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CVD Management“The Treatment Gap”
Dr Allan Moffitt & Pauline Sanders-Telfer
(on behalf of Metro-Auckland Clinical Governance Forum)
15 November 2017
What we will cover
Metro-Auckland Clinical Indicators (CVD, Diabetes)• History & context • Agreed indicators • Why these – treatment gap!• Definition & data issues• Regional adherence reports• Performance – how are we doing?• Focus on improving management
– SLMF; LDT (Modified DIP); Diabetes SLA Team
• Enablers• Lessons Learnt
Outcomes & Quality Framework
• Started in 2014 - implemented 2015
• Borrowed on prior work (MHN, AH+, ProCare)
• Focus on reducing inequalities
• As chair of MACGF at the time
• Agreement in principle to indicator set
• Initial false start
• Took quite a while to implement
Closing the Equity Gap
Life Expectancy: What is contributing to the gaps in equity. CMDHB 2005 [Source: G. Jackson, CMDHB, Public Health Team, CMDHB 2005]• Smoking• CVD• Diabetes
0.5 0.6
1.6 1.9
2.10.5
1.5
0.3
0.7
0.7
3.9
1.5
-
2
4
6
8
10
12
Maaori Pacific
LE
ga
p (
ye
ars
)
Remainder
Diabetes
Cancer (non-lung)
Smoking-related
lung disease
Cardiovascular
disease
Infant mortality
CVDRA Clinical Performance – Dec. 2012 to Jun. 2014
0
25
50
75
100
125
Nu
m. o
f p
ract
ice
s
PerformanceDec-12 Jun-13 Dec-13 Jun-14
CVDRA where are we now?
The Treatment Gap
• Initial data suggested that we did reasonably well on anyone indicator
• But bundled together there was a much bigger gap from ideal
Secondary Prevention
• 40-50 % gap for Triple Therapy in secondary prevention
Primary Prevention
• 60-57 % gap for Dual Rx (BP lowering and Statin)
PHO variation:Triple therapy
Slide courtesy of Dr Andrew Kerr
Statin initial prescribing, dispensing and long-term maintenance after ACS admission (n = 1846) ANZACS-QI 11
Slide Courtesy of Dr Andrew Kerr
NRA Regional Adherence Reports
• Data from TestSafe – the DHBs health summary portal including laboratory results and dispensing data.
• Used Predict to i.d. Prior CVD ‘tick’ in Predict
• Encrypted so cannot use for improvement
• Data issues
• However useful way of presenting outliers and those to target for best impact!
CVD Primary Prevention medication -Individual practice variation : Funnel plots
70% is MACGF target.Cardiac Network target is increase by 5%.
Funnel plots – use dispensing data
Secondary prevention for known CVD (where ticked)
CVD Prevention medication - Individual Practice variation :Outlier practices
The Indicators
• Diabetes
– Glycaemic Control (HbA1c <64 mmol/mol)
– ACE inhibitor for those with Microalbuminuria
– Blood pressure control (BP <140 mmHg systolic)
• Cardiovascular Disease
– Secondary Prevention – Triple Rx for known CVD
– Primary Prevention – Dual Rx (BP & Statin) for those with 5yr CVD Risk >20%
Secondary Prevention - MACGF
2* Prevention Ethnic Variation
Individual Practice variation 2*
Primary Prevention & Dual Rx
Ethnic Variation 1* Prevention
Data issues
• Disease Coding cf. Predict
• Prescribing cf. Dispensing
• Upper Age limits
• Laboratory coding changes e.g. HbA1c
• Took a long time to get standardisation to compare ‘like with like’
• Now regular quarterly reporting – collated by A-WDHB Planning, Funding & Outcomes team.
Nursing Management
Nurse Led Care & Nurse Led Clinics
Supportive team environment
Virtual Clinics
CVD/DM courses
Structured process
Understanding and analysing data
Current DHB Funding Enablers
Diabetes ManagementPlanned Proactive Care Model
Diabetes Care Improvement Packages• Outcomes based and volumes based
CVD ManagementPlanned Proactive Care Model
Volumes based
Future DHB Funding Enablers
CVD Management
Focus on Management vs Screening alone
Equity focus
SLM ProgrammeDM Management
Volumes and outcomes based
SLM Programme
PHC Enablers
CVD & DM Management• Transparency across the system
• Data availability and analysis
• Quality Improvement – Safety in Practice CVD Bundle
• Models of Care
• SMO and NS availability
• Technology
Learnings
• Keeping the patient/family in the centre• No blame/shame environment• Transparency across the system• Data Sharing agreement• Data definitions• IT capability – PHO / DHB• Consistent PMS Coding• Data analysis at practice level
What does it mean? What difference can I make?
• Collaborative environment
Learnings
“Let not the perfect, be the enemy of the good”
