^the treatment gap - primary healthprimaryhealth.org.nz/3ctreatmentgap.pdfcvd management ^the...

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

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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”

Finally……….

Thank you so much for your help!PaulineS@alliancehealth.org.nz

AllanM@procare.co.nz