title: CrowdED: crowding metrics and data visualization in the emergency department.
abstract: OBJECTIVE
  Emergency department (ED) crowding metrics were validated in our facility and a new technique of data visualization is proposed.
  
  
  METHODS
  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.
  
  
  METHODS
  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.
  
  
  METHODS
  The key outcome variables were patient reneging (number of patients who left before being seen by a physician) and ambulance diversion status.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: VA San Diego Healthcare System, Health Services Research & Development, San Diego, CA 92161, USA. lgreci@ucsd.edu
authors.forename: Laura S
authors.initials: LS
authors.lastname: Greci
authors.forename: Christina E
authors.initials: CE
authors.lastname: Parshalle
authors.forename: Alan
authors.initials: A
authors.lastname: Calvitti
authors.forename: Marian J
authors.initials: MJ
authors.lastname: Renvall
mesh_terms: D000328:Adult; D000552:Ambulances / Q000706:statistics & numerical data; D001509:Bed Occupancy / Q000706:statistics & numerical data / Q000639:trends; D002140:California; D003430:Cross-Sectional Studies; D003441:Crowding; D016208:Databases, Factual / Q000706:statistics & numerical data*; D004632:Emergency Medical Services / Q000706:statistics & numerical data*; D005260:Female; D020445:Hospitalists / Q000706:statistics & numerical data; D006787:Hospitals, Veterans / Q000145:classification / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D009741:Nursing Staff, Hospital / Q000706:statistics & numerical data; D010043:Outcome and Process Assessment, Health Care / Q000706:statistics & numerical data* / Q000639:trends; D010360:Patient Transfer / Q000706:statistics & numerical data; D012044:Regression Analysis; D013997:Time Factors; D016526:Workload / Q000706:statistics & numerical data