Browsing by Author "Emery, Jon"

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    Adoption, acceptance, and use of a decision support tool to promote timely investigations for cancer in primary care
    (2024) Chima, Sophie; Hunter, Barbara; Martinez-Gutierrez, Javiera; Lumsden, Natalie; Nelson, Craig; Manski-Nankervis, Jo-Anne; Emery, Jon
    Background The complexities of diagnosing cancer in general practice has driven the development of quality improvement (QI) interventions, including clinical decision support (CDS) and auditing tools. Future Health Today (FHT) is a novel QI tool, consisting of CDS at the point-of-care, practice population-level auditing, recall, and the monitoring of QI activities.Objectives Explore the acceptability and usability of the FHT cancer module, which flags patients with abnormal test results that may be indicative of undiagnosed cancer.Methods Interviews were conducted with general practitioners (GPs) and general practice nurses (GPNs), from practices participating in a randomized trial evaluating the appropriate follow-up of patients. Clinical Performance Feedback Intervention Theory (CP-FIT) was used to analyse and interpret the data.Results The majority of practices reported not using the auditing and QI components of the tool, only the CDS which was delivered at the point-of-care. The tool was used primarily by GPs; GPNs did not perceive the clinical recommendations to be within their role. For the CDS, facilitators for use included a good workflow fit, ease of use, low time cost, importance, and perceived knowledge gain. Barriers for use of the CDS included accuracy, competing priorities, and the patient population.Conclusions The CDS aligned with the clinical workflow of GPs, was considered non-disruptive to the consultation and easy to implement into usual care. By applying the CP-FIT theory, we were able to demonstrate the key drivers for GPs using the tool, and what limited the use by GPNs.
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    AI Based Cancer Detection Models Using Primary Care Datasets
    (2022) Ristanoski, Goce; Emery, Jon; Martinez Gutierrez, Javiera; McCarthy, Damien; Aickelin, Uwe
    Cancer is one of the most common and serious medical conditions with more than 144 000 Australians having been diagnosed with cancer in 2019. The non-specific nature of cancer symptoms and its low prevalence make cancer diagnosis particularly challenging, especially for primary care physicians/General Practitioners (GPs). Ongoing research in cancer diagnosis places a heavy focus on understanding the epidemiology of cancer symptoms. With GPs being the first point of contact for most patients, prediction models using the patient's medical history from primary care data can be a useful decision tool for early cancer detection. Our work both investigates the opportunities to use primary care data, specifically pathology data, for developing such decision tools and tackles the challenges coming from uncertainty in the data such as irregular pathology records. We present opportunities using the results within the frequently ordered full blood count to determine relevance to a future cancer diagnosis. By using several different pathology metrics, we show how we can generate features suitable for AI models that can be used to detect cancer 3 months earlier than current practices. Though the work focuses on patients with lung cancer, the methodology can be adjusted to other types of cancer and other data within the medical records. Our findings demonstrate that even when working with incomplete or obscure patient history, hematological measures contain valuable information that can indicate the potential of cancer diagnosis for up to 8 out of 10 patients. The use of the proposed decision tool presents a way to incorporate pathology data in the current cancer diagnosis practices and to incorporate various pathology tests or other primary care datasets for similar purposes.
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    Data Resource Profile: Victorian Comprehensive Cancer Centre Data Connect
    (Oxford Univ. Press, 2023) Lee, Alex; McCarthy, Damien; Bergin, Rebecca J.; Drosdowsky, Allison; Martinez Gutiérrez, Javiera; Kearney, Chris; Philip, Sally; Rafiq, Meena; Venning, Brent; Wawryk, Olivia; Zhang, Jianrong; Emery, Jon
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    Optimising a clinical decision support tool to improve chronic kidney disease management in general practice
    (2024) Hunter, Barbara; Davidson, Sandra; Lumsden, Natalie; Chima, Sophie; Martínez Gutiérrez, Javiera; Emery, Jon; Nelson, Craig; Manski-Nankervis, Jo-Anne
    Early identification and treatment of chronic disease is associated with better clinical outcomes, lower costs, and reduced hospitalisation. Primary care is ideally placed to identify patients at risk of, or in the early stages of, chronic disease and to implement prevention and early intervention measures. This paper evaluates the implementation of a technological intervention called Future Health Today that integrates with general practice EMRs to (1) identify patients at-risk of, or with undiagnosed or untreated, chronic kidney disease (CKD), and (2) provide guideline concordant recommendations for patient care. The evaluation aimed to identify the barriers and facilitators to successful implementation. Methods Future Health Today was implemented in 12 general practices in Victoria, Australia. Fifty-two interviews with 30 practice staff were undertaken between July 2020 and April 2021. Practice characteristics were collected directly from practices via survey. Data were analysed using inductive and deductive qualitative analysis strategies, using Clinical Performance - Feedback Intervention Theory (CP-FIT) for theoretical guidance. Results Future Health Today was acceptable, user friendly and useful to general practice staff, and supported clinical performance improvement in the identification and management of chronic kidney disease. CP-FIT variables supporting use of FHT included the simplicity of design and delivery of actionable feedback via FHT, good fit within existing workflow, strong engagement with practices and positive attitudes toward FHT. Context variables provided the main barriers to use and were largely situated in the external context of practices (including pressures arising from the COVID-19 pandemic) and technical glitches impacting installation and early use. Participants primarily utilised the point of care prompt rather than the patient management dashboard due to its continued presence, and immediacy and relevance of the recommendations on the prompt, suggesting mechanisms of compatibility, complexity, actionability and credibility influenced use. Most practices continued using FHT after the evaluation phase was complete. Conclusions This study demonstrates that FHT is a useful and acceptable software platform that provides direct support to general practice in identifying and managing patients with CKD. Further research is underway to explore the effectiveness of FHT, and to expand the conditions on the platform.
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    Optimization of a Quality Improvement Tool for Cancer Diagnosis in Primary Care: Qualitative Study
    (2022) Chima, Sophie; Martinez-Gutierrez, Javiera; Hunter, Barbara; Manski-Nankervis, Jo-Anne; Emery, Jon
    Background: The most common route to a diagnosis of cancer is through primary care. Delays in diagnosing cancer occur when an opportunity to make a timely diagnosis is missed and is evidenced by patients visiting the general practitioner (GP) on multiple occasions before referral to a specialist. Tools that minimize prolonged diagnostic intervals and reduce missed opportunities to investigate patients for cancer are therefore a priority.
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    Should I Take Aspirin? (SITA): randomised controlled trial of a decision aid for cancer chemoprevention
    (2024) Onwuka, Shakira R.; McIntosh, Jennifer; Macrae, Finlay; Chondros, Patty; Boyd, Lucy; Wijesuriya, Rushani; Saya, Sibel; Karnchanachari, Napin; Novy, Kitty; Jenkins, Mark A.; Walter, Fiona M.; Trevena, Lyndal; Gutierrez, Javiera Martinez; Broun, Kate; Fishman, George; Marker, Julie; Emery, Jon
    Background Australian guidelines recommend that people aged 50-70 years consider taking low-dose aspirin to reduce their risk of colorectal cancer (CRC). Aim To determine the effect of a consultation with a researcher before an appointment in general practice using a decision aid presenting the benefits and harms of taking low- dose aspirin compared with a general CRC prevention brochure on patients' informed decision making and low- dose aspirin use. Design and setting Individually randomised controlled trial in six general practices in Victoria, Australia, from October 2020 to March 2021. Method Participants were recruited from a consecutive sample of patients aged 50-70 years attending a GP. The intervention was a consultation using a decision aid to discuss taking aspirin to reduce CRC risk while control consultations discussed reducing CRC risk generally. Self-reported co-primary outcomes were the proportion of individuals making informed choices about taking aspirin at 1 month and on low-dose aspirin uptake at 6 months, respectively. The intervention effect was estimated using a generalised linear model and reported with Bonferroni-adjusted 95% confidence intervals (CIs) and P-values. Results A total of 261 participants (86% of eligible patients) were randomised into trial arms (n n = 129 intervention; n = 132 control). Of these participants, 17.7% (n n = 20/113) in the intervention group and 7.6% (n n = 9/118) in the control group reported making an informed choice about taking aspirin at 1 month, an estimated 9.1% (95% CI = 0.29 to 18.5) between- arm difference in proportions (odds ratio [OR] 2.47, 97.5% CI = 0.94 to 6.52, P = 0.074). The proportions of individuals who reported taking aspirin at 6 months were 10.2% (n n = 12/118) of the intervention group versus 13.8% (n n = 16/116) of the control group, an estimated between-arm difference of -4.0% (95% CI = -13.5 to 5.5; OR 0.68 [97.5% CI = 0.27 to 1.70, P = 0.692]). Conclusion The decision aid improved informed decision making but this did not translate into long-term regular use of aspirin to reduce CRC risk. In future research, decision aids should be delivered alongside various implementation strategies.