gms | German Medical Science

GMDS 2012: 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

16. - 20.09.2012, Braunschweig

Data quality for managers of medical supply centers

Meeting Abstract

  • Gregor Endler - Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl Informatik 6 – Datenmanagement, Erlangen, Deutschland
  • Philipp Baumgärtel - Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl Informatik 6 – Datenmanagement, Erlangen, Deutschland
  • Johannes Held - Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl Informatik 6 – Datenmanagement, Erlangen, Deutschland
  • Richard Lenz - Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl Informatik 6 – Datenmanagement, Erlangen, Deutschland

GMDS 2012. 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Braunschweig, 16.-20.09.2012. Düsseldorf: German Medical Science GMS Publishing House; 2012. Doc12gmds112

DOI: 10.3205/12gmds112, URN: urn:nbn:de:0183-12gmds1121

Published: September 13, 2012

© 2012 Endler et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.


Outline

Text

Introduction: It is a current trend in healthcare for medical practitioners to affiliate in cooperative medical supply centers to increase their power to compete [1]. In such an integration scenario [2], data quality is an important concern: On the one hand, integration can benefit from data quality, on the other hand, many data quality problems only become evident when comparing several sources of data [3]. Moreover, for certain use cases in economic controlling certain kinds of data quality are more important than others: some data quality problems translate directly to decreased income for the center. Our goal is to establish useful quality considerations, as well as ways to assess them.

Methods: We conducted interviews with practice managers, asking about their notions of data quality and their most pressing needs for improvement. Data quality is traditionally measured along several dimensions, many of which are well established [4], although the exact definitions vary. Constructing metrics for these is commonly regarded as highly non-trivial [5].

Results: Our interviews exposed currency, correctness and completeness [4] as the dimensions practice managers are most interested in. Completeness is separated into several sub-dimensions [3]. One of these is population completeness [6], which denotes the percentage of real-world entities that have a distinct corresponding entry in the database. Measurements along this dimension take an exceptional position in a clinical context since, according to practice managers, they can directly influence a supply center’s revenue. As a guiding example, consider tracking the benefits a medical unit provides to its patients. As long as the sum of the proceeds of these benefits stays below a certain budget, the Association of SHI Physicians reimburses the full sum. If the proceeds go over budget, however, only a fractional amount is remitted. For this reason, the dimension of completeness is tied closely to the remaining budget capacity. As long as a medical unit stays below its budget, population completeness of the creditable benefits is highly important for the practice manager. According to our interviews, an accurate value enables the manager to countersteer if necessary. Unfortunately, we cannot establish a reference relation [7] due to the distributed and independent nature of our data sources. A comprehensive source with guarantees of completeness does not inevitably exist in a medical supply center. That is why it is necessary to estimate rather than measure the population completeness. We developed, and are currently evaluating, a completeness metric to support the centers’ economic planning. The metric is based on the evaluation of past account data and the prediction of future occurrences of deductible benefits.

Conclusion: Population completeness is an important consideration for economic planning in medical supply centers. In this domain, completeness of data may be impossible to measure. Therefore, we need to develop methods to estimate population completeness, a non-trivial challenge. The metric we developed ties in with our general approach to data quality [8] inspired by Wang’s TDQM [9].

Acknowledgements: The project is supported by the German Federal Ministry of Education and Research (BMBF), project grant No. 01EX1013D.


References

1.
Hellmann W, Eble S. Gesundheitsnetzwerke managen – Kooperation erfolgreich steuern. Medizinisch Wissenschaftliche Verlagsgesellschaft; 2009.
2.
Endler G, Langer M, Purucker J, Lenz R. Smooth migration instead of forced integration: A real-time ERP system for medical supply centers and networked practices. In: 56. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 6. Jahrestagung der Deutschen Gesellschaft für Epidemiologie (DGEpi); 2011 Sep 26-29; Mainz, Deutschland.
3.
Batini C, Scannapieco M. Data Quality: Concepts, Methodologies and Techniques. Springer; 2006.
4.
Scannapieco M, Missier P, Batini C. Data Quality at a Glance. Datenbank-Spektrum. 2005;14:6-14.
5.
Dustdar S, Pichler R, Savenkov V, Truong HL. Quality-aware Service-Oriented Data Integration: Requirements, State of the Art and Open Challenges. SIGMOD Rec. 2012;41(1):11-9.
6.
Pipino LL, Lee YW, Wang RY. Data quality assessment. Commun ACM. 2002l;45:211-8.
7.
Scannapieco M, Batini C. Completeness in the Relational Model: a Comprehensive Framework. In: IQ; 2004. p. 333-45.
8.
Endler G, Langer M, Purucker J, Lenz R. An Evolutionary Approach to IT Support for Medical Supply Centers. In: Proceedings der 41. Jahrestagung der Gesellschaft für Informatik e.V. (GI); 2011.
9.
Wang RY. A product perspective on total data quality management. Communications of the ACM. 1998;41:58-65.