| Project/Area Number |
13672363
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Medical sociology
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| Research Institution | Kagoshima University |
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Principal Investigator |
KUMAMOTO Ichiro Kagoshima University, Faculty of Medicine, Professor, 医学部, 教授 (40225230)
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| Co-Investigator(Kenkyū-buntansha) |
MURANAGA Fuminori Kagoshima University, University Hospital, Faculty of Medicine, Research Associate, 医学部附属病院, 助手 (00325812)
UTO Yumiko Kagoshima University, Faculty of Medicine, Assistant Professor, 医学部, 助教授 (50223582)
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| Project Period (FY) |
2001 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥4,100,000 (Direct Cost: ¥4,100,000)
Fiscal Year 2002: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2001: ¥3,100,000 (Direct Cost: ¥3,100,000)
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| Keywords | pharmacoepidemiology / data warehouse / agent risk evaluation / hospital information system / OLAP / post marketing surveillance / PMS |
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| Research Abstract |
By this study, we developed a pharmacoepidemiology data warehouse system as a support system to evaluate a effect and risk of agent after marketing. This system integrated information of patient, diagnosis, prescription, injection, clinical laboratory, nursing and operation system in Kagoshima University hospital information system, and we could do analysis of medication history and a test result by this system quickly and easily. To evaluated efficiency of evaluation support features of effect and a risk of agent of this system, we extracted data of the patient who received prescription of medicine that was written as not prescribing it for a person of advanced age in a Merck manual. We extracted data from a pharmacoepidemiology data warehouse with an OLAP (On Line Analytical Processing) tool called BusinessObjects by a client PC.
As a result, we were able to detect several prescription examples which fitted combination warning to a person of advanced age. The extract time of data was around 2 minutes for about 30 seconds or more. Because we did not interrupt business system even if we processed it with a pharmacoepidemiology data warehouse, we were able to execute data extraction in business time. These results seemed that the pharmacoepidemiology data warehouse was extremely superior in easy operability and a performance in data extract in comparison with business system. This system was useful as an evaluation support tool of effect and a risk of agent.
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