1. Boarding Times and Patient Safety: A quantifiable and generalizable model David Wein, MD MBA Associate Facility Medical Director Tampa General Hospital TEAMHealth Southeast Jason Wilson, MD Chief Resident University of South Florida EM Residency Program

2. Background ED overcrowding is a nationwide problem that affects patient flow, safety and satisfaction. ED overcrowding and extraneous wait times are largely dependent on extra-departmental hospital functions that lead to long boarding periods in the ED Long waiting room times are predictive of greater numbers of patients leaving prior to medical evaluation.

3. Long waiting room times correlate with larger numbers of patients leaving prior to physician evaluation Long waiting room times correlate with larger numbers of patients leaving prior to physician evaluation r 2 =0.567, p < 0.001 r 2 =0.567, p < 0.001

4. Study Goals and Design In this study, we attempt to model the variables that predict long waiting room times and high left-without treatment (LWOT) volumes by examining the relationship between boarding times (admit hold hours) and overall total bed hours available in our department. Does the risk of a patient leaving prior to medical evaluation change significantly at some percentage of bed hours burdened by admit-holds? The goal of this study is to create a generalizable model that can be extrapolated to other emergency departments and used by ED administrators in discussion with hospital leadershi p.

5. Methods 180 days of ED census data were obtained from our large urban department180 days of ED census data were obtained from our large urban department We tested a null hypothesis that there is no relationship between the proportion of bed hours taken up by admit holds with LWOTs by examining the change in slope at different levels of total admit hold hours using simple linear regressionWe tested a null hypothesis that there is no relationship between the proportion of bed hours taken up by admit holds with LWOTs by examining the change in slope at different levels of total admit hold hours using simple linear regression The change in slope approach was used to avoid assumptions regarding the distribution of the dataThe change in slope approach was used to avoid assumptions regarding the distribution of the data The level of admit hold recorded as a percentage of total bed hours available in our department was calculateThe level of admit hold recorded as a percentage of total bed hours available in our department was calculate

6. Methods After finding the percentage associated with a statistically significant change in the slope, we take that number of admit hold hours and create a binary variable (greater than hours or less than the number) in order to test the odds ratio that LWOTs increase greater than 2% at that levelAfter finding the percentage associated with a statistically significant change in the slope, we take that number of admit hold hours and create a binary variable (greater than hours or less than the number) in order to test the odds ratio that LWOTs increase greater than 2% at that level The 2% number was chosen as this is likely a level of LWOTs irrespective of waiting room timesThe 2% number was chosen as this is likely a level of LWOTs irrespective of waiting room times

7. Results We found a significance in the change in slope in the regression comparing admit hold hours to LWOTs when admit-hold hours reached 8.5% of total daily ED bed hoursWe found a significance in the change in slope in the regression comparing admit hold hours to LWOTs when admit-hold hours reached 8.5% of total daily ED bed hours In other words, there were 822 total bed hours available and when admit-holds took up 100 or more of those hours, there was a significant change in the slope when compared to the slope between all admit-hold hours and LWOTs.In other words, there were 822 total bed hours available and when admit-holds took up 100 or more of those hours, there was a significant change in the slope when compared to the slope between all admit-hold hours and LWOTs.

8. Results Admit Hold HoursPercent of Total Hours SlopeP Value 38-157 hours4.6% - 19%34.8300.01 100+8.5%44.879< 0.01 (difference in slope) this is an abbreviated version of the table in the final version, the slope at each percent of hours will be shown along with the p-value This table just shows the point where the change in slope reached statistical significance

9. Results The risk of leaving without treatment also became significantly greater very close to this same level (OR 2.445, 95% CI 2.21 – 2.73)The risk of leaving without treatment also became significantly greater very close to this same level (OR 2.445, 95% CI 2.21 – 2.73) In the final study we will show OR at each percentage of admit hold hoursIn the final study we will show OR at each percentage of admit hold hours 2X2 TableLWOTNot LWOT% LWOTTotals Admit Hold Hours > 100 793691910.2%7712 Admit Hold Hours < 100 668143114.5%14979 Totals14612123022691 Odds Ratio2.355495% CI2.21 to 2.73 Chi-Square285.5921 degree of Freedom P < 0.001

10. Conclusions and Future Directions Add 180 day additional days of sequential data and see if same results are found between each 180 day subset and in a 360 day larger sample Test this model using data from other Emergency Departments Examine whether potential solutions to decrease boarding times result in a decrease in observed LWOTs through a future prospective study