Case Study: Acute PREDICT - Auckland · Case Study: Acute PREDICT Cardiovascular Prevention Program...
Transcript of Case Study: Acute PREDICT - Auckland · Case Study: Acute PREDICT Cardiovascular Prevention Program...
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Case Study: Acute PREDICT
Cardiovascular Prevention Program andAcute Coronary Syndrome database
Andrew Kerr and Andrew McLachlan, Cardiology Dept
Middlemore Hospital
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Themes
• Motivation
• Team approach
• Willingness to learn and adapt
• Data collection as part of the clinicalwork-flow ideally to drive decision support
• Electronic data collection, decision supportand reporting
• Program development driven by results anddata
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The Treatment Gap
Discrepancy between ideal
CVD risk management and
what happens in real life
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Counties Manukau
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55%
40%
62%
36%
50%
22%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Perc
enta
ge a
chie
ved
Prescribed Aspirin Prescribed Statin Systolic BP< 130
Management Indicators in High Risk Patients
CVD or Equivalent Risk > 15%
Sinclair and Kerr, NZMJ 2006
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CCU audit highlights an “issue”
• Increasing CCU staff turnover/less experienced nurses
• Shorter but more intensive inpatient stays
• Poor uptake of cardiacrehabilitation
• Primary care input on dischargeunknown/variable.
100%
52%4%
0%
0%
20%
40%
60%
80%
100%
Documented lifestyle interventions
Smoking
Cardiac Rehab
Diet
Activity
Weight loss
BP/lipid lowering
78%
64%
24%
0% 0%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Assessment documented
Smoking addressed
Weight
Height
BMI
Waist circumference
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The “golden” 24 hours
• Robinson (2001) argues that healthfunding for health promotion is targetedat primary health care and nurses in acutecare have slowly developed a belief thathealth education is no longer their role.
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Acute Predict Project
• To implement an electronic clinical decision supportsystem (Acute Predict™) to more systematically managepeoples cardiovascular disease (CVD) risk factors inacute care.
• Run by nursing and junior medical staff
• To collect and analyse data to help strategically plan aconsidered response.
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The PREDICT CVD ECDS Program
• ECDS vision shared by multiple stakeholders» ProCare
» CMDHB/WDHB
» University of Auckland
» Enigma
» Ministry of Health
» National Heart Foundation
» National Cardiovascular Advisory Group
» New Zealand Guidelines Group
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Predict Electronic Program
• Use Information Technology to provide on-line CVD riskassessment and management advice which is:
– Fast
– User Friendly
– Guideline and evidence based
– Patient Individualised
• Using the PREDICT tool integrated with PMS– PROMPT
– CMDHB CCM
– Acute Predict– CVD/DM Predict
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The Acute PREDICT implementation process
A Plan, Do, Study, Act quality improvement cycle.
2004
2007
Frequent training
sessions including
one on one support
to develop
“CHAMPIONS”
Monthly “user” group
identify issues and
develop action plans
Monthly audit
and feedback
results to CCU
team
Celebrate
Success’s
Regular marketing to
keep Predict in
peoples thoughts
Ongoing development of software
by Enigma publishing Ltd acting
on user feedback
Patient resources and
information
Nurse surveys and
act on feedback
0%
10%
20%
30%
40%
50%
60%
70%
80%
Feb
May
July
Aug
Sept Oc
tNov
Dec
Jan FebMarch
2005-2006
month
% of total
CCU/SDU
population
screened
The things you can do to reduce your risk of heart or stroke problems in the future include:
Stay smokefree
Keep as active as you can , on as many days a week as you are able.
Try and keep your weight stable or lose weight if you are too heavy.
Follow a healthy eating plan
Take the medications you have been prescribed regularly.
Talk to someone if you are stressed or feeling low
Your doctor and nurses are happy to help you
keep as well as you can.
Who carries responsibility for managing your information?
The Acute CVD PREDICT Administrator, who can be contacted C/O
CCU, Middlemore Hospital, Pr ivate Bag 94052, South Auckland Mail Centre, if you have any c
Together we can make a difference.
Cardiovascular disease (CVD) includ ing heart attacks and strokes and diabetes are major problem in New
Zealand today. The good news is that there are many things that both you and your doctors and nurses can do to reduce your risk of heart attack or stroke or the
complications of diabetes.
Goal 1 - Increase use
of PREDICTGoal 2- Improve workflow
process
Goal 3- Close loop-
Get PREDICT info to GP
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ACS data base within AcutePredict
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KPIs• Finalised patients on Statin
• Finalised patients on Aspirin
• Finalised IHD/CABG/PTCA patients on Beta
• Finalised patients on ACE and/or ATII blocker
• Current smokers who got smoking cessation advice
• Door to balloon < 90 min for primary PCI STEMI
• Door to needle < 30 min for thrombolysed STEMI
• Revascularisation (PCI or CABG referral)
• Aspirin< 24h
• ACE and/or ATII blocker in patients with EF <40%at D/C
• Beta-blocker in patients with EF <40% at D/C
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Cardiac out patients
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Innovations increasingscreening
COP monthly PREDICT screenings
0
10
20
30
40
50
60
70
80
12/06. 1/07. 2/07. 3/07. 4/07. 5/07. 6/07. 7/07.
DM
non DM
CVD risk
questionnaire
introduced
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Gout clinics
Patients with >15% CVD risk on management
n = 42
0%
20%
40%
60%
80%
100%
Aspirin Statin BP therapy
non DM n=26
DM n=16
Over 50% >15%/High risk
Significant treatment gaps
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Incidence of ACS admission to CCU perhead of CMDHB population
0
2
4
6
8
10
12
35-44 y 45- 54 y 55 - 64 y
NZ Euro/Other NZ Maori Pacific All Asian
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Modifiable risk: Smoking
0
10
20
30
40
50
60
70
15-44 45-54 55-64 65-74
Sm
oki
ng
(%
)
Other
Maori
Pacific
Indian
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Modifiable risk: BMI
20
22
24
26
28
30
32
34
36
38
40
15-44 45-54 55-64 65-74
Me
an
BM
I
Other
Maori
Pacific
Indian
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Modifiable risk: Diabetes
0
10
20
30
40
50
60
15-44 45-54 55-64 65-74
Dia
be
tes
(%
)
Other
Maori
Pacific
Indian
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Predict database
NHI + Predict data
Encrypted NHI
+ baseline data
NHI & eNHI
NZHIS
eNHI is linked to outcomes data
(hospital admissions, deaths)
eNHI
+ outcomes data
University of Auckland
Combines Predict data
with NZHIS outcomes
GP practice
RA and
management advice
Linkage of risk to outcomesapproved by Multicentre Ethics Committee
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Results: events in risk groups in first30,878 patients
Risk <10%
68%
Risk 10-<15%
11%
Risk 15+%
9%
Hx CVD
12%
47%
27%26%
74% of events occur in 32% of the people, 26% in low-risk people
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The Framingham score & NZ Guidelineadjustments: useful for CVD risk
prediction in NZ?
J B Broad, R J Marshall, S Wells,
A J Kerr, T Riddell, R Jackson
on behalf of HRC-Predict Co-Investigators
Work in progress: Preliminary results
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Results: est. 5-year incidence
For prior CVD 5-year risk is: 20 + 1.3*Framingham score
Mean est. 5-year incidence for Hx CVD is 28.4% (95%CI 26.3 to 30.4)
0
10
20
30
40
50
60
<5 5-<10 10-<15 15-<20 20+
Framingham score (5-year risk, %)
Cu
mu
lati
ve
in
cid
en
ce
(%
)
of
CV
D e
ve
nt
in 5
ye
ars
(9
5%
CI)
for Hx CVD
no Hx CVD
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Gotta keep moving…
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Where to next?
• Support CVD risk screening in other areas of thehospital
• Support development of nursing workforce toassess and manage CVD risk as part of anadvanced role
• Next audit loop – use data reports/exceptionreporting to identify problem areas and designprograms to address these
• Move towards electronic integration of PREDICTwith primary care
• Develop HF/AF eCDS
• ? Electronic care planning
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Acknowledgements• School of Population Health – particularly Rod Jackson,
Sue Wells, Tania Riddell, Jo Broad
• Many many GPs, practice nurses and clinicalmedical/nursing specialists
• Primary Healthcare organisations –esp ProCare,HealthWest,
• Enigma Publishing
• New Zealand Guidelines Group
• National Cardiovascular Advisory Group
• Maori Cardiovascular Group
• Ministry of Health Clinical Services Directorate
• National Heart Foundation
• Diabetes NZ
• Counties Manukau District Health Board esp ChronicCare Management programme, Middlemore HospitalCardiology and Diabetes Services
• Medtech Global Ltd
• Health Research Council