| Project/Area Number |
18K11529
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 62010:Life, health and medical informatics-related
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| Research Institution | Kyushu University |
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Principal Investigator |
Ahmed Ashir 九州大学, システム情報科学研究院, 准教授 (30457444)
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| Co-Investigator(Kenkyū-buntansha) |
久住 憲嗣 九州大学, システムLSI研究センター, 准教授 (10380685)
イスラム ラフィクル 九州大学, 大学病院, 学術研究員 (20815906)
横田 文彦 九州大学, 持続可能な社会のための決断科学センター, 講師 (50760451)
福田 晃 九州大学, システム情報科学研究院, 教授 (80165282)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | remote healthcare / portable health clinic / error detection / Healthcare data / data error / triage / acceptance range / Remote Healthcare / Error Detection / Personalized Triage / Remote healthcare / Medical data errors / Acceptance Range |
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| Outline of Final Research Achievements |
Remote healthcare data carries more errors than usual clinical data. Most of the errors occur due to the human input errors by the healthcare workers. We came up with a solution to predict three different acceptance ranges- (a) for a particular community (b) for an age group and gender and (c) for a particular person, we call them Human Acceptance Range (HAR), Group Acceptance Range (GAR) and Personal Acceptance Range (PAR).
To evaluate GAR, we prepared an experimental field in Bangladesh. By organizing PHC service campaign (four times) we gathered 999 healthcare data from similar patients. The 1st phase found 1.62% unusual and 0.00% error data. The 2nd phase found 1.00% unusual and 0.00% error data. The 3rd phase found 1.00% unusual and 0.00% error data. The 4th phase found 1.63% unusual and 0.00% error data. A major observation is that the error rate was 0.00% in every case and unusual data was reduced to 4.23%.
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| Academic Significance and Societal Importance of the Research Achievements |
Detecting and reducing errors in remote healthcare systems has been a challenge. The findings of this research are expected to improve the efficiency of remote healthcare systems. This improved approach will help increase the efficiency of remote healthcare systems and also reduce cost and time.
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