General Practice and Primary Care - Research Publications
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ItemOptimization of a Quality Improvement Tool for Cancer Diagnosis in Primary Care: Qualitative Study(JMIR PUBLICATIONS, INC, 2022-08)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. OBJECTIVE: This study aims to explore the usefulness and feasibility of a novel quality improvement (QI) tool in which algorithms flag abnormal test results that may be indicative of undiagnosed cancer. This study allows for the optimization of the cancer recommendations before testing the efficacy in a randomized controlled trial. METHODS: GPs, practice nurses, practice managers, and consumers were recruited to participate in individual interviews or focus groups. Participants were purposively sampled as part of a pilot and feasibility study, in which primary care practices were receiving recommendations relating to the follow-up of abnormal test results for prostate-specific antigen, thrombocytosis, and iron-deficiency anemia. The Clinical Performance Feedback Intervention Theory (CP-FIT) was applied to the analysis using a thematic approach. RESULTS: A total of 17 interviews and 3 focus groups (n=18) were completed. Participant themes were mapped to CP-FIT across the constructs of context, recipient, and feedback variables. The key facilitators to use were alignment with workflow, recognized need, the perceived importance of the clinical topic, and the GPs' perception that the recommendations were within their control. Barriers to use included competing priorities, usability and complexity of the recommendations, and knowledge of the clinical topic. There was consistency between consumer and practitioner perspectives, reporting language concerns associated with the word cancer, the need for more patient-facing resources, and time constraints of the consultation to address patients' worries. CONCLUSIONS: There was a recognized need for the QI tool to support the diagnosis of cancer in primary care, but barriers were identified that hindered the usability and actionability of the recommendations in practice. In response, the tool has been refined and is currently being evaluated as part of a randomized controlled trial. Successful and effective implementation of this QI tool could support the detection of patients at risk of undiagnosed cancer in primary care and assist in preventing unnecessary delays.
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ItemThe SMARTscreen Trial: a randomised controlled trial investigating the efficacy of a GP-endorsed narrative SMS to increase participation in the Australian National Bowel Cancer Screening Program(BMC, 2022-01-12)BACKGROUND: Increasing participation in the Australian National Bowel Cancer Screening Program (NBCSP) is the most efficient and cost-effective way of reducing mortality associated with colorectal cancer by detecting and treating early-stage disease. Currently, only 44% of Australians aged 50-74 years complete the NBCSP. This efficacy trial aims to test whether this SMS intervention is an effective method for increasing participation in the NBCSP. Furthermore, a process evaluation will explore the barriers and facilitators to sending the SMS from general practice. METHODS: We will recruit 20 general practices in the western region of Victoria, Australia to participate in a cluster randomised controlled trial. General practices will be randomly allocated with a 1:1 ratio to either a control or intervention group. Established general practice software will be used to identify patients aged 50 to 60 years old who are due to receive a NBCSP kit in the next month. The SMS intervention includes GP endorsement and links to narrative messages about the benefits of and instructions on how to complete the NBCSP kit. It will be sent from intervention general practices to eligible patients prior to receiving the NBCSP kit. We require 1400 eligible patients to provide 80% power with a two-sided 5% significance level to detect a 10% increase in CRC screening participation in the intervention group compared to the control group. Our primary outcome is the difference in the proportion of eligible patients who completed a faecal occult blood test (FOBT) between the intervention and control group for up to 12 months after the SMS was sent, as recorded in their electronic medical record (EMR). A process evaluation using interview data collected from general practice staff (GP, practice managers, nurses) and patients will explore the feasibility and acceptability of sending and receiving a SMS to prompt completing a NBCSP kit. DISCUSSION: This efficacy trial will provide initial trial evidence of the utility of an SMS narrative intervention to increase participation in the NBCSP. The results will inform decisions about the need for and design of a larger, multi-state trial of this SMS intervention to determine its cost-effectiveness and future implementation. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12620001020976 . Registered on 17 October 2020.
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ItemAI Based Cancer Detection Models Using Primary Care Datasets(ENGINEERING & TECHNOLOGY PUBLISHING, 2022-04)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.