電子病歷系統建置後急診患者高量資訊危險因子分析

Department of Emergency Medicine, Medical Center & School of Medicine, Kaohsiung Medical University

目的：分析急診患者電子病歷醫療資訊量，了解實況，並尋找高量資訊危險因子。方法：2011年5月，本院急診開始使用電子病歷。回溯性抽取2011年6月至2013年3月間，逢3、6、9、12月第一週急診患者。計算其該次就診時必要的基本資訊、影像檢查、檢驗檢查、及生理學檢查次數。除影響較深遠的住院及住院手術外，只採計該次就診前六個月內者。若未就診再往前回溯，但每項資訊最多以三次為限。資料分析採用敘述性統計、單因子變異數分析、及多元邏輯迴歸分析。結果：總共分析14,613人次就診。必要資訊量並未明顯地與日俱增。患者類別中一般急症資訊量最多，創傷其次、兒童急診最少（P＜0.001; P=0.016）。檢傷分類等級中一級患者資訊量最多、二級其次、三級再其次（P＜0.001）。年齡分組以中老老人資訊量最多、次而為初老者、成人、幼兒，兒童資訊量最少（P＜0.001）。高量資訊危險因子依序為一般急症（OR: 5.38; 95% CI: 4.64-6.24）、中老老人（OR: 4.15; 95% CI: 3.68-4.68）、檢傷一級（OR: 4.11; 95% CI: 3.23-5.23）、初老老人（OR: 2.87; 95% CI: 2.54-3.23）、檢傷二級（OR: 2.05; 95% CI: 1.85-2.27）、檢傷四級（OR: 1.91; 95% CI: 1.46-2.49）、上班時段（OR 1.74-2.29）、及週間時段（OR 1.20-1.31）。結論：電子病歷系統讓我們進入巨量資訊時代。本研究並未觀察到必要資訊量與日俱增。一般急症、高嚴重度、老年患者，以及平日上班時段的患者，常伴隨較高量資訊。

Objectives: To evaluate medical data volume in emergency department (ED) patients after implementation of the electronic health record (EHR) system, to measure data volume of different patients, and to identify huge-data risk factors. Methods: EHRs were implemented in the studied ED since 05/2011. We retrospectively retrieved visits in the first week of March, June, September and December from 06/2011 to 03/2013. We measured the essential data volume pertaining to elementary information, images, laboratory and physiological examinations within six months before the index visit. If no data were found, we limited the collection of previous data to 3. In-patient surgeries and hospitalizations were counted without time limit. We used descriptive statistics, one-way analysis of variance, and multiple logistic regression for analyses. Results: We retrieved 14,613 visits for analysis. No continuous increase of data was identified. Medical patients had the most data, followed by trauma, and pediatric patients (P＜0.001 and, P=0.016 respectively). Triage-I patients had the most data, followed by triage-II, and triage- III patients (P＜0.001). The old old had the most data, followed by the young old, adults, toddlers, and children (P＜0.001). Huge-data-related factors were medical emergencies (OR: 5.38; 95% CI: 4.64-6.24), the old old (OR: 4.15; 95% CI: 3.68-4.68), triage-I (OR: 4.11; 95% CI: 3.23-5.23), the young old (OR: 2.87; 95% CI: 2.54-3.23), triage-II (OR: 2.05; 95% CI: 1.85-2.27), triage-IV (OR: 1.91; 95% CI: 1.46-2.49), and office-time (OR 1.74-2.29) and weekday visits (OR 1.20-1.31). Conclusions: EHRs bring us to the era of big data. No continuous increase of essential data was observed in this study. Huge data were related to medical emergencies, critical triages, old age, and office time visits.