Slides from National eHealth Summit, 30 Sept 2015 at Carton House, Kildare: Professor Gary Courtney, Lead, National Acute Medicine Programme (NAMP). #eHealthSummit15 http://www.ehealthsummit.ie http://mhealthinsight.com/2015/09/25/mhealth-insights-from-the-ehealth-summit/

4. Or to put it another way
‘Our Health Service is
Unfair, unaffordable and unsustainable…’
Dr James Reilly, T.D. Minister of Health
2014

5. Opportunities
New Minister
New Secretary General
New HSE Directorate Structure (5)
New National Clinical Leads (5)
New Presidents RCPI and RCSI
New Hospital Groups
Money Follows The Patient/ABF
NQAIS Medicine
Mathematical Flow Modelling
New GP Contract
Economic upswing

6. Leo the Pirate

7. Change (Reform)
“All change is difficult,
even from worse to better!
Richard Hooker
Want to make enemies?
Try changing something!
Pres. Woodrow Wilson

9. National Acute Medicine Programme
Described new model of care with 4
intervention areas
1). Ambulatory Care (AMAU, 1/7)
2). Short Stay Units (< 2/7)
3). Inpatient wards (3-14/7)
4). Complex discharges (> 14/7)

10. Why AMAUs work
• Multidisciplinary Team working
• Clearly defined roles/responsibilities
• Mindsets of staff (can do attitude)
• Investigate to discharge, NOT admit to investigate
• Ability to change ‘tempo’ in response to unit demands
• Coordinated/organized
• Older patient focused
• Systems set up for Safety
• & Quality
• & Speed
• Reduce LOS
• Improve PET

11. Medical AvLOS for 2009, 2010, 2011, 2012, 2013
7
7.2
7.4
7.6
7.8
8
8.2
8.4
8.6
8.8
9
Jan
10
Jan
11
Jan
12
Jan-
13
Time Period
AvLOS(days)
Data Source: HIPE, ESRI
© Acute Medicine Programme HSE Ireland

20. ‘every system is perfectly
designed to get the results it
gets’
0
2
4
6
8
10
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
%ofattendees
Arrivals
Departures

21. Hospital C : % of Inpatient Discharges by Length of Stay
Category for Acute Medicine
Combined Emergency and Elective Admissions
0 Days
12%
1-2 Days
24%
3-5 Days
22%
6-9 Days
17%
10-14 Days
10%
>14 Days
15%
Hospital C : % of BDU by Length of Stay Category for Acute
Medicine Combined Emergency and Elective Admissions
1-2 Days
4%
3-5 Days
11%
6-9 Days
16%
10-14 Days
14%
>14 Days
54%
0 Days
1%

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Background
•DIT /3S Flow Study for new Mater ED Building
•AMP commissioned project with DIT/3S in 2013
•Originally project designed to;
Include 6 hospitals, data analysis, build flow models (ED to hospital
discharge), scenario modelling, optimisation modelling
•Project had to be confined to Tallaght Hospital (ED/AMAU/MSSU
not other wards), up to the scenario modelling point of development

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System Development
•Data from SYMPHONY system
•Interviews with staff
•Build flow model i.e. where patients go/timing, staff utilisation,
capacity utilisation etc.
•Validation of model
•Scenario simulation

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Data Phase
Modelling Phase
Sensitivity Analysis
Distribution of patients in ED & AMAU by clinical group
2%
2%
41%
1%2%
12%
5%
3%
2%
2%
10%
12%
4%
Patients in ED
96%
Patients in AMAU

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Data Phase
Modelling Phase
Sensitivity Analysis
Medical Patients Discharge Destinations from ED & AMAU
37%
54%
8%
0%
ED
Ward Home Other Die
32%
49%
18%
AMAU
Ward Home SSU

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Data Phase
Modelling Phase
Sensitivity Analysis
Distribution of triage groups for medical patients

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Process Times of medical patients in ED & AMAU
0 50 100 150 200 250 300
From Reg. To Triage
From Triage to Dept.
From ED to AMAU
From entrance to be seen by
ED/AMAU Clinician
To be seen by a speciality doctor
Bed Requested
Time to leave dept
Minutes
Medical Patients in AMAU Medical Patients in ED
1.5 hours more for
medical pts in ED
than pts in AMAU
3 hours more for
medical pts in ED
than pts in AMAU
2.5 hours more for medical
pts to be transferred to
AMAU from ED
Data Phase
Modelling Phase
Sensitivity Analysis

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PET Breakdown in ED
0
200
400
600
800
1000
1200
Hours
PET breakdown for patients in ED Medical Patients
All Patients
Data Phase
Modelling Phase
Sensitivity Analysis
0
20
40
60
80
100
120
140
160
180
Hours
PET breakdown for medical patients in AMAU
≈ 5hrs
≈ 10 hrs
≈ 8hrs

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Number of patients in ED/age group
0
500
1000
1500
2000
2500
<20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 75-80 80-90 90-100
Age
Age Distribution of patients in ED
Medical Patients
All Patients
Data Phase
Modelling Phase
Sensitivity Analysis
0
20
40
60
80
100
120
140
160
180
<20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 75-80 80-90 90-100
Age
Age Distribution of medical patients in AMAU

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Patient Flow Analysis
Patients Arrival
(Ambulance)
Patients Arrival
(Walk-In)
ED
23% 77%
CDU AMAU
14-16 patients
Die
AMAU Review
Clinic
SSU
18%
Wards
Other Hospital Convalescense Nursing Home Home
54%
30% of discharged
patients go to
review clinic
70%
2%2%6%
2%
32%
Dayward
OPD
Waiting List
4 patients
3 patients
7 patients
37%
50%
Die
Home Home
Average of 121 patients served daily in EDAverage of 121 patients served daily in ED
Data Phase
Modelling Phase
Sensitivity Analysis
17%

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Data Phase
Modelling Phase
Sensitivity Analysis
Simulation Model

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Data Phase
Modelling Phase
Sensitivity Analysis
Scenario Design - KPIs
PET KPIs
1) PET of patients in AMAU
2) PET of medical patients in ED
3) PET of non-medical patients in ED
Productivity KPIs
1) Percentage of medical patients routed to the AMAU

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Sensitivity Analysis
Patients who should be routed to the AMAU have the
following characteristics:
1) Presented to the ED from 9:00-18:00
2) Medical Patients
3) Triaged as category 2 or 3
4) They walked in to the ED
63% of the above patients are properly “allocated” to
AMAU pathway
37% of those patients gets “misallocated” and go to the ED
path instead
Data Phase
Modelling Phase
Sensitivity Analysis

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Scenarios
Using the following variables:
1. With “misallocation”/without “misallocation”
2. Number of Consultants (+1, +2)
3. Opening Hours (12 hrs, 18 hrs, and 24 hrs)
4. SSU Capacity (12 beds, 18 beds, and 24 beds)
5. Combined Scenario
• 18 beds in AMAU
• 24 beds in SSU
• Opening 18 hrs/weekday
• Add 2 consultants
Data Phase
Modelling Phase
Sensitivity Analysis

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Scenarios
Using the following variables:
1. With “misallocation”/without “misallocation”
2. Number of Consultants (+1, +2)
3. Opening Hours (12 hrs, 18 hrs, and 24 hrs)
4. SSU Capacity (12 beds, 18 beds, and 24 beds)
5. Combined Scenario
• 18 beds in AMAU
• 24 beds in SSU
• Opening 18 hrs/weekday
• Add 2 consultants
Data Phase
Modelling Phase
Sensitivity Analysis

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Data Phase
Modelling Phase
Sensitivity Analysis
Increase No. of Consultants
Mis-Allocation
With Mis-allocation Without-Misallocation
With With With Without Without Without
No. Consultants 0 +1 +2 0 +1 +2
KPIs %
change
%
change
%
change
%
change
%
change
PET PET (All-Non) 7.53 7.48 -1% 7.51 0% 7.09 -6% 6.55 -13% 6.48 -14%
PET (All-MED) 9.78 9.51 -3% 9.51 -3% 9.17 -6% 8.24 -16% 8.27 -15%
PET (All-AMAU) 4.75 3.16 -34% 3.04 -36% 5.48 15% 3.73 -21% 3.51 -26%
Productivity
% Med in AMAU 17% 18% 9% 18% 9% 20% 19% 26% 57% 27% 63%

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Data Phase
Modelling Phase
Sensitivity Analysis
Change AMAU Opening Hours
Factors
Mis-Allocation With Misallocation Without Misallocation
Opening hrs 9-21 9-24 0-24 9-21 9-24 0-24
KPIs % change % change % change % change % change
PET
PET (All-Non) 7.53 6.99 -7% 5.88 -22% 7.09 -6% 6.87 -9% 5.39 -28%
PET (All-MED) 9.78 8.91 -9% 7.26 -26% 9.17 -6% 8.68 -11% 6.78 -31%
PET (All-AMAU) 4.75 5.05 6% 5.34 12% 5.48 15% 5.82 22% 6.16 30%
Productivity % Med in AMAU 17% 22% 33% 33% 100% 20% 19% 25% 51% 38% 126%

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Data Phase
Modelling Phase
Sensitivity Analysis
Better Combinations Scenario
Factors
Mis-Allocation Base Scenario with without
AMAU Capacity 11 18 18
SSU Capacity 12 24 24
Opening hrs 9-9 9-12 9-12
Consultants 0 +2 +2
KPIs % change % change
PET
PET (All-Non) 7.53 6.88 -9% 5.53 -27%
PET (All-MED) 9.78 8.49 -13% 6.89 -30%
PET (All-AMAU) 4.75 3.09 -35% 3.95 -17%
Productivity % Med in AMAU 17% 24% 45% 37% 123%

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Recommendations
•Use system in hospital redesign
•Extend model development to optimisation stage
•Extend model to include inpatient wards
•Analyse data in other hospitals and customise model for
other hospitals
•Joint hospital group project ?

41. Conclusion - we need to:
Re-engineer healthcare systems based on
cooperation, interdisciplinary working and
mathematical flow modelling
Achieve highest quality of safe, efficient care with
lower mortality and length of stay with improved
patient outcomes and increased staff satisfaction
Develop Acute Physicians who can manage
complex, co-morbid illnesses, leading
multidisciplinary teams and who embrace
performance improvement skills as core
competencies

42. That €10K could come in handy...