Door to Doc (D2D) Reduces ED Patient “Walkout” Rate
Twila BurdickBanner Health
Objectives
• Recognize ED patient flow as a patient safety problem
• Describe improved patient flow using ED Door to Doc (D2D) Split Patient Flow model
• Discuss approach for implementing D2D in diverse ED settings
• Show impact of D2D on patient safety
ED Patient Safety Challenge• Capacity constrained EDs
– Increased visits– Holding inpatients
• Long waits for patients arriving– Patients deteriorating in the waiting room– Patient complaints, patient dissatisfaction
• Patients leaving without treatment– self-diagnosis is not safe!– Long waits to see an ED physician related to
LWOTs
Improving ED Safety
• Challenge:– How to get patients seen by ED physicians
sooner in overcrowded, busy EDs across Banner Health
• Idea:– Apply “science of throughput” to change
patient flow and reduce delays – Start with D2D “straw model” – Implement in diverse EDs
Designing D2DED Improvement/Design Oversight Team
Care Transformation
Clinical Risk
Intervention
ThroughputAHRQ Grant
Productivity
Common Launch:
•Learning Session
•Clarification of Multiple Outcomes
•Oversight Process Defined
•Phasing Described
Work Design
•Work Team with Representation from all Stakeholders
•Dedicated, nearly full time effort for up to 4 weeks
•Start with “straw model” based on best practices
Physician Work Group
Consistent application
of automation
Discharge Process to reduce
risk and returns
ConsistentProcess and Measures
Process Description for LayoutED Improvement/Design Support Team
Stakeholder
Review
Strategic Svs Leadership
Conf
Consistent Productivity
Measures
ED Call Coverage
Facility Issues, Designs
Behavioral Health Patients
Door to Doc Care Process
Patient Arrives
1. Quick Reg (PFS Rep) and
Quick Look (RN)
2. Sicker?(ESI 1 or 2)
3. Patient escorted to
Intake Space
(RN or Tech)
4. MSE/focused assessment, Orders
& Documentation(RN and Physician)
7. Specimen Collection
5. ED Bed Required?
6. Diagnostic Testing Required?
9. Procedure/Treatment
8. Medical Imaging
13. Patient
escorted to ED Bed
14. MSE/Focused Assessment,
Orders, Specimen Collection, Procedure and Documentation
(RN, Tech, Physician)Full Registration & Co-Pay
Collection(PFS Rep)
15. Testing
16. Treatment
17.Patient meets
Results Waiting Criteria
10.Move patient
to Results Waiting Area
11.Review of Results
19.Patient to Discharge Room for Informed Discharge
20.Patient to IP
Unit/IP Holding Unit
21.Transfer to
another facility
Patient leaves the
EDB
B
B
No
No
Yes
Yes
A
A
Yes
No
12.Medical Decision Making
18. Patient
Remains in ED Bed
No
• Quick Registration
• Quick Look (ESI)
• Split Patient Flow
• Intake area for Less acute patients
• Joint medical screening
• Patient moves to testing and treatment
• Informed discharge
• IP care for admitted patients
Common ED Characteristics
• ED arrival volume patterns are predictable by hour of the day. – 9 am-9 pm peak (30%
higher)
• Relative Length of Use by acuity level has a similar pattern across facilities
Multiplicative Indices for Arrival Rate by Hour of Day
0.000.250.500.751.001.251.501.752.00
0 1 2 3 4 5 6 7 8 91
01
11
21
31
41
51
61
71
81
92
02
12
22
3
Hour of Day
Hospital A
Hospital B
Hospital C
Hospital D
Hospital E
Hospital F
Hospital G
Similar LOU Pattern by Acuity5 Hospitals
0%20%40%60%80%
100%120%140%160%180%
1 2 3 4 5
Acuity
LO
U In
dex
Systems View: Queuing Model
Hospital Exit
Inpatient Transitional
Care
rIH = 1-rIDrID
rOD = 1-fRE = 80%= fA /(rRO*fRE+rRI)
Quick Look
Intake/ Discharge
IPED
rROrRI = 1-rRO
fRE = 20%
LWOT
Ambulance Diversion
0%
Results Waiting
rOW = (f3+f4) / (f3+f4+f5)
rWO = 100%
Choosing ‘Patient Safe’ Capacities
Recommendations for discussion are in bold
Improvement in Patient Safety through Reduced LWOTs
Banner Health Hospitals - LWOT Percentage Pre/post New Process Implementation
9.84%
14.24%
1.51%
4.03%
5.02%
0.56%
0.99%
3.06%
0.64%
1.78%
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
15%
BBMC BDMC BMMC McKee NCMC
Hospital
Per
cen
t L
WO
T
59% Improvement
42% Improvement
35% Improvement
63% Improvement
65% Improvement
Emergency Department Patients That Leave Without TreatmentSample Size = 24 months
Banner Health: Banner Baywood Medical CenterMesa, Arizona, United States of America
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
15%
16%
17%
18%
19%
20%
21%
Jul-
05
Aug
-05
Sep
-05
Oct
-05
No
v-05
De
c-05
Jan
-06
Fe
b-0
6
Mar
-06
Apr
-06
May
-06
Jun
-06
Jul-
06
Aug
-06
Sep
-06
Oct
-06
No
v-06
De
c-06
Jan
-07
Fe
b-0
7
Mar
-07
Apr
-07
May
-07
Jun
-07
Month-Year (month)
% o
f P
atie
nts
th
at L
eft
Wit
ho
ut
Tre
atm
ent
Door to DocImplementation
Line = 30% improvement Emergency Department Patients That Leave Without TreatmentSample Size = 24 months
Banner Health: Banner Desert Medical CenterMesa, Arizona, United States of America
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
15%
16%
17%
18%
19%
20%
21%
Jul-
05
Aug
-05
Sep
-05
Oct
-05
No
v-05
De
c-05
Jan
-06
Fe
b-0
6
Mar
-06
Apr
-06
May
-06
Jun
-06
Jul-
06
Aug
-06
Sep
-06
Oct
-06
No
v-06
De
c-06
Jan
-07
Fe
b-0
7
Mar
-07
Apr
-07
May
-07
Jun
-07
Month-Year (month)
% o
f P
atie
nts
th
at L
eft
Wit
ho
ut
Tre
atm
ent
Line = 30% Improvement
Door to DocImplementation
Emergency Department Patients That Leave Without TreatmentSample Size = 24 months
Banner Health: Banner Mesa Medical CenterMesa, Arizona, United States of America
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
15%
16%
17%
18%
19%
20%
21%
Jul-
05
Aug
-05
Sep
-05
Oct
-05
No
v-05
De
c-05
Jan
-06
Fe
b-0
6
Mar
-06
Apr
-06
May
-06
Jun
-06
Jul-
06
Aug
-06
Sep
-06
Oct
-06
No
v-06
De
c-06
Jan
-07
Fe
b-0
7
Mar
-07
Apr
-07
May
-07
Jun
-07
Month-Year (month)
% o
f P
atie
nts
th
at L
eft
Wit
ho
ut
Tre
atm
ent
Line = 30% Improvement
Door to DocImplementation
Emergency Department Patients That Leave Without TreatmentSample Size = 24 months
Banner Health: North Colorado Medical CenterGreeley, Colorado, United States of America
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
15%
16%
17%
18%
19%
20%
21%
Jul-
05
Aug
-05
Sep
-05
Oct
-05
No
v-05
De
c-05
Jan
-06
Fe
b-0
6
Mar
-06
Apr
-06
May
-06
Jun
-06
Jul-
06
Aug
-06
Sep
-06
Oct
-06
No
v-06
De
c-06
Jan
-07
Fe
b-0
7
Mar
-07
Apr
-07
May
-07
Jun
-07
Month-Year (month)
% o
f P
atie
nts
th
at L
eft
Wit
ho
ut
Tre
atm
ent
Line = 30% Improvement
Door to DocImplementation
LWOTs and D2D Time
• There is a strong relationship between LWOT% and D2D time.
• A linear model (shown) explains 93% of the LWOT% /D2D data variation (correlation coefficient = 0.96).
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
0 15 30 45 60 75 90 105 120 135 150 165 180 195
D2D (min)
LW
OT
%
Toolkit ED
ED A
ED B
ED C
ED D
ED E
ED G
Predicted
Lessons Learned• ED Patient Safety can be improved
– The D2D SPF design reduces LWOTS • Collaboration is key
– ASU Industrial Engineering– Physicians, Staff
• Implementation is the hardest part – Changing minds along with process
• Leadership matters– High level organizational commitment
• Keep measuring and monitoring– Ongoing improvement should occur
• The SPF D2D model can apply in many EDs• A toolkit with interactive modeling tools is available at www.BannerHealth Innovations.org
Acknowledgements
• This project was funded under grant U18 HS 15921 from the Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services.
• Special thanks to the many people who have made this improvement possible! Especially Professor Jeff Cochran!