Role of Diagnostic Test-Osman Sianipar-CE and BU-Clinical Pathology (2015)
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Transcript of Enhancing the quality of pathology test requesting and ... · Enhancing the quality of pathology...
Enhancing the quality of
pathology test
requesting and test
result management in
Australian general
practice Euan 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
Enhancing the quality of pathology in Australian general practice
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Suggested citation:
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
Enhancing the quality of pathology in Australian general practice
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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
Accuracy ............................................................................................................................ 9
Enhancing the quality of pathology in Australian general practice
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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 SUMMARY
Pathology 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 aim
This 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 review
A 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
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
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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 protocol
The 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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ABBREVIATIONS
Acronyms 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
Background
Pathology, 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 aim
This 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
Aim
The 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 strategy
A 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
the studies that matched these criteria were independently reviewed and sorted based on the
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inclusion criteria. Reference lists of included studies were hand searched for further relevant
studies.
Analysis
For 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).
Results
Thirteen 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 Findings
Clinician Survey
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
Clinician Survey
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
Clinician Survey
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%
Clinician Survey
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
Clinician Survey
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
Clinician Survey
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 imprecision
Clinician Survey
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
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Study design Author Year Participants Key Findings
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
Observational 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 statins
RCT 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.
The smallest real change between two consecutive laboratory results that can be accurately
Enhancing the quality of pathology in Australian general practice
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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 (10th-
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.
Enhancing the quality of pathology in Australian general practice
7
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,
Enhancing the quality of pathology in Australian general practice
8
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.
Enhancing the quality of pathology in Australian general practice
9
APPRASIAL OF GENERAL PRACTICE DATA QUALITY
Aim
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 test result
management.
Study setting
MEGPN 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 extract
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. 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 quality
Each 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.
Enhancing the quality of pathology in Australian general practice
10
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
Enhancing the quality of pathology in Australian general practice
11
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. Convergent
Convergent 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. Discriminate
No overlap between data that were not supposed to relate to each other was found.
Data contents
The 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 female
Enhancing the quality of pathology in Australian general practice
12
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 linkage
Medical 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
Enhancing the quality of pathology in Australian general practice
13
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.
Enhancing the quality of pathology in Australian general practice
14
PROTOCOL FOR INVESTIGATING QUALITY USE OF
PATHOLOGY IN AUSTRALIAN GENERAL PRACTICE
Aim
To 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
Enhancing the quality of pathology in Australian general practice
15
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.
Enhancing the quality of pathology in Australian general practice
16
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
Enhancing the quality of pathology in Australian general practice
17
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
Enhancing the quality of pathology in Australian general practice
18
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.
Stage 2 – 7 Months
Data linkage and quality analysis - the data will be linked and the quality of the data assessed.
Stage 3 – 9 Months
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.
Stage 4 – 11 Months
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.
Stage 5 – 6 Months
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.
Enhancing the quality of pathology in Australian general practice
19
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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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 database
Software 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 PatientID
Year_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 PatientID
Resource_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
Patient_UUID Deidentified PatientID
Resource_ID Deidentified ID of the user/clinician who added the record
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 PatientID
Request_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 PatientID
Result_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 test
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Software Table Name Field Name Description
Path_Group Pathology test grouping
Prescription GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from
Patient_UUID Deidentified PatientID
Resource_ID Deidentified ID of the user/clinician who added the record
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
Patient_UUID Deidentified PatientID
Resource_ID Deidentified ID of the user/clinician who added the record
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
Patient_UUID Deidentified PatientID
Resource_ID Deidentified ID of the user/clinician who added the record
Request_No GP clinics reference number for the pathology request and test result
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Software Table Name Field Name Description
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 PatientID
Postcode 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 PatientID
UserID 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
Patient_UUID Deidentified PatientID
UserID Deidentified ID of the user/clinician who added the record
RequestedTests GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from
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Software Table Name Field Name Description
UserID Deidentified ID of the user/clinician who added the record
ProviderName The name of the laboratory company that the test request is being made to
RequestDate Date of request
Patient_UUID Deidentified PatientID
RequestID 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 PatientID
LOINCCode 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 PatientID
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Software Table Name Field Name Description
RecordID Unique identifier of script
UserID Deidentified ID of the user/clinician who added the record
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
Patient_UUID Deidentified PatientID
UserID Deidentified ID of the user/clinician who added the record
VisitID Unique identifier for visit
VisitReason GRHANITE_Site Unique deidentified variable relating to the GP Clinic where the data came from
Patient_UUID Deidentified PatientID
Reason 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
UserID Deidentified ID of the user/clinician who added the record
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)
Patient_UUID Deidentified PatientID
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Software Table Name Field Name Description
Notation 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
Enhancing the quality of pathology in Australian general practice
30
Talavera Road, North Ryde, Sydney,
Australia
T: (02) 9850 2400 F: (02) 9850 2499
CRICOS Provider Number 00002J
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