patients in the health insurance funds was not

implemented in a single German region. In some

federal states, the economic feasibility study has been

switched to target agreements, which only include a

certain part of the drug ordinances and thus leads to a

limited degree of justice between doctors. Target

agreements have a normative character and only take

regional characteristics into account to a certain

extent. In Schleswig-Holstein, in addition to a target

agreement with a compensatory effect, an MRG

(morbidity related groups) analysis was carried out in

2020, in which patient-centered features regarding the

diseases and the prescribed drugs were taken into

account and thus a risk compensation with regard to

the health insurance companies was fully

implemented. For the most part, the fairness of the

examination for doctors with regard to health

insurance companies has not yet been adequately

resolved.

REFERENCES

Busse, R., Panteli D., Henschke C. (2015). Arzneimittel-

versorgung in der GKV und 15 anderen europäischen

Gesundheitssystemen: Ein systematischer Vergleich,

Universitätsverlag der TU Berlin.

Bratzke, Spies, Krebs (2012). Morbiditätskomponente bei

Arznei- und Heilmittelbudgets einführen, Deutsche

Ärztetag 2012, Drucksache V I – 37.

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

(2017). Comparison of Different Implementations of a

Process Limiting Pharmaceutical Expenditures

Required by German Law. Proceedings of HealthInf.

GKV-Versorgungsstärkungsgesetz (GKV-VSG) (2015).

BGBl. I S. 1211, 2015

Gottwald, N. (2015). Damoklesschwert Richtgrößen-

prüfung. Im Dschungel der 25-Prozent-Regel, HNO-

Nachrichten, Springer.

Korzilius, H. (2015). Arzneimittelrichtgrössen, Ärzte sollen

angstfrei verordnen, Dtsch Arztebl 112.

Ostermann, Th., R.Schuster, R. (2015). An Information-

theoretical Approach to Classify Hospitals with

Respect to Their Diagnostic Diversity using Shannon’s

Entropy. HealthInf.

Prüfvereinbarung (2016). Prüfvereinbarung gemäß §§ 106

Abs. 1, 106a Abs. 4 und 106b Abs. 1 SGB V

wischender Kassenärztlichen Vereinigung Schleswig-

Holstein (KVSH), Bad Segeberg und den

Krankenkassen(-verbänden). www.kvsh.de.

Schuster, F., Ostermann, Th., Schuster R., Emcke, T.

(2017). Deviations in Birth Rates with Respect to the

Day of the Week and the Month for a 100 Year Period

Regarding Social and Medical Aspects in Explaining

Models. Proceedings of HealthInf.

Schuster, F., Ostermann, Th., Emcke, T. (2017). Age and

Gender Structures for ICD 10 Diagnoses in Outpatient

Treatment using Shannon Entropy, GMDS, German

Medical Science.

Schuster, R. (2015). Morbidity Related Groups (MRG) and

drug economic efficiency index - a new concept after

the age of “Richtgrößen” benchmarks in Germany,

GAA-Jahrestagung, German Medical Science.

Schuster, R., Heidbreeder, M., Emcke, T. (2017).

Relations of Morbidity Related Groups (MRG), ICD 10

diagnoses and age structures in outpatient treatment.

GMDS, German Medical Science.

Schuster, R., Emcke, T., Arnstedt, E.v., Heidbreder M.

(2016). Morbidity Related Groups (MRG) for

epidemiological analysis in outpatient treatment, IOS

Press 783-787.

Schuster, R., Emcke, T., Schuster, F. (2016). Vergleich der

Richtgrößenauswertungen für Arzneimittel im

Vertragsärztlichen Bereich mit Statusgruppen und

Altersgruppen. GMDS-Jahrestagung, German Medical

Science.

Sinowatz F. (2015). Ressourcenallokation in der haus-

ärztlichen Versorgung: eine empirisch-ethische

Untersuchung, Dissertation, LMU München:

Medizinische Fakultät.

Urt. LSG Sachsen-Anhalt (2013) v. 15.01.2014 - L 9 KA

5/12.

Urt. BSG Juni (2013). http://www.medical-tribune.de:

Amtsermittlungspflicht bei Praxisbesonderheiten.

Urt. SG Dresden (2013) v. 11.12.2013, Az. S 18 KA 31/10,

S 18 KA 71/10, S 18 KA 266/10, S 18 KA 268/10 und

S 18 KA 269/10.

Wersborg, T. (2006). Morbiditätsbezogene Richtgrößen zur

Steuerung einer bedarfsgerechten und wirtschaftlichen

Arzneimittelversorgung innerhalb der gesetzlichen

Krankenversicherung in Deutschland, Dissertation,

LMU München: Medizinische Fakultät.

HEALTHINF 2022 - 15th International Conference on Health Informatics

396