Using Simulation to Manage Unscheduled Care

Using Simulation to Manage

Unscheduled Care

Guest Speaker:

Dr Paul Schmidt

Consultant Acute Physician, Portsmouth Hospitals NHS Trust

Powerful. Flexible. Fast.

SIMUL8 Corporation | SIMUL8.com | [email protected]

Presenters

Paul Schmidt

Consultant Acute Physician,

Portsmouth Hospitals NHS Trust


Housekeeping

1. Audio

2. Q & A

Recording available on SIMUL8Healthcare.com


Introduction to the case study

Overview of context and challenges for unscheduled care operations

Principles for a rational operational strategy

Use of Modelling and Simulation

Outcomes

Lessons learnt and next steps

ACUTE NHS TRUST

South Coast of England▪ 600,000 catchment

▪ 105, 000 ED attendances

▪ 40,000 inpatients

Emergency Clinical Service Centre (CSC)

▪ Emergency Department

▪ Acute Medicine Unit

▪ Single management structure since 2010

▪ Clinical directors for ED and AMU

Increasing and changing nature of demand Trauma & injuries

True new non-trauma emergencies

Chronic medical co-morbidities with acute deterioration

Frail elderly

Ambulant and exercising choice

Patients with chaotic lives

ED access block

Acute medicine Emergency Ambulatory Care (AEC)

(Dis)continuity of care?

Asplin et al (2002)

Functional divide designed for clinicians rather than patients

Operational inefficiencies

Not aligned to patients

Inefficient use of skills

Complex patient flow

Failure to meet targets

Cost overrunsLow morale

Staff turnoverFailure to recruit

Damage to reputation

Create focused Services around customers with similar requirements requiring similar processes

The greater the pooling of demand and processors, the more effective a given system will be in minimising queues. (Erlang queue theory)

Service time variation increases the length of the queue

Reduced service time variation increases resource utilisation

Very high utilisation levels increase risk that peaks in demand could cause exponential growth in queue length

Avoid cut-outs unless completely justified. Only use for unique processes/services

MedicalAcute Med UnitEDOutliersOutliers

MedicalAcute Med UnitED MedicalAcute Med UnitEDOutliers

Sorting (streaming)Prioritising (triage)ED Queues

Acute Med UnitED MedicalOutliers

GP

ED vs GP admissionsChoosing outliers

Inconsistent overburdening (mura) of staff at different times

Very high staff utilisation or process constraint (muri)

Non-value adding activities (muda)(waiting times, clinical rework, unnecessary transfers)

Inventory (patients for beds)

Batching and overproduction (consultant WRs)

Desynchronised flow (to specialist beds)

Unnecessary complexity

Add valueFeel valued

Use skillsWork in a good team

STAFF

PATIENTS

ContinuityGood communicationNo unnecessary waitsGood quality care Trust in staff

OPERATION

Fewer moves

Simpler processes

Good use of skills

Less work in progress

High staff utilisation

Patient-centred

Less variation

CORPORATE

Safety, avoid critical events

Impact on elective services

Financial constraints

Meet targets

Reputation

ALIGNMENT

Flow/ED access block/4-hour breaches Patient safety: direct and overnight

admissions Bed capacity allocation Staffing and staff utilisation Job content of consultants Staff buy-in and identification with new

organisation Cultures different in ED and AMU

Understand demand

SegmentationTime

Test designs

Simulation

models

Key performance

indicators

Coherent redesign

Simplify &

cut waste

Stream to skills

Patient access

Fewest transfers

Align

Co-production

Synchronise flow

86.7%

by 4 hrs 94.6%

96.7%

• 3-stage to 2-stage process

• All patients through ED (including GP

referrals and Ambulatory patients)

• Patients separated by their expected

total LOS

• ≤ 48 hrs – Short-Stay

• > 48 hrs – Medical Spec

• Assessment (and resources) bought

forward to ED

• MAU becomes Short-Stay ward – for

execution of care only

Alignment of Demand and Services in an Integrated Model

(All Emergency CSC Activity)

Injuries

Own Transport 20,919

999 calls 7,944

Trauma &

Orthopaedic CSC

Surgical Spec

Wards

4,957 cases

Gastro 2409

Uro/Vasc 215

Cardioresp 107

Neuro 79

Various 182

Acute Medicine

Short stay ward

13819 cases

Cardioresp 5028

Neuro 2316

Adverse reactions 1744

Unknown/misc 1414

Gastrointestinal 1026

Uro/Vasc 454

Multi-systems 452

Skin/soft tissue 443

Metabolic 288

Mental health 268

Skin/soft tissue trauma 194

Various 192

Trauma /Injury Unit

28,906 cases

Soft tissue injuries 21578

Multi-trauma

Orthopaedic problems 7328

Falls that needs injuries treated

or ruled out

Non Trauma Assessment Unit

29,603 cases

Cardioresp 9033

Gastrointestinal 6781

Neuro 6413


Unknown/misc 2174

Urology/Vascular 1124

Metabolic 986

Multisystem 672

Mental health 94

Ambulatory Unit

29,916 cases

Skin/ soft tissue 8099

Unknown/misc 7265

Cardioresp 5300

ENT 1674

Eye 1549

Neuro 1476

Uro/Vasc 883


Obs/gynae 842

Dental 725

Multi-system 671

Mental health 574

Non-trauma:

Own transport

Own Transport 31,021

Non-Trauma

Ambulance Transport

999 calls 28,541

Urgent GP amb 1,827

Medical/DMOP

Spec Wards

12,186 cases

Cardioresp 3750

Neuro 2704

Gastro 1352

Metabolic 497

Multisystem 375

Uro/Vasc 354

Unknwn/Misc 347

Adv reactions 145

4324 1912

23,817 7,833 20,970

9256 1964 2663 2993 9523

Trauma Obs

ward 1902

210

210

239

GP Calls 10,000

Diverted 2,000

5500 3500

Patient access

Streaming into

focused services

Co-production by

ED and acute

physicians

Consultant decision

prior to admission

Assessment

Cells

Single transfer

OutliersMedicalAcute Med UnitED

00:00

01:12

02:24

03:36

04:48

06:00

0 24 48 72 96 120 144 168

Real

Sim

New

Average Std Dev Lower CI Upper CI

Real 02:38 0.01 02:36 02:40

Sim 02:38 0.04 02:30 02:45

New 02:29 0.02 02:23 02:34

Wed Thurs Fri Sat SunTuesMon

0

100

200

300

400

500

600Real Sim NEW

Difference = 4000 fewer breaches

4 hr target

00:00

00:28

00:57

01:26

01:55

02:24

02:52

0 24 48 72 96 120 144 168

Real

Sim

NEW

Average Std Dev Max Min

Real 01:16 00:20 02:07 00:36

Sim 00:57 00:35 02:32 00:18

NEW 00:32 00:19

0

200

400

600

800

1000

1200

1400

ED/AC to Home Home from MAU/ ShortStay

Non-MedicalAdmission*

Specialty

Min

ute

s

Existing

Merged

Desynchronised

flow

• Mean ED LOS reduced by 12 min/patient

• Time to nurse assessment reduced by 9 min/patient

• Time to doctor assessment almost halved

• Doctor workload reduced by 6.6% - equivalent

of having 2.5 extra doctors

• Queues reduced: ED and AMU

• Transfers of care reduced by 16.8%

• Time to end destination reduced for all destinations –

more than halved for medical specialties

Interaction with Specialties▪ Constraints imposed by specialty bed supply

▪ Impact of reduced transfers

▪ Synchronised flow

Staffing▪ Optimised rotas

▪ Staff utilisation

▪ Job content

Bed capacity▪ How many?

▪ Where?

Actual bed provisionAverage Bed Use

Utilisation

Existing Integrated Existing Integrated Existing Integrated

4 hour assessment cells 40 55 17.2 21.8 43% 39.70%

Short stay beds 67 50 41.3 31.83 61.60% 63.60%

Specialty bed provision (excl Rehab)

742 758 699.3 713.7 94.20% 94.20%

Total inpatient beds (excl Rehab) 809 808 757.8 767.33*

Gives confidence improvement is possible Requires whole hospital change, not tinkering

with parts

Simplify and redirect flow

Staffing models will have to changes

Change staff rotas before making capital investments

Right size the capacity at each stage.

South Central Strategy Health Authority for project funding

James Newbold, data analyst for untiring efforts creating and testing the SIMUL8 models

Questions?

Please forward any topics you would like to

see covered to: [email protected]

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Transcript of Using Simulation to Manage Unscheduled Care