Authors:
Reinhard Schuster
1
;
Thomas Ostermann
2
;
Marc Heidbreder
1
and
Timo Emcke
3
Affiliations:
1
Medical Advisory Board of Statutory Health Insurance in Northern Germany, Germany
;
2
Witten/Herdecke University, Germany
;
3
Association of Statutory Health Insurance Physicians, Germany
Keyword(s):
Morbidity Related Groups (MRG), Outpatient Treatment, ATC Classification System, International Classification of Diseases (ICD-10), Determination of Main Diagnoses, Distance Structure.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
A patient’s (basic) Morbidity Related Group (MRG) is defined by the drug class (first four characters of the
international Anatomic Therapeutic Chemical [ATC] Classification System) with the highest costs per quarter
with respect to a physician. The morbidity of a patient is thereby represented by the drug most important
economically. We consider the relation of those case groups with diagnoses (ICD-10-GM) on the individual
and group level. In analogy to the DRG Systems (Diagnosis-related group) a degree of severity with respect
to age, multimorbidity and treatment intensity is defined. We compare multimorbidity and age structures of
MRGs and ICD-10 using a distance measure given by the fraction of patients with respect to their MRG and
ICD-10. Main diagnoses like in hospital treatment are not given in outpatient care. MRG classification data
can be used in order to algorithmically construct an outpatient care equivalent. Individual MRG components
as points in a vector space can be u
sed to determine the ”biological age“ of groups of individuals with respect
to in- or decreased morbidity.
(More)