Using Data to Assess and Improve Operational Performance in the Emergency Department Jody Crane, MD,...

Using Data to Assess and Improve Operational Performance in the

Emergency Department

Jody Crane, MD, MBAjcranemd@gmail.com

Chuck Noon, PhDcnoon@utk.edu

Objectives - Web and Action

• Summarize demand/capacity issues within the ED with respect to nursing and physicians hours and bed demand

• Determine the best capacity plan by hour of day based on characteristics of patient demand and determine the ideal schedule of staff

• Create an operational plan to drive future management decisions

Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008

Objectives for Today

• To begin to understand basic concepts behind ED operations

• To provide an overview of analysis with EDucate©

• To orient participants to the data necessary to populate EDucate Inputs ©

• To provide guidance for collecting and validating data for EDucate Inputs ©

What is EDucate©?

• The Emergency Department Utilization and Capacity Assessment Tool in Excel©

• A tool to give Emergency Departments an accurate assessment of their current operational state to help guide improvement and operational redesign

• It is the most comprehensive, data-driven assessment tool currently available to help EDs focus on targets for operational improvement

EDucate© Summary

• EDucate© can be an effective tool for ─Fast Track and Triage planning ─Matching demand to capacity throughout your

process flow─Determining the correct team size─Determining the impact of holds, diversions,

and walkouts on staffing, throughput, and revenue

─Creating “what-if” scenarios for future projects

Concepts Behind EDucate©

• EDucate© was developed based on ─A recognition that ED demand is random yet

varies predictably throughout a day─A view that ED operations can be modeled as

a network of queues, with each queue having its own arrival and service characteristics

─The belief that understanding current demand/capacity misalignment is the first step in operational improvement

Server

A Simple Queue

Server

Person (MD, nurse, tech, transporter, housekeeper,

etc.) or Resource (bed, scanner, equipment, etc.)

A Simple Queue

ServerCustomerArrivals

A Simple Queue

ServerCustomerArrivals

Person (patient, call, etc.) or Thing (lab sample, soiled room, tray to be picked,

A Simple Queue

Server

A Simple Queue

Server

CustomerDepartures

A Simple Queue

Server

Queue(waiting line)Customer

Arrivals

CustomerDepartures

A Simple Queue

Server

Arrivals

CustomerDepartures

A Simple Queue

ServiceRate (

Server

Arrivals

CustomerDepartures

A Simple Queue

Arrival

Rate (

A Simple Queue

Server

Arrivals

CustomerDepartures

Arrival

Rate ( ServiceRate (

Avg Number

in Queue (Lq )

Avg. Wait Time

in Queue (Wq )

Triage Example 1Suppose we have a triage operation staffed by a single nurse. Patients arrive and wait in the waiting area if the triage nurse is busy triaging other patients. When a patient is seen by the triage nurse, the triage activity occurs in a single encounter.

Data was gathered and, on average, 6 patients arrive per hour. The average time it takes to triage a patient is 12 minutes.

So, will there be any waiting?

Data was gathered and, on average, 6 patients arrive per hour. The average time it takes to triage a patient is 12 minutes (equivalent to rate of 5 per hour).

So, will there be any waiting? Most definitely.

Data was gathered and, on average, 4 patients arrive per hour. The average time it takes to triage a patient is 12 minutes (again, a “service rate” of 5 patients/hour).

So, will there be any waiting?

Triage Example 2 (cont’d)It depends. Let’s look at two situations.

Situation 1 – A patient arrives exactly every 15 minutes and the time to triage each patient is exactly 12 minutes.

Will there be any waiting? If so, how much?

ARRIVAL TIMES

0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480

Triage Example 2 (cont’d)It depends. Let’s look at two situations.

Situation 1 – A patient arrives exactly every 15 minutes and the time to triage each patient is exactly 12 minutes.

Will there be any waiting? If so, how much?There will be NO waiting.

ARRIVAL TIMES

0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480

Triage Example (cont’d)Situation 2 – • Patients arrive according to a random arrival process (a Poisson process) at an average rate of 4 patients per hour.

ARRIVAL TIMES

0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480

ARRIVAL TIMES

0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480

ARRIVAL TIMES

0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480

Poisson Arrival Examples

From a sample of arrival data

ARRIVAL TIMES

660 690 720 750 780 810 840 870 900

ARRIVAL TIMES

460 490 520 550 580 610 640 670 700

Arrival data from a California hospital. Mondays, 2pm-6pm.

Triage Example (cont’d)Situation 2 – • Patients arrive according to a random arrival process (a Poisson process) at an average rate of 4 patients per hour. • Triage (service) times are distributed according to an Exponential distribution with a mean of 12 minutes.

Service Time (minutes)

Exponential Distribution of Triage Times

From a sample of ED Triage Times.

Distribution of Observed Triage Times (n=777)

100120

140160180

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Minutes

Average = 5.06Std.Dev. = 4.97

Time study data from MWH.

Triage Example (cont’d)Situation 2 – • Patients arrive according to a random arrival process (a Poisson process) at an average rate of 4 patients per hour. • Triage (service) times are distributed according to an Exponential distribution with a mean of 12 minutes.

Will there be any waiting? If so, how much?

How can we estimate the waiting?

• We can use steady-state formulas for certain queuing interfaces. For example, in cases with Poisson arrivals, Exponentially distributed service times, and 1 server, the estimated wait in front of triage can be calculated using the formula:

• Alternatively, we could use any one of a number of computer simulation packages.

• Or, we can use a spreadsheet like QueueCalc which uses the steady-state formulas.

Wq = / (-)

With the Coefficient of Variations set to 1, we areAssuming maximal variation with Poisson arrivals and Exponentially distributed service times. Values of 0 would imply no variation.Infinite Queue Approximation Worksheet

INPUTS

KEY OUTPUTS

Triage ExampleIn the triage example, we had an arrival rate of 4/hour and a service rate of 5/hour (average triage time of 12 minutes). Assuming maximal variation, we can use QueueCalc to estimate steady-state values for:

1. Server Utilization __80%__

2. Average Wait Time (Wq) _.8 hour___

3. Average Line Length (Lq) _3.2___

Triage Example RevisitedIn the triage example, we had an arrival rate of 4/hour and a service rate of 5/hour. If the number of visits to the ED grew by 15%, what would happen to the waiting times?

1. Server Utilization _______

2. Average Wait Time (Wq) ________

3. Average Line Length (Lq) ________

Triage Example RevisitedIn the triage example, we had an arrival rate of 4/hour and a service rate of 5/hour. If the number of visits to the ED grew by 15%, what would happen to the waiting times?

1. Server Utilization __92%__

2. Average Wait Time (Wq) _2.3 hours___

3. Average Line Length (Lq) _10.6___

Queuing Interface Principles

For queues with the arrival rate less than the service rate, and full-time server availability,…

─…there will be no waiting if there is no variation

─…variation can be present in the arrival process and/or the service process

─…as variation increases, waiting increases

─…in queues with considerable variation, waiting will increase dramatically as utilization increases and, hence, alignment of capacity with demand is key.

Typical Queue Behavior

WaitingTime

(minutes)

The average rate of arrivals varies fairly predictably throughout a day…

Average Rate of Arrivals (Mondays, 168.5 per day)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour of Day

Based on one year of arrival data for a California hospital with 58,000 visits.

… but the actual count of arrivals for any given hour or day can vary considerably. This is arrival variation.

19 1 1

18 1 1 1 2

16 2 1 1 1 3 1 1

15 2 1 1 1 1 1 1 1 1 1

14 1 2 8 3 7 3 1 2 1 9 1 1

13 6 3 6 6 1 2 2 1 2 3 3 1 1 1

12 4 3 7 4 6 3 1 3 5 5 6 9 2 3 1 1

Count 11 3 4 6 5 8 4 6 5 5 4 2 4 4 2 1 2

10 1 7 10 5 4 6 7 5 6 3 7 6 9 2 3 3 1

9 1 1 1 2 4 7 5 8 5 3 6 10 14 5 4 4 8 10 12 4

8 3 2 1 6 3 5 6 5 10 11 6 5 8 8 6 11 9 6 5

7 4 1 2 1 1 3 6 3 1 5 4 4 7 6 6 6 4 7 9 6 8 6

6 4 6 4 3 2 3 12 8 4 7 1 5 5 1 5 4 5 1 4 3 9 5 8

5 12 5 6 1 3 1 4 5 5 7 5 2 2 4 3 3 3 5 5 3 1 5

4 10 15 12 10 10 5 4 6 3 2 3 3 3 2 1 3 2 2 5 13

3 8 13 11 16 12 17 10 9 1 2 1 1 1 1 1 2 6 5

2 9 6 14 15 11 11 20 8 4 1 1 1 1 1 1

1 3 3 5 8 10 10 6 1 1

0 1 7 6 1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour of Day (Mondays only)

• Medium sized 24-hour Emergency Department with 95 visits per day, on average.

Example Problem - Situation

Average Arrivals

• Medium sized 24-hour Emergency Department with 95 visits per day, on average.

• Physicians can treat 2.5 patients per hour, on average. So, 38 (computed as 95/2.5) physician-hours required per day.

• Therefore, staffing 2 physicians on duty 24-hours per day should provide sufficient capacity cushion (estimated utilization of 79%, computed as 38/48).

Average Arrivals

Average Capacity

• Makes sense, but how will it really perform?

• Simulate for one year with assumed Poisson arrivals and Exponentially distributed service times.

Example Problem

Example Problem – Performance

(average waiting time by hour of

arrival)

Average patient wait is 74 minutes

Longest lines and waits occur at 9-11pm

Average patient wait is 74 minutes

(average waiting time by hour of

arrival)

Average Arrivals Capacity

But that is not where the problem was caused.

Example Problem - Resolution

Average Arrivals Capacity New Capacity

Example Problem – Performance

(average waiting time by hour of arrival)

Average patient wait is 43 minutesCopyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008

Typical Queue Behavior

Giving This

Getting ThisWaitingTime

(minutes)

An ED is a network of queues. Balance is crucial.

Network of Queues

• ED operations can be viewed as a network of queues, each of which must be properly configured to align capacity with demand.

• The completion through one server creates an immediate arrival for the next server if handoffs are clear & direct.

S1 S2S4

Computer Simulation Results

1 Nurse 1 Doctor

4 BedsTreatment Times

1 Nurse 1 Doctor

4 BedsTreatment Times

1 Nurse 1 Doctor

4 Beds30 min. Average

time betweenarrivals

Waiting Room

Treatment Times

1 Nurse 1 Doctor

Waiting Room

Treatment Times

Simulation

1 Nurse 1 Doctor

Waiting Room

Patient Averages

3.3 hours in Waiting Room

Patient Averages

1.7 hours in Exam Room

Treatment Times

1 Nurse 1 Doctor

Waiting Room

Patient Averages

Treatment Times

Total LOS of 5.0 HOURS

1 Nurse 1 Doctor

Waiting Room

Treatment Times 8

1 Nurse 1 Doctor

Waiting Room

Patient Averages

Treatment Times 8

1.4 Hours 2.6 Hours

1 Nurse 1 Doctor

Waiting Room

Patient Averages

Treatment Times 8

1.4 Hours 2.6 Hours

Caution! This assumes no increase in service times!

1 Nurse 1 Doctor

Waiting Room

Patient Averages

Treatment Times 8

1.4 Hours 2.6 Hours

Let’s include a slight increase in service times.

28 1010

1 Nurse 1 Doctor

Waiting Room

Patient Averages

Treatment Times 8

3.7 Hours 3.1 Hours

Total LOS of 6.8 HOURS!

28 1010

Let’s include a slight increase in service times.

min.27

1 Nurse 1 Doctor

Waiting Room

Treatment Times

2311 11

min.27

1 Nurse 1 Doctor

Waiting Room

Patient Averages

Treatment Times

1.4 Hours 1.6 Hours

2311 11

What do You Have to Tell the Tool?

Inputs

1 hour 1 hour1 hour 1 hour

MD Demand

3-hour Length Of Stay

MD spends 30 minutes per patient,

but when?

MD Demand

but when?

Assume all 30 minutes

in first hour?

MD Demand

but when?

Unrealistic!

Assume all 30 minutes

in first hour?

MD Demand

but when?

10 Minutes in first hour

10 Minutes in second

10 Minutes in third

MD Demand

but when?

Better, but not exactly the way we do things!

10 Minutes in second

10 Minutes in third

MD Demand

but when?

6 Minutes in second

9 Minutes in third

MD Demand

but when?

specify how MD (or RN) time is spent within a patient’s LOS.

6 Minutes in second

9 Minutes in third

50% in first 33% 20% in second 33% 30% in third 33%

Inputs

Sources of Data

• Arrivals data (monthly, daily, hourly) – Most registration systems

• Walkouts (daily, time of day) – Most registration systems

• Lab performance (order to collect, collect to receive, receive to results) – Most hospital ordering systems

• Radiology performance (order to procedure start, proc start to proc end, proc end to results signed) – Most radiology systems

ED Intervals

• Door to triage

• Triage start to triage end

• Triage end to bed placement

• Bed placement to MD

• MD start to MD dispo

• MD dispo to discharge

Door to Doc

Interval

In-ED Time

ED Intervals

• Most EDIS can give you this information

ED Intervals

• What if you don’t have an EDIS?• You have the following options:

─ Patient Tracking Infrared RFID Ultrasound Wideband

─ Retrospective chart review Time consuming ? Accuracy (depends on written documentation)

─ Sampling/Observation

Sampling

• There are multiple forms and methods of sampling in the ED

• Some require lengthy period of observation

• Some require some math and smarts

• In many cases, a mix can be used to develop the inputs

Time Observation Sheet

Patient Based TrackingName:____________________________ MR #:____________________________ Initial Entry to Emergency Department Date:_____________________________ Time:_____________________________ Triage Time you were placed in triage room:____________________ Time nurse entered triage room:_________________________ Time nurse left the triage room:_________________________ Time MD entered triage room:__________________________ Time MD left the triage room:__________________________ Time you left the triage room:___________________________ Main Emergency Department Time you were placed in a room:_________________________ Time the nurse entered the room:_________________________ Time the physician entered the room:______________________ X-rays/CT Time you left the room for x-ray:_________________________ Time you returned from x-ray:___________________________ Lab Time labs were drawn:_________________________________ Discharge Time doctor told you that you would be discharged:__________ Time you left the room:_________________________________

•Patient-completed tool•Accompanied by timer•Some minimal reward

upon completion•Starbuck’s card

•Money ($5)•Most will cooperate,

especially if you let them know it might help their

future visits

Sampling for Intervals

• Take snapshots with the following information:─ Number of patients waiting to be triaged─ Number in triage─ Number triaged waiting to be seen─ Number in beds─ Number awaiting discharge

Sampling for Intervals

• Once you have taken multiple snapshots you will have sufficient data for the following:─ Take your overall LOS─ Take the average number of patients in each

area─ Divide this number by the total in the department─ You will then have % distribution─ Multiply this by the overall LOS to arrive at the

length of each interval

Sampling for Intervals - Example

• Your sampling results in the following averages:

• Overall LOS – 180 minutes─ # to be triaged 5─ # in triage 1─ # to be seen 10─ # in beds 20─ # awaiting discharge 5

• Your sampling results in the following averages:

• Overall LOS – 180 minutes─ # to be triaged 5 12%─ # in triage 1 4%─ # to be seen 10 24%─ # in beds 20 49%─ # awaiting discharge 5 12%

Total 41

• Your sampling results in the following averages:• Overall LOS – 180 minutes

─ # to be triaged 5 22min─ # in triage 1 4min─ # to be seen 10 44min─ # in beds 20 88min─ # awaiting discharge 5 22min

Total 41 180min

What can EDucate Tell You?

Information on Daily ED Arrivals and Admissions

Arrivals by Various Modalities (Triage, EMS, FT)

ED Size/Annual Visits per Bed

Bed Demand

Bed Demand Profile

Bed Demand/Capacity

Bed Demand/Capacity (Boarders)

RN and MD to Bed Ratios

RN and MD Staffing vs. Patient Demand

Graphical representation of Main ED and FT MD and RN demand relative to capacity by

hour of day

RN and MD Staffing vs. Patient Demand (Boarders)

RN and MD Staffing vs. Patient Demand (Boarders+Lunches)

RN to MD Ratios

Admissions / Hospitalist Demand/Capacity Matching

Inpatient Capacity and Admit Process Assessment

The Effect of Boarding on RN FTEs

The $$$ Related to LWOBS and Diversions

The Right Combo of Bed,RN,MD

Summary Page

process flow─Determining the correct team size─Determining the impact of holds, diversions,

and walkouts on staffing, throughput, and revenue

─Creating “what-if” scenarios for future projects

aligned in order to achieve a highly functional ED that flows: Triage, MDs, RNs, and Beds (related to ancillary TAT)

• This tool will help identify where and when demand/capacity may be mismatched

• Garbage in/garbage out─Try to get exact data─If you don’t have exact data, that’s ok, but make your

best estimates and understand the subsequent limitations

Using Data to Assess and Improve Operational Performance in the Emergency Department Jody Crane, MD,...

Documents

Transcript of Using Data to Assess and Improve Operational Performance in the Emergency Department Jody Crane, MD,...

Homeless LGBTQ Youth and Public Libraries Julie Ann Winkelstein, PhD jwinkels@utk.edu.

Jody Fisher - Jazz Skills.pdf

Jump start with Jody

WNDC_Neighbors_Kate and Jody

Jody Lazarski- PowerPoint is back

© 2007, Jody Crane, MD, MBA 1 Introduction to the Advanced ED Operations 5-Day Course Jody Crane, MD, MBA jcranemd@gmail.com Chuck Noon, PhD cnoon@utk.edu.

Chuck Jones - Chuck Amuck - Johnson

Transcripts - Jody Peter Amblard

Jody Turner & Karri Winn

ER COLLET CHUCK Hydraulic Chuck

Arboriculture Resources, Certifications, and Techniques David S. Vandergriff dgriff@utk.edu.

A VIEW FROM THE VILLAS · Jody Anderson, Treasurer Ellyn Wrzeski, Secretary Chuck Ebann, Director Fred Fitzpatrick, Director (815)685-2026 (815)353-1196 (815)276-4166 (815)451-0024

Jody Dixon from Jody G Photography introduced us to Teak ... · 5/3/2016 · Jody Dixon from Jody G Photography introduced us to Teak & Twine. ... $34.99 (on sale til May 8th) ...

Jody Turner @ OMD Predicts

Plant Community Dynamics in the Northern Great Plains ......Kelly Krabbenhoff, KDK Consulting Bob Patten, ND State Univ. Stan Boltz, NRCS (SD) Chuck Stanley, NRCS (CNTSC) Jody Forman,

Chuck - 4x01 - Chuck Versus the Anniversary.HDTV.LOL.en

Jody Bradley

Campus Transformation Project / Updates Staenberg Kooper ... · Bobette & Jay Lerner Alan J. Levine Bonnie & Steve Levinger Lisa & Chuck Lucoff Trenton Magid Jody & Buzz Malashock

Jody barrow

TERA: PAMS Reporting By Michael McGuire mcguire@utk.edu