| Project/Area Number |
16K12541
|
|---|
| Research Category |
Grant-in-Aid for Challenging Exploratory Research
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Web informatics, Service informatics
|
|---|
| Research Institution | National Center for Global Health and Medicine |
|---|
Principal Investigator |
Murai Shinsuke 国立研究開発法人国立国際医療研究センター, その他部局等, 医師 (00506644)
|
|---|
| Project Period (FY) |
2016-04-01 – 2020-03-31
|
| Project Status |
Completed (Fiscal Year 2019)
|
| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
|
|---|
| Keywords | 医療サービス / 行政サービス / 品質管理 / サービスサイエンス / 医療の質・安全 / 質改善 / 可視化 / ヘルスシステム |
|---|
| Outline of Final Research Achievements |
For the delay in routine health information systems (RHIS), it was difficult to assume an appropriate statistical distribution. Also there were very few longitudinal studies that track the behavior of delays overtime. This study investigated the behavior of “lead time” and “delay” of report submissions in one province for the past 19 years by using the permutation test that does not require the previous assumption of a statistical distribution. This study found that (1) the proportion of the submitted reports was high when we did not consider the due date, (2) systemic delays occurred due to the strict due date given by the standard of RHIS, (3)when the system was updated, similarity with the previous standard operational procedures affected the effect of improvement and (4) large change like computerization divided reporters in two groups that adapted computerization and that did not.
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究は、特別な統計分布の仮定を要しない順列組み合わせ検定を用いて、改善効果の検出が可能であり、改善課題が抽出できることを保健情報システムの報告遅れを例に実証した。行政および医療現場で扱うサービスの質の挙動は、産業界とは異なり、十分なサンプル数が確保できなかったり、あらかじめ統計分布がわかっているサービスが少なかったりする。本研究のような特別な統計分布を仮定しない手法で改善効果を検出することで、誤った統計分布の仮定に基づいた意志決定のリスクを軽減できると考える。
|
