Evidence map›Paper›PMID 28468629›Full record

ArticleImplementation science : IS2017

A methodological protocol for selecting and quantifying low-value prescribing practices in routinely collected data: an Australian case study.

Jonathan Brett, Adam G Elshaug, R Sacha Bhatia, Kelsey Chalmers, Tim Badgery-Parker, Sallie-Anne Pearson

Abstract read
In one paragraph

Article in Implementation science : IS, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  6. Measuring low-value care in hospital discharge records: evidence from China.The Lancet regional health. Western Pacific · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jonathan BrettMedicines Policy Research Unit, Centre for Big Data Research in Health, University of New South Wales, Level 1, AGSM Building (G27), Sydney, NSW, 2052, Australia.
Adam G ElshaugMenzies Centre for Health Policy, School of Public Health, University of Sydney, Level 6 The Hub, Charles Perkins Centre D17, Sydney, NSW, 2006, Australia.
R Sacha BhatiaDepartment of Medicine, University of Toronto, Toronto, Canada.
Kelsey ChalmersMenzies Centre for Health Policy, School of Public Health, University of Sydney, Level 6 The Hub, Charles Perkins Centre D17, Sydney, NSW, 2006, Australia.
Tim Badgery-ParkerMenzies Centre for Health Policy, School of Public Health, University of Sydney, Level 6 The Hub, Charles Perkins Centre D17, Sydney, NSW, 2006, Australia.
Sallie-Anne PearsonMedicines Policy Research Unit, Centre for Big Data Research in Health, University of New South Wales, Level 1, AGSM Building (G27), Sydney, NSW, 2052, Australia. sallie.pearson@unsw.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGrowing imperatives for safety, quality and responsible resource allocation have prompted renewed efforts to identify and quantify harmful or wasteful (low-value) medical practices such as test ordering, procedures and prescribing. Quantifying these practices at a population level using routinely collected health data allows us to understand the scale of low-value medical practices, measure practice change following specific interventions and prioritise policy decisions. To date, almost all research examining health care through the low-value lens has focused on medical services (tests and procedures) rather than on prescribing. The protocol described herein outlines a program of research funded by Australia's National Health and Medical Research Council to select and quantify low-value prescribing practices within Australian routinely collected health data.

methodsWe start by describing our process for identifying and cataloguing international low-value prescribing practices. We then outline our approach to translate these prescribing practices into indicators that can be applied to Australian routinely collected health data. Next, we detail methods of using Australian health data to quantify these prescribing practices (e.g. prevalence of low-value prescribing and related costs) and their downstream health consequences. We have approval from the necessary Australian state and commonwealth human research ethics and data access committees to undertake this work. DISCUSSION: The lack of systematic and transparent approaches to quantification of low-value practices in routinely collected data has been noted in recent reviews. Here, we present a methodology applied in the Australian context with the aim of demonstrating principles that can be applied across jurisdictions in order to harmonise international efforts to measure low-value prescribing. The outcomes of this research will be submitted to international peer-reviewed journals. Results will also be presented at national and international pharmacoepidemiology and health policy forums such that other jurisdictions have guidance to adapt this methodology.

Indexed as

Cost-Benefit AnalysisQuality Indicators, Health CareAustraliaData CollectionDrug PrescriptionsHumansPractice Patterns, Physicians'Low-value careMedicinesPrescribingProtocolRoutinely collected data

Identifiers

PMID28468629
PMCPMC5415810

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.