Myths And Barriers To Optimizing Ed Patient Flow

PART 1

Joseph Twanmoh, MD, MBA Senior Vice President, MS2

Information

Release Date: January 13, 2015 Termination Date: January 13, 2015

Hardware/Software Requirements

PC Microsoft Windows 2000 SE or above. Internet Explorer (v5.5 or greater), or Firefox Flash Player Plug-in (9.0 or later) Check your version here. Sound Card & Speakers 800 x 600 Minimum Monitor Resolution (1024 x 768 Recommended) Adobe Acrobat Reader*

MAC MAC OS 10.2.8 Safari or Firefox Flash Player Plug-in (9.0 or later) Check your version here. Sound Card & Speakers 800 x 600 Minimum Monitor Resolution (1024 x 768 Recommended) Adobe Acrobat Reader* Internet Explorer is not supported on the Macintosh. * Required to view printable (PDF) version of the lesson.

Information

Contact Information

The George Washington University Office of Continuing Education in the Health Professions (CEHP) Em: cehp@gwu.edu Ph: (202) 994-4285

Policy on Privacy & Confidentiality

http://www.gwu.edu/privacy-policy

Copyright

http://www.gwu.edu/copyright

Accreditation Information

Accreditation

The George Washington University School of Medicine and Health Sciences is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

The George Washington University School of Medicine and Health Sciences designates this live internet activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Instructions for Obtaining Credit

At the end of this webinar, you will receive an email for completing the online course evaluation. Your certificate of credit will be available immediately after you complete the evaluation.

Disclosure Statement In accordance with the Accreditation Council for Continuing Medical Education's Standards for Commercial Support, The George Washington University Office of Continuing Education in the Health Professions (CEHP) requires that all individuals involved in the development and presentation of CME activity content disclose any relevant financial relationships with commercial interest(s). CEHP identifies and resolves all conflicts of interest prior to an individual’s participation in an educational activity.

The following faculty, planners, and staff report that they have no relevant financial relationships with commercial interest(s):

Joseph Twanmoh , MD, MBA

Jesse Pines, MD (Course Director)

Danielle Lazar (Staff)

Leticia Hall (Staff)

Commercial Support This activity received no commercial support.

Objectives

• Review the most commonly used strategies to improve ED patient flow.

• Does the evidence support the practice? • Where are they applicable? • Why does it fail?

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 2

Myth vs. Reality?

• Direct To Bed • More Beds • Fast Tracks

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 3

Direct to Bed Survey

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 4

Direct to Room: One Department’s Approach to Improving ED Throughput, Bertoty et. al., J Emerg Nurs. 2007 Feb;33(1):26-30

• 47,000 visit ED • 31 beds • Prospective, before-after interventional • Immediate bedding when possible

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 5

Avg. Time (min.) Jan-Mar 2005

Avg. Time (min.) May-July 2005

Improvement (min.)

Arrival to Bed 20.49 14.8 5.69 Avg. Length of Stay 259 239 20

“The new triage system, DTR, has been a success. The average LOS …has dropped 7.7%”

Direct to Room: One Department’s Approach to Improving ED Throughput, Bertoty et. al., J Emerg Nurs. 2007 Feb;33(1):26-30

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 6

Strategies for Dealing With Emergency Department Overcrowding: A One Year Study, Takakuwa et. al., J Emer Med. May 2007:337-342

• 47,000 Adult ED • Prospective, before-after intervention • Immediate bedding when available,

bedside registration

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 7

“There was a significant decrease in time from triage-to-room after bedside registration was implemented.”

Takakuwa et. al., J Emer Med. May 2007:337-342

FIGURE 1 The effect of bedside registration on time from triage to room by time of day.

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 8

Impact of Rapid Entry and Accelerated Care at Triage on Reducing Emergency Department Patient Wait Times, Lengths of Stay, and Rate of Left Without Being Seen, Chan et. al., Annals Emer Med, Dec. 2005

UC San Diego 37,000

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 9

Direct To Bed Takeaways

• One size does not fit all – What is your Door to Bed time?

• Only works when you have open beds

– 1500 patients per bed

• Wrong patient/wrong bed

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 10

Space Survey: Do You Need A Bigger ED?

Emory University Hospital Emergency Department expands Woodruff Health Sciences Center | Dec. 20, 2013

Updated: 1:39 p.m. Wednesday, Feb. 12, 2014 | Posted: 6:34 p.m. Tuesday, Feb. 11, 2014 Hospital unveils emergency department upgrades Fort Hamilton now has 35 emergency beds after $5 million project.

THE NEW JFK EMERGENCY PAVILION BUILDING THE FUTURE OF EMERGENCY MEDICINE As one of the busiest emergency departments in Central New Jersey, JFK is responding to the dramatic boost in volume in the emergency department by nearly doubling its square footage to 60,000 square feet and renovating the existing space to improve patient flow, privacy and quality of care.

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 11

The Effect of Emergency Department Expansion on Emergency Department Overcrowding, Han et. al., Academic Emergency Medicine, April 2007

• 45,000 visits per year • 28 to 54 beds • Pre/Post Study over 1 year • Ambulance Diversion

– 100%/10/10

• LWOTs • Physician in Triage (1p-9p)

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 12

Results: No Improvement

Pre-expansion Post-expansion Difference

AMBULANCE DIVERSION

Total duration per month (hr.) 106.9 117.9 10.9

No. of episodes per month 14.2 16.2 2.0

Duration per episode (hr.) 7.5 7.3 -0.2

Daily patient volume 125 144 29

Left without being seen 3.5% 2.7% -0.8

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 13

Blame It On Boarding

Pre-expansion Post-expansion

No. of ED admit requests per hour 1 1

No. of admission holds 3 5

Admission hold LOS (hr.) 3.0 4.1

Occupancy (%) 85.0 77.9

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 14

When Overcrowding Paralyzes An Emergency Department, Managed Care / June 2006

Pre-Expansion Post-Expansion

St. Joseph Medical Center, Towson, MD

Annual Volume 40,000 42,000

No. of Beds 20 40

Ambulance Diversion Hours/Month 120 287

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 15

Adding more beds to the emergency department or reducing admitted patient boarding times: which has a more significant influence on emergency department congestion? Ann Emerg Med. 2009 May;53(5):575-85.

Table 4A. Length of stay with varying the number of ED beds

Departure Rate/min 23 Beds 28 Beds Difference, min

1 Patient/20 240 min 247 min +7

1 Patient/15 218 min 225 min +7

Table 4B. Length of stay with varying the admitted patient departure rate. No. Beds 1 Patient/20 min 1 Patient/15 min Difference, min

23 240 min 218 min -22

28 247 min 225 min -22

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 16

15% Increase in Daily Patient Census

Table 5A. Length of stay with varying the number of ED beds

Departure Rate/min 23 Beds 28 Beds Difference, min

1 Patient/20 482 min 504 min +22

1 Patient/15 404 min 422 min +18

Table 5B. Length of stay with varying the admitted patient departure rate.

No. Beds Patient/20 min 1 Patient/15 min Difference, min

23 482 min 404 min -78

28 504 min 422 min -82

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 17

When do you need more beds?

• > 2,000 patients per bed • After you’ve fixed your patient flow

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 18

Survey Question: Is Your Fast Track Really Fast?

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 19

Streaming by case complexity: evaluation of a model for emergency department fast track. Emerg Med Australas. 2008;20:241–249

• Rural, academic 40,000 visit ED • New FT

– 3 beds/4 recliners – Total beds 25 to 24

• 16 h/day • 1 MD (11.1%)/2 RN (20.3%) • 14.5% increase in volume during study period

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 20

Improvement due to Fast Track? PRE

(MAY-OCT 2005) POST

(MAY-OCT 2006) DIFFERENCE

Waiting Time (min) 54.5 31.7 -22.8

Treatment Time (min) 240.6 194.1 -46.5

LWBS 6.2% 3.1% -3.1%

STUDY PERIOD FAST TRACK MAIN ED

No. of Patients 6,062 12,442

Hours of Operation 16/day 24/day

Patients per Hour 2.08 2.85

Streaming by case complexity: evaluation of a model for emergency department fast track. Emerg Med Australas. 2008;20:241–249

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 21

Evaluation of the fast track unit of a university emergency department. Nash, et. al. J Emerg Nurs. 2007;33:14–20

• Urban, academic 80,000 visit ED • Minor Care replaced with Fast Track • 8 beds • NP’s 8a-12a, 1 or 2 per shift

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 22

Fast Track Slower than Minor Care

MINOR CARE

SEP 03-FEB 04 FAST TRACK

SEP 04-FEB 05 DIFFERENCE

Length of Stay 4.68 hrs. 4.36 hrs. -.32 hr. (p=.08) Total Patients 9,130 5,995 3,135 Patients per hour 3.13 2.06 1.07

Evaluation of the fast track unit of a university emergency department. J Emerg Nurs. 2007;33:14–20

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 23

Fast Track: Has it changed patient care in the emergency department? Kwa, et. al., Emerg Med Australas. 2008;20:10–15

• Urban, academic 53,000 visit ED • 8 bed FT (New Space) • Attending, Resident, 1-2 RN • 8a-10p daily • Pre/Post 6 mos. Study (Dec.-May)

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 24

CONCLUSION: “Fast Track allows lower-acuity patients to be seen quickly without a negative impact on high acuity patients.”

Avg. Daily Census 17 Hours per Day 14 Patients/Hour 1.2 Patients/Hour/Provider 0.6

Fast Track: Has it changed patient care in the emergency department? Kwa, et. al., Emerg Med Australas. 2008;20:10–15

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 25

Impact of streaming “fast track” emergency department patients, O’Brien, et. al., Aus. Health Rev. 2006;30:525–532

• Urban, academic 43,000 visit ED • 9a-10p M-F; 9:30a-6p weekends • 3 beds and 1 chair (new space) • Junior ED doctor and nurse • 12 week observation

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 26

Results

• Decreased LOS for discharged patients 20 min.

• No change for admitted patients O’Brien, et. al., Aus. Health Rev. 2006;30:525–532

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 27

Fast Track Performance

“During the 12-week trial period, an average of 123.5 patients per week were streamed through the fast track area.”

Daily Census 17.6

Avg. FT hours per day 11.7

Patients per hour 1.5

O’Brien, et. al., Aus. Health Rev. 2006;30:525–532

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 28

Fast Track Conclusions

• Mixed reviews on effectiveness • Silo mentality • Multiple queues • Utilization issues • Different expectations

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 29

Summary

• Direct to Bed • Adding more beds • Fast tracks

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 30

Part 2

• Provider Triage • Advanced Nursing Triage Protocols • PCOT • Advanced front end flow model

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 31

Questions?

jrt@ms2group.com

Myths and Barriers to Optimizing ED Patient Flow | Part 1 | 32