Evidence map›Paper›PMID 30728010›Full record

ArticleBMC health services research2019

Factors affecting general practitioners' decisions to adopt new prescription drugs - cohort analyses using Australian longitudinal physician survey data.

Yuting Zhang, Susan J Méndez, Anthony Scott

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
3.7field-weighted citation impact, top 7% of its field
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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

  1. Pooled it
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  4. Evaluating Risk and Benefit Sensitivity for Cognitive Treatments.Journal of cognitive enhancement : towards the integration of theory and practice · 2025
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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

3 authors at 1 institution in 1 country.

Yuting ZhangMelbourne Institute: Applied Economic and Social Research, Faculty of Business and Economics, The University of Melbourne, 5th floor, FBE Building 111 Barry St, Carlton, VIC, 3010, Australia. yuting.zhang@unimelb.edu.au.ORCID http://orcid.org/0000-0002-6460-6779
Susan J MéndezMelbourne Institute: Applied Economic and Social Research, Faculty of Business and Economics, The University of Melbourne, 5th floor, FBE Building 111 Barry St, Carlton, VIC, 3010, Australia.
Anthony ScottMelbourne Institute: Applied Economic and Social Research, Faculty of Business and Economics, The University of Melbourne, 5th floor, FBE Building 111 Barry St, Carlton, VIC, 3010, Australia.
The University of Melbourne · AU

Funding

Department of Health, Australian Government Australian-American Health Policy Fellowship
6 · The paper itself

Abstract

backgroundWe investigate factors affecting Australian general practitioners' decisions to adopt novel oral anticoagulants (NOACs) for the prevention of stroke/systemic embolism among patients with atrial fibrillation. Australia has a national homogeneous review and coverage system, which enables us to distinguish physician level factors while maintaining system level factors and patient coverage information constant.

methodsWe conduct a cohort analyses by using longitudinal physician survey data from the Medicine in Australia: Balancing Employment and Life panel survey of Australian physicians (MABEL). MABEL data contain rich physician-level information such as age, gender, education, risk preferences, personality, physicians' communications with other medical professionals, and other practice characteristics. Importantly, the survey data were linked, with physician's consent, to actual utilization data from the Australian Pharmaceutical Benefits Scheme and the Medicare Benefits Schedule between January 1, 2012 and December 31, 2015. We measure speed (days until first time prescribing) of adopting NOACs. We estimate a Cox proportional hazard model to estimate factors affecting the adoption speed.

resultsSeveral factors predict earlier adoption of NOACs: being male, more likely to take clinical risk, higher prescribing volume, being a principal or partner in the practice instead of an employee, spending less time in a typical consultation, and practicing in more affluent areas or areas with a higher proportion of older patients. GPs in Queensland are more likely to adopt NOACs and more likely to be extensive early adopters compared to other GPs. Other characteristics including physician personality, family circumstances, their involvement with public hospitals and teaching activities, and the distance between physician practice location to other clinics in the area are not statistically associated with earlier adoption.

conclusionsOur paper is one of the first to study the relationship between GPs' risk preferences, personality and their decisions to adopt new prescription drugs. Because NOACs are commonly prescribed and considered more cost-effective than their older counterpart, understanding factors affecting physicians' decisions to adopt NOACs has direct policy implications. Our results also highlight that even with universal coverage for prescription drugs, access to new drugs is different among patients, partially because who their doctors are and where they practice.

Indexed as

AnticoagulantsGeneral PractitionersPrescription DrugsAdultAgedAttitude of Health PersonnelAustraliaFemaleHealth Services ResearchHumansLongitudinal StudiesMalePractice Patterns, Physicians'AnticoagulantsPrescription DrugsAdoption of new prescription drugsAustraliaGeneral practitioners

Identifiers

PMID30728010
PMCPMC6366109
OpenAlexW2919645905

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.