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Clinical Informatics Applications

Outline. Informatics Roots and evolutionEmergence of clinical informaticsData management and data miningCarolina data warehouse for health. Informatics. A discipline which is concerned with effective and efficient use of computing to promote discovery, creativity, decision-making, and product

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Clinical Informatics Applications

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    1. Clinical Informatics & Applications Javed Mostafa Biomedical Research & Imaging Center School of Information & Library Science Translational & Clinical Sciences Institute May 15, 2009 EPID 896 Clinical Research Curriculum seminar

    2. Outline Informatics Roots and evolution Emergence of clinical informatics Data management and data mining Carolina data warehouse for health

    3. Informatics A discipline which is concerned with effective and efficient use of computing to promote discovery, creativity, decision-making, and productivity A wide variety of sub-disciplines exists

    4. An analogy

    5. Few Informatics Examples

    6. Informatics in Relation to Medicine & Health Many associated domains exist, sometimes leading to confusion .. . Bionformatics Health informatics Biomedical informatics Medical informatics Clinical informatics Additionally … nursing informatics, public health informatics …

    7. Clinical Informatics American Medical Informatics Association (AMIA) recently approved the Core Content of of Clinical Informatics Clinical Informaticians transform health care by analyzing, designing, implementing, and evaluating information and communication systems … that enhance individual, population health outcomes, improve patient care, and strengthen the clinician-patient relationship

    8. Critical Areas of Clinical Informatics Care – provision of service to an individual Health system – organization, policies, quality, data management

    9. Critical Areas in CI: Information Systems System development & integration Networks Security Data representation, manipulation, and sharing

    10. A Key Challenge in CI: Data Management Volume of data growth is rapid Type of data is heterogeneous Need systematic way to aggregate For retrieval and analysis To support decision making, quality control, and long-term projects such as research

    11. Evolution of Data Management

    12. Relational Model Relation is a term that comes from mathematics and represents a simple two-dimensional table. Representation based on logical associations only! No pointers … Relation = Table

    13. Relational Model 1980-1990+ E.F. Codd proposed the Relational Model Simple and elegant and scales with ease Combined with Structured Query Languages (SQL) offers a powerful mechanism for data organization and access

    14. DW Multidimensional Model

    15. Multidimensional Star Schema Star schema: Consists of a fact table with a single table for each dimension.

    16. DW OLAP OLAP – OnLine Analytical Processing Fast analysis of shared multidimensional information (FASMI) Data mining is a critical aspect of OLAP

    17. DW Data Mining Prediction: Determine how certain attributes will behave in the future. Identification: Identify the existence of an item, event, or activity. Classification: Partition data into classes or categories. Optimization: Optimize the use of limited resources. Referred to as PICO …

    18. Carolina Data Warehouse for Health Evolution UNC health care system started developing electronic medical records almost 20 years ago Inpatient and outpatient care in UNC hospitals, clinics and affiliated satellite practices throughout central North Carolina Paperless with full nursing notes, physician order entry, progress notes, laboratory, procedure notes, discharge summaries, medication lists, and the ability to write prescriptions available on-line 24/7 used by over 1900 physicians, 3000 nurses, with hundreds of thousands of patients each year Two years ago UNC Health Care System (UNCHCS) initiated development of an enterprise-wide data warehouse, the Carolina Data Warehouse for Health (CDW-H), to meet the dual challenges of enhancement of quality of care and clinical research with our patient populations (invested > $7 million so far)

    19. CDW – H Strategic Vision

    20. CDW-H: As It Is Now … A retrospective, persistent record of cleansed, transformed, and stored data originating from operational systems The “one source of truth” for reporting, analytic, and data mining Data organized logically into subject areas for the user’s benefit without regard to its source system Reports, analytics, and decision making will be consistent across the entire organization

    21. CDW-H: As It Is Now … Data is refreshed periodically (24-48 hrs) and is not real time data CDW is not designed to replace or augment daily operational activities, but to support those activities through analytical retrospective processes Designed to address overall organizational priorities under the governance of the CDW Oversight and Operations Committees

    22. CDW-H: As It Is Now … Major Subject Areas in CDW include:

    23. Data Set Size Number of Tables in Staging area: 219 Number of Columns in Staging area: 3,849 Number of Tables in ADS: 202 Number of Columns in ADS: 2,840 Number of Tables in Inpatient Datamart: 81 Number of Columns in Inpatient Datamart: 1,581 Number of Tables in Diabetes Datamart: 21 Number of Columns in Diabetes Datamart: 504 Total number of unique Patients: 1.8 Million Total number of unique Accounts: 4.5 Million

    24. Data Marts Focused subset of atomic store data to support specific analytical requirements …… The data is organized by Dimension and Facts Fact Tables contain the desired detailed information Diabetes Facts: Last A1c, Last LDL, BP, Bilateral Amputee, Onset Date, Insulin Use, Micro Albumin, etc. Dimensions are distinct threads of information that allow the facts to be summarized in specific ways Diabetes Dimensions: Patient, Clinic, Provider, Date, Visit, etc. Dimensions are expanded fully to provide the aggregation required For example, the date dimension would specify the calendar date, the day of the week, weekday / weekend, month, quarter, and year.

    25. Topics Covered in the Diabetes Data Mart

    26. Diabetes: Dimensions and Facts

    27. Research Portal: Gateway for Researchers and Students An application to expose the various key features of the CDW-H in a user friendly way Metadata and business terms A portal to find useful related resources and services related to the CDW-H Currently, offers a Cohort Discovery Service as a pre-research step

    28. Medical Record Access: Challenges

    29. Access & Approval

    30. Summary of Access Rules The following table summarizes the basic documentation requirements

    31. Cohort Selection Demo Project Summary Descriptions: Need to determine which woman with digital mammograms performed at UNC between May 2007 and June 2008 who also have a documented history or new diagnosis of cardiovascular disease Logon to portal Construct cohort query Review the results Refine cohort query Review the results

    32. Logon to portal

    45. TraCS Service Center Please visit: http://tracs.unc.edu Check the Research Resources area … A set of consultants Clinical Research Analysts System/Business Analysts DB Programmer

    46. Questions? Javed jm@unc.edu

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