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Detecting inpatient falls by using natural language processing of electronic medical records.
http://hdl.handle.net/10191/30128
http://hdl.handle.net/10191/301285bde5d7f-99a2-45b2-8771-7553c858373a
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
12_448-448.pdf (524.3 kB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
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| 公開日 | 2014-11-11 | |||||
| タイトル | ||||||
| タイトル | Detecting inpatient falls by using natural language processing of electronic medical records. | |||||
| タイトル | ||||||
| タイトル | Detecting inpatient falls by using natural language processing of electronic medical records. | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Natural language processing | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Text mining | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Falls | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Adverse events | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Incident reports | |||||
| 資源タイプ | ||||||
| 資源 | http://purl.org/coar/resource_type/c_6501 | |||||
| タイプ | journal article | |||||
| 著者 |
Toyabe, Shin-ichi
× Toyabe, Shin-ichi |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | BACKGROUND: Incident reporting is the most common method for detecting adverse events in a hospital. However, under-reporting or non-reporting and delay in submission of reports are problems that prevent early detection of serious adverse events. The aim of this study was to determine whether it is possible to promptly detect serious injuries after inpatient falls by using a natural language processing method and to determine which data source is the most suitable for this purpose. METHODS: We tried to detect adverse events from narrative text data of electronic medical records by using a natural language processing method. We made syntactic category decision rules to detect inpatient falls from text data in electronic medical records. We compared how often the true fall events were recorded in various sources of data including progress notes, discharge summaries, image order entries and incident reports. We applied the rules to these data sources and compared F-measures to detect falls between these data sources with reference to the results of a manual chart review. The lag time between event occurrence and data submission and the degree of injury were compared. RESULTS: We made 170 syntactic rules to detect inpatient falls by using a natural language processing method. Information on true fall events was most frequently recorded in progress notes (100%), incident reports (65.0%) and image order entries (12.5%). However, F-measure to detect falls using the rules was poor when using progress notes (0.12) and discharge summaries (0.24) compared with that when using incident reports (1.00) and image order entries (0.91). Since the results suggested that incident reports and image order entries were possible data sources for prompt detection of serious falls, we focused on a comparison of falls found by incident reports and image order entries. Injury caused by falls found by image order entries was significantly more severe than falls detected by incident reports (p<0.001), and the lag time between falls and submission of data to the hospital information system was significantly shorter in image order entries than in incident reports (p<0.001). CONCLUSIONS: By using natural language processing of text data from image order entries, we could detect injurious falls within a shorter time than that by using incident reports. Concomitant use of this method might improve the shortcomings of an incident reporting system such as under-reporting or non-reporting and delayed submission of data on incidents. | |||||
| 書誌情報 |
BMC Health Services Research en : BMC Health Services Research 巻 12, p. 448-1-448-8, 発行日 2012-12 |
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| 出版者 | ||||||
| 出版者 | BioMed Central | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 14726963 | |||||
| 書誌レコードID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA12034989 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | info:doi/10.1186/1472-6963-12-448 | |||||
| 権利 | ||||||
| 権利情報 | (C) 2012 Toyabe; licensee BioMed Central Ltd. | |||||
| 著者版フラグ | ||||||
| 値 | publisher | |||||
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Cite as
Toyabe, Shin-ichi, 2012, Detecting inpatient falls by using natural language processing of electronic medical records.: BioMed Central, 448-1-448-8 p.
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