‘transformed health through data & insights’ · •patcat data: 1,500,000 (approx.)...

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‘Transformed Health Through Data & Insights’Western Sydney HIU

Shahana FerdousiManager, Western Sydney HIUDec 07, 2017

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Presentation Overview:

1.Demonstrate state of development and readiness of HIU

2.Demonstrate utility of this work in informing evidence based decision making

3.Current Governance and data and security arrangements

4.Discussion and input regarding governance and data utilisation

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•Ensure everything we do has a positive impact and provides value for money

•Understand and meet population health requirements

•Provide the tools for primary care professionals to do their jobs

•Develop cross-system networks, tools and services to share intelligence, expertise and experience

•Support openness and innovation

•Work in collaboration efficiently

Key Commitments:

PHN Performance Framework

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How we can do that:

By creating a common shared platform

By creating Health Intelligence Unit

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Health Intelligence Unit:

Why do we exist? The purpose of data sharing

To capture, translate and share data with all internal and external system partners with a consistent view to support, inform, evaluate and improve health and wellbeing of Western Sydney population.

• Foster data driven quality improvement• Adopt and implement NPHF and PHNPF co-design and establish reporting

system• Support evidence based practice• Eliminate existing siloes or fragmented views of health data• Support joined needs assessment to avoid duplication and replication

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Health intelligence Unit:

Strategic Intent:

• Partnership• Analysing, interpreting and

contextualising data to understand population health and its determinants, treatment, outcomes and trends of Western Sydney

• “Go to’ source for linked dataset, primary care and LHD data, all AIHW data at all level

• Evidence and knowledge translation for best practice and benchmarking by reliable validated, nationally consistent, accessible data

• Strengthening workforce capacity

• Research• Information advocacy

Vision:

• Integrated and coordinated health care aligned with the Q Aim

• Data enabling a single system view to support resource allocation across clinicians, primary and acute care

• Highly informed general practitioners able to continually monitor progress through timely access to data and insights

• Greater transparency on quality and quantity of care and informing continuous improvement in service delivery

• The Western Sydney population has improved health and wellbeing through better risk factor prediction and management of population level programs and interventions

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Health Intelligence Unit:

State of Maturity: How?

By creating hub and satellite model in partnership with WSLHD that will collaborate, collect and store data in a secure data repository, and analyse and deliver reports and evidence based information to support the system partners. HIU will be equipped with

Workforce-Skilled and experienced workforce with a broad range of domain and technical expertise

Facts - Population Health Status of Western Sydney, benchmarking national, regional and other PHN data sets

Analysis - Health Intelligence output and publications Care Coordination- Building relationships across the health care

system, including specialty care, LHNs, hospitals, community services and supports

Resources - Quick link to journals and other materials Evidence - Find and use evidence effectively Research - PEER Research and support

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Health Intelligence Unit:What’s plan? What do we do?

Internal External

Essential

PHN Reporting-NA, Activity Plan, 6&12 months reporting

WSDMI- Monitoring & Surveillance, Research, Case Conferencing

BI Tool- all dashboards

Data Linkage with MoH

Surveys including HappyOrNot

Clinical Research Group-SCHN

AD hoc data request IC Demonstrator-QAim Dashboard

In Progress

M&E for commissioning PCMH Evaluation

NMHSPF, PHNPF Suicide Prevention ModelIC Demonstrator- ROI

QPIP

Nice to have

Joined Needs Assessment

Local level linked dataset for IC and HCH enrolled patients

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What Data We Hold?

• PATCAT Data: 1,500,000 (Approx.) deidentified patient records

every month from 140 general practices

• LinkedEHR Data: 1295 (Oct, 2017) care plan records & patient

metrics from 46 practices

• CRM-ChilliDB Data: Practice Profile of 352 general practices

including practice details, accreditation status, training/capacitybuilding status, contact details of 1201 GPs, 442 Nurses and 150 AHPsworking in Western Sydney PHN area

• Linked Data: 272,202 primary and acute care linked data from 16

general practices with a potential of depth and reach expansion of theproject

• Penelope Data: 15,000 ATAPS & PIR records (identified) of mental

health patients

• HealthPathways Data: 324 live Health Pathways

• Folio : Contains data for 159 commissioned providers

• Tenderlink: 159 organisation registered for commissioning

purposes

• BI 360 & NAV: All finance data for WentWest

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PHN priority areas: What We Are Doing?

PHNs have a mandate to focuson six priority areas, whichinclude: Population health Aboriginal health Mental health Aged care (Chronic diseases) (Child Health) eHealth Health workforce

Commissioning

Embedded

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• QAim Data Dashboard for General Practices

• Linked Data Dashboard• Integrated Care Dashboard• Case Conferencing

Dashboard• Population Health Needs

Assessment• Mental Health Needs

Assessment• Population Health Atlas• Mental Health Atlas• WentWest KPIs & M&E tool• Evaluation of different

programs

What Do We Measure and How?

Examples:

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Potential data sources and methods: What data we need?

Acute Care

Emergency Department Data Collection (EPDC)

Admitted Patient Data Collection (APDC)

Non Admitted Patient Data (NAP)Subacute Non Admitted Patient Data (SNAP)

Private Hospital Data

Secure Analytics for Population Health Research & Intelligence (SAPHaRI)

Hospital Based GP After Hours Clinic Data

Primary Care

Allied Health Practice

General Practice Data

Medicines for Secondary Prevention

Outpatient Clinic

Specialist Medical Practice

Home and Community Care Services

Indigenous Health

Outcomes

Death

Recurrent Hospital Admission

Linked Dataset

Recurrent ED Presentation

PHN

MBS

PBS

APDC

EPDC

NAP

SNAP The National Death Index, NSW Health, Others

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HIU- Other available resources: Technical capability analysing and reporting Unique Valuable Data Assets BI Platform and BI Tool Linked dataset from NSW GP Data Linkage Project Dedicated skilled workforce

HIU- Further Needs: Data Governance Council, Advisory Committee &

Working Committee, Data Stewards, Developers Regional Data Sharing Agreement Data Governance model TOR for each group Identify data needs, analytic models, indicators and

publications Workforce capacity building Technical support for sharing information

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A possible governance model

WentWest/WSPHN Data

Governance Committee

Data Governance and Security

Advisory Council

Joint WSPHN and WSLHD

Steering Committee

WSLHDData for DecisionMaking

Taskforce

Membership of keySystem players and Authorities:

• NSW Health• PHNs in data

linkage work• LHD• RACGP• AMA• Etc ..discussion

Bolster membershipand outline here

List proposedcross membershipfrom WSLHD and WSPHN

List groups fromLHD currently involved

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Data Governance

Data Governance Committee (DGC)

Data is recognised

Committee formation

Vision

TOR

Alignment Liaison & Recognised by

SMT

Roles & Responsibilities

Individual system owner

DRC

Defined Stewardship

Corporate Data Model

Defined Ownership

Policies and Processes

Framework

Policies

Processes

Agreed workflow

Regular Review

Program

Data issues are raised and considered

Cover Local Initiative

Organisational awareness on DG

& DM

2nd Iteration-Refine Process

Continual improvement

Reporting and Quality Assurance

Performance Measures quality

reporting

Data architecture & Data Life cycle

Master Data Repository

DQA

Work as an exception

reporting basis

Health intelligence Unit: Proposed Data Governance maturity model

Initial

Repeatable

Defined

Managed

Optimised

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HIU Data Governance:

0

1

2

3

4

5

Data Gov Reporting& Quality Assurance

Data Owenship andStewardship Roles

and Resp

Framework Policies& Processes

Data GovernanceProgram

Data GovernanceCommittee

Vision DG Maturity Target DG Maturity End Dec 2017

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Benefits/Advantage of Data Sharing (Achievements):

Quality improvement Remove siloes of health data Reduce duplication; encourage co funding, co design, joined

needs assessment, ROI Contribution to the process of integrating care Risk Stratification Improve understanding of the patient journey and outcome Improved understanding of predictors of health deterioration

through a more granular set of predictors from both primary and acute care

Save time, cost and other resources Value proposition to all system partners in Western Sydney

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Thank You

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Examples of data

tools and visualisation

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Patient Experience of Care- QAim 3 Safe &

effective care Timely &

equitable access

Patient & family needs met

Quality & Population Health-QAim 1 Improved health

outcomes Reduced disease

burden Improvement in

individual, behavioural and physical health

Sustainable Cost- QAim 2 Efficiency &

effectiveness Increased

resourcing to primary care

Return on innovation cost invested

Improved Provider Satisfaction-QAim4 Joy satisfaction &

meaning of work Increased

clinician & stuff satisfaction

Evidence of leadership and team based care

Quality improvement culture

Quadruple Aim: what do we

measure?

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Business Intelligence – Qlik Sense

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24


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After Hours After HoursBusiness Hours

ED presentations by time of arrival

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Data Driven Quality Improvement:

53

57

55

82

68

51

85

87

93

93

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0 20 40 60 80 100

Clinical indicators or…

Routine surveys of…

Medication Reviews,…

GP Management Plans…

MBS Items, PIPs, SIPs

Practice demographics…

Overall, only half of GPs

are regularly reviewing important data

2016 2017

50

58

44

60

DataCleansing

Data Usage DataDashboard

PracticeFinancialModelling

Data utilisation continues

to be an area for improvement.

Percent

Q: Which of the following practice performance and patient care measures does your practice routinely review?

Q: Which of the following data driven improvement activities would you or your practice team benefit from assistance with?

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Business Intelligence – ICP

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LinkedEHR Dashboard:

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Suicide Prevention Model

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Happy or Not

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Maturity: Data Governance Committee

Level 1Initial

Level 2Repeatable

Level 3Defined

Level 4Managed

Level 5Optimised

Individual program areas reacting to data issues when they are raised. No proactive data Planning.

An informal group of data champions or data subject matter experts without budget or central function advising functional areas and projects. Need for Data Governance recognised and pushed by 1 or 2 visionaries.

A vision for Data Governance is defined but not fully bought into across the organisation data.

Executive level sponsorship established and full terms of reference for a Data Governance Committee established. Accountabilities for all aspects of data are defined and workflows established.

Data Governance fullyrecognised by Senior Management Team with regular meetings and decisions communicated by Data Governance Committee.

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Maturity: Data Ownership and Stewardship Roles and ResponsibilitiesLevel 1

InitialLevel 2Repeatable

Level 3Defined

Level 4Managed

Level 5Optimised

No clear ownership has been assigned. Individual system owner and or technicians or analysts assumed to be responsible.

Data champions or super users with passion for data emerge in business functions. Limited collaboration for shared data, common data policies and responsibilities.

Data ownership and stewardship is defined and loosely applied to a Master data subject area. Responsibilities for data has now become part of the role.

Corporate data model developed, data subject areas defined. Major data subjects have data owners/stewards appointed with major responsibilities.

All data subject areas have data owners. The majority of data subject areas are actively stewarding in accordance with policies and standards.

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Maturity: Principles, Policies & Standards

Level 1Initial

Level 2Repeatable

Level 3Defined

Level 4Managed

Level 5Optimised

No published framework, policies and standards specially covering relevant component data subjects.

A limited number of formal policies emerge. Limited traction in turning policies/ framework into action.

Framework, policies and processes for most data subjects agreed and published. Processes adopted and being rolled out.

Processes put in place to assure frameworkand policies are being adopted and achieved. Dispensations and issues resolved via agreed workflow involving data owners.

Data Governance framework, Policies and Processes are regularly reviewed and approved by the Data Governance Committee. Changes readily adopted in operations and projects.

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Maturity: Data Governance Program

Level 1Initial

Level 2Repeatable

Level 3Defined

Level 4Managed

Level 5Optimised

Data issues are raised and considered as part of requirements for projects. Shared data subject areas not considered. No cross business area mandate for data.

Individual data projects within business areas cover local initiatives. Interaction regarding shared data and ownership is primarily within one business unit. Limited interaction outside of business unit.

Data Governance and data management strategy across the organisation developed and communicated. Formal program is kicked off to establish DG Processes.

Major components of DG now covered. 2nd

iteration to refine processes and management taking place. Constant communication regarding DG continues.

DG program completed with continuous improvement of Governance component through review and refine cycle. Regular communication and updated training is ongoing.

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Maturity: Data Governance Reporting & Assurance

Level 1Initial

Level 2Repeatable

Level 3Defined

Level 4Managed

Level 5Optimised

Limited, ad hoc and varied level of Data Governance and quality reporting. Where it exists, is aligned to local initiatives of functional areas and processes.

Standards being definedand enacted for projects related to Data Governance. Quality, operational reporting of data issues and architecture.

A shared widely accessiblerepository exists for data related documents and data models. Detail requirements for data quality measures and metrics are developed.

Models, data related documents and data quality measures are regularly reviewed and approved. Processes put in place to deliver assurance and to audit documentation.

Data Governance Committee is now working on an exception reporting basis. Few assurance and audit issues are apparent but where they exist are resolved quickly.

‘transformed health through data & insights’ · •patcat data: 1,500,000 (approx.)...

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