VA San Diego Healthcare System, Health Services Research & Development, San Diego, CA 92161, USA.
J Public Health Manag Pract. 2011 Mar-Apr;17(2):E20-8. doi: 10.1097/PHH.0b013e3181e8b0e9.
Emergency department (ED) crowding metrics were validated in our facility and a new technique of data visualization is proposed.
A sequential cross-sectional study was conducted in our ED during October 2007. Data were collected every 2 hours by a research assistant and included patient arrivals and acuity levels, available inpatient and ED beds, ambulance diversion status, staff present, and patient reneging. The charge nurse and an attending physician also completed a single-question crowding instrument. Pearson correlation coefficients were calculated and logistic regression were performed to test the usefulness of the crowding score and test significance of the data visualization trends.
SETTING/PARTICIPANTS: Our ED is an adult, level-III, veterans administration ED in urban southern California. It is open 24 hours per day, has 15 treatment beds with 4 cardiac monitors, and typically sees about 30 000 patients per year.
MAIN OUTCOME MEASURE(S): The key outcome variables were patient reneging (number of patients who left before being seen by a physician) and ambulance diversion status.
Average response rate was 72% (n = 227) of sampling times. Emergency Department Work Index, demand value, lack of inpatient beds, census, patients seen in alternate locations, and patient reneging correlated significantly (P < .01) with the crowding instrument. Staff workload ranks predicted patient reneging (odds ratio 6.0, 95% confidence interval 2.3-15.4). The data visualization focused on common ED overcrowding metrics and was supported by logistic regression modeling.
The demand value, ED Work Index, and patient reneging are valid measures of crowding in the studied ED, with staff workload rank being an easy, 1-question response. Data visualization may provide the site-specific crowding component analysis needed to guide quality improvement projects to reduce ED crowding and its impact on patient outcome measures.
在本机构验证了急诊（ED）拥挤度指标，并提出了一种新的数据可视化技术。
2007 年 10 月在本 ED 进行了一项连续的横断面研究。研究助理每 2 小时收集一次数据，包括患者到达和严重程度、可用于住院和 ED 的床位、救护车转院情况、工作人员人数和患者流失情况。值班护士和主治医生还完成了一个单问题拥挤度工具。计算了 Pearson 相关系数，并进行了逻辑回归，以测试拥挤评分的有用性和数据可视化趋势的检验显著性。
地点/参与者：我们的 ED 是加利福尼亚州南部城市的一家成人三级退伍军人事务部 ED。它每天 24 小时开放，有 15 张治疗床，其中 4 张带有心脏监护仪，通常每年接待约 30000 名患者。
关键的观察结果变量是患者流失（在被医生看到之前离开的患者人数）和救护车转院情况。
平均响应率为 72%（n = 227）的采样时间。急诊部门工作指数、需求值、缺乏住院床位、普查、在其他地方就诊的患者和患者流失与拥挤工具显著相关（P <.01）。工作人员工作量等级预测患者流失（优势比 6.0，95%置信区间 2.3-15.4）。数据可视化重点关注常见的 ED 过度拥挤指标，并得到逻辑回归模型的支持。
需求值、ED 工作指数和患者流失是研究 ED 拥挤度的有效衡量标准，工作人员工作量等级是一个简单的、1 个问题的回答。数据可视化可能提供特定于站点的拥挤度成分分析，以指导质量改进项目，减少 ED 拥挤及其对患者结果测量的影响。
