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RESEARCH ARTICLE
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A quality improvement project to increase treatment rates of osteoporosis in general practice

Patrick Bolton A * , Markus Seibel B , Daniel Moses C , Michael Moore D and Brendan Goodger E
+ Author Affiliations
- Author Affiliations

A Complex Care Physician Randwick Hospitals Campus and Centre for Primary Health Care and Equity, UNSW, NSW, Australia.

B Concord Repatriation General Hospital and Concord Medical School, The University of Sydney, Sydney, NSW, Australia.

C Graduate School of Biomedical Engineering, UNSW, NSW, Australia.

D General Practitioner, Hornsby, NSW, Australia.

E Central Eastern and Southern Primary Health Network, Randwick, NSW, Australia.

* Correspondence to: Patrick.Bolton@health.nsw.gov.au

Australian Health Review https://doi.org/10.1071/AH24214
Submitted: 5 August 2024  Accepted: 26 September 2024  Published: 17 October 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of AHHA.

Abstract

Objective

This study tests a model to improve the management of patients with an osteoporotic fracture.

Methods

Patients with fractures potentially due to osteoporosis were identified from imaging reports using computerised near natural language processing. A coordinator notified the referring GP about the finding and provided follow-up to remind GPs of the need for management. This provided an opportunity to assess action taken by the GP.

Results

Near natural language processing efficiently detected fractures in patients at risk of osteoporosis. GPs reported that they are managing osteoporosis in over 40% of patients identified. Notification of GPs coincided with a small increase in osteoporosis management.

Conclusion

Information technology can identify patient populations with clinically important risks such as osteoporosis. Methods to engage GPs to optimally address this risk have yet to be developed.

Keywords: general practice/primary care, machine learning, osteoprosis, prevention.

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