The development of health care data warehouses to support data mining.

Clinical data warehouses offer tremendous benefits as a foundation for data mining. By serving as a source for comprehensive clinical and demographic information on large patient populations, they streamline knowledge discovery efforts by providing standard and efficient mechanisms to replace time-consuming and expensive original data collection, organization, and processing. Building effective data warehouses requires knowledge of and attention to key issues in database design, data acquisition and processing, and data access and security. In this article, the authors provide an operational and technical definition of data warehouses, present examples of data mining projects enabled by existing data warehouses, and describe key issues and challenges related to warehouse development and implementation.

[1]  R Verma,et al.  Life cycle of a data warehousing project in healthcare. , 2001, Journal of healthcare information management : JHIM.

[2]  Dean F Sittig,et al.  Application of Information Technology j MediClass : A System for Detecting and Classifying Encounter-based Clinical Events in Any Electronic Medical , 2005 .

[3]  Linda K Goodwin,et al.  Data mining methods for improving birth outcomes prediction. , 2002, Outcomes management.

[4]  Jules J. Berman.  Concept-Match Medical Data Scrubbing , 2009 .

[5]  Mark E. Frisse,et al.  Scaling an expert system data mart: more facilities in real-time , 1998, AMIA.

[6]  David F. Lobach,et al.  Medical data mining: knowledge discovery in a clinical data warehouse , 1997, AMIA.

[7]  Narasimhaiah Gorla,et al.  Features to consider in a data warehousing system , 2003, CACM.

[8]  Catherine Moore,et al.  Case Report: Standardizing Laboratory Data by Mapping to LOINC , 2006, J. Am. Medical Informatics Assoc..

[9]  Fabiane Bizinella Nardon,et al.  Knowledge Sharing and Information Integration in Healthcare using Ontologies and Deductive Databases , 2004, MedInfo.

[10]  W. H. Inmon,et al.  Building the data warehouse , 1992 .

[11]  Prakash M. Nadkarni,et al.  Data Extraction and Ad Hoc Query of an Entity– Attribute–Value Database , 2000 .

[12]  Mark Levene,et al.  Why is the snowflake schema a good data warehouse design? , 2003, Inf. Syst..

[13]  Jocelyn G Dewitt,et al.  Development of a Data Warehouse at an Academic Health System: Knowing a Place for the First Time , 2005, Academic medicine : journal of the Association of American Medical Colleges.

[14]  Isaac S. Kohane,et al.  Scaling a data retrieval and mining application to the enterprise-wide level , 1999, AMIA.

[15]  Cynthia Brandt,et al.  Research Paper: UMLS Concept Indexing for Production Databases: A Feasibility Study , 2001, J. Am. Medical Informatics Assoc..

[16]  C Breen,et al.  Implementing a data warehouse at Inglis Innovative Services. , 2001, Journal of healthcare information management : JHIM.

[17]  Barry Robson,et al.  Data mining and clinical data repositories: Insights from a 667, 000 patient data set , 2006, Comput. Biol. Medicine.

[18]  Perry L. Miller,et al.  Metadata-driven creation of data marts from an EAV-modeled clinical research database , 2002, Int. J. Medical Informatics.

[19]  Hagop S. Mekhjian,et al.  Using an Information Warehouse to screen patients for Clinical trials: A prototype , 2005, AMIA.

[20]  J. Berman.  Concept-match medical data scrubbing. How pathology text can be used in research. , 2003, Archives of pathology & laboratory medicine.

[21]  R E Reynolds,et al.  Case study: a data warehouse for an academic medical center. , 2001, Journal of healthcare information management : JHIM.

[22]  Ulysses J. Balis,et al.  Development and evaluation of an open source software tool for deidentification of pathology reports , 2006, BMC Medical Informatics Decis. Mak..

[23]  J. Gilbertson,et al.  Evaluation of a deidentification (De-Id) software engine to share pathology reports and clinical documents for research. , 2004, American journal of clinical pathology.

[24]  S. Schell.  Creation of clinical research databases in the 21st century: a practical algorithm for HIPAA Compliance. , 2006, Surgical infections.

[25]  Hans-Jörg Schek,et al.  A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces , 1998, VLDB.

[26]  J. Spinelli,et al.  Co-morbidity data in outcomes research: are clinical data derived from administrative databases a reliable alternative to chart review? , 2000, Journal of clinical epidemiology.

[27]  Peter Rob,et al.  Database systems : design, implementation, and management , 2000 .

[28]  Vivian S. Gainer,et al.  A Visual Interface Designed for Novice Users to find Research Patient Cohorts in a Large Biomedical Database , 2003, AMIA.

[29]  Avi Silberschatz,et al.  Application of Information Technology: Dynamic Tables: An Architecture for Managing Evolving, Heterogeneous Biomedical Data in Relational Database Management Systems , 2007, J. Am. Medical Informatics Assoc..

[30]  G Tusch,et al.  Data warehouse and data mining in a surgical clinic. , 2000, Studies in health technology and informatics.

[31]  Alan R. Hevner,et al.  The Catch data warehouse: support for community health care decision-making , 2003, Decision Support Systems.

[32]  Sandra Pelletier,et al.  Mapping From a Clinical Data Warehouse to the HL7 Reference Information Model , 2003, AMIA.

[33]  J. Marc Overhage,et al.  SPIN Query Tools for De-identified Research on a Humongous Database , 2005, AMIA.

[34]  L. Iezzoni.  Assessing Quality Using Administrative Data , 1997, Annals of Internal Medicine.

[35]  K. El Emam,et al.  Evaluating Common De-Identification Heuristics for Personal Health Information , 2006, Journal of medical Internet research.

[36]  John D. Williams,et al.  The data warehouse as a foundation for population-based reference intervals. , 2003, American journal of clinical pathology.

[37]  George Hripcsak,et al.  Mining a clinical data warehouse to discover disease-finding associations using co-occurrence statistics , 2005, AMIA.

[38]  Robert A. Weinstein,et al.  Application of Information Technology: Development of a Clinical Data Warehouse for Hospital Infection Control , 2003, J. Am. Medical Informatics Assoc..

[39]  Henry C. Chueh,et al.  Optimizing healthcare research data warehouse design through past COSTAR query analysis , 1999, AMIA.

[40]  C S Ledbetter,et al.  Toward best practice: leveraging the electronic patient record as a clinical data warehouse. , 2001, Journal of healthcare information management : JHIM.