Presentation - ICMCC

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Presentation - ICMCC

Interactive Framework For

Accident and Emergency

(A&E) Simulation Model


Presentation Structure

• Overview

• Issues and approach

• Interactive data input interfaces

• Generic model

• Initial validation and testing

• Further work


Overview – Modelling Applications to Health Care

• Assist understanding internal dynamics of hospitals

• Insight into non-linear hospital structures

• Assist understanding of internal processes

• A decision making tool

• Test new and or different scenarios in a safe environment


Overview - Challenges to Overcome

• Poor modelling adoption in health care

• Understand health care client needs

• Provide clients with answers and insights to their business

• Assist communication


Key Issues to Address

• Simplify Models - Manage levels of abstraction

• Simple, clear inputs

• Clear outputs

• Visual and interactive

• Solid models

• Statistical analysis


Approach

• Built Generic Accident & Emergency (A&E) model

• Excel sheets to input data – Familiarisation and clarity

• Input data (Pathways, Arrival Time, Resources)

• Input data run simulation model (Simul8)

• Visually show patients flowing through A&E area

• Analyse data


Modelling Goals

• Key elements successful simulation in health care

• Communication and participation

• User-friendly simulation software

• Act as a decision making tool

• Overlay simulation model onto A&E layout


A&E Layout


Pathways

• Separate pathways for ambulance and ambulant entries

• Pathways show percent routing to each area

• Areas include

• Minor injuries

• Rapid assessment (major injuries)

• Resuscitation

• Adults and children (Paediatrics)


Input – Ambulant Pathways Example

Treatment 1 Treatment 2 Treatment 3 Treatment 4

Treatment 5

Ambulant

56

25

0

0

19

Minors (Adult) (1)

Minors (Paeds) (2)

X-Ray (3)

Discharge (4)

wt25

100

0

0

0

100

0

0

70

Discharge (1) 100 Minors (Adult) (1) 50 Discharge (1) 95 Discharge (1)

wt1 wt2 wt3

X-Ray (2) 95 Discharge (2) 2 MAU (2) 5 In-Patient (2)

wt7

wt4

MAU (3)

5 In-Patient (3) 48 SAU (3) 95 Discharge (3)

wt8

wt5

SAU (4) 95 Discharge (4)

5 In-Patient (4)

wt9

wt6

5 In-Patient (5) 95 Discharge (4) 95 Discharge (5)

wt10 wt12 wt13

Discharge (5) 100 Minors (Paeds) (6) 5 PAU (5) 5 In-Patient (6)

wt11

X-Ray (6)

PAU (7)

Minors (Adult) (8)

Discharge (7)

wt26 wt22 wt23

5

PAU (13) 5 In-Patient (11)

wt15

In-Patient (8)

wt16

Discharge (9)

wt17

MAU (10)

SAU (11)

To Rapid Assesment (5) 30 Minors (Paeds) (9) 95 Discharge (12)

95

5

95

2

3

95

5

95

5

95

Discharge (6)

wt18

In-Patient (7)

wt19

Discharge (8)

wt20

In-Patient (9)

wt21

Discharge (10)

wt24

wt14

Enter route percentage (all

route columns must add up

to 100%. Enter green text

boxes only - red text boxes

adjust automatically.

Enter Ambulant Arrival

Times - Sheet 2 of 6


Arrival Times

• Model can capture hourly arrivals

• Over a 24 hour period

• Over a 7 day week


Input – Arrival Time (Ambulant Example)

Enter ambulant patient arrivals per hour (Walk In)

Calculated ambulant Inter-arrival Time

Time (hrs) Mon Tues Wed Thur Fri Sat Sun Mon Tues Wed Thur Fri Sat Sun

00:00 - 00:59 3.45 3.45 3.45 3.45 3.45 3.45 3.45 17.39 17.39 17.39 17.39 17.39 17.39 17.39

01:00 - 01:59 3.12 3.12 3.12 3.12 3.12 3.12 3.12 19.23 19.23 19.23 19.23 19.23 19.23 19.23

02:00 - 02:59 3.12 3.12 3.12 3.12 3.12 3.12 3.12 19.23 19.23 19.23 19.23 19.23 19.23 19.23

03:00 - 03:59 2.25 2.25 2.25 2.25 2.25 2.25 2.25 26.67 26.67 26.67 26.67 26.67 26.67 26.67

04:00 - 04:59 1.85 1.85 1.85 1.85 1.85 1.85 1.85 32.43 32.43 32.43 32.43 32.43 32.43 32.43

05:00 - 05:59 2.03 2.03 2.03 2.03 2.03 2.03 2.03 29.56 29.56 29.56 29.56 29.56 29.56 29.56

06:00 - 06:59 2.54 2.54 2.54 2.54 2.54 2.54 2.54 23.62 23.62 23.62 23.62 23.62 23.62 23.62

07:00 - 07:59 3.92 3.92 3.92 3.92 3.92 3.92 3.92 15.31 15.31 15.31 15.31 15.31 15.31 15.31

08:00 - 08:59 6.58 6.58 6.58 6.58 6.58 6.58 6.58 9.12 9.12 9.12 9.12 9.12 9.12 9.12

09:00 - 09:59 7.70 7.70 7.70 7.70 7.70 7.70 7.70 7.79 7.79 7.79 7.79 7.79 7.79 7.79

10:00 - 10:59 9.27 9.27 9.27 9.27 9.27 9.27 9.27 6.47 6.47 6.47 6.47 6.47 6.47 6.47

11:00 - 11:59 8.68 8.68 8.68 8.68 8.68 8.68 8.68 6.91 6.91 6.91 6.91 6.91 6.91 6.91

12:00 - 12:59 8.65 8.65 8.65 8.65 8.65 8.65 8.65 6.94 6.94 6.94 6.94 6.94 6.94 6.94

13:00 - 13:59 8.03 8.03 8.03 8.03 8.03 8.03 8.03 7.47 7.47 7.47 7.47 7.47 7.47 7.47

14:00 - 14:59 8.10 8.10 8.10 8.10 8.10 8.10 8.10 7.41 7.41 7.41 7.41 7.41 7.41 7.41

15:00 - 15:59 8.21 8.21 8.21 8.21 8.21 8.21 8.21 7.31 7.31 7.31 7.31 7.31 7.31 7.31

16:00 - 16:59 7.67 7.67 7.67 7.67 7.67 7.67 7.67 7.82 7.82 7.82 7.82 7.82 7.82 7.82

17:00 - 17:59 7.56 7.56 7.56 7.56 7.56 7.56 7.56 7.94 7.94 7.94 7.94 7.94 7.94 7.94

18:00 - 18:59 7.52 7.52 7.52 7.52 7.52 7.52 7.52 7.98 7.98 7.98 7.98 7.98 7.98 7.98

19:00 - 19:59 6.00 6.00 6.00 6.00 6.00 6.00 6.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00

20:00 - 20:59 5.63 5.63 5.63 5.63 5.63 5.63 5.63 10.66 10.66 10.66 10.66 10.66 10.66 10.66

21:00 - 21:59 5.67 5.67 5.67 5.67 5.67 5.67 5.67 10.58 10.58 10.58 10.58 10.58 10.58 10.58

22:00 - 22:59 5.12 5.12 5.12 5.12 5.12 5.12 5.12 11.72 11.72 11.72 11.72 11.72 11.72 11.72

23:00 - 23:59 4.07 4.07 4.07 4.07 4.07 4.07 4.07 14.74 14.74 14.74 14.74 14.74 14.74 14.74

Enter ambulant Patient Arrival Time


Treatment Time and Resources

• Treatment time derived from data and or Subject-Matter Experts

• Resources modelled the number of bed, bays, cubicles in each area

• Resources assumed inclusion of associated staff

• Resources assumed inclusion of associated treatment resource


Input – Resources

Treatment Time

Derived by

Subject Matter

Expert

Triage 5 3

Minor Adult 120

Minor Adult - post X-Ray 10

Minor Paed 120

Minor Paed - post X-Ray 10

X-Ray 10 3

MAU 300 7

SAU 300 10

PAU 300 6

Resus 100 8

Rapid Assessment - Medical 120

Rapid Assessment - Medical - post X-Ray 10

Rapid Assessment - Surgical 120

Rapid Assessment - Surgical - post X-Ray 10

Paeds Assessment 120

Paeds Assessment - post X-Ray 10

Number of bays,

cubicles or beds

10

8

18

12

8


The Model


Validation and Verification

• Animation and operational graphics used to validate

• Model trialled using data from a proposed hospital build


Assumptions – Initial Trial

• Simplified model for initial trial

• Subject-Matter Expert data used for initial trial

• Allocated resource included staff and equipment to treat patient

• All supplementary treatment undertaken with treatment area

• After treatment all patients discharged

• Three Triage Nurses


Model Input Summary

• Arrival times matched to proposed hospital build

• Pathway proportions derived from proposed hospital build

• Treatment times –from Subject-Matter Expert

• Trial run for 24 hours, 50 times

• Model warm-up period 24 hours to ensure steady-state

• Subject-Matter Expert major adult time used for rapid assessment time


Treatment Times – From Subject-Matter Expert (SME)

Area

Most Optimistic

Time

Most Likely

Time

Most Pessimistic

Time

Triage 5 10 15

Resus 30 30 300

Major - Adults 60 180 240

Major - Paeds 30 120 150

Minor - Adult 5 90 240

Minor - Paeds 5 90 240


Output Sample – Summary Queue Times, Discharge

Times and Utilisation

Average Queue

Time (minutes)

Average Time To

Discharge

(minutes)

% Utilisation

Triage 0.04 119 16

Resus 0.00 163 16

Rapid Assessment Medicine 0.00 163 23

Rapid Assessment Surgical 0.00 162 17

RA Paed 0.00 84 7

Minor Adult 12.22 128 60

Minor Paed 0.40 117 33

Hospital X – Major - 164 -

Hospital X – Minor - 89 -


Output Summary Observation

• Minor adults showed the longest average queue time

• Minor adults showed the largest utilisation

• Rapid Assessment showed no queues and with low utilisation

• Rapid Assessment adults results similar to Trust major data 2004


Output Sample – Minor Adults

• Sample data showed that 96

percent of minor adults were

discharged within 4 hours

• The most likely discharge time

between 51 and 100 minutes

• Average time for discharge 128

minutes

Proportion

0.3

0.25

0.2

0.15

0.1

0.05

0

0.15

Discharge Minor Adults - Sample Data

0.25

0.24

0 to 50 51 to 100 100 to

150

0.18

151 to

200

0.14

201 to

250

Time in Minutes (Arrival to Discharge)

0.04

251 to

300

0.00

More


Output Sample – Minor Adults

• The longest queues

occurred in the evening

• In the afternoon queue

sizes were shorter but

more likely

• Overall the probability of

queuing low

• Approximately 98% of time

the queue size was less

than 8 patients

Number in Queue

(Q)

Probability

1

0.8

0.6

0.4

18.00

16.00

14.00

12.00

10.00

8.00

6.00

4.00

2.00

0.00

0.939

Minor Adult Queue - 10 Bays (Trial 50 Days)

0 2 4 6 8 10 12 14 16 18 20 22 24

Hours

Average Q Maximum Q Q Probability

Minor Adult Queue Probability

0.60

0.50

0.40

0.30

0.20

0.10

0.00

Q Probability

0.2

0

0.041 0.018 0.003 0.000

0 to 4 5 to 8 9 to 12 13 to 16 More

Queue Size


Further Study for Rapid Assessment Medicine

• Large resource quota (18 beds)

• No queues

• Low utilisation

• Re-run model with say 10 beds…


Rapid Assessment Re-run With 10 Beds

Rapid Assessment

Medicine 18 Beds

Rapid Assessment

Medicine 10 Beds

Average Queue Time

0 0.94

(minutes)

Maximum Queue Time

0 12.74

(minutes)

Average Discharge

163 164

Time (minutes)

Maximum Discharge

236 241

Time (minutes)

% Utilisation 23 41


Rapid Assessment Re-run With 10 Beds

• Maximum number in queue 3

• Probability of queue low

• % Utilisation nearly doubled to

41%

Number in Queue

3.50

3.00

2.50

2.00

1.50

1.00

0.50

0.00

Ra_Medical Queue - X Bays (Trial 50 Days)

0 2 4 6 8 10 12 14 16 18 20 22 24

Hours

Mean Q Max Q Probability Q

0.12

0.10

0.08

0.06

0.04

0.02

0.00

Q Probability


Further Work

• Obtain more data to fully validate and verify the model

• Expand the model to include observation areas

• Incorporate staff resources

• Investigate incorporating related treatment areas i.e. X-Ray


Summary

• Assisted understanding internal dynamics of hospitals

• Insight into non-linear hospital structures

• Assisted understanding of internal processes

• Acts decision making tool

• Tests new and or different scenarios in a safe environment

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