Using ABF information to
assess the viability of a new
service
Stephen Cole
Director, ABF Financial Reporting
Gold Coast Health
Objectives of ABF Analysis
 Challenge of a new service:
 Are we providing quality care
 Are we getting value for money
 ABF can assist by demonstrating
 Efficiency of a new service
 Existing tools can reinforce this by providing
 Statistical evidence
 Provide KPIs
 To support introduction of other new services
Why a Medical Assessment Unit
 Meet the National NEAT targets
 Address increasing number ED presentations
 Decrease incidence of delayed ambulance off load
 Earlier Medical Consultation
 To provide cost effective care
 Prevention of unplanned complex discharges
 Reduce length of stay, readmissions, mortality and
morbidity rates
 Improve weekend discharge rate
Helen Cooper DDON Medicine HRT Innovation Awards 2012
Key Changes Implemented
 The MAU is a 28 bed unit with 10 hard wired cardiac monitors and 18
telemetry units.
 Length of stay (LOS) is a maximum of 48hrs, average MAU LOS is approx 19
hours
 The MAU has seen 9020 admissions in just over twelve months. An average
of 24 admissions a day
 Clinical Nurse Consultant led acceptance of referrals from ED
 On duty dedicated General Physician, two consultant ward rounds daily
 Seven day a week Allied Health team
 24/7 administration support
 Dedicated support service staff
 Early discharge supported by daily medical outpatient clinics
 Priority for medical imaging, pathology and clinical measurements
Helen Cooper DDON Medicine HRT Innovation Awards 2012
Key Changes Implemented
Helen Cooper DDON Medicine HRT Innovation Awards 2012
The Problem
 UK model
 Not comparable to any similar MAU model in Queensland
 Expensive
 Approximately twice the FTEs per bed of conventional Acute
Medical Unit
 Pervasive
 It impacted on a number of areas within the hospital
Challenges for analysis
 Difficult to isolate the impact
 Cannot be isolated from other medical wards
 Look at performance prior and post implementation
 Requires consistency in:
• Costing processes
• ABF weights
 Limited timeframe for analysis
 Precluded detailed analysis of ABF data
• Multivariate analysis.
Tools and Analysis
 Tools
 Transition II (SDSM) costing information
 Microsoft Excel 2003/2010
 Analysis
 Standard descriptive stats
 Comparison costs
 Statistical Analysis
• T-Test
How was it performing
 Length of stay changes
 Cost per bed day
 How many WAUs per patient bedday
 Cost per WAU
 Cost of transferred patients
Length of stay changes
Implementation of MAU
Implementation of MAU
Implementation of MAU
Cost per bed day
Implementation of MAU
Cost per exact bed day
Implementation of MAU
How many WAUs per patient bed day
Implementation of MAU
Cost per WAU
Implementation of MAU
Cost per WAU
 2 sample t-Test
(assuming unequal variances)
 Medical admissions Robina Hospital 2012 only
before
Mean
Variance
Observations
Hypothesized Mean Difference
$4,776.802958
14530024.19
1082
0
df
t Stat
1404
6.89649054071710
P(T<=t) one-tail
0.00000000000402
t Critical one-tail
1.64593965343783
P(T<=t) two-tail
0.00000000000804
t Critical two-tail
1.96165506919397
after
$3,923.009
13040258
6871
Cost of transferred patients
Lessons
 Use exact LOS if available
 Use cost per WAU
 Don’t straddle years/major changes if possible
 Use the tools you have available
Outcomes
 MAU is being trialled at Gold Coast Hospital Southport
 In preparation for move to Gold Coast University Hospital
 Robina Hospital is consistently meeting NEAT targets
Stephen Cole
Director, ABF Financial Reporting
Gold Coast Health
stephen_cole@health.qld.gov.au
Questions