The Relevance of Data Warehousing and Data Mining in the Field of Evidence-based Medicine to Support Healthcare Decision Making

Evidence-based medicine is a new direction in modern healthcare. Its task is to prevent, diagnose and medicate diseases using medical evidence. Medical data about a large patient population is analyzed to perform healthcare management and medical research. In order to obtain the best evidence for a given disease, external clinical expertise as well as internal clinical experience must be available to the healthcare practitioners at right time and in the right manner. External evidence-based knowledge can not be applied directly to the patient without adjusting it to the patient-s health condition. We propose a data warehouse based approach as a suitable solution for the integration of external evidence-based data sources into the existing clinical information system and data mining techniques for finding appropriate therapy for a given patient and a given disease. Through integration of data warehousing, OLAP and data mining techniques in the healthcare area, an easy to use decision support platform, which supports decision making process of care givers and clinical managers, is built. We present three case studies, which show, that a clinical data warehouse that facilitates evidence-based medicine is a reliable, powerful and user-friendly platform for strategic decision making, which has a great relevance for the practice and acceptance of evidence-based medicine.





References:
[1] Sackett D.L., Rosenberg W.M., Gray J.A., Haynes R.B., Richardson
W.S., "Evidence-Based Medicine: what it is and what it isn-t
"(editorial). BMJ. 196; 312 (7023) 71-72 (www.pubmed.com)W.-K.
Chen, Linear Networks and Systems (Book style). Belmont, CA:
Wadsworth, 1993, pp. 123-135.
[2] Inmon W.H., "Building the Data Warehouse", Second Edition, J.Wiley
and Sons, New York, 1996.
[3] Shams K., Farishta M. ; "Data Warehousing: Towards knowledge
management", Top Health Inf Manage. 2001 Feb, 21 (3): 24-32,
(PubMed - indexed by MEDLINE)
[4] Abidi S.S.R., Abidi S.R., "A Case of Supplementing Evidence Base
Medicine with Inductive Clinical Knowledge: Towards Technology
Enriched Integrated Clinical Evidence System", 14th IEEE Symposium
on Computer-Based Medical Systems, (CBMS-2001), 26-27 July 2001,
Bethesda (USA)
[5] Frawley W., Piatetsky-Shapiro G. and Matheus C., "Knowledge
Discovery in Databases: An Overview", AI Magazine, Fall 1992, pgs
213-228.
[6] Schuerenberg B. K., "Clearing the Hurdles to Decision Support", Health
Data Management, May 2003.
[7] Roeden N. et al., "Clinical Pathways", Medizincontrolling/DRG
Research Group, Universitätsklinikum Münster,
http://drg.unimuenster.de/de/behandlungspfade/cpathways_reisebericht.
html
[8] Wu R., Peters W., Morgan M.W., "The next generation of clinical
decision support: linking evidence to best practice", J Healthc Inf Manag
2002 Fall;16(4):50-5.
[9] Craig J.C., Irwing L.M., Stockler M.R., "Evidence-based medicine:
useful tools for decision making", The Medical Journal of Australia
(http://www.mja.com.au), MJA 2001; 174:248-253M. Young, The
Techincal Writers Handbook. Mill Valley, CA: University Science,
1989.
[10] Stolba N., Banek M. and Tjoa A M., "The Security Issue of Federated
Data Warehouse in the Area of Evidence-Based Medicine", ARES 2006,
The First International Conference on Availability, Reliability and
Security, April 20.-22. 2006, Vienna, submitted for publication
[11] Health Informatics Research (HIR) group, School of Computing and IT,
University of Western Sydney, (http://www.cit.uws.edu.au/hir/)
[12] Open Clinical, "Knowledge Management for medical care",
http://www.openclinical.org/clinicalpathways.html
[13] Lin F., Chou S., Pan S., Chen Y., "Mining Time Dependency Patterns in
Clinical Pathways", 33rd Hawaii International Conference on System
Sciences, Jan 4-7 2000 Page(s):8 pp. vol.1, IEEE CNF