using data to assess and improve operational performance in the emergency department jody crane, md,...
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Using Data to Assess and Improve Operational Performance in the
Emergency Department
Jody Crane, MD, [email protected]
Chuck Noon, [email protected]
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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
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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 ©
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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
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Server
A Simple Queue
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Server
Person (MD, nurse, tech, transporter, housekeeper,
etc.) or Resource (bed, scanner, equipment, etc.)
A Simple Queue
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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ServerCustomerArrivals
A Simple Queue
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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ServerCustomerArrivals
Person (patient, call, etc.) or Thing (lab sample, soiled room, tray to be picked,
etc.)
A Simple Queue
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Server
A Simple Queue
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Server
CustomerDepartures
A Simple Queue
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Server
Queue(waiting line)Customer
Arrivals
CustomerDepartures
A Simple Queue
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Server
Queue(waiting line)Customer
Arrivals
CustomerDepartures
A Simple Queue
ServiceRate (
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Server
Queue(waiting line)Customer
Arrivals
CustomerDepartures
A Simple Queue
Arrival
Rate (
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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A Simple Queue
Server
Queue(waiting line)Customer
Arrivals
CustomerDepartures
Arrival
Rate ( ServiceRate (
Avg Number
in Queue (Lq )
Avg. Wait Time
in Queue (Wq )
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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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?
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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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 (equivalent to rate of 5 per hour).
So, will there be any waiting? Most definitely.
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Triage Example 2Suppose 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, 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?
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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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
Time
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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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
Time
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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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.
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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ARRIVAL TIMES
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480
Time
ARRIVAL TIMES
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480
Time
ARRIVAL TIMES
0 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480
Time
Poisson Arrival Examples
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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From a sample of arrival data
ARRIVAL TIMES
660 690 720 750 780 810 840 870 900
Time
ARRIVAL TIMES
460 490 520 550 580 610 640 670 700
Time
Arrival data from a California hospital. Mondays, 2pm-6pm.
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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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.
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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0%
5%
10%
15%
20%
25%
30%
35%
Service Time (minutes)
Fre
qu
en
cy
Exponential Distribution of Triage Times
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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From a sample of ED Triage Times.
Distribution of Observed Triage Times (n=777)
02040
6080
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
Co
un
t
Average = 5.06Std.Dev. = 4.97
Time study data from MWH.
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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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?
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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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 = / (-)
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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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
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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___
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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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) ________
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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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___
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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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.
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Typical Queue Behavior
WaitingTime
(minutes)
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The average rate of arrivals varies fairly predictably throughout a day…
Average Rate of Arrivals (Mondays, 168.5 per day)
0
2
4
6
8
10
12
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
Av
era
ge
Ra
te (
pts
/ho
ur)
Based on one year of arrival data for a California hospital with 58,000 visits.
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… but the actual count of arrivals for any given hour or day can vary considerably. This is arrival variation.
22 1
21 1
20 1
19 1 1
18 1 1 1 2
17 1
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)
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• Medium sized 24-hour Emergency Department with 95 visits per day, on average.
Example Problem - Situation
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Example Problem - Situation
0
1
2
3
4
5
6
7
8
12
:00
AM
1:0
0 A
M
2:0
0 A
M
3:0
0 A
M
4:0
0 A
M
5:0
0 A
M
6:0
0 A
M
7:0
0 A
M
8:0
0 A
M
9:0
0 A
M
10
:00
AM
11
:00
AM
12
:00
PM
1:0
0 P
M
2:0
0 P
M
3:0
0 P
M
4:0
0 P
M
5:0
0 P
M
6:0
0 P
M
7:0
0 P
M
8:0
0 P
M
9:0
0 P
M
10
:00
PM
11
:00
PM
Average Arrivals
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• 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).
Example Problem - Situation
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Example Problem - Situation
0
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12
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AM
1:0
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11
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Average Arrivals
Average Capacity
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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• Makes sense, but how will it really perform?
• Simulate for one year with assumed Poisson arrivals and Exponentially distributed service times.
Example Problem
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Example Problem – Performance
(average waiting time by hour of
arrival)
Average patient wait is 74 minutes
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Longest lines and waits occur at 9-11pm
Average patient wait is 74 minutes
(average waiting time by hour of
arrival)
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Example Problem - Situation
0
1
2
3
4
5
6
7
8
12
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AM
1:0
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M
2:0
0 A
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M
4:0
0 A
M
5:0
0 A
M
6:0
0 A
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7:0
0 A
M
8:0
0 A
M
9:0
0 A
M
10
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11
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12
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2:0
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7:0
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8:0
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9:0
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PM
11
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PM
Average Arrivals Capacity
But that is not where the problem was caused.
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Example Problem - Resolution
0
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AM
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M
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M
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9:0
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11
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PM
Average Arrivals Capacity New Capacity
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Example Problem – Performance
(average waiting time by hour of arrival)
Average patient wait is 43 minutesCopyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Typical Queue Behavior
Giving This
Getting ThisWaitingTime
(minutes)
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
An ED is a network of queues. Balance is crucial.
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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
S3
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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D
Computer Simulation Results
N1
1 Nurse 1 Doctor
N2
4 BedsTreatment Times
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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D
Computer Simulation Results
N1
27
min.
1 Nurse 1 Doctor
N2
9
min.
4 BedsTreatment Times
9
min.
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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D
Computer Simulation Results
N1
27
min.
1 Nurse 1 Doctor
N2
9
min.
4 Beds30 min. Average
time betweenarrivals
Waiting Room
Treatment Times
9
min.
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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D
Computer Simulation Results
N1
27
min.
1 Nurse 1 Doctor
N2
9
min.
4 Beds30 min. Average
time betweenarrivals
Waiting Room
Treatment Times
9
min.
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Simulation
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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D
Computer Simulation Results
N1
27
min.
1 Nurse 1 Doctor
N2
9
min.
4 Beds30 min. Average
time betweenarrivals
Waiting Room
Patient Averages
3.3 hours in Waiting Room
Patient Averages
1.7 hours in Exam Room
Treatment Times
9
min.
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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D
Computer Simulation Results
N1
27
min.
1 Nurse 1 Doctor
N2
9
min.
4 Beds30 min. Average
time betweenarrivals
Waiting Room
Patient Averages
3.3 hours in Waiting Room
Patient Averages
1.7 hours in Exam Room
Treatment Times
9
min.
Total LOS of 5.0 HOURS
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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D
Computer Simulation Results
N1
27
min.
1 Nurse 1 Doctor
N2
9
min.
4 Beds30 min. Average
time betweenarrivals
Waiting Room
Treatment Times 8
9
min.
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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D
Computer Simulation Results
N1
27
min.
1 Nurse 1 Doctor
N2
9
min.
4 Beds30 min. Average
time betweenarrivals
Waiting Room
Patient Averages
3.3 hours in Waiting Room
Patient Averages
1.7 hours in Exam Room
Treatment Times 8
1.4 Hours 2.6 Hours
9
min.
Total LOS of 4.0 HOURS
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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D
Computer Simulation Results
N1
27
min.
1 Nurse 1 Doctor
N2
9
min.
4 Beds30 min. Average
time betweenarrivals
Waiting Room
Patient Averages
3.3 hours in Waiting Room
Patient Averages
1.7 hours in Exam Room
Treatment Times 8
1.4 Hours 2.6 Hours
9
min.
Total LOS of 4.0 HOURS
Caution! This assumes no increase in service times!
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D
Computer Simulation Results
N1
27
min.
1 Nurse 1 Doctor
N2
9
min.
4 Beds30 min. Average
time betweenarrivals
Waiting Room
Patient Averages
3.3 hours in Waiting Room
Patient Averages
1.7 hours in Exam Room
Treatment Times 8
1.4 Hours 2.6 Hours
9
min.
Total LOS of 4.0 HOURS
Let’s include a slight increase in service times.
28 1010
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D
Computer Simulation Results
N1
27
min.
1 Nurse 1 Doctor
N2
9
min.
4 Beds30 min. Average
time betweenarrivals
Waiting Room
Patient Averages
3.3 hours in Waiting Room
Patient Averages
1.7 hours in Exam Room
Treatment Times 8
3.7 Hours 3.1 Hours
9
min.
Total LOS of 6.8 HOURS!
28 1010
Let’s include a slight increase in service times.
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D
Computer Simulation Results
N1
9
min.27
min.
1 Nurse 1 Doctor
N2
9
min.
4 Beds30 min. Average
time betweenarrivals
Waiting Room
Treatment Times
2311 11
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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D
Computer Simulation Results
N1
9
min.27
min.
1 Nurse 1 Doctor
N2
9
min.
4 Beds30 min. Average
time betweenarrivals
Waiting Room
Patient Averages
3.3 hours in Waiting Room
Patient Averages
1.7 hours in Exam Room
Treatment Times
1.4 Hours 1.6 Hours
2311 11
Total LOS of 3.0 HOURS
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
What do You Have to Tell the Tool?
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
Inputs
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Inputs
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
Inputs
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
Inputs
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
Inputs
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
Inputs
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1 hour 1 hour1 hour 1 hour
MD Demand
3-hour Length Of Stay
MD spends 30 minutes per patient,
but when?
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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1 hour 1 hour1 hour 1 hour
MD Demand
3-hour Length Of Stay
MD spends 30 minutes per patient,
but when?
Assume all 30 minutes
in first hour?
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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1 hour 1 hour1 hour 1 hour
MD Demand
3-hour Length Of Stay
MD spends 30 minutes per patient,
but when?
Unrealistic!
Assume all 30 minutes
in first hour?
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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1 hour 1 hour1 hour 1 hour
MD Demand
3-hour Length Of Stay
MD spends 30 minutes per patient,
but when?
10 Minutes in first hour
10 Minutes in second
hour
10 Minutes in third
hour
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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1 hour 1 hour1 hour 1 hour
MD Demand
3-hour Length Of Stay
MD spends 30 minutes per patient,
but when?
10 Minutes in first hour
Better, but not exactly the way we do things!
10 Minutes in second
hour
10 Minutes in third
hour
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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1 hour 1 hour1 hour 1 hour
MD Demand
3-hour Length Of Stay
MD spends 30 minutes per patient,
but when?
15 Minutes in first hour
6 Minutes in second
hour
9 Minutes in third
hour
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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1 hour 1 hour1 hour 1 hour
MD Demand
3-hour Length Of Stay
MD spends 30 minutes per patient,
but when?
15 Minutes in first hour
EDUCATE© allows you to
specify how MD (or RN) time is spent within a patient’s LOS.
6 Minutes in second
hour
9 Minutes in third
hour
50% in first 33% 20% in second 33% 30% in third 33%
Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
Inputs
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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
© 2007, Jody Crane, MD, MBA
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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
© 2007, Jody Crane, MD, MBA
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ED Intervals
• Most EDIS can give you this information
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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
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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
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Time Observation Sheet
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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
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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
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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
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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
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Sampling for Intervals - Example
• 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
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Sampling for Intervals - Example
• 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
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What can EDucate Tell You?
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Information on Daily ED Arrivals and Admissions
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Arrivals by Various Modalities (Triage, EMS, FT)
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ED Size/Annual Visits per Bed
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1 hour 1 hour1 hour 1 hour
Bed Demand
3-hour Length Of Stay
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1 hour 1 hour1 hour 1 hour
Bed Demand
3-hour Length Of Stay
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1 hour 1 hour1 hour 1 hour
Bed Demand
3-hour Length Of Stay
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1 hour 1 hour1 hour 1 hour
Bed Demand
3-hour Length Of Stay
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1 hour 1 hour1 hour 1 hour
Bed Demand
3-hour Length Of Stay
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1 hour 1 hour1 hour 1 hour
Bed Demand
3-hour Length Of Stay
Bed Demand Profile
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Bed Demand/Capacity
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Bed Demand/Capacity (Boarders)
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RN and MD to Bed Ratios
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RN and MD Staffing vs. Patient Demand
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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)
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RN and MD Staffing vs. Patient Demand (Boarders+Lunches)
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RN to MD Ratios
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Admissions / Hospitalist Demand/Capacity Matching
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Inpatient Capacity and Admit Process Assessment
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The Effect of Boarding on RN FTEs
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The $$$ Related to LWOBS and Diversions
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
The Right Combo of Bed,RN,MD
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Summary Page
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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
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Copyright Jody Crane, MD, MBA, Chuck Noon, PhD 2008
EDucate© Summary• There are 4 main components that have to be
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