How Simulation Can Help to Improve Bed Capacity Management

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Overview

• Current challenges

• Case studies

• Learning

• Benefits

• Future thoughts

Headlines

Challenges

• Long waits in emergency departments

• Balancing elective vs. emergency demand

• Wrong bed wrong time

• Discharge placements

• Cancelled electives

• COMPLEX

NHS IMASSupporting Improvements in Emergency Care

Purpose

• Educational simulations to

demonstrate best practice

concepts

• Increase adoption of best

practice concepts

• Highlight which improvement

concept to focus efforts

The Simulation

• Arrivals (Acute Only) – Hour of Day/Day of

• Patient Types – Age and GP referral

The Simulation

• LOS – Samples likely number of midnight

• Hour of day and day of week dependent

• Discharge – Samples likely discharge hour

• Calculates time until midnight, adds on

midnight stays and hour of discharge

The Simulation

• Constraints – total number of beds

• Key results

Scenarios

• Users select from pre

defined scenarios

• Scenarios cover key

improvements to help

alleviate bed constraints

• Users can also upload

own data to test

GP/ PCP Referrals

• Referrals arriving 5pm• ED saturated at this point • Move to 1pm

Discharges

• High % of patients

discharge towards

end of day

• Ability to move the

curve forward by ‘x’

Over 75’s

• Better management of

over 75’s

• Reducing LOS of those

with 14+ midnight stays

• Support being available in

the community

7 Day Working

• Reduced number of

discharges over

weekend

• Staff available to make

the discharge decisions

• Apply a weekday LOS

profile to Sat/Sun

admissions

Results

Royal Free HospitalManaging Elective and Emergency Admissions

Purpose

• Understand bed

consumption by specialty

• Understand bed

consumption by elective

and emergency

• Reduce ED waits

• Reduce elective

cancellations

The Simulation

Arrivals Emergency – Hour of day/Day of Week by specialty

and emergency type.

Elective – Schedules by specialty, Day of Week and

month. Arrival time sampled separately.

Patient Types

• Admission type

• Arrival type

• Specialty

The Simulation

– Dependent on admission type, specialty ,

hour of day and day.

– If 0 Midnight stays samples resample from

separate LOS distribution

The Simulation

Constraints

• Number of beds by ward

• Ward dependent on specialty

• Top specialty attendances to ward used to create priority matrix

• Resource Pools representing wards created for each specialty

• Checks bed availability, if no preferred wards remain takes any bed

from the pool

Ward Identifier

The Simulation

Discharge specialty’s

• Specialty change

• Ward Movements

• LOS sampled from

predicted

discharge

specialty

The Simulation

• Overflow wards

– Available at certain times

– Overnight only

• % Routed to Short Stay Wards

– Not part of simulated beds

• Bed changeover time

• Emergency patients are priority for beds

Results

• Select specialty/ward• Select Admission Type• Select Time Interval

View Results by

• Max Bed Occupancy • Admissions• Discharges

Whole Hospital Simulation

Module 1 Module 2 Module 3 Module 4

Lessons

• Simple vs. Complex Simulations

– What are the minimum requirements?

• Arrival Data

– Only shows time bed was occupied

– Simulate delay

– Shift arrival curve

• LOS Data

– Time Dependent, Specialty Dependent, Ward Dependent,

Admission Dependent

– Data Sparse for some specialties

Lessons

• Discharge time – batching

• Rules – Difficult to define

– Triggers

– Simulation driving understanding

Lessons

• Specialty vs. Wards

– Visual representation important

• Validation

– Bed Census

– Closed Wards

Lessons

Scenario Assumptions – How do you model

7 day working?

Actually increased

capacity required

Lessons

Number of midnight

stays appear to be

driven by a push to

discharge on Friday.

Clear the system before

weekend rush.

If 7 day working, no rush to discharge

before Friday. What is a clinically

appropriate LOS?

Benefits of using simulation

• Interconnectedness – See the effect in

ED, on Bed Utilization and on waiting lists

• Complex Routes and Rules – Simulation

helped engage and define complexities in

process

• Visualization

• Experimentation

Future

• Generic bed modeling simulation

– How complex/simple does this need to be?

• Connecting to real time data

• Linking modules

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