PHC Informatics stream symposium program

Optimising use of primary care EHR data collected during routine care:

issues, opportunities and the scope for collaboration

When and where

Date: 31st October 2014

Time: 0930 – 1530 hours

Place: John Reid Theatre, AGSM Building, UNSW Randwick (Gate 11)

Purpose

To identify the potential for improving the quality and use of primary care EHR data through

coordinated research and development

Objectives: by the end of the symposium participants will:

Have agreed on the core current and emerging uses of primary care EHR data

Be able to describe current initiatives, activities and tools to pursue this

Have agreed on the main challenges facing optimal use of primary care EHR data

Have identified opportunities for collaborative research and development to address these

challenges and further develop the use of primary care EHR data.

Program

Time Activity Responsible person

0900 – 1000 Arrivals / Welcome / Intro / coffee Gawaine Powell Davies

1000 – 1045 Using primary care EHR data: common purposes, methods, and challenges

The Learning Health Neighbourhood

Data quality assessment and management

Governance: who, what and how

Mark Harris, Teng Liaw, and PHCI/ePBRN team

1045 – 1100 Morning tea

1100 – 1200 Common purposes, methods/tools and challenges

MAGNET

MedicineInsight

Improvement Foundation

BEACH

Priorities for small group activities

Gawaine

Chris Pearce

Margaret Williamson

Colin Frick

Helena Britt

Gawaine

1200 – 1300 Identify common methods & tools small groups

1300 – 1330 Lunch All

1330 – 1430 Identify / prioritise common challenges small groups

1430 – 1530 Next steps: a coordinated research and development plan?

Gawaine

Workshop participants (alphabetical order)

Title First Name Last Name Organisation Email Address

Dr Amit Arora UNSW CPHCE amit.arora@unsw.edu.au

Mr Michael Blanca SWSML MBlancia@swsml.com.au

Ms Katie Bowden Prince of Wales Hospital agga.bowden@sesiahs.health.nsw.gov.au

A/Prof Helena Britt BEACH, Uni of Sydney helena.britt@sydney.edu.au

Dr Tim Churches Sax Institute tim.churches@saxinstitute.org.au

Ms Robyn Cook POW Clinical School UNSW Medicine robyn.cook@unsw.edu.au

Mr Michael Falster Centre for Health Research, UWS aggart.falster@uws.edu.au

Dr Dale Ford Improvement Foundation Australia dale.ford@improve.org.au

Mr Colin Frick Improvement Foundation Australia colin.frick@improve.org.au

Prof Mark Harris UNSW CPHCE m.harris@unsw.edu.au

Mr Jitendra Jonnagaddala UNSW SPHCM z3339253@unsw.edu.au

Mrs Chandni Joshi Univeristy of New South Wales c.joshi@unsw.edu.au

Ms Helen Kehoe AIHW agga.kehoe@aihw.gov.au

Dr Tim Luckett UNSW t.luckett@unsw.edu.au

Mr Andrew McAlister Mental Health Drug and Alcohol Office amcal@doh.health.nsw.gov.au

Ms Lisa McGlynn Australian Institute of Health & Welfare bianca.smola@aihw.gov.au

Ms Nicki Meagher UNSW nicki.meagher@unsw.edu.au

Dr Hamish Meldrum Ochre Health aggar@ochrehealth.com.au

A/Prof Chris Pearce MAGNET, IEMML, Monash University drchrispearce@mac.com

Dr Sarah Potter University of Sydney sarah.potter@sydney.edu.au

A/ Prof Gawaine Powell Davies UNSW CPHCE g.powell-davies@unsw.edu.au

Prof Pradeep Ray WHO Collaborating Centre (eHealth) p.ray@unsw.edu.au

Mr Jeremy Spindler AIHW aggar.spindler@aihw.gov.au

Ms Jane Taggart UNSW CPHCE j.taggart@unsw.edu.au

Ms Maureen Thornhill Ochre Health mthornhill@ochrehealth.com.au

Mr Lyle Turner MAGNET, Monash University

Dr Margaret Williamson NPS MedicineInsight mwilliamson@nps.org.au

Ms Louise York Australian Institute of Health & Welfare bianca.smola@aihw.gov.au

Dr Hairong Yu UNSW CPHCE h.yu@unsw.edu.au

Background reading and resource materials

CONTENTS

1. URLS FOR INFORMATION ABOUT RELEVANT PHC DATA PROJECTS IN AUSTRALIA 4

A. EPBRN: HTTP://CPHCE.UNSW.EDU.AU/RESEARCH-STREAMS/PRIMARY-HEALTH-CARE-INFORMATICS 4

B. MAGNET: HTTP://WWW.MED.MONASH.EDU.AU/GENERAL-PRACTICE/MAGNET/ 4

C. MEDICINEINSIGHT: HTTP://WWW.NPS.ORG.AU/ABOUT-US/WHAT-WE-DO/MEDICINEINSIGHT 4

D. IMPROVEMENT FOUNDATION: WWW.IMPROVE.ORG.AU 4

E. BEACH: HTTP://SYDNEY.EDU.AU/MEDICINE/FMRC/ABOUT/INDEX.PHP 4

2. INDICATIVE DATA QUALITY ASSESSMENT FRAMEWORK: HTTP://WWW.EDM-FORUM.ORG/DATAQUALITY 4

3. SELECTED PUBLICATIONS ON PURPOSE, METHODS AND CHALLENGES 4

GENERAL APPROACHES AND PURPOSES 4

TOOLS AND METHODS 5

GOVERNANCE 6

4. SLIDE DECK FROM WORKSHOP PRESENTATIONS 6

5. OTHER RELEVANT INFORMATION 7

1. BEACH 7

2. MEDICINEINSIGHT 10

1. URLs for information about relevant PHC data projects in Australia

a. ePBRN: http://cphce.unsw.edu.au/research-streams/primary-health-care-

informatics

b. MAGNET: http://www.med.monash.edu.au/general-practice/magnet/

c. MedicineInsight: http://www.nps.org.au/about-us/what-we-do/medicineinsight

d. Improvement Foundation: www.improve.org.au

e. BEACH: http://sydney.edu.au/medicine/fmrc/about/index.php

2. Indicative Data Quality Assessment framework: http://www.edm-

forum.org/dataquality o DQ Assessment Framework document from project funded by Patient Centred

Outcomes Research Institute (PCORI) USA

o A useful framework to harmonise with

3. Selected publications on purpose, methods and challenges

General approaches and purposes

Britt HC & Miller GC. The Bettering the Evaluation and Care of Health (BEACH) program: where to from

here? Med J Aust 2013; 198(3):125-6.

Britt HC & Miller GC. The Bettering the Evaluation and Care of Health (BEACH) program: where to from

here?[letter in reply]. Med J Aust 2013; 199(4):240

de Lusignan S, Pearce C, Shaw N, Liaw ST, Michalakidis G, Vicente M, Bainbridge M. What are the barriers to conducting international research using routinely collected primary care data? Stud Health Technol Inform. 2011; 165:135-140. DOI 103233/978-1-60750-735-2-135

de Lusignan S, Liaw ST, Krause P, Curcin V, Vicente M, Michalakidis G, Agreus L, Leysen P, Shaw N, Mendis K. Key concepts to assess the readiness of data for International research: Data quality, lineage and provenance, extraction and processing errors, traceability, and curation. IMIA Yearbk of Med Informatics 2011: pp 112-121.

Harrison C, Britt H, Miller G, Henderson J. Prevalence of chronic conditions in Australia. PLoS ONE

2013;8(7):e67494. Epub 2013 Jul 23.

Liaw ST, Rahimi A, Ray P, Taggart J, Dennis S, de Lusignan S, Jalaludin B, Yeo AE, Talaei-Khoei A. Towards an ontology for data quality in integrated chronic disease: a realist review of the literature. Int J Med Inform 2013; 82(1): 10–24 http://dx.doi.org/10.1016/j.ijmedinf.2012.10.001

Liaw ST, Taggart J, Dennis S, Yeo AET. Data quality and fitness for purpose of routinely collected data – a case study from an electronic Practice-Based Research Network (ePBRN). Proceedings of the American Medical Informatics Association Annual Symposium 2011, Washington DC.

Liaw ST, Chen HY, Maneze D, Taggart J, Dennis S, Vagholkar S, Bunker J. Health reform: is current electronic information fit for purpose? Emergency Medicine Australasia 2011 (Sep): (doi: 10.1111/j.1742-6723.2011.01486.x)

Liaw ST. Computerised decision support in general practice – a research journey. Aus Fam Physician 2011; 40 (9): 711

Liyanage H, de Lusignan S, Liaw ST, Kuziemsky CE, Mold F, Krause P, Fleming D, Jones S. Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks. IMIA Yearbook 2014, pp 27-35. DOI: http://dx.doi.org/10.15265/IY-2014-0016

Liyanage, H., Liaw, S.-T., Kuziemsky, C., Terry, A. L., Jones, S., Soler, J. K., & de Lusignan, S. The Evidence-base for Using Ontologies and Semantic Integration Methodologies to Support Integrated Chronic Disease Management in Primary and Ambulatory Care: Realist Review. Yearbook of Medical Informatics. 2013; 8(1), 147–54

Liyanage H, Liaw ST, Kuziemsky C, de Lusignan S. Ontologies to improve chronic disease management research and quality improvement studies – a conceptual framework. Proceedings Medinfo 2013

Liyanage H, Liaw ST, de Lusignan S. Reporting of studies conducted using observational routinely collected data (RECORD) statement: call for contributions from the clinical informatics community. Informatics in primary care 2012; 20:221-224.

Liyanage H, Liaw ST, de Lusignan S. Accelerating the development of an information ecosystem in health care, by stimulating the growth of safe intermediate processing of health information (IPHI). Informatics in primary care 2012; 20(2):81-86

Taggart J, Liaw ST, Dennis S, et al. The University of NSW electronic Practice Based Research Network: Disease registers, data quality and utility. Stud Health Technol Inform 2012, 178, 219-27

Tools and methods

Britt H. Miller GC. Valenti L. et al Evaluation of imaging ordering by general practitioners in Australia,

2002–03 to 2011–12. General practice series no.35. Sydney: Sydney University Press, 2014.

de Lusignan S, Liaw ST, Michalakidis G, Jones S. Defining data sets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: an ontology driven approach. Informatics in Primary Care 2012: 20, 127

Harrison C, Britt H, Miller G, Henderson J. Examining different measures of multimorbidity, using a large

prospective cross-sectional study in Australian general practice. BMJ Open. 2014 Jul 11;4(7):e004694. doi:

10.1136/bmjopen-2013-004694.

Huang N, Liaw ST, Taggart J, Yu H, Williamson M, Gillies MB. Designing a fit-for-purpose data extraction tool for MedicineInsight. Paper presentation Int Soc Pharmacoepidemiology (ISPE) Conference 2013

Liaw ST, Taggart J, Yu H, de Lusignan S, Kuziemsky C, Hayen A. Integrating electronic health record information to support integrated care: practical application of ontologies to improve the accuracy of diabetes disease registers. J Biomed Informatics July 2014; DOI: http://dx.doi.org/10.1016/j.jbi.2014.07.016

Liaw ST, Taggart J, Yu H, de Lusignan S. Data extraction from electronic health records – existing tools may be unreliable and potentially unsafe. Aust Fam Physician 2013; 42 (11):820-823

Comino EJ, Duong TT, Taggart JR, Liaw ST, Ruscoe W, Snow JM, Harris MF. A preliminary study of the relationship between general practice care and hospitalisation using a diabetes register, CARDIAB. Aust Health Review. Sept 2012

Liaw ST, Chen HY, Maneze D, Taggart J, Dennis S, Vagholkar S, Bunker J. The quality of routinely collected data: using the “principal diagnosis” in emergency department databases as an example. Electronic Health Informatics Journal 2012 (Jan)

Miller GC, Valenti L, Britt H, Bayram C. Drugs causing adverse events in patients aged 45 or older: a

randomised survey of Australian general practice patients. BMJ Open 2013;3(10):e003701

Pearce C, Shearer M, Gardner K, Kelly J. A division's worth of data. Aust Fam Physician. 2011

Mar;40(3):167-70. http://www.med.monash.edu.au/general-practice/magnet/

Rahimi A, Liaw ST, Ray P, Taggart J, Yu H. Validating an ontology-based algorithm to identify patients with Type 2 Diabetes Mellitus in electronic health records. Int J Medical Informatics 2014 (DOI: http://dx.doi.org/10.1016/j.ijmedinf.2014.06.002)

Rahimi A, Ray P, Paramesh N, Taggart J, Yu H, Liaw ST. Development of a methodological approach for data quality ontology in diabetes management. Int J e-Health & Med Communication (IJEHMC) [in press: accepted April 2014]

Rahimi A, Liaw ST, Ray P, Taggart J, Yu H. Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review. Decision Analytics 2014, 1-5. http://www.decisionanalyticsjournal.com/content/1/1/5

Governance

Liaw ST, Pearce C, Liyanage H, Liaw SSG, de Lusignan S. An integrated organisation-wide data quality management and information governance framework: theoretical underpinnings. Informatics in Primary Care Sep 2014; 21(4): 1-8. http://dx.doi.org/10.14236/jhi.v21i4.87:

Liaw ST. Clinical decision support systems: data quality management and governance. pp 362-369. In

Hovenga E, Grain H (eds). Health information governance in a digital environment. IOS Press 2013.

DOI:10.3233/978-1-61499-291-2-362.

4. Slide deck from workshop presentations This will be made available on the day as well as on the UNSW CPHCE website

5. Other relevant information

1. BEACH

by Helena Britt BA PhD, Associate Professor & Director, Family Medicine Research Centre, Sydney

School of Public Health

The Family Medicine Research Centre at the University of Sydney was established in August 1999 to

undertake health services research in general practice and primary care in Australia.

The Centre was formed from the Family Medicine Research Unit which has carried out research in

the Department of General Practice since 1990, which in turn grew from the research work of the

Department conducted since 1977. The Centre is part of the School of Public Health and is located

offices in Parramatta, NSW. Our work centres on health services research and development.

We are a multidisciplinary team with undergraduate and postgraduate educational backgrounds in

medicine (general practice & orthopaedics), psychology, statistics, epidemiology, public health,

health information management; market research, communication, and information technology.

http://sydney.edu.au/medicine/fmrc/about/index.php

BETTERING THE EVALUATION AND CARE OF HEALTH (BEACH)

The BEACH Program is a continuous national cross sectional study of general practice clinical activity

collects. Information collected includes:

characteristics of the GPs

patients seen

reasons people seek medical care

problems managed, and for each problem managed (direct link)

medications prescribed, advised, provided, clinical treatments and procedures

referrals to specialists and allied health services

test orders, including pathology and imaging

The BEACH database currently includes about 1.6 million GP-patient encounter records, from about

16000 GP participants representing about 10,0000 individuals. It uses a cross-sectional, paper based

data collection system developed and validated over 30 years at the University of Sydney. Data

generated is used by researchers, government , industry and NGOs.

What BEACH does, that no other national general practice based study does, is provide linkage of

management actions to morbidity – linked by the GP, not though ‘logical’ guesswork of secondary

data analysts. The BEACH relational database is represented in the attached diagram.

More information: http://sydney.edu.au/medicine/fmrc/beach/index.php

CLASSIFICATION AND TERMINOLOGY IN PRIMARY CARE

Classification provides a method of distributing coded concepts in a sorted and meaningful manner.

A good classification structure facilitates both immediate and longitudinal data management and

retrieval across a number of different groups. Quality health research requires the use of a reliable

and suitable classification system.

Staff at the Family Medicine Research Centre (FMRC) are heavily involved in the development and

application of medical and pharmaceutical classifications for the Australian health care sector,

particularly primary care/general practice.

Three members of staff are on the Wonca International Classification Committee.

A/Prof Helena Britt is leading the international development of ICPC-3

The FMRC distributes, licences, and educates about ICPC-2 in Australia on behalf of Wonca.

The FMRC developed, distributes and updates ICPC-2 Plus ( the Plus terminology classified to ICPC-2), designed on the basis of terms used by GPs in >3 million encounters records, used: in the BEACH program; by about 20% of GPs in their EHRs; throughout the Northern Territory primary and community health services, allied health workers in Perth, to name a few.

FMRC has recently led a project for the International Health Terminology S DO (IHTSDO), working

with an international team, to develop a GP RefSet of SNOMED concepts to be used as a basic core

on which to build individual national subsets of concepts for general practice/family medicine.

The GP/FP RefSet is due for release by the IHTSDO in the next few weeks.

More information on classifcation: http://sydney.edu.au/medicine/fmrc/classifications/index.php

FMRC PUBLICATIONS: FULL LIST

http://sydney.edu.au/medicine/fmrc/publications/index.php

Books: 35 (and 2 in press)

http://sydney.edu.au/medicine/fmrc/publications/books/GP-series/index.php

Refereed articles in recognised journals: 163

http://sydney.edu.au/medicine/fmrc/publications/refereed-articles/index.php

Other brief articles in recognised journals (for GP readership): 134

http://sydney.edu.au/medicine/fmrc/publications/other-publications/index.php

Bytes from BEACH: 11

Usually critical comment on published articles, or public statements, which BEACH data disproves.

http://sydney.edu.au/medicine/fmrc/beach/bytes/index.php

SAND Abstracts: 210

Abstracts of findings from each of 2010 substudies conducted in the BEACH program –

based on patient self report + GP knowledge + GP record;-- not based on encounter data).

http://sydney.edu.au/medicine/fmrc/publications/sand-abstracts/keyword-list.php

RECENT PAPERS OF INTEREST FOR THIS WORKSHOP

1. ***Britt HC & Miller GC. The Bettering the Evaluation and Care of Health (BEACH) program:

where to from here? Med J Aust 2013; 198(3):125-6.

2. ***Britt HC & Miller GC. The Bettering the Evaluation and Care of Health (BEACH) program:

where to from here?[letter in reply]. Med J Aust 2013; 199(4):240

3. ** Britt H. Miller GC. Valenti L. et al Evaluation of imaging ordering by general practitioners in

Australia, 2002–03 to 2011–12. General practice series no.35. Sydney: Sydney University Press,

2014.

4. Miller GC, Valenti L, Britt H, Bayram C. Drugs causing adverse events in patients aged 45 or older:

a randomised survey of Australian general practice patients. BMJ Open

2013;3(10):e003701Harrison C, Britt H, Miller G, Henderson J. Prevalence of chronic conditions in

Australia. PLoS ONE 2013;8(7):e67494. Epub 2013 Jul 23.

5. Harrison C, Britt H, Miller G, Henderson J. Examining different measures of multimorbidity, using

a large prospective cross-sectional study in Australian general practice. BMJ Open. 2014 Jul

11;4(7):e004694. doi: 10.1136/bmjopen-2013-004694.

The 2013–14 BEACH reports will be available through the FMRC web site on 11 Nov 2014.

BEACH RELATIONAL DATABASE:

The encounter

date

direct (face to face)

— Medicare/DVA item

number(s) claimable — workers compensation

— other paid

— no charge

indirect (e.g. telephone)

Patient substudies (SAND)

risk factors — body mass — smoking status — alcohol consumption

other topics

Management of each problem

Medications (up to four per problem)

prescribed

over-the-counter advised

provided by GP

— drug class

— drug group

— generic

— brand name

— strength

— regimen

— number of repeats

— drug status (new/continued)

Other treatments (up to two per

problem)

procedural treatments

clinical treatments (e.g. advice,

counselling)

practice nurse involvement

Other management

referrals (up to two) — to specialists — to allied health professionals — to emergency departments — hospital admissions

pathology tests ordered (up to five)

imaging ordered (up to three)

GP characteristics

age and sex

years in general practice

country of graduation

direct patient care hours/week

FRACGP status (yes/no)

FACRRM status (yes/no)

currently a registrar (yes/no)

clinical use of computers

Practice characteristics

practice size (no. & FTE GPs)

practice nurse(s) (no. & FTE)

after-hours arrangements

postcode

presence of other health services

Problems managed

diagnosis/problem label

problem status (new/old)

work-related problem status

The patient

age and sex

practice status (new/old)

Commonwealth concession

card status

Repatriation health card status

postcode of residence

NESB/Indigenous status

reasons for encounter

Note: FRACGP – Fellow of the Royal Australian College of General

Practitioners; FACRRM – Fellow of the Australian College of Rural

and Remote Medicine; FTE – full-time equivalent; DVA – Department

of Veterans’ Affairs; NESB – non-English-speaking background;

SAND – Supplementary Analysis of Nominated Data.

Figure 1: The BEACH relational database

2. MedicineInsight

by Margaret Williamson PhD, Manager MedicineInsight, National Prescribing Service

About MedicineInsight

MedicineInsight is a unique, Australian Government funded program that aims to improve

understanding of prescribing behaviour in Australian general practice.

Through this understanding, MedicineInsight will assist general practitioners in improving the quality

and safety of their prescribing where required, with the ultimate goal of achieving better health

outcomes for all Australians.

How does MedicineInsight work?

MedicineInsight collects anonymous patient and clinical care information from general practices. The

program links the information on patient diagnosis to the medicines prescribed, the health services

provided and the clinical impact.

MedicineInsight analyses this information and reports back on:

when, what dose, and for what type of patient medicines are being used the effects of a medicine(s) on an individual how new medicines are adopted once released into the community alignment with recommended best practice any adverse events changes in prescribing behaviour (for example, before and after new evidence or guidelines

become available).

This offers significant potential for prescribers, government and public health organisations to improve

policy and clinical practice, assist the relevance and effectiveness of the Pharmaceutical Benefits

Scheme (PBS), and achieve better health outcomes.

MedicineInsight will not enable NPS or government organisations to monitor individual GP's

prescribing patterns or identify individual patients. All information gathered is anonymous.

Benefits to health professionals

MedicineInsight provides GPs with tailored treatment data on their patients, in an accessible and

useful format. Equipped with this new information, GPs can make fully informed decisions to improve

the quality and safety standards of patient care.

The program also aims to improve the quality of data that is recorded by general practices in existing

clinical management software programs.

MedicineInsight, as with other NPS programs, is designed to improve patient outcomes at an

individual and nationwide level.

Ethics approval

The safe and secure transfer of information and the ethical considerations of patients and GPs are

governed by a robust framework. The pilot phase of MedicineInsight received approval from the Royal

Australian College of General Practitioners (RACGP) Ethics Committee on 10 January 2013. This

approval ensures all ethical issues have been considered during the program’s design and

development and that each step is being conducted ethically (for example, confidentiality and storage

of data).

A CASE STUDY

MedicineInsight — General Practice reportsA unique, data-driven view into general practice prescribing to help improve the quality and safety of patient care

How MedicineInsight helped Shepparton Medical CentreThe staff at the University of Melbourne's Shepparton Medical Centre wanted a clearer view of how medicines are used across their practice. They joined MedicineInsight in March 2013 and six months later received a practice report about the care they provided to patients with type 2 diabetes. These unique insights stimulated conversation about how to implement practice processes to more easily identify patients at risk of type 2 diabetes and provide improved care for patients already diagnosed. Three months later they received a follow-up report allowing them to track improvements.

The clinical issueGeneral practitioners (GPs) typically treat one patient at a time, so it is difficult to gain a whole-of-practice perspective on patient care to inform quality improvement initiatives. For practices to identify where improvements could be made, they need to better understand GP prescribing and care patterns across the practice. For example, how medicines are being prescribed and for whom, and the effects of new medicines in patient populations. This type of data would provide a clearer picture of patient care and help practices compare performance against known standards to identify areas for improvement.

The MedicineInsight SolutionMedicineInsight is creating an unprecedented evidence base on GP prescribing patterns and other clinical measures at a whole-of-practice level. It involves secure collection of non-identifiable patient data from practice computers during non-work hours. The program links patient conditions, diagnoses and treatments over time to show how patients are being cared for compared to best practice and to other participating practices. Results are presented by an NPS MedicineWise facilitator at an all-of-practice meeting and practice staff use the report information, along with experience, to define areas and actions for improvement.

Quality improvement is an essential part of service delivery in general practice, but unless we measure, it’s hard to know exactly what to improve and whether we have achieved improvement.1

Why NPS MedicineWise?

Practice needPractices recognise the need to take a whole-of-practice view of patient care to ensure they are providing the best and safest care for their patients, but often they don't have time. They also realise that a long term view of activity is needed as evidence for change and for monitoring the effects of systems and activities.

ChallengesPractices and patients worry about privacy when it comes to health data collection and GPs are reluctant to have their day-to-day practice disturbed. We needed to develop an IT solution that could seamlessly integrate with clinical desktop systems, collect data on an ongoing basis, and de-identify patient records all without disturbing day-to-day practice.

Results and outcomesMedicineInsight began successfully collecting patient data from practices across Australia in March 2013. Reports are now being delivered to practices who are benefiting from this unique view into their current activity and how they compare to other practices. It is proving to be a powerful way to benchmark patient care activities and measure changes over time.

Next stepsBy mid 2015, we aim to have 500 practices participating in and benefiting from MedicineInsight. More broadly, we will share aggregated and anonymised data to help shape health policy and research and ensure healthcare investments are being directed where they are needed most.

With MedicineInsight we are able to, in a very anonymised and unthreatening way, look at what we do and learn from this to develop more effective ways of prescribing. It helps us provide safer, better and more efficient care for our patients and has really been worth it.

Dr Derek Wooff, Shepparton Medical Centre, VIC.

We are a trusted and globally recognised organisation helping people make the best decisions about medicines and other medical choices to achieve better health and economic outcomes. Well informed health professionals and a health-savvy population are key to achieving this.

As the only independent organisation in Australia working at a national level to positively change attitudes and behaviours around the use of medicines and medical tests, we ensure consumers and health professionals are equipped to make the best decisions when it counts.

Independent, not-for-profit and evidence based, NPS MedicineWise enables better decisions about medicines and medical tests. We are funded by the Australian Government Department of Health. ©2014 National Prescribing Service Limited trading as NPS MedicineWise ABN 61 082 034 393.

Level 4/176 Wellington Pde East Melbourne VIC 3002 PO Box 21 East Melbourne VIC 3002

1300 721 726

03 9416 3325

medicineinsight@nps.org.au

www.nps.org.au

""

References1. Landgren, F, A guide to using data for health care quality improvement, The Victorian Quality Council June 2008.

1

Practice Report Snapshot Each MedicineInsight practice report is generated from practice data collected and analysed by NPS

MedicineWise. Each report links patient conditions and treatments over time to show how medicines are

being prescribed. The results are compared to best practice and to other practices participating in the

program.

Reports are presented at a whole-of-practice meeting by an NPS MedicineWise facilitator. At the meeting,

practice staff discuss report information and use it, along with clinical experience, to generate actions for

improvement if required.

Follow up reports are distributed at regular intervals to allow practices to track improvements.

Examples of the type of information provided in MedicineInsight practice reports are illustrated below. All

examples are taken from a report that focuses on type 2 diabetes.

1. Information on your patients

Including:

Number of patients with chosen condition

Demographics of patients with chosen condition

Recording rate of lifestyle risk factors for patients with chosen condition (e.g. smoking, BMI, waist circumference)

Age and sex profile of patients with type 2 diabetes

2

2. Information on key clinical measures for disease states

Including:

Monitoring rates for clinical measurements (e.g. HbA1c, BP, lipids, other medical tests)

Achievement of best practice targets

Practice to practice comparison of HbA1c results for type 2 diabetes

In practice GP comparison of HbA1c monitoring

3

3. Information on what types of medicines are prescribed

Including:

Prescription rates for medicines relevant to chosen condition Medicines use and resulting clinical measurements which may inform if patient review or therapy

intensification is required

Top 10 glucose-lowering medicines prescribed in your practice for patients with type 2 diabetes (last

12 months)

HbA1c results and glucose-lowering medicines prescribed within your practice for patients with type 2 diabetes (may inform patient review or therapy intensification)

4

4. Information on how care is coordinated

Including:

How often patients present for care Who provides care Opportunities for business revenue

Number of and average length of consultations for patients with type 2 diabetes

5. Information on data quality

Including:

Completeness of data entry into clinical desktop system

Quality of data entered into clinical deskop system for all patients at practice

3. ePBRN practice report

XXXXXXX Medical Service

26th Aug 2014

SWSLHD / UNSW General Practice Unit UNSW Centre for Primary Health Care and Equity and

School of Public Health and Community Medicine

2

This report provides results of the data extracted from your practice in August 2014 and compares it with other general practices in the electronic Practice Based Research Network (ePBRN) situated in the Fairfield area (10 practices). It provides information on the quality of your clinical records and summaries about your patient population and diabetes patients. AIM To provide you with feedback on:

the completeness and correctness of your patient records

the profile of your diabetes patients

the risk profile of your patient population

Your results are compared with your all participating ePBRN practices.

DATA A range of data with no identifying information were extracted from the structured fields in your clinical system including:

Social determinants – age, gender, ATSI, marital status, occupation, postcode

Risk factors – smoking, alcohol, BMI, BP, family history

Consultations – number, reason for visit

Pathology – tests ordered, results

Prescriptions – Medications prescribed

Management – diabetes review, medication review This is a report based on the active patients as defined by the RACGP (3 visits in last 2 years)

The blue highlighted areas indicate where your practice rates are higher compared with all

ePBRN practices and the green areas indicate where your practice has lower rates. The

orange highlighted areas indicate where changes have occurred since the last report

SUMMARY A majority of your patients are female and most are aged between 20 – 64 yrs.

We identified 12.6% of your patients having Type1 or 2 diabetes.

Your practice meets the RACGP targets for the recording of gender, DOB, Aboriginal and TSI

and height and weight. It is not meeting the targets for alcohol and BMI and only meets the

targets for allergies and smoking for adults and not paediatrics – see tables below).

There seem to be a higher rate of patients with their last recorded SBP >140 and a lower rate

of HDL ≤ 1.0 mmol/L compared with all practices in the ePBRN. However, this may be due to

differences in the recording of these attributes.

3

1. PRACTICE POPULATION

Age and gender of RACGP active patients XXXXXX ePBRN

Aug 2014 (n =2,734)

Aug 2014 (n=43,097)

F M F M

No. % No. % No. % No. %

0 - 5 yrs 106 6.9 131 10.9 1790 7.5 1973 10.3

6 - 19 yrs 220 14.4 197 16.3 3286 13.8 3204 16.7

20 - 44 yrs 489 32.0 299 24.8 9132 38.3 6149 32.0

45 - 64 yrs 425 27.8 359 29.8 6513 27.3 5113 26.6

65 - 74 yrs 147 9.6 112 9.3 1643 6.9 1551 8.1

75 yrs+ 140 9.2 108 9.0 1482 6.2 1252 6.5

Total 1528 55.9 1206 44.1 23847 55.3 19242 44.6

Comment: A majority of your patients are female and most are aged between 20 – 64 yrs. Your practice has a higher proportion of patients over 75 yrs compared with the ePBRN practices 2. DIABETES REGISTER

A) The following table shows the RACGP active diabetes patients identified in various places in MD3 (excluding GDM).

XXXXXXX Aug 2014

ePBRN Aug 2014

No. % No. %

Reason for contact 198 57.6 2530 56.1

Past history – diabetes recorded at reason for contact or reason for script

301 87.5 3347 74.2

HbA1c results available 11 3.2 2957 65.5

- HbA1c ≥ 6.5 6 1.7 1921 42.6 Fasting BGL ≥ 7.0 183 53.2 1165 25.8

Random BGL ≥ 11.1 1 0.3 641 14.2

Diabetes prescriptions 156 45.3 3130 69.4

- Insulin only 22 6.4 216 4.8

- Oral hypoglycaemics 133 38.7 2806 62.2 - Insulin and oral hypoglycaemics 42 12.2 501 11.1

Total diabetes patients (% of all RACGP active patients) 344 12.6 4513 10.5

Comment: We identified 12.6% of your patients having Type1 or 2 diabetes which is slightly higher that the ePBRN practices. Most of your diabetes patients have a diabetes diagnosis recorded in Past History. Few were identified as having an HbA1c result recorded but a much higher proportion had a BGL ≥7.0 recorded compared with the ePBRN. A lower proportion of your diabetes patients had a diabetes related prescription, particularly for oral hypoglycaemics.

4

B) Age and gender of RACGP active diabetes patients identified in Table 2A.

XXXXXX ePBRN

Aug 2014 (n=344) Aug 2014 (n=4511)

F M F M

No. % No. % No. % No. %

0 - 5 yrs 0 0 0 0 3 0.1 1 0

6 - 19 yrs 2 1.1 0 0 24 1 14 0.6

20 - 44 yrs 21 11.5 13 8.0 363 15.6 195 8.9

45 - 64 yrs 70 38.5 70 43.2 887 38.2 910 41.5

65 - 74 yrs 35 19.2 35 21.6 497 21.4 561 25.6

75 yrs+ 54 29.7 44 27.2 546 23.5 510 23.3

Total 182 52.9 162 47.1 2320 51.4 2191 48.5

Comment: Your diabetes patient profile is similar to your practice population profile with a greater proportion of older patients compared with the ePBRN.

5

3. DATA QUALITY

Table 3A shows the completeness and correctness of your practice records with a

comparison to all Medical Director practices in the ePBRN (n=11). Completeness is defined

as having at least 1 record per patient.

A) DATA COMPLETENESS FOR ALL RACGP ACTIVE PATIENTS

Target %* XXXXXX ePBRN

No. % No. %

All Patients 9779 100 168127 100

RACGP Active Patientsa 2734 27.96 43097 25.63

EHR Active Patientsb 3451 35.29 109120 64.9

Gender 100 2734 100 43089 99.98

DOB 100 2734 100 43097 100

Aboriginal & TSI Routinely record 2031 74.29 21254 49.32

Country of birthc 75^ 30 1.1 1034 2.4

Allergies 90 2424 88.66 38903 90.27

Smoking status 75 2047 74.87 28788 66.8

Alcohol assessment 75 772 35.3 6450 17.66

Alcohol consumption 75 484 17.7 2812 6.52

Systolic 1809 66.17 24507 56.86

Diastolic 1809 66.17 24506 56.86

Height Working towards 1669 61.05 15899 36.89

Weight Working towards 1689 61.78 18674 43.33

Waist circumference 643 23.52 2783 6.46

BMI Working towards 1078 39.43 9918 23.01

Notes: * RACGP Standards for General Practice (4th edition); indicates meeting the RACGP Standards for General Practice aRACGP defined active patients have had 3 visits in the past 2 years; b EHR active patients defined as being active in Medical Director; cOnly available to practices using Pracsoft; dMD3 defaults to non-smoker for children <10 yrs. RACGP recommendations for recording ages: Smoking ≥12yrs, alcohol ≥ 14yrs, BMI ≥6yrs and BP≥18yrs

Comment: Your practice meets the targets for the recording of gender, DOB, Aboriginal and

TSI and height and weight. It is not meeting the targets for alcohol and BMI and only meets

the targets for allergies and smoking for adults and not paediatrics – see tables below). Your

practice has higher rates for a number of the attributes compared with the ePBRN practices.

The patients who are recorded as active in Medical Director is close to the proportion of

RACGP active patients.

6

B) DATA COMPLETENESS FOR PAEDIATRIC PATIENTS (0-16 YEARS) Target %* XXXXXX ePBRN

No. % No. %

All Patients 1976 100 32069 100

RACGP Active Patientsa 570 28.85 8714 27.17

EHR Active Patientsb 848 42.91 23978 74.77

Gender 100 570 100 8714 100

DOB 100 570 100 8714 100

Aboriginal & Torres Strait Islander

Routinely record 427 74.91 3859 44.29

Country of birthc 75^ 13 2.28 223 2.56

Allergies 90 427 74.91 7089 81.35

Smoking status 75 235 41.23 4583 52.59

Alcohol assessment 75 3 0.87 37 0.58

Alcohol consumption 75 0 0 7 0.08

Systolic 44 7.72 513 5.89

Diastolic 44 7.72 513 5.89

Height Working towards 313 54.91 4029 46.24

Weight Working towards 359 62.98 5125 58.81

Waist circumference 0 0 0 0

BMI Working towards 68 11.93 946 10.86

C) DATA COMPLETENESS FOR ADULT PATIENTS (≥17 YEARS) Target %* XXXXXX ePBRN

No. % No. %

All Patients 7768 100 135825 100

RACGP Active Patientsa 2163 27.85 34382 25.31

EHR Active Patientsb 2598 33.44 85107 62.66

Gender 100 2163 100 34374 99.98

DOB 100 2163 100 34382 100

Aboriginal & Torres Strait Islander

Routinely record 1603 74.11 17394 50.59

Country of birthc 75^ 17 0.79 811 2.36

Allergies 90 1997 92.33 31814 92.53

Smoking status 75 1812 83.77 24205 70.4

Alcohol assessment 75 769 41.73 6413 21.27

Alcohol consumption 75 484 22.38 2805 8.16

Systolic 1765 81.6 23994 69.79

Diastolic 1765 81.6 23993 69.78

Height Working towards 1356 62.69 11870 34.52

Weight Working towards 1330 61.49 13549 39.41

Waist circumference 643 29.73 2783 8.09

BMI Working towards 1010 46.69 8972 26.1

7

D) DATA CORRECTNESS

Correctness is defined as a valid and appropriate record (eg. height is in metres,

centimetres and/or mm and is within an appropriate range for age)

Target %* XXXXXX ePBRN

No. % No. %

EHR Active Patientsb 3451 100 109120 100

Gender 2734 100 43089 100

DOB 2733 99.963 43096 99.998

Aboriginal & Torres Strait Islander

2031 100 21254 100

Country of birthc NULL NULL NULL NULL

Allergies NULL NULL NULL NULL

Smoking status 2047 100 28788 100

Alcohol assessment NULL NULL NULL NULL

Alcohol consumption 480 99.17 2793 99.32

Systolic 1808 99.94 24503 99.98

Diastolic 1808 99.94 24504 99.99

Height 1652 98.98 15533 97.7

Weight 1686 99.82 18578 99.49

Waist circumference 640 99.53 2768 99.46

BMI 1069 99.17 9829 99.1

Notes: aRACGP defined active patients have had 3 visits in the past 2 years; b EHR active patients

defined as being active in Medical Director; cOnly available to practices using Pracsoft; NA – currently

not

Comment: There are high rates of correctness for these attributes

4. RISK FACTORS Table 4 shows the proportion of all RACGP active patients with risk factors for heart disease and diabetes. (Patients with no recorded information are excluded ).

Risk factors

XXXXXX ePBRN

No. % No. %

Aboriginal &TSI Status

Aboriginal 9 0.4 78 0.4

TSI 0 0 9 0

Aboriginal and TSI 4 0.2 13 0.1

Non indigenous 2018 99.4 21154 99.5

Total patients with ATSI record 2031 74.3 21254 49.3

Smoking status

Never smoked 1307 63.8 19426 67.5

Smoker 222 10.8 4701 16.3

Ex smoker 518 25.3 4661 16.2

Total patients with smoking record

2047 74.9 28788 66.8

Alcohol consumption

>2 drinks on a day 143 29.5 876 31.2

Total patients with alcohol record 484 17.7 2812 6.5

Systolic BP (SBP) >140 375 20.7 2849 11.6

Total patients with SBP record 1809 66.2 24507 56.9

Diastolic BP (DBP)

>90 136 7.5 1060 4.3

Total patients with DBP record 1809 66.2 24506 56.9

Waist circumference

Men ≥ 94cm 219 34.1 1094 39.3

Women ≥80cm 286 44.5 1265 45.5

Total patients with waist record 643 23.5 2783 6.5

BMI

Overweight (25 to 29.9) 327 30.3 2926 29.5

Obese (30 to 39.9) 321 29.8 3017 30.4

Severe obesity (40+) 54 5.0 573 5.8

Total patients with BMI recorded 1078 39.4 9918 23.0

Lipids

TC > 4 mmol/L 1259 80.9 16104 79.1

Total patients with TC recorded 1556 56.9 20370 47.3

LDL > 2.5 mmol/L 942 61.7 11416 63.9

Total patients with LDL recorded 1526 55.8 17869 41.5

HDL ≤ 1.0 mmol/L 185 12.1 3356 18.7

Total patients with HDL recorded 1533 56.1 17981 41.7

TG ≥ 1.5 mmol/L 504 32.4 7206 35.2

Total patients with TG recorded 1556 56.9 20457 47.5

Blood Glucose 5.5 – 6.9 mmol/L fasting record 0 0 1772 18.2

≥7.0 mmol/L fasting 135 8.4 897 9.2

Total patients with fasting BG record

1612 59.0 9750 22.6

>11.1 mmol/L non-fasting 1 2.6 342 2.2

Total patients with non-fasting BG 38 1.4 15697 36.4

Comment: Difficult to interpret results due to some low rates of recording and differences in recording rates between XXXXXX and the ePBRN practices. However, there seem to be a higher rate of patients with their last recorded SBP >140 and a lower rate of HDL ≤ 1.0 mmol/L compared with the ePBRN.

MAGNET GP Data

Associate Professor Chris Pearce PhD, MFM, MBBS, FRACGP, FACRRM, FAICD, FACHI

cpearce@iemml.org.au 0417 032 618

MAGNET Data (from the POLAR program)

Data Hierarchy: 1.  Care of the Patient 2.  Clinical Governance 3.  Population Health 4.  Policy and Strategy 5.  Research 6.  Administration

There is no ‘Secondary Use of Data’; all use of data can be useful, taking into account need and context

Pearce C, Shearer M, Gardner K, Kelly J. A division's worth of data. Aust Fam Physician. 2011 Mar;40(3):167-70. http://www.med.monash.edu.au/general-practice/magnet/

POLAR

•  50 practices •  200+ GPs •  1,000,000+

patients •  Longitudinal

and patient centred data

•  Geographic coverage

•  10 years of data quality activities

https://iemml.org.au/polar-data

Challenges Coded vs Uncoded Diagnosis vs Reason For Encounter vs NBI Workflow vs Academic Rigour Always a gap – How relevant is that? The Public Interest…