‘Transformed Health Through Data & Insights’ · •PATCAT Data: 1,500,000 (Approx.)...
Transcript of ‘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:
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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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Business Intelligence – Qlik Sense
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Business Intelligence – Qlik Sense
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Business Intelligence – Qlik Sense
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After Hours After HoursBusiness Hours
ED presentations by time of arrival
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Data Driven Quality Improvement:
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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 – Qlik Sense
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Business Intelligence – ICP
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LinkedEHR Dashboard:
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Business Intelligence – Qlik Sense
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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.