Enhancing the quality of pathology in Australian …€¦ · Web viewd Royal College of Pathologists of Australasia Quality Assurance Program, St Leonards, Sydney, NSW, Australia

Enhancing the quality of pathology test requesting and test result management in Australian general practiceEuan McCaughey,a Julie Li,a Ling Li,a Meredith Makeham,a Robert Borotkanics,a Douglas Boyle,b

Adam Mcleod,c Tony Badrick,d Johanna I Westbrook,a Andrew Georgioua

a Centre for Health Systems and Safety Research, Australian Institute of Health Innovation,

Macquarie University, Sydney, NSW, Australia

b GRHANITETM Health Informatics Unit, Health and Biomedical Informatics Centre, University of

Melbourne, Melbourne, VIC, Australia

c Melbourne East GP Network, Burwood East, Melbourne, VIC, Australia

d Royal College of Pathologists of Australasia Quality Assurance Program, St Leonards, Sydney,

NSW, Australia

This project was funded by an Australian Government Department of

Health: Quality Use of Pathology Program grant

Suggested citation:

Enhancing the quality of pathology in Australian general practice

McCaughey EJ, Li J, Li L, Makeham M, Borotkanics RJ, Boyle D, Mcleod A, Badrick T, Westbrook JI

and Georgiou A. Enhancing the quality of pathology test requesting and test result management

in Australian general practice. Report to Commonwealth of Australia, Department of Health,

Quality Use of Pathology Committee. Australian Institute of Health Innovation, Macquarie

University, Sydney. October 2016.

© Centre for Health Systems and Safety Research, Published October 2016

Centre for Health Systems and Safety Research, Australian Institute of Health Innovation,

Macquarie University

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Table of Contents

EXECUTIVE SUMMARY........................................................................................................1

Project aim..........................................................................................................................1

Literature review................................................................................................................ 1

Data extract........................................................................................................................2

Project setting.................................................................................................................2

Quality analysis...............................................................................................................2

Data contents..................................................................................................................2

Key findings.....................................................................................................................2

Development of study protocol..........................................................................................2

ABBREVIATIONS.................................................................................................................3

BACKGROUND & AIMS.......................................................................................................4

Background.........................................................................................................................4

Project aim..........................................................................................................................5

Key performance indicators................................................................................................5

Data quality.....................................................................................................................5

Development of protocol................................................................................................6

SYSTEMATIC LITERATURE REVIEW EVALUATING THE QUALITY OF PATHOLOGY RESULT INTERPRETATION BY GENERAL PRACTITIONERS.................................................................7

Aim..................................................................................................................................... 7

Search strategy...................................................................................................................7

Analysis...............................................................................................................................2

Results................................................................................................................................ 2

Critical appraisal..............................................................................................................2

Key recommendations........................................................................................................8

APPRASIAL OF GENERAL PRACTICE DATA QUALITY............................................................9

Aim..................................................................................................................................... 9

Study setting.......................................................................................................................9

Data extract........................................................................................................................9

Evaluation of data quality...................................................................................................9

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Accuracy..........................................................................................................................9

Comparability................................................................................................................10

Completeness................................................................................................................10

Conformity....................................................................................................................10

Consistency...................................................................................................................10

Relevance......................................................................................................................11

Timeliness..................................................................................................................... 11

Usability........................................................................................................................ 11

Validity.......................................................................................................................... 11

Data contents................................................................................................................... 11

Data linkage......................................................................................................................12

Key findings.......................................................................................................................13

PROTOCOL FOR INVESTIGATING QUALITY USE OF PATHOLOGY IN AUSTRALIAN GENERAL PRACTICE..........................................................................................................................14

Aim................................................................................................................................... 14

Study protocol.................................................................................................................. 14

Context..........................................................................................................................14

Setting........................................................................................................................... 14

Significance................................................................................................................... 14

Methods........................................................................................................................16

Timeline........................................................................................................................ 18

Outcomes......................................................................................................................19

REFERENCES..................................................................................................................... 19

APPENDICES..................................................................................................................... 22

Appendix 1: Centre for Health Systems and Safety Research (CHSSR) overview..............22

CHSSR Overview............................................................................................................22

Mission..........................................................................................................................22

Aims.............................................................................................................................. 22

Appendix 2: Tables and fields extracted from MEGPN database......................................23

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EXECUTIVE SUMMARYPathology is a vital branch of medical science, influencing up to 70% of critical decisions involving patient care.1 The past decade has seen a dramatic increase in the volume of pathology tests ordered by Australian General Practitioners (GPs),2 amounting to an additional 4 million tests per year.2 This has raised concerns about the substantial costs and risks associated with potentially unnecessary tests and the incorrect management of results, including unnecessary patient discomfort and increased risk of unnecessary additional tests and procedures. While individual studies have identified variation in the way GPs interpret and act upon pathology results,3-5 herein referred to as test result management, there has been no comprehensive review that has synthesised this evidence. Furthermore, despite a number of initiatives intended to enhance how GPs use pathology,6 there has been no comprehensive data-driven assessment of how well pathology is used, and results managed, in Australian general practice. Such assessments have been hampered by the fragmented nature of Australian general practice data.

Project aimThis collaboration between Macquarie University, the University of Melbourne, Melbourne East GP Network (MEGPN) and the Royal College of Pathologists of Australasia Quality Assurance Program, aims to combine best practice evidence, reliable data and expertise in data management, information systems and quality improvement infrastructures to:

Synthesise the best available evidence of how GPs interpret and act upon pathology results;

To extract and assess the contents of a sample of Australian general practice data to examine whether it can be used to investigate the quality of pathology requesting and result management;

Develop a research protocol to examine the quality of pathology requesting and result management in Australian general practice.

Literature reviewA literature review was undertaken to investigate how GPs interpret and act upon pathology results for patients with Diabetes Mellitus (DM) and Cardiovascular Disease (CVD). Fourteen studies were included in the review, seven of which employed clinical surveys and seven of which had a quantitative design. Of the seven surveys: five found that GPs demanded unrealistically high precision from pathology tests; three identified large variation in the change between two consecutive results that GPs regarded as indicating a change in a patient’s wellbeing; three found that GPs generally acted on smaller increases than decreases in International Normalized Ratio and Glycohemoglobin A1c; three found large variation in the use of repeat testing for diagnostic conformation; and two found that GPs generally overestimated the risk of complications associated with DM and CVD based on pathology results. Of the seven quantitative studies: four observational studies found that GPs often failed to initiate appropriate treatment for patients with DM and CVD and two intervention studies found that

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providing GPs with feedback relating to their pathology test ordering and interpretation practices and the addition of educational messages to pathology results both improved clinical outcomes. Overall, evidence about how well GPs manage results and the impact this has on patient outcomes remains weak and inconclusive. However, this review identified a number of areas where interventions could support GPs to improve the management of laboratory test results, including feedback to GPs and the addition of educational messages to test result reports.

Data extract

Project setting

Data were extracted from 50 Australian general practices, with these practices covering a metropolitan area of 319 km2, with an estimated population of 626,314.7

Quality analysis

Data were extracted from both Medical Director and Best Practice relating to: patient, practice and GP demographics; visits to GPs (including the reason for visit); patient diagnoses; pathology requests; pathology results; and medication prescriptions. The data were assessed for accuracy, comparability, completeness, conformity, consistency, relevance, timeliness, usability and validity. One site, which provides only after hours care, was removed from further analysis as staff at this site were responsible for ordering pathology but not for managing the pathology results (this was done by the patient’s regular GP).

Data contents

The final data, which covered visits in the period from 1st January 1990 to the 27th March 2015, contained information relating to a total of:

49 practices 3,845 GPs1

1,759,909 patients 25,697,006 visits 35,357,785 individual pathology test results from 12,555,807 pathology tests 15,780,941 prescriptions

Key findings

The quality of the data in the data extract was verified by the research team, with the data found to be of sufficient quality for further analysis.

Development of study protocolThe findings from both the literature review and data extract were combined to inform the design of a study protocol. This protocol will be used in a proposed study that will aim to generate the most accurate and comprehensive data on how well pathology is used and the results managed in Australian general practice.

1 This includes GPs no longer at each practice. Furthermore, when a patient changes practice and asks for their patient record to be transferred to this new practice, their previous GP will be regarded as being a staff member at both the old and new practice

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ABBREVIATIONSAcronyms Description ACR Albumin–Creatinine Ratio BG Blood Glucose CVD Cardiovascular Disease DM Diabetes Mellitus GP General Practitioner HbA1c Glycohemoglobin A1c INR International Normalized Ratio MA Microalbuminuria MEGPN Melbourne East GP Network PHN Primary Health Network UA Urine Albumin VKA Vitamin K Antagonist

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BACKGROUND & AIMS

BackgroundPathology, the science of the causes and effects of disease, is an important and valuable branch of medical science, estimated to contribute to 60-70% of all critical decisions involving patient treatment.1 The past decade has seen a dramatic increase in the number of pathology tests being ordered by Australian General Practitioners (GPs), with approximately 25.5 million more tests ordered in 2015 compared to 2005.2 As a result, the Australian government, through Medicare, spent $2.5bn on pathology services in 2014-2015. While this increase may be linked to enhanced diagnostic technologies that offer superior patient insights,9 it has raised major concerns about the costs and risks associated with unnecessary tests. Further, questions have been voiced about the capacity of GPs to accurately interpret and take appropriate clinical actions in response to the results generated from this vast amount of tests, a process herein referred to as test result management. In the case of unnecessary testing, these risks include increased patient discomfort and increased risk of harm due to inappropriate treatment and unnecessary additional tests and procedures.10 The incorrect management of pathology results can also lead to incorrect treatment and unnecessary additional tests and procedures and delayed, missed or incorrect diagnoses.10 Therefore, for pathology tests to be of value, appropriate use of pathology testing and proper management of results is crucial.

In primary care, pathology requesting, result interpretation and subsequent patient management is the responsibility of the GP.11 Cardiovascular Disease (CVD) and Diabetes Mellitus (DM), are two chronic conditions frequently monitored by GPs. Together these conditions were estimated in 2004 to account for 9.9% and 1.3% of the total global burden of disease, respectively.12 There are numerous clinical guidelines available to assist GPs in providing appropriate, evidence-based, laboratory testing for patients with these conditions,13,14 with the most commonly ordered tests being Blood Glucose (BG),15 Glycohemoglobin A1c (HbA1c),15 Urine Albumin (UA),16 Albumin–Creatinine Ratio (ACR)17 and International Normalized Ratio (INR).18,19 For patients with DM, BG facilitates daily monitoring of metabolic control, while HbA1c enables an estimate of metabolic control over a longer period of time.15 Regular monitoring of HbA1C for patients with DM is crucial, as each 1% elevation in HbA1C for these patients increases the risk of a cardiovascular event by 18%,20 death by up to 14%21 and retinopathy or renal failure by 37%.22 Microalbuminuria (MA) and macroalbuminuria serve as predictors of renal disease and CVD in DM,16 with UA16 and ACR17 recommended as screening tests to detect their presence. Vitamin K Antagonists (VKAs) are a group of oral anticoagulants, the most common of which is Warfarin, used to manage patients with CVD.23 During VKA treatment, the risk of major bleeding increases by 42% for each one point increase in INR,18 with the risk of thromboembolism increasing with the use of low-intensity therapy (INR 1.5–1.9) compared to conventional-intensity therapy (INR 2.0–3.0).24 Therefore, VKA treatment requires strict dose adjustment within narrow therapeutic intervals to avoid bleeding or thromboembolic complications.19 Misinterpretation of the results of these tests may lead to suboptimal patient care. Despite individual studies identifying variation in the way GPs interpret pathology results, there has been no review which has synthesised this evidence.

In Australia, most GPs work in private practices. The private nature of practices means that there

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is little standardisation over how each practice operates. As a result, GPs in Australia are free to select their own practice software, with a number of different packages currently used across the country. Furthermore, patient data are normally stored in individual practices. The fragmented nature of these data has, to date, prevented any comprehensive data-driven analysis of whether pathology is used and interpreted in line with current best-practice guidelines in Australian general practice.

Project aimThis collaboration between Macquarie University, the University of Melbourne, Melbourne East GP Network (MEGPN) and the Royal College of Pathologists of Australasia aimed to combine best-practice evidence, reliable data and expertise in data management, information systems and quality improvement infrastructures to:

Perform a systematic literature review to synthesise evidence of the accuracy with which GPs interpret and act upon pathology results;

Assess the reliability of pathology referral data from a selection of Australian general practices; and

Develop a research protocol to facilitate an extensive examination of the quality of pathology requesting and result management in Australian general practice.

Key performance indicators

Data quality

The quality of data can be verified by analysing its accuracy, comparability, completeness, conformity, consistency, relevance, timeliness, usability and validity (see Table 1).25,26

Table 1. Data quality indicator

Quality Indicator Definition Accuracy A measure of how well information in data reflects what it is supposed to

measure.25

Comparability The extent to which data are uniform over time and use standard conventions.25

Completeness The proportion of all potential data that were available.25

Conformity How well the data conform to expected formats, such as standardised nomenclature.25

Consistency How well data agree across different data sets, and the extent of agreement between different data sets that are measuring the same thing.25 This is particularly pertinent to this analysis, as a high level of consistency is required to enable triangulation of data from different sources through data linkage.

Relevance How well data meet the current and future analytics needs of the organisation.25

Timeliness How recent and up-to-date the data are for analysis.25

Usability How easy it is to access, use and understand data.25

Validity The extent to which data measure what they claim to be measuring. This can be further divided into convergent and discriminate validity.26 Convergent validity is the degree to which multiple uses of the same concept are in agreement and discriminate validity is the degree of overlap between data that should not relate to each other.26

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Development of protocol

We believe that data-driven studies designed to assess the quality with which pathology is used and managed in Australian general practice have been hampered by the fragmented nature of Australian general practice data. Future data-driven studies into the quality use of pathology in Australian general practice require a robust protocol supported by best-practice evidence and which makes use of high quality data. Development of such a protocol is a key aim of this study.

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SYSTEMATIC LITERATURE REVIEW EVALUATING THE QUALITY OF PATHOLOGY RESULT INTERPRETATION BY GENERAL PRACTITIONERS

AimThe aim of this systematic literature review was to synthesise the best available evidence of how GPs interpret and act upon pathology results for patients with DM and CVD.

Search strategyA systematic search of MEDLINE, CINAHL, EMBASE, Evidence Based Medicine Reviews (EBMR), ProQuest and Scopus was performed in May 2016.27 Peer-reviewed studies published between January 2000 and December 2015 were included if they analysed GPs’ interpretation of BG, HbA1c, UA or ACR for patients with DM or INR for patients with CVD. Articles were excluded if they were: not in English, duplicates, experiments on animals, case reports, abstracts, editorials or reviews.

The search was performed by combining terms relating to: general practice, pathology, interpretation and DM or CVD. The exact search strategy is shown in Table 2.

Table 2 Systematic review search strategy 1. EXP General Practice/ 2. Primary Health Care/ 3. General Practitioners/ 4. Physicians, Family/ 5. Physicians, Primary Care/ 6. General adj practi$.ti,ab. 7. General adj physician$.ti,ab. 8. Family adj practi$.ti,ab. 9. Family adj physician$.ti,ab. 10. Primary adj healthcare$.ti,ab. 11. Primary adj health adj care$.ti,ab. 12. Primary adj care$.ti,ab. 13. OR/1-12 14. EXP Pathology/ 15. Laboratories/ 16. Pathology.ti,ab. 17. Laborator$.ti,ab. 18. OR/14-17 19. 13 AND 18 20. Medication Therapy Management/ 21. EXP Quality Assurance, Health Care/ 22. EXP Disease Management/ 23. Monitor$.ti,ab. 24. Interpret$.ti,ab. 25. Manage$.ti,ab. 26. Follow$.ti,ab. 27. Assess$.ti,ab.

28. Screen$.ti,ab. 29. Treat$.ti,ab. 30. OR/20-29 31. 19 AND 30 32. Blood Glucose Self-Monitoring/ 33. EXP Diabetes Mellitus/ 34. Hemoglobin A, Glycosylated/ 35. EXP Glucose/ 36. EXP Albumins/ 37. Warfarin/ 38. International Normalized Ratio/ 39. EXP Cardiovascular Diseases/ 40. Diabetes.ti,ab. 41. HbA1C.ti,ab. 42. Glucose.ti,ab. 43. Albumin$.ti,ab. 44. Warfarin.ti,ab. 45. INR.ti,ab. 46. International adj Normalised adj

Ratio.ti,ab. 47. Cardiovascular adj disease$.ti,ab. 48. OR/32-47 49. 31 and 48 50. limit 49 to yr="2000 - 2015" 51. limit 50 to english language

EXP: Explode, ti: title, ab: abstract, $: and anything after, adj: must be followed by

Titles and abstract of all articles returned using the terms outlined in Table 2 were independently reviewed and sorted based on the predefined inclusion criteria. The full text of

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the studies that matched these criteria were independently reviewed and sorted based on the inclusion criteria. Reference lists of included studies were hand searched for further relevant studies.

AnalysisFor the purpose of this analysis, management of laboratory results is defined as the process of interpreting laboratory results and subsequently providing appropriate care based on these results. Included studies were combined into two groups, namely clinician surveys and observational and interventional studies. The quality of each study was assessed using The Effective Public Health Practice Project (EPHPP) framework28 (shown to be suitable for randomised and non-randomised studies29).

ResultsThirteen articles met the inclusion criteria. Twelve (92.9%) were from Europe16,23,30-40 and one was from Australia41 (see Table 3).

Table 3. Studies in systematic review classified by continent and country

Continent (studies) Country (studies) Study Author Europe (12) Norway (5) Kristoffersen et al. 30; Skeie et al.32; Aakre et al. 31;

Skeie et al.33; Kristoffersen et al.23

Europe (12) Denmark (2) Schroll et al.34; Kristensen et al.35

Europe (12) Germany (1) Haller et al.16

Europe (12) Russia (1) Boystov et al.39

Europe (12) Netherlands (1) Hellemons et al.37

Europe (12) United Kingdom (1)

Foy et al.40

Europe (12) Portugal (1) Cortez-Dias et al.38

Australasia (1) Australia (1) Thomas et al.41

Seven studies employed a clinician survey design,16,23,30-33,41 four were cross-sectional observational studies,35,37-39 one was a cohort study34 and one was a Randomised Controlled Trial (RCT).40 Ten articles investigated the use of laboratory testing for patients with DM16,31-35,37,38,40,41 and six the use of laboratory testing for patients with CVD.23,30,34,38-40 Four articles were published between 2000 and 2007,23,32,33,41 with the remaining nine published between 2008 and 201516,30,31,34,35,37-40 (see Table 4).

Critical appraisal

Of the seven clinician surveys,16,23,30-33,41 six were rated as of weak quality (primarily due to all six having a response rate of <60%)16,23,30-33 and one was moderate.41 Of the four observational studies,35,37-39 one was rated as of weak quality (due to a lack of randomisation and non-validated data collection methods39) and three as moderate.35,37,38 The one cohort study and one RCT were both rated as of strong quality.34,40

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Table 4. Summary of included studies. Quality was graded according to the Effective Public Health Practice Project (EPHPP) framework28

Study design Author Year Participants Key FindingsClinicianSurvey

Kristoffersen et al.30

2012 2473 GPs & 543 specialists

Most GPs would change VKA dose if INR was at or just outside therapeutic range Annual stroke and bleeding risk was overestimated and varied significantly No correlation between bleeding risk and dose reduction or number of days to a new INR measurement

ClinicianSurvey

Haller et al.16 2010 800 GPs, 450 cardiologists & 450 diabetoligists

Association of MA with kidney damage was well recognised, but association with other organ damage poorly recognised

Large variation in suggested criteria to confirm MA Inter-country and inter-disciplinary variation in % of patients with MA

ClinicianSurvey

Aakre et al.31 2008 Median CDs for an increase or decrease in UA were similar in most countries and did not vary by reporting unit Large intra-country variation in CDs, with similar variation in each country GPs required analytical imprecision of <14%

ClinicianSurvey

Kristoffersen et al.23

2006 1547 GPs Median time to next INR followed guidelines for a stable patient, but was too long for a patient with a supratherapeutic INR

Only 29% of GPs identified the correct INR therapeutic range for a patient with a mechanical heart valve, with 63% identifying the correct range for a patient with pulmonary embolism

There was substantial variation in CDs Bleeding risk was overestimated and varied significantly

ClinicianSurvey

Thomas et al.41 2006 348 GPs GPs routinely estimate kidney function in a low proportion of patients with DM Good correlation between GP-estimated values of kidney function and those derived from patient notes, with GPs

able to identify individuals with impaired function in 83% of cases Only 50% of patients were correctly categorised by GPs as having impaired kidney function

ClinicianSurvey

Skeie et al.32 2005 2538 GPs Importance of a repeat BG test after a value close to limit of therapeutic range varied significantly between countries

CDs for BG were similar across countries with large intra-country variations, CDs for HbA1c were also similar across countries with larger intra-country variation

GPs demanded an unattainable level of analytical imprecisionClinicianSurvey

Skeie et al.33 2000 444 GPs CD was higher for a decrease compared to an increase in HbA1c GPs require a maximum analytical imprecision of 2.2% 22% of GPs act on changes in HbA1c less than the analytical imprecision

Observational Boystov et al.39 2013 1000 consecutive outpatients

Warfarin and statin prescription for patients with arterial fibrillation and hypercholesterolemia was inadequate Statin prescription was similar in patients with total cholesterol >5.0 mmol/l and >6.2 mmol/l Only 0.6% of patients with total cholesterol >6.2 mmol/l received statins in high doses

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Study design Author Year Participants Key FindingsObservational Hellemons et al.37 2013 14120 patients

with DM Treatment was initiated in only 14.3% of patients with repeat increased albuminuria Appropriate action was taken in only 16.5% of patients with incident increased albuminuria

Observational Cortez-Dias et al.38 2010 16856 patients 9.8% of diabetic patients with DM were not treated with antidiabetic therapy Only 57.4% of DM patients with indications for statin therapy were prescribed statins

Observational Kristensen et al.35 2008 2473 patients with DM

24% of patient with HbA1C >8% had no new HbA1c measurement or treatment within 3 months and 25% had no treatment within 1 year

Median time to second test was shorter for patients with HbA1c >8% than HbA1C <8%.Cohort Schroll et al.34 2012 8320 patients

with DM Clinician feedback led to a significant reduction in risk of having high HbA1c or total cholesterol and not

being prescribed antidiabetic medication or statinsRCT Foy et al.40 2011 8690 patients

with DM Addition of educational messages to pathology tests led to a significant increase in likelihood of further HbA1c test

or foot inspection and a statistically significant reduction in diastolic BP and likelihood of MA patient having controlled BP

Educational messages had no significant effect on: mean HbA1c or cholesterol, likelihood of controlled HbA1c, cholesterol or BP, further cholesterol test, systolic BP

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Clinician surveys

Kristoffersen et al.23 presented 1547 Norwegian GPs with two scenarios relating to the management of patients with a stable and supratherapeutic INR, with a second study presenting similar scenarios to 2473 (82%) GPs and 543 secondary care specialists from 12 countries.30 Skeie et al.33 surveyed 444 Norwegian GPs to evaluate the interpretation of HbA1c and BG for a patient with DM and possible DM, respectively, with a second study presenting the same scenarios to 538 GPs across seven European countries.32 Aakre et al.31 surveyed 2078 GPs across nine European countries to investigate the use of UA to detect MA, while Haller et al.16 surveyed 800 GPs, 450 cardiologists and 450 diabetologists across five European countries to investigate how MA is measured and its relationship to organ damage. Thomas et al.41 surveyed 348 Australian GPs to assess their ability to estimate kidney function from laboratory data and to identify patients with poor kidney function. The results of these surveys are reported under the following themes: risk estimates; critical differences and analytical variation; repeat testing; therapeutic ranges and dosing; and diagnosis of secondary complications.

Risk estimates

Two studies that asked GPs to estimate the risk of complications associated with CVD based on laboratory test results found that GPs overestimated these risks.23,30 When 1547 Norwegian GPs were asked to estimate the risk of a bleed within 48 hours for a patient with a supratherapeutic INR (5.9), the median estimated risk was 75 times higher than the current best estimate from the literature.23 A similar international survey of 2473 GPs found that GPs overestimated the risk of a stroke or bleed by 2-3 times, with no correlation between the perceived risk of bleed and suggested VKA dose reduction (r=−0.07) or time to a new INR measurement (r=0.06).30 Risk estimates were found to be more accurate for GPs: dosing VKA at least once a week; familiar with the CHADS2 score; and using clinical decision support.30

Haller et al.16 asked 800 GPs, 450 cardiologists and 450 diabetologists across five European countries to identify the risk between Microalbuminuria and additional complications. While the association between Microalbuminuria and kidney damage was recognised by at least 93.8% of clinicians in each country, the risks of Microalbuminuria to the heart, eyes and brain and association with microvascular and macrovascular complications were recognised by <50% of clinicians in each country.16

Critical differences and analytical variation

Four surveys asked GPs to state the change in two consecutive laboratory results that would indicate that a patient’s condition had improved or deteriorated, termed the Critical Difference (CD).23,31-33 All four studies found large intra- and inter-country variation in CDs.23,32,33 Three studies also found that GPs proposed smaller CDs for increases than decreases in INR and HbA1c,23,32,33 probably due to increased INR being associated with an increased risk of a major bleed and increased HbA1c being associated with an increased risk of DM complications.18,20-22 CDs for an increase and decrease in BG32 and UA31 were similar across countries. Aakre et al.31 found that CDs for UA did not vary by reporting unit, while Skeie et al.33 found intra-country variation in CDs between GPs who used Point of Care Testing (PoCT) and those who did not.

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The smallest real change between two consecutive laboratory results that can be accurately detected depends on within-subject variation (attainable from published studies) and analytical precision (the analytical variation of the device and inter-subject variation).23 As CD is directly related to within-subject variation and analytical imprecision,23 five studies used CDs to calculate the required analytical imprecision expected by GPs.23,30-33 All five studies found that GPs demanded unrealistically low analytical variation, with the expected analytical imprecision at 95% confidence often too small to accurately calculate. Skeie et al.33 also found that 22% of 444 Norwegian GPs would act on changes in HbA1c less than analytical imprecision.

Repeat testing

Two studies, one involving 1547 Norwegian GPs and one 2473 GPs from 13 countries, asked GPs to state the time to next INR for a stable and supratherapeutic patient. They found that the median time for the stable patient (4 weeks) corresponded with current guidelines (3-4 weeks).23,30 While there was large variation for the patient with a supratherapeutic INR (10 th-90th percentiles: 3-8 days23 inter-country range: 2-7 days, inter-clinician range: 1-14 days30), the findings were in line with current guidelines, which range from between one and 14 days.30 There was also no intra-country differences in suggested time to next INR between GPs using clinical decision support compared to those relying on clinical experience.30

Three studies found large variation in adherence to clinical guidelines on repeat testing,16,31,32 with one international study of 2538 GPs finding a significant (p<0.001) inter-country variation in the perceived importance of a repeat BG test after recording a BG value that was close to the therapeutic limit.32 It was also found that after a positive detection of MA in a UA test, only 62% of 2078 Norwegian GPs would follow clinical guidelines and employ a repeat test,31 and that only 21.5% of 800 European GPs knew that two of three tests had to be positive.16

Therapeutic ranges and dosing

When asked the therapeutic INR range for a patient with a mechanical heart valve, only 29% of 1547 Norwegian GPs stated the correct range (2.5-3.5).23 The suggested therapeutic range for a patient with pulmonary embolism was more homogenous, with 63% of these GPs stating the correct range (2.0-3.0). This may be because while there is strong consensus on the therapeutic INR range for pulmonary embolism, different therapeutic ranges have been suggested for mechanical heart valves. A further study of 2473 GPs from 13 countries found that for a patient with a stable INR, most clinicians would change the dose of VKA if the INR result increased or decreased to a level at or just outside the therapeutic range.30 A further study of 2473 GPs from 13 countries found that for a patient with a stable INR, 30–40% of GPs in Belgium and Hungary would change the medication dose when INR was still within the target range, compared to <10% in the other countries.30 Furthermore, INR values of ≤1.7 and ≥3.5 were tolerated by 50% of GPs in Denmark, compared to up to 15% and 30% in other countries. This finding from Denmark is concerning due to the increased risk of complications at these values. For a patient with a supratherapeutic INR, there was considerable variation in the suggested VKA dose reduction during the first two days (inter-country range 53%-100%, inter-clinician range 9-100%).30 However, there was no intra-country differences between GPs using clinical decision support compared to those relying on clinical experience.

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Diagnosis of complications

Thomas et al.41 found that while only 24% of patients with DM had routine estimates of kidney function, there was good correlation (R2=0.72) between GP-estimated values of kidney function for all patients and those derived from laboratory test results in patients’ medical records. GPs were able to identify individuals with a creatinine clearance of <60 mL/min in over 83% of cases, with a specificity of 90%. This suggests that GPs are able to estimate kidney function, they just do not always feel the need to do so.41

Observational and interventional studies

The results of the four observational studies are reported under the theme of appropriate treatment, while the results of the cohort study and RCT are reported under the theme of interventions to support test result interpretation.

Appropriate treatment

While appropriateness of treatment can be influenced by a wide range of factors, including patient attendance at follow-up appointments, four observational studies investigated whether GPs delivered treatment in line with current clinical guidelines based on their laboratory results. A study of 1000 patients treated by GPs and cardiologists in Russian outpatient clinics revealed that VKA (4.4%) prescription for patients with arterial fibrillation and statin (51.1%) prescription for patients with hypercholesterolemia was inadequate.39 Inadequate treatment was also found in a study of 2473 DM patients treated by Danish GPs, where 24% of patients with high (>8%) HbA1c had no new measurement or pharmacological treatment within 3 months and 25% had no treatment within 1 year.35 Additionally, a study of 16,856 patients being treated by Portuguese GPs found that 9.8% of DM patients were not treated with anti-diabetic therapy and that 42.6% of DM patients with a positive indication for statin therapy were not prescribed statins.38 This finding is similar to that of a study of 182 Dutch GPs that showed that for unmedicated patients with repeated and incident increased albuminuria, appropriate action (repeat measurement and/or treatment) was performed for only 16.5% and 14.3%, respectively.37 However, these results must be judged with caution, as pharmacological treatment may have been substituted for lifestyle interventions in some patients. Therefore, a lack of treatment may not always indicate poor interpretation of results.

Interventions

A cohort study of 8320 Danish DM patients, which was classified as of strong quality, provided GPs with annual feedback on the proportion of patients with critical pathology values who were not medicated. This feedback significantly (P<0.001) reduced the risk of a patient having an HbA1c >7.0% and not being on antidiabetic medication, or having a total cholesterol >4.5mmol/L and not being prescribed statins.34

A cluster RCT of 8690 British DM patients showed that the addition of educational messages to HbA1c and ACR results led to a statistically significant increase in the likelihood of further HbA1c tests (IRR 1.06, 95% CI: 1.01-1.11) and foot inspection (IRR 1.26, 95% CI: 1.18-1.36) and a statistically significant decrease in diastolic BP (-0.52 mmHg, 95% CI: -0.73 -0.32).40 However,

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there was no effect of educational messages on HbA1c, the proportion of patients with HbA1c <6.35%, cholesterol level, systolic blood pressure or the likelihood of: cholesterol being within target range; further cholesterol tests; and BP <140/80 mmHg. As pre-trial clinical values were already reasonable, the findings suggest that there may be a threshold in clinical performance beyond which educational messages do not work or have only modest effects.40 Furthermore, the inclusion of educational messages actually reduced the likelihood of MA patients having a BP of <130/80 mmHg (OR 0.88, 95% CI: 0.78-0.99).

Key recommendations Evidence about how well GPs manage results and the impact this has on patient

outcomes remains weak and inconclusive. As such, there is a pressing need to produce new evidence about how GPs manage laboratory test results and the impact on decision-making and patient outcomes.

GPs may benefit from interventions designed to enhance: how they estimate risks based on pathology results; their identification of therapeutic ranges; their awareness of the need for repeat tests; and the level of test result precision that is currently available.

Improved feedback to clinicians about their test ordering and results management practices, and the addition of educational messages to pathology results may improve the appropriate use of pathology, enhance test result management, and as such improve subsequent patient outcomes.

Quality of patient care may also be improved through increased awareness of current evidence based clinical guidelines relating to appropriate test result management practices.

Clinical decision support may also assist GPs to initiate appropriate care based on pathology results. Specific target areas are clinical decision support to assist with: dosing, risk estimates, identifying correct therapeutic ranges and identifying patients who require treatment initiation.

All of the aforementioned strategies, based on the currently limited evidence base, could be implemented by either laboratories or local health care providers, and may support improved GP result interpretation. However, there is a clear need for well-designed interventional studies to provide clearer direction as to effective strategies to support test result management, particularly strategies which support improved patient care and outcomes.

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APPRASIAL OF GENERAL PRACTICE DATA QUALITY

AimTo extract and assess the contents of a sample of Australian general practice data to examine whether it can be used to investigate the quality of pathology requesting and test result management.

Study settingMEGPN is an independent organisation providing clinical services and support to general practice and Primary Health Networks (PHNs). The data in this study is from practices covering a metropolitan area of 319 km2, with an estimated population of 626,314.7 Due to the fragmented nature of other Australian general practice data, the centrally stored data from these practices is believed to provide some of the most comprehensive information relating to pathology ordering and results management available nationally.

Data extractGeneRic HeAlth Network Information Technology for the Enterprise (GRHANITE), is a world-leading middleware (software that acts as a bridge between a database and computer programs) developed by researchers at The University of Melbourne for interfacing with healthcare databases.42 A unique aspect of this software is that it is able to irrevocably de-identify patient information, making it ideal for use in health informatics research. GRHANITE was used to extract de-identified data from 50 general practices in MEGPN. Practices were combined based on whether they used Medical Director or Best Practice software, providing the research team with two datasets. An overview of the specific fields extracted from Medical Director and Best Practice is provided in Appendix 2.

Evaluation of data qualityEach dataset was analysed for accuracy, comparability, completeness, conformity, consistency, relevance, timeliness, usability and validity. It should be noted however that the data were evaluated prima facia, and specific laboratory procedures were not evaluated within the scope of this project.

Accuracy

Accuracy is a measure of how well information in data reflects what it is supposed to measure.25 To determine the accuracy of the data, the contents of all fields were analysed and suspect entries identified. Such entries were further analysed by the research team and, after liaison with MEGPN, data deemed to be inaccurate, incomplete or irrelevant, were removed from the dataset. This process led to the removal of one site (Site 15) from the data. This site, which provides only after- hours care, was removed from the data analysis as staff at this site were responsible for ordering pathology but not for managing the pathology results (this was done by the patient’s regular GP). Three hundred and eighteen patients in Medical Director and 25 in Best Practice had a date of birth before 1887, and 787 had a visit date prior to 1990, with these data judged to be inaccurate.

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Comparability

Comparability relates to the extent to which data are uniform over time and use standard conventions.25 The Medical Director and Best Practice data were found to use similar standard conventions over the whole extract period, with only a small amount of recording required. An example of this is patient Gender, which was coded as 1 and 2 in Best Practice, and M and F in Medical Director. Overall, the data were regarded as comparable.

Completeness

Completeness relates to the proportion of all potential data that were available.25 This is particularly important for data linkage, as incomplete data can prevent accurate triangulation of different datasets. In this project, completeness of the data were analysed by liaising with MEGPN, to confirm that the data contained information relating to all GP visits and pathology orders over the time period. The results from this analysis of completeness are reported below for each of the software providers.

Medical Director: 7.8% of users had no stated job role; 1.3% of patients had no gender code. 7.8% of patients had no valid year of birth; 31.7% of diagnoses had no SNOMED code. It should be noted that SNOMED does not come from the practice software and is additional mapping completed by MEGPN post data extraction. Therefore, while all diagnoses have a SNOMED code of some form, this high percentage of incomplete data may be caused by: errors in spelling, abbreviation or other incorrect use; some diagnosis may not be mapped as they are low in number; the field is often used incorrectly and contains irrelevant data.

Best Practice: 34.3% of staff members had a job role of ‘guest’; 2.4% of patients had a gender code of blank; 19.8% of diagnoses had no SNOMED code.

Combined data: 41.3% of prescriptions had either no Anatomical Therapeutical Chemical class, or were classed as other. This may be because of issues with spelling, abbreviation, etc.; issues with brand versus generic name being used incorrectly in the relevant field; or over the counter and natural medicines being entered into this field, which do have a direct match in the Anatomical Therapeutical Chemical class code.

Conformity

Conformity relates to how well the data conform to expected formats, such as standardised nomenclature.25 All pathology data were found to use standard formats for data entry. Visit data were largely standardised, except for the fields relating to: Resource Type; Reason for medication; Frequency, Strength, Dose and Quantity of Medication; Reason for Pathology Ordering and the Ordered Test Name. These were all found to be free text. Therefore, while the data extract was generally found to have good conformity, this could be further improved by standardisation of the aforementioned fields.

Consistency

Consistency relates to how well data agree across different data sets, and the extent of agreement between different data sets that are measuring the same thing.25 This is particularly pertinent to this analysis, as a high level of consistency is required to enable triangulation of data from different sources through data linkage. It was found that after the

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accuracy of the data had been established (see Section Accuracy above) overlapping data between all the data fields, such as patient unique identifier, gender, year of birth, were in agreement. Therefore, the data were regarded as consistent.

Relevance

Relevance reflects how well data meet the current and future analytics needs of the organisation.25 It was found that the data contained the required fields for the analyses proposed in this project, and were therefore regarded as being relevant.

Timeliness

Timeliness reflects how recent and up-to-date the data are for analysis.25 The extracted data related to patient visits up to 27th March 2015. Analysis was performed in October 2016. Therefore, the data were regarded as having adequate timeliness.

Usability

Usability is concerned with how easy it is to access, use and understand data.25 All data were provided to the research team in a format compatible with standard spreadsheet and statistical software formats (e.g. R), making it easy to both access and use. Where data were coded, the University of Melbourne and MEGPN were able to provide information relating to the interpretation of any coding schemes. A data dictionary for this project was also developed, in collaboration with MEGPN and the University of Melbourne, to enhance the usability of the data extract (see Appendix 2).

Validity

Construct validity is the extent to which data measure what they claim to be measuring. This can be further divided into convergent and discriminate validity.26

1. ConvergentConvergent validity is the degree to which multiple uses of the same concept are in agreement.26 It was found that overlapping data, such as patient unique identifier, gender, year of birth, were in agreement.

2. DiscriminateNo overlap between data that were not supposed to relate to each other was found.

Data contentsThe final data, which covered visits in the period from 1st January 1990 to the 27th March 2015, contained information relating to:

49 practices

3,845 GPs out of 8030 total staff. However, it should be noted that this includes staff no longer at each practice. Furthermore, when a patient changes practice and asks for their patient record to be transferred to this new practice, the staff member who previously treated them will be regarded as being a staff member at both the old and new practice.

1,759,909 patients, 53.6% of whom were female17


25,697,006 visits. For the sites using Medical Director (16,707,286 visits) the median time that the patient’s record was open (providing an indirect measure of visit duration) was 287 seconds, with a mean of 690 seconds. For the sites using Best Practice (8,971,677 visits) the median time that the patient’s record was open was 193 seconds, with a mean of 622 seconds.

35,357,785 individual pathology test results from 12,555,807 pathology tests. The seven most frequent tests associated with these results were: Full Blood Count (n=8,009,260 individual results), Liver Function Tests (n=3,043,318 individual results), Urea Electrolytes Creatinine (n=2,993,920 individual results), Lipids (n=1,732,792 individual results), Blood Gases (n=642,133 individual results), Iron Studies (n=478,878 individual results), Glucose (Excluding HbA1c) (n=456,038 individual results).

15,780,941 prescriptions. The four most frequently prescribed Anatomical Therapeutic Chemical (ATC) groups of medications were: Lipid Modifying Agents (n=547,595), Drugs For Peptic Ulcer And Gastro-Oesophageal Reflux Disease (GORD) (n=506,781), Other Beta-Lactam Antibacterials (n=505,881) and Opioids (n=502,688).

Data linkageMedical Director and Best Practice store data in different tables relating to common variables, such as a table for medication, a table for pathology requests a table for pathology results etc. (see Appendix 2 for list of tables used within this project). While each table in Medical Director and Best Practice is discrete, it was found that there were a number of common variables that could be used to combine data across one or more different tables:

Patient_UUID is a unique identifier relating to each patient which runs across all tables in each data set (except for the users table, which is only related to GPs).

Pathology ID is a Unique record ID of the pathology report that links the data in the Medical Director tables Pathology and Pathology_Atom.

Request_No is a GP clinics reference number for the pathology request and test result that links the data in the Medical Director tables Pathology and Request.

ReportID is the ID given to the results that links the data in the Best Practice tables INVESTIGATIONS and REPORTVALUES.

RequestID is a Unique identifier of the pathology request that links the data in the Best Practice tables REQUESTEDTESTS and INVESTIGATIONS.

VisitID is a unique identifier for the visit that links the data in the Best Practice tables VISITS and VISITREASON

While all tables contain the Patient_UUID and a date, no direct variable was identified that linked visits with prescriptions, pathology tests or pathology test results. Therefore, to link these data advanced data linkage algorithms, which consider the likelihood of two events being directly related, may need to be considered.

It should also be noted that pathology tests ordered by another provider (e.g. cardiologist) will be sent to the GP for review, even though the GP did not order the pathology test. There is no easy way to identify who ordered the pathology test within the data. Assuming that all

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pathology tests reviewed by GPs were also ordered by these GPs will result in an overestimation of the number of pathology tests that a GP ordered.

Key findings The data were generally accurate, comparable, complete, had acceptable conformity,

consistent, relevant, timely, usable and valid.

The quantity of data was judged suitable to be used for a large-scale investigation of the quality of pathology ordering and result management in Australian general practice.

Incomplete data relating to patient gender, year of birth and SNOMED diagnoses may limit some of the analysis and interpretation of data.

Alternative methods for linking currently discrete data will need to be considered.

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PROTOCOL FOR INVESTIGATING QUALITY USE OF PATHOLOGY IN AUSTRALIAN GENERAL PRACTICE

AimTo use information gained from the aforementioned literature review and data extract to develop a study protocol to facilitate the generation of the most accurate and comprehensive data on how well pathology is used and the results managed in Australian general practice and to provide a framework for the monitoring and improvement of these factors.

Study protocol

Context

Primary Health Networks (PHNs) were launched in 2015 to drive primary health care reform and to improve management of federal health funding. They serve as the hub of all GP activity. MEGPN is an independent organisation providing clinical services and support to general practice and PHNs. MEGPN use GRHANITE software to extract data from a range of general practices to provide services including a data warehousing activity to PHNs.

Setting

The data in the proposed study will primarily come from East Melbourne PHN, which covers a metropolitan area of 3,956 km2 comprising a population of over 1.5 million people. Due to the fragmented nature of other Australian general practice data, the centrally stored data from these practices is believed to provide some of the most comprehensive information relating to pathology ordering and results management available nationally, and as such, is the most appropriate database to use for the proposed project. MEGPN will act as a liaison between the research team and East Melbourne PHN.

Significance

As well as increasing the risk of injury, unnecessary patient discomfort and the generation of false-positive results, unwarranted and unnecessary pathology tests represent a major financial burden to the healthcare provider. Furthermore, incorrect interpretation of test results increases the risk of unnecessary treatment or a significant diagnosis being missed. The proposed project aims to generate comprehensive data about the volume and type of pathology tests that are ordered in Australian general practice, and provide an indication of the proportion of tests that are interpreted in line with current clinical guidelines. It is anticipated that widespread dissemination of the findings from this proposed project will enable GPs to analyse their own test ordering and test result management practices and, where necessary, adjust these to improve patient outcomes. It is believed that this will enhance pathology use and interpretation in general practice, helping to improve the safety and effectiveness of patient care.

The proposed identification of sources of variation in pathology ordering within the proposed project should serve to enhance understanding of why doctors order pathology tests that are unnecessary, or do not concur with current clinical guidelines, and why results are incorrectly

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interpreted. It is believed that this will enable PHNs and the Commonwealth Department of Health to develop appropriate guidance to avoid these scenarios. The clinical translation of the framework developed during the proposed project should lead to a reduction in unnecessary pathology tests, and unnecessary procedures associated with incorrect interpretation of pathology results. It is hoped that this evidence-based framework will also provide PHNs and the Commonwealth Department of Health a platform for monitoring and improving pathology usage after the conclusion of this project. Overall, it is believed that the proposed project will make a major contribution to the evidence-based and informed use of pathology both nationally and internationally, which should reduce variation and lead to a direct improvement in the safety, effectiveness and quality of patient care and a significant cost saving for the healthcare provider.

Furthermore, we believe that the proposed project has the potential to benefit numerous stakeholders across Australia, including, but not limited to:

Patients: A reduction in the number of inappropriate pathology tests and an improvement in results interpretation based on the results of the proposed project would improve the safety and effectiveness of patient care. Specifically, a reduction in unnecessary pathology tests should decrease the risk of injury, patient discomfort and false-positive results, and the associated unnecessary further testing and treatment. Enhanced interpretation of test results should decrease the risk of unnecessary treatment or, conversely, a significant diagnosis being missed.

General practice: Providing GPs with comprehensive data on the average number of pathology tests ordered in Australian general practice gleaned from the proposed project, along with an indication of the proportion of tests that were interpreted in line with current clinical guidelines, should enable them to analyse and improve their own practices.

Pathology laboratories: Referrals for inappropriate or unnecessary tests place a significant burden on pathology services. Enhanced use and interpretation of pathology based on the results of the proposed project should reduce the number of such referrals.

Primary Health Networks (PHNs): Information relating to how well pathology is used and interpreted across East Melbourne PHN, coupled with the evidence-based framework generated in the proposed project, will enable PHNs to continually assess the effectiveness of their pathology services after the conclusion of this project. Results highlighting the appropriateness and variation of pathology test use and interpretation across East Melbourne PHN generated from the proposed project should also help to inform decisions for quality improvement in other PHNs, by highlighting areas that may need additional support.

Commonwealth Department of Health: Departments of Health should be able to use the results from the proposed project to influence macro-level decision-making. The evidence-based framework generated in the proposed project may be most effective if applied broadly across entire jurisdictions in the healthcare system to continually monitor and enhance the appropriate use of pathology after the conclusion of this project. Such jurisdiction-wide monitoring should lead to a direct improvement in the quality of pathology and should impact favourably on patient outcomes.

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Methods

GeneRic HeAlth Network Information Technology for the Enterprise (GRHANITE), is a world leading middleware (software that acts as a bridge between a database and computer programs) developed by researchers at The University of Melbourne for interfacing with healthcare databases.42 A unique aspect of this software is that it is able to irrevocably de-identify patient information, making it ideal for use in health informatics research. In the proposed project, GRHANITE will be used to extract de-identified data from the databases of PHNs in Melbourne. These data will cover a period of up to 6 years (depending on when current general practice software was implemented at each site) and will contain the following information:

Patient and encounter

Detailed information of patient encounters, including a unique non-identifiable patient ID, patient demographic information such as age and sex, date/time of encounter, descriptors for the illness (presenting problem), and comorbidities.

Pathology/Medical imaging

Detailed information of test requests, including a unique non-identifiable patient ID, test name and test code, date/time that test was ordered and the result made available, viewed and acknowledged by clinician and whether any error or problem was detected with the sample or the documentation.

Medications

A list of current and new prescriptions, including a unique non-identifiable patient ID, medication name, date/time that prescriptions were ordered.

These factors were all identified in a systematic literature review (see page 7) as possible indicators of the quality use of pathology.

Data linkage and quality analysis

In the first stage of the proposed project, complex data linkage algorithms will be employed to combine all information relating to each patient into one large dataset, termed the linked dataset. Each row in this linked dataset will relate to a patient encounter, containing some, or all, of the aforementioned information relating to Patient and Encounter, Pathology/Medical Imaging and Medications. A quality analysis of pathology data, which have previously been identified by MEGPN as an area warranting further investigation, will be performed. Examples of the quality analysis of the pathology data that will be employed in the proposed project will include an assessment of whether all pathology results are supplied with a complete and appropriate reference range, and whether all pathology providers are using the same test names, reference ranges and reporting units for each test. This information will be fed back to East Melbourne PHN to help improve the quality of the data that they collect from each practice.

Assessing test ordering: After the quality of the linked data has been confirmed (with further data extraction performed if necessary to address any quality issues) it is proposed that it will be used to provide a comprehensive assessment of the pathology test ordering profile across

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East Melbourne PHN. The first step towards this objective will be to use the combined patient and pathology data to identify how often each pathology test is ordered. This will enable the identification of tests that are ordered despite clinical guidelines advising against their routine use, such as routine thyroid function testing for asymptomatic patients.43 It is proposed that the number of pathology tests ordered per patient encounter will then be examined at both an individual GP and practice level. This will enable an initial assessment of the degree of variation in pathology test ordering between individual GPs and the 49 included practices, with the aforementioned systematic literature review (see page 10) identifying large variation in how pathology is used by different GPs. Descriptive statistics will then be used to denote the patient (i.e. age and gender) and clinical characteristics (i.e. reason for visit and comorbidities) associated with GPs’ pathology test ordering practices. Statistical models (e.g. linear regression modelling) will be used to investigate sources of variation in pathology test ordering practices across East Melbourne PHN. Examples of the sources of variation that will be investigated are: gender; patient age; reason for visit; diagnosis; and comorbidities (all data routinely collected and warehoused by MEGPN), all of which were highlighted in the aforementioned systematic literature review as being possible sources of variation in how GPs use pathology. Identification of such sources of variation will enable insights into the relationship between test ordering and clinical guidelines. It will further identify areas to reduce variability, thereby reducing potential risk of patient harm. Finally, such analyses have the potential to identify therapeutic areas that may require focused, quality improvement attention.

Test result management: Our systematic literature review (see page 7) identified significant variation in how test results are managed in general practice. In the proposed project, the linked dataset will be used to examine GP pathology result management and provide an indication of the proportion of test results that were managed in a way that is consistent with guidelines. This will be achieved by comparing any action taken by the GP after receiving a pathology result (i.e. no action, prescription, repeat test or referral to specialist) to recommended actions in current clinical guidelines, with tests that have no consensus guidelines excluded. Statistical models (e.g. linear regression modelling) will then be used to investigate sources of variation that may affect how GPs interpret pathology results, such as number of years of experience, patient diagnosis and patient comorbidities. As such, the proposed study would provide the first quantitative assessment of the quality of pathology result interpretation in Australian general practice, information that will be crucial for improving practice. As with pathology test usage, identification of variation in result interpretation should increase understanding of how test results are interpreted, enabling the development of best-practice guidelines to enhance patient care.

Building the framework: The results of the proposed project will be used to develop an evidence-based framework to support the appropriate use of pathology. This framework will facilitate the translation of the results and recommendations from the proposed project into clinical practice and provide tools for the ongoing monitoring and enhancement of pathology in general practice. The framework will provide a range of key indicators for monitoring the use of pathology (e.g. total volume, number of associated prescriptions) and will describe how to extract these data. It will also outline ways in which the key indicators can be improved, and the relevance of these indicators to patient care. This should enable Australian GPs to

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continually monitor their pathology usage and results interpretation, and to compare their performance to that achieved in other, similar, practices. This should allow GPs to adjust their practices where necessary to improve patient outcomes. The widespread clinical translation of this framework should also lead to a reduction in unwarranted pathology tests and incorrect interpretation of pathology results, which will improve patient safety and lead to a significant cost saving. This framework would also serve as an important platform for future research studies.

Clinical translation

The results of the proposed project would be disseminated to GPs and laboratories throughout Australia. Furthermore, the evidence-based framework would be translated into good clinical practice guidelines, ensuring the effective clinical translation of results from this project.

Timeline

The planned duration of the proposed project is 3 years.

Stage 1 – 3 Months

Data extraction - develop a protocol for extracting all data relevant for the project and negotiate with PHNs to secure access to relevant data. Gain ethical approval for study.


Data linkage and quality analysis - the data will be linked and the quality of the data assessed.


Assessing test ordering - the linked dataset will be analysed to provide a comprehensive assessment of the pathology test ordering profile across East Melbourne PHN and to identify sources of variation.


Test result management - the linked dataset will be used to analyse result interpretation in the light of the subsequent action taken by the GP compared to recommended actions in current clinical guidelines. Statistical modelling will be employed to identify sources of variation.


Building the framework and clinical translation - the results of the proposed project will be used to generate an evidence-based framework for the monitoring and enhancement of pathology in general practice. This will include detailed findings about the quality of pathology referrals and interpretation, including explanations of the clinical and organisational implications of these findings, and sufficient information to replicate this project in another jurisdiction.

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Outcomes

The proposed project will provide the most comprehensive data to date relating to how well pathology results are used and interpreted in Australian general practice. As well as identifying sources of variation in pathology usage and result management, it will aim to facilitate the development of strategies to avoid these scenarios. The proposed project will also seek to provide an evidence-based framework to monitor and enhance the appropriate use of pathology in general practice. Finally, the proposed project will aim to generate a robust platform for future monitoring and improvement initiatives and provide the basis for other states and countries to undertake similar studies.

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43. RACGP Red Book Taskforce. Guidelines for preventive activities in general practice. Melbourne, Australia2012.

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APPENDICES

Appendix 1: Centre for Health Systems and Safety Research (CHSSR) overview

CHSSR Overview

The Centre for Health Systems and Safety Research (CHSSR) conducts innovative research aimed at understanding and improving the way in which health care delivery and patient outcomes are enhanced through the effective use and exchange of information. It is one of three research centres that form the Australian Institute of Health Innovation (AIHI) at Macquarie University.

Mission

The Centre’s mission is to lead in the design and execution of innovative health systems research focused on patient safety and the evaluation of information and communication technologies in the health sector, to produce a world-class evidence base which informs policy and practice.

Aims

The Centre’s research is underpinned by a systems perspective, exploiting highly innovative and wide-ranging research methods. Its research team is characterised by its talent and enthusiasm for working within and across discipline areas and sectors. The Centre has a focus on translational research, aimed at turning research evidence into policy and practice, while also making fundamental contributions to international knowledge.

The Centre’s research program has four central aims:

Produce research evidence of the impact of information and communication technologies (ICT) on the efficiency and effectiveness of health care delivery, on health professionals’ work and on patient outcomes

Develop and test rigorous and innovative tools and approaches for health informatics evaluation

Design and apply innovative approaches to understand the complex nature of health care delivery systems and make assessments of health care safety

Disseminate evidence to inform policy, system design, practice change and the integration and safe and effective use of ICT in healthcare

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Appendix 2: Tables and fields extracted from MEGPN databaseSoftware Table Name Field Name Description

Medical Director

Patient GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

Patient_UUID Deidentified PatientIDYear_of_Birth The year the patient was born

Gender_Code The patients identified gender (M=Male, F=Female)

Postcode The postcode of the practice the patient visited

Resource GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

Resource_ID Deidentified ID of the user/clinician who added the record

Resource_Type Users job role/category within the GP clinic

Diagnosis GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

Patient_UUID Deidentified PatientIDResource_ID Deidentified ID of the user/clinician who added the record

Diagnosis_date Date diagnosis was recorded

Diagnosis_Type Type of diagnosis (reason for visit/contact, procedure, reason for medication)

SNOMED_text SNOMED diagnosis grouping

INR GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from


Recorded_Date Date of INR reading

INR_value INR reading value

Pathology GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

Collection_date Date that the pathology sample was taken

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Software Table Name Field Name Description

Checked Indicates if the test result has been checked (0=Not checked, 1=Checked)

Checked_Date Date when the test result was checked within the GP clinic

Checked_By_ID ID of the user who checked the test result within the GP clinic

Lab_Name Name of the laboratory company that performed the test and produced the results report

Report_Date Date test result report was created by the laboratory company

Pathology_ID Unique record ID of the pathology report

Patient_UUID Deidentified PatientIDRequest_No GP clinics reference number for the pathology request and test result

Request_Date Date test was requested

Stamp_user_ID User who updated the record

Test_Name Name that the laboratory company has given to the test result

Lab_Normal Indicates if the test result is normal or abnormal (N=Normal, Y=Abnormal)

Pathology Atom GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

Test_Name Atomised test result name

Result Atomised test result value

Normal_Range The safe range that the test result should sit within

Abnormal_Flags Indicates if the test result is considered to be abnormal (N=Normal, L=Low, H=High)

Patient_UUID Deidentified PatientIDResult_Date Date of the pathology test result

Pathology_ID Unique record ID of the pathology report

Units Units that the test result was report in

LOINC Logical Observation Identifiers Names and Codes standard for the test30



Path_Group Pathology test grouping

Prescription GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from


Script_Date Date prescription was printed on

Strength Amount of active ingredient/s that is contained within the drug

Dose Amount of the drug the patient needs to take

Frequency How often the patient needs to take the medication

Quantity Amount contained within the medication packaging

Repeats The number of repeat prescription scripts given to the patient

Reason_Code Reason code as to why the medicate was prescribed

Reason Reason medication was prescribed

MedicationLevel3 Medication grouping

Progress GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from


Visit_Date Date of visit

Duration Length of visit (in seconds)

Request GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from


Request_No GP clinics reference number for the pathology request and test result31



Lab_Name The name of the laboratory company that the test request is being made to

Reason Reason why pathology has been ordered

Tests Name of the pathology tests requested

Best Practice

Patients GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

Sexcode The patients identified gender (0= Blank, 1=Female, 2=Male, 3=Other, 4=Unknown)YOB The year the patient was born

Patient_UUID Deidentified PatientIDPostcode The postcode of the practice the patient visited

Users GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

UserID Deidentified ID of the user/clinician who added the record

GroupCode Users job role/category within the GP clinic

PastHistory GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

Patient_UUID Deidentified PatientIDUserID Deidentified ID of the user/clinician who added the record

Created Date diagnosis was recorded

SNOMED_Text SNOMED diagnosis grouping

INRValues GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

RecordDate Date of INR reading

INRValue INR reading value


RequestedTests GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from32




ProviderName The name of the laboratory company that the test request is being made to

RequestDate Date of request

Patient_UUID Deidentified PatientIDRequestID Unique identifier of the pathology request

ReportValues GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

ResultValue Atomised test result value

ResultName Atomised test result name

Patient_UUID Deidentified PatientIDLOINCCode Logical Observation Identifiers Names and Codes standard for the test

ReportDate Date of the pathology test result

ReportID ID given to the results

AbnormalFlag Indicates if the test result is considered to be abnormal (L=Low, H=High)

Range The safe range that the test result should sit within

Units Units that the test result was report in

Path_group Pathology test grouping

ScriptItems GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

ScriptDate Date prescription was printed on

Repeats The number of repeat prescription scripts given to the patient

Frequency How often the patient needs to take the medication

Quantity Amount contained within the medication packaging

Patient_UUID Deidentified PatientID33



RecordID Unique identifier of script


ScriptID Unique identifier for prescribed medication

Strength Amount of active ingredient/s that is contained within the drug

Dose Amount of the drug the patient needs to take

MedicationLevel3 Medication grouping

Visits GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

Duration Length of visit (in seconds)

Visit_Date Date of visit


VisitID Unique identifier for visit

VisitReason GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from

Patient_UUID Deidentified PatientIDReason Reason for Visit

VisitID Unique identifier for visit

CreatedBy User who created the record (equivilant of UserID)

Investigations GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from


Action Action required by the clinic to ensure results are given by appropriate user within practice to the patient (0=Not marked, 1=No action, 2=Reception to advise, 3=Nurse to advise, 4=Doctor to advise, 5=Send Routine Reminder, 6=Non-Urgent Appointment, 7=Urgent Appointment)

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Patient_UUID Deidentified PatientIDNotation Outcome of the test result given to the patient (0=Not marked, 1=Normal, 2=Abnormal, 3=Stable,

4=Acceptable, 5=Unacceptable, 6=Being Treated, 7=Seeing specialist)ActionDate Date test result has required action assigned to it

TestName Name that the laboratory company has given to the test result

NormalFlag Indicates if the test result is normal or abnormal

CollectionDate Date that the pathology sample was taken

ProviderName Name of the laboratory company that performed the test and produced the results report

ReportDate Date test result report was created by the laboratory company

ReportID ID given to the results

RequestDate Date test was requested

RequestID Unique identifier of the pathology request

CheckDate Date when the test result was checked within the GP clinic

CheckedBy ID of the user who checked the test result within the GP clinic

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Talavera Road, North Ryde, Sydney, Australia

T: (02) 9850 2400 F: (02) 9850 2499

CRICOS Provider Number 00002J mq.edu.au

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Documents

Enhancing the quality of pathology in Australian …€¦ · Web viewd Royal College of Pathologists of Australasia Quality Assurance Program, St Leonards, Sydney, NSW, Australia