Big data in primary care

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“Big data” in primary care

How should it be governed?

What should it be governed for?

School of Public Health & Community Medicine CPHCE

Who are we?

• Consumer

• Clinical medical

• Clinical nurse

• Clinical allied health

• Managerial

• Policy

• Decision maker

• Others

Why are we workshopping?

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Recognise that GP/PC EHR data will be used

beyond the clinician-patient relationship.

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This secondary use has implications for

clinicians and managers as data creators.

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Data quality management, provenance and

governance processes and structures must

be transparent and acceptable to consumers,

GPs and their representative organisations.

Program

When? What? Who?

2.15 – 2.20pm Introductions & Setting the scene Teng Liaw

2.20 – 2.40pm Current initiatives & governance models

• Established data collection perspective Helena Britt

• Medicare Local/PHN perspective Chris Pearce

• Local health neighbourhood perspective Teng Liaw

• Linked national collections now possible Dougie Boyle

2.45 – 3.05pm Small groups: levels custodianship and

stewardship Small groups

3.05 – 3.45pm Bringing it together All groups

Setting the scene

Governance principles and scope

Teng Liaw

Definitions of key terms

Curation: Manage and promote the use of data from their point of creation, to ensure they are fit for contemporary purpose, and available for discovery and use

Information Ecosystem: A network that is continuously sharing information, optimising decisions, communicating results and generating new insights for businesses

Data Quality Management: Define DQ standards, data collection strategies and assessment of collected data using DQ indicators

Data Governance: specifies who holds the decision rights and accountability for an organisation’s decisions about its data assets.

Information Governance: Ensures necessary safeguards for, and appropriate use of, patient and personal information.

Data custodian:person/group responsible for decisions on providing access to and determining uses of data on behalf of the organisation and stakeholders

Data Stewardship: Attends to and takes the past into account to influence the future, stretching from data planning to sampling, from data archive to use and reuse. This includes the care of data and information infrastructure, and involves data definitions, data requirements and quality assurance as well as user feedback, redesign and data exchange.

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Integrated governance

Data

• Data quality management (DQM)

• Information governance (FFP)

Clinical

• Quality: cost-effectiveness

• Safety: patients and clinicians

Corporate

• Accountable Medical Homes/Orgns

• Strategy, resources, metrics

Scope:

“big data” challenges



collect data that can become useful and actionable

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Interoperability of devices, applications and services

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integrate sensing, clinical and service design to

ensure actionable data.

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linkages of health with social/environmental data

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extract meaningful knowledge: policy & practice

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efficient databases, tools and interfaces to allow

access to appropriate data at point of care.

OECD 2011

Current initiatives &

governance models

Program Objective Denominator Governance Tools Other

AIHW

Specific non-routinely collected practice level data (not from EHR)

National sample Program level AIHW in-house tools.

www.aihw.gov.au/ data/

BEACH

Specifically collected encounter (patient) level data (not from EHR)

Sample of 1000 GPs at a time from national database

Program level In-house BEACH tools. www.sydney.edu.a u/medicine/fmrc/ab out/index.php Improvement Foundation

Collection of practice level

EHR data National sample Program level

PEN-CAT extraction tool / Canning www.improve.org. au Electronic Practice Based Research Network (ePBRN)

Collection and linkage of patient level EHR data from primary and secondary care datasets in Integrated Health Neighbourhood (IHN) IHN = hospital, ambulatory care, community health and GP services Program level. Local Health District (LHD) and UNSW ethics committees. Planned: ML/LHD joint governance.

GRHANITE™ extraction & linkage tool. SQL / XML. SAS / SPSS. Semantic Web tools. www.cphce.unsw. edu.au/research- streams/primary- health-care-informatics MAGNET (Note: POLAR is a portal to MAGNET)

Medicare Local‐based collection of practice level EHR data with links to other services Medicare Local (ML) Note: MLs now superseded by Primary Health Networks (PHN) Data governance at program level. Multiple ethics approvals.

Was using PEN-CAT extraction tool, but now using GRHANITE™ www.med.monash. edu.au/general-practice/magnet/ Medicine Insight

Collection of patient level EHR data National sample (Target: 500 practices) Data governance at program level. RACGP ethics. GRHANITE™ extraction tool www.nps.org.au/a bout-us/what-we-do/medicineinsight

Some existing Australian programs to collect and use primary care data

National data

collection perspective

Helena Britt

The BEACH program

Governance:

‘The action, manner, or power of governing :principles

of good governance’

.(www.thefreedictionary.com/governance).

Includes:

The ethical, legal and social issues around any data – big or small And pertains to its collection, management, and use.

BEACH:

cross sectional, encounter-based, not longitudinal patient based, continuous, paper based. Running continuously since April 1998, now in Year 18. Began as University of Sydney-AIHW collaboration (1998-2010) ran under the AIHW Act

In 2010 agreement dismantled, University of Sydney continues alone. and adopts much the same rules.

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The BEACH program

AIHW Act

• approved by bothAIHW and University Ethics committees, • data undiscoverable (cannot be called to court, does not exist) • all staff sign confidentiality agreements: breach can result in jail and fine. • limitations are set on release of line data and on reporting,

so cannot possibly allow identification of individual GP, practice or patient. Since 2010

GPs: OPT IN system, when approached through random sampling

Patients OPT IN: supplied ‘Patient information sheet’ which informs, custodian, purpose, uses, funders, with contact details for ethical concerns to Ethics Committee University of Sydney

Ethics: Sydney University Human Ethics Committee, five weekly

applications of sub-studied not covered by the broad approval, and annual reports to Committee.

Ask ourselves: Yes, its approved but is it ETHICALin the broadest sense?

BEACH Advisory Board: includes RACGP, AMA, ACRRM, CCHF, and rep

from each of the organisations supporting BEACH (3 meetings per year).

Still have confidentiality agreements

The BEACH program

Data management

• Select only those data elements that we need to

collect-(unethical to collect data for its own sake),

• Safe data transfer with de-identificaiton of both

clinician/practice and patient.

• Different PEOPLE can access identifying information

(e.g. who is the GP or practice), from those who have

access to the clinical data, in which identify practice/GP/

patinets only by project identified by project ID

• Locked secure data storage, encryption at data transfer.

• Third party patient matching when needed (e.g,

CHeReL)

The BEACH program

• Quality control checks: pregnant males, prostate

problems in females; children on HRT etc. • Validity checks: what are you trying to represent? Do

these data represent what you SAY they are representing?

• Reliability checks against other data sources: e.g. prevalence estimates of diagnosed selected diseases

in the database x age-sex, compared with national data sources.

• Keep specific record of cleaning methods applied for provision to the end user. Make sure you cleaning rules are uniformly applied in all cases

The BEACH program

Making the data meaningful and useable for an end-user

• If data files: stripped of D0B, addresses, etc.

• Care with how many geographic variables you provide (e.g. GP practice PHN, SEIFA, LHD, size could well identify a practice in some less populous areas of Australia.

• Provide them with the files with a standardised structure: • Data dictionary with formal definitions of each data field, and copies of

full code sets used, using international standards whenever possible • Written detailed methods of every step of data collection, data

manipulation and data management processes; full description of limitations.

Note: we gave up line data provision: on phone 8 hours per day The public good

• AND to what extent are you responsible for correct use and interpretation of the data you provide to others? (examples)

Medicare Local /

PHN perspective

Danielle Mazza on behalf of

Chris Pearce

POLAR: Population Level

Analysis and Reporting

• Population Level Aggregation & Reporting

• Service run by MEGPN (and presumably

the PHN in the future) – delivering

information to practices.

• 50 practices and expanding, 1 million

patients

• Extensive cover of Eastern Melbourne

• Covered by Practice agreements

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20

MAGNET

• Research collaboration between MEGPN

and Monash

• National advisory group

• Jointly managed

• Ethics approved

• Multiple projects in place

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Data Heirarchy/Capability

• Support Clinical Interventions

• Clinical Governance

• Population Based Decision Support

• Policy and Strategy

• Research

• Administration

Local health neighbourhood

perspective

Teng Liaw

A Network of Health

Neighbourhoods

Decrypted

-hashed

identifiers.

Secure Data Repository

Doubly encrypted Records linked and processed

GPs + hospital + CH

=>

Health Neighbourhood

Opt out consent

Feedback to services to improve DQ

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ePBRN governance

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UNSW and SWSLHD HREC approved and

monitor ePBRN:

geographic scope

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Steering Committee includes

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ML/PHN, LHD, Clinician, Consumer

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Participating GPs sign MoU:

roles & responsibilities

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Standard operating procedures:

access, privacy, confidentiality, security, risk management, incident management, etc

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DQ Assessment framework & protocols

“Top-down” & “Bottom-up” model?

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Health neighbourhood

= unit of governance

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PHN/LHD = network of neighbourhoods

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State (e.g. BHI) = network of PHN/LHD?

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National (e.g. AIHW) = network of PHN/LHD?

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integrated governance

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Scope: OECD challenges

National data collections

now possible

Dougie Boyle

Some of the challenges to overcome

• National data means working with over 7,000 private

businesses (practices)

• There are many GP system vendors, database

schemas can changing regularly, very limited

standardisation, multiple terminologies

• Real privacy concerns and limited expertise to deal

with such concerns at the practice level

• Minimal technical experience in the community /

practices

• Aggregate v’s de-identified individual-level data

extractions

What has changed that makes this

workshop important?

• New tools mean linked data and national data collection

are a technical feasibility – including implementing

consent mechanisms

• Data strategies for PHN’s are in-flux – nobody wants 31

separate organisations going their own way

• Increasing scales of data collection raises appropriate

concern about how such data can be governed and the

interests of stakeholders represented

• There is limited experience in Quality Assurance and

Terminology standardisation with such data – how do we

harness this?

Impact of different levels of infrastructure distribution and governance

One-stop shop National QA Terminology Standards Cheaper Flexibility Stakeholder Control

NATIONAL

LEVEL

STATE LEVEL

PHN LEVEL

The GOOD The BAD

The UGLY: Where’s the middle ground?

Big Brother Bureaucratic Legislative barriers Costly Poor QA No Standards

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Small groups

2.45 – 3.05pm

Facilitators

Levesque, Furler, Boyle, Britt, Liaw

How do we set the balance in data management and

governance if we are to have any hope of having a

national, quality assured and standardised primary

care resource?

1. Where should data custodianship lie? Can we have a single store of data or do we need the data managed in a more distributed model? (can you standardise and QA distributed data effectively?)

2. Decisions about ‘who governs data’ and ‘what can be done with it’ can be independent of where data sits. What would a workable governance model look like that may be acceptable to stakeholders (from patients

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federal government)?