loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Thomas Ostermann 1 and Reinhard Schuster 2

Affiliations: 1 Witten/Herdecke University, Germany ; 2 University of Luebeck, Germany

ISBN: 978-989-758-068-0

Keyword(s): Entropy, Diagnostic Diversity, Hospital Comparison, Classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Clinical Problems and Applications ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: In Germany hospital comparisons are part of health status reporting. This article presents the application of Shannon’s entropy measure for hospital comparisons using reported diagnostic data. We used Shannon’s entropy to measure the diagnostic diversity of a hospital department by means of reported ICD–9–codes. Entropy values were compared both with respect to the hospital status (i.e. primary, secondary, tertiary or specialized hospital) and specialisations (e.g. surgery, gynaecology). There were relevant differences in entropy values between the different types of hospitals. Primary hospitals differed from specialized hospitals (0.535 ± 0.09 vs. 0.504 ± 0.07). Furthermore, specialized departments like obstetrics or ophthalmology did generate lower entropy values than area-spanning departments like paediatrics or general internal medicine, having significantly higher values. In conclusion, we showed how entropy can be used as a measure for classifying hospitals. Besides of hospital comparisons, this approach can be implemented in all fields of health services research for measuring variability in nominal or ordinal data. The use of entropy as a measure for health services research and classification algorithms should be encouraged to learn more about this measure, which unreasonably has fallen into oblivion in health services research. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.207.137.4

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ostermann, T. and Schuster, R. (2015). An Information-theoretical Approach to Classify Hospitals with Respect to Their Diagnostic Diversity using Shannon’s Entropy .In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 325-329. DOI: 10.5220/0005197103250329

@conference{healthinf15,
author={Thomas Ostermann. and Reinhard Schuster.},
title={An Information-theoretical Approach to Classify Hospitals with Respect to Their Diagnostic Diversity using Shannon’s Entropy },
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},
year={2015},
pages={325-329},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005197103250329},
isbn={978-989-758-068-0},
}

TY - CONF

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - An Information-theoretical Approach to Classify Hospitals with Respect to Their Diagnostic Diversity using Shannon’s Entropy
SN - 978-989-758-068-0
AU - Ostermann, T.
AU - Schuster, R.
PY - 2015
SP - 325
EP - 329
DO - 10.5220/0005197103250329

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.