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%

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Jul-

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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

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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%

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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

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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!