Javiera Martinez-Gutierrez

PURPOSE: Multicancer detection (MCD) tests aim to detect different cancer types using a single test. However, evidence on their potential for screening asymptomatic populations remains limited. We consolidated evidence from prospective cohort studies evaluating blood-based MCD tests in primarily asymptomatic adults to contextualize upcoming randomized controlled trial results. MATERIALS AND METHODS: We updated and extended a prior review (to September 2023), conducting comprehensive Medline/Embase searches to February 1, 2026. Key outcomes included cancers detected and not detected by MCD tests, false-positive MCD tests, and diagnostic investigation pathways. Risk of bias (RoB) was assessed using a modified Quality Assessment of Diagnostic Accuracy Studies-2 tool. RESULTS: From 2,723 screened records (244 previously shortlisted to 2023, 2,479 records for 2023-2026), we included 18 articles (12 studies, nine MCD tests); of these, 11 articles had not appeared in prior reviews. Cancer detection rates varied widely between studies, for example, new MCD–test–detected invasive cancers diagnosed ≤12 months post-test ranging from 18.8 (95% CI, 11.0 to 30.1) to 43.8 (95% CI, 29.4 to 62.8) per 10,000 tested, with MCD–test–detected invasive stage I to II cancers ranging from 9.0 (95% CI, 4.2 to 17.2) to 21.0 (95% CI, 11.6 to 35.5) per 10,000 tested. False-positives exceeded MCD–test–detected cancers (eg, approximately 1.6-fold in PATHFINDER, 1.5-fold K-DETEK, 4.2-fold DETECT-A, 8.1-fold SeekInCare studies). Diagnostic investigation pathways were prespecified/suggested in four of eight interventional studies. Where reported, the median time to diagnostic resolution varied from <0.1 months to 4 months for MCD–test–detected cancers, with substantially higher 75th percentiles (3.1-7 months), and similar patterns were observed for false-positive MCD tests. No study was judged to have overall low RoB. CONCLUSION: Substantial heterogeneity in cancer yield metrics likely reflects differences in MCD technologies, diagnostic pathways, follow-up duration, and background standard-of-care screening. Long-term follow-up, randomized trials, fully-paired test comparisons, and implementation research are essential to determine the potential of MCD tests for population screening.

Background: Delayed cancer diagnosis leads to poorer outcomes. Unintended weight loss (UWL) is a nonspecific symptom associated with cancer and other serious conditions, which can make it complex to identify the underlying cause. Clinical decision support systems (CDSSs) can provide evidence-based recommendations to facilitate timely investigation and diagnosis. Objective: This study aimed to pilot the implementation of a CDSS for improving early cancer detection in primary care patients with UWL. Methods: Five practices reviewed patients identified by the UWL CDSS. Staff were interviewed on the acceptability and feasibility of the CDSS. Interviews were analyzed thematically using 2 relevant frameworks: the acceptability of health care interventions and the sociotechnical model for evaluation of digital interventions framework. Clinical audits assessed the correct identification of UWL, follow-up rates, and patient characteristics. Results: Of 60 patients identified by the CDSS as potentially having UWL, 36 (60%) had true UWL. Among the misclassified cases, most patients (16/55, 30%) were intentionally trying to lose weight; this intention was documented as free text in the clinical notes for 98% (58/60) of patients and in a structured field for only 20% of patients. Of the 36 patients, 5 (14%) were actively recalled by their practices for further follow-up; the others (31/36, 86%) were not recalled, as most were deemed already under appropriate follow-up. By 6 months, 94% (34/36) of the cohort had received follow-up care regardless of whether they had been formally recalled. One-third (12/36, 33%) of patients had no additional symptoms, while 36% (13/36) had recorded additional abdominal symptoms. Mental health conditions accounted for 19% of diagnoses linked to UWL consultation. Practice staff were generally receptive to the UWL CDSS concept. It was particularly appreciated by practices with strong quality improvement processes and prior CDSS experience. A high proportion of misclassified patients, poor workflow integration, and conflicting patients’ agendas were cited as implementation barriers. Conclusions: Our study revealed challenges and potential benefits of implementing a CDSS for identifying patients with UWL at risk of cancer in primary care. While integration issues and misclassification of patients were noted, high rates of follow-up care were observed regardless of the CDSS. These high follow-up rates prompt consideration of whether they reflect Australian primary care and whether a CDSS with these characteristics is the most appropriate approach to support early cancer detection. Future research should focus on improving CDSS integration with existing workflows, enhancing correct identification through access to clinical notes and the use of more sophisticated digital methods (eg, AI). These findings highlight the potential of CDSS in improving patient care and the complexities involved in their successful implementation.

Background: Early detection in primary care could improve pancreatic cancer survival, but diagnosis is often delayed due to the low prevalence of the disease, the nonspecific nature of early symptoms, and the broad range of conditions and volume of consultations managed by general practitioners (GPs). In Australia, improving pancreatic cancer outcomes, including via earlier diagnosis, is a priority being progressed under the National Pancreatic Cancer Roadmap developed by Cancer Australia. Computerized clinical decision support systems (CDSSs) have shown promise in aiding timely cancer diagnosis; however, barriers to adopting CDSS such as mistrust of the recommendations or not being embedded in the clinical workflow remain. Simulation techniques, which offer flexible and cost-effective ways to evaluate digital health interventions, can be used to test CDSS before real-world implementation. Objective: This study aims to assess the acceptability and feasibility of identifying patients with symptoms associated with pancreatic cancer through a CDSS within a simulated environment. Methods: We developed a CDSS that interacted with an electronic health record used in general practice to identify patients with symptoms, which may indicate pancreatic cancer (unintended weight loss or new-onset diabetes), in a simulation laboratory for digital interventions. We tested it by inviting GPs (n=11) to use the CDSS, with patient actors simulating specific clinical scenarios. We then interviewed GPs about the interaction to assess the acceptability and feasibility of the CDSS in their clinical practice. We used thematic analysis and 2 relevant frameworks to analyze the data. Results: GPs found the CDSS easy to use, unobstructive, and effective as a prompt to consider investigations for people with risk factors for pancreatic cancer. However, they expressed concerns about possible overtesting, financial costs, and the potential for anxiety in patients with a very low probability of having cancer. Conclusions: While GPs found the tool useful and compatible with their workflow, concerns about overtesting, lack of evidence, and cost-effectiveness were identified as barriers. GPs favored a stepwise approach to investigations rather than immediate imaging. Despite the overall acceptability of the tool, additional evidence to underpin clinical recommendations is necessary before implementing a CDSS with these specific recommendations for pancreatic cancer in primary care.

Background: Pancreatic cancer is often diagnosed at an advanced stage with poor survival. Risk assessment tools have been developed to aid early diagnosis of pancreatic cancer in primary care settings (QCancer ^® , electronic Risk Assessment Tool [eRAT], and the Queensland Institute of Medical Research [QIMR] Berghofer Pancreatic Cancer Decision Support Tool [QPaC Tool]) but have not been validated in the Australian setting. Aim: To estimate and compare the performance of these tools for identifying patients with undiagnosed pancreatic cancer using Australian primary care data. Design and setting: A cohort study was conducted using linked primary care and cancer registry data from Victoria, Australia. Method: Patients presenting to primary care with signs and/or symptoms included in the tools (recorded in the primary care ‘reason for encounter’) were included. Diagnostic accuracy statistics for each tool (and their individual signs and symptoms) were compared. Results: Patients with pancreatic cancer were more likely ( P ® was 0.8% (95% CI = 0.54 to 1.15). Conclusion: When applied to Australian primary care data, none of the tools were strongly predictive of pancreatic cancer. New diagnostic models incorporating additional data could potentially improve their predictive performance.

Background: Early cancer detection is crucial, but recognizing the significance of associated symptoms such as unintended weight loss in primary care remains challenging. Clinical decision support systems (CDSSs) can aid cancer detection but face implementation barriers and low uptake in real-world settings. To address these issues, simulation environments offer a controlled setting to study CDSS usage and improve their design for better adoption in clinical practice. Objective: This study aimed to evaluate a CDSS integrated within general practice electronic health records aimed at identifying patients at risk of undiagnosed cancer. Methods: The evaluation of a CDSS to identify patients with unintended weight loss was conducted in a simulated primary care environment where general practitioners (GPs) interacted with the CDSS in simulated clinical consultations. There were four possible clinical scenarios based on patient gender and risk of cancer. Data collection included interviews with GPs, cancer survivors (lived-experience community advocates), and patient actors, as well as video analysis of GP–CDSS interactions. Two theoretical frameworks were employed for thematic interpretation of the data. Results: We recruited 10 GPs and 6 community advocates, conducting 20 simulated consultations with 2 patient actors (2 consultations per GP: 1 high-risk consultation and 1 low-risk consultation). All participants found the CDSS acceptable and unobtrusive. GPs utilized CDSS recommendations in three distinct ways: as a communication aid when discussing follow-up with the patient, as a reminder for differential diagnoses and recommended investigations, and as an aid to diagnostic decision-making without sharing with patients. The CDSS’s impact on patient–doctor communication varied, facilitating and hindering interactions depending on the GP’s communication style. Conclusions: We developed and evaluated a CDSS for identifying cancer risk in patients with unintended weight loss in a simulated environment, revealing its potential to aid clinical decision-making and communication while highlighting implementation challenges and the need for context-sensitive application.

Background: Pancreatic cancer causes non-specific symptoms, potentially leading to delays in diagnosis. Decision support tools may help primary care practitioners to triage patients for pancreatic imaging. Aim: To investigate the sensitivity of three different tools for identifying patients who may have pancreatic cancer. Design & setting: An observational study in Australia. Method: We investigated the performance of the Risk Assessment Tool (RAT) for pancreatic cancer, the QCancer ^® tool, and a tool developed through a consensus process led by QIMR Berghofer (the QPaC Tool). We applied these tools to people with pancreatic cancer who were interviewed about their symptoms on first presentation to a clinician. We designated patients as ‘flagged’ by each tool if they met specific criteria, and calculated the percentage flagged (that is, the sensitivity). Participants with jaundice were excluded from analyses of QCancer ^® . Results: We included 190 participants in analyses of the RAT and QPaC Tool (142 in analyses of QCancer ^® ). The sensitivity of the QPaC Tool and the RAT were 54% and 27%, respectively. QCancer ^® had a sensitivity of 14%, at a probability threshold of 1%; in the same 142 participants, QPaC and the RAT flagged 44% and 7%, respectively. Conclusion: The QPaC Tool was the most sensitive, largely owing to its inclusion of severe epigastric pain and emphasis on diabetes, but it has unknown specificity. More research is needed to determine whether any tool could reduce delays in diagnosis; in the interim, the QPaC Tool may support clinicians to consider pancreatic cancer in their differential diagnoses.

Objective: The rapid growth in the cancer survivor population in Chile and Latin America raises new challenges in addressing their care needs. This study assesses the health status and compares the quality of care and quality of life in cancer survivors at a primary care network and a private cancer centre in Santiago, Chile. Design: Retrospective cohort study. Setting: Three primary care clinics and one cancer centre in Chile. Participants: All breast and colorectal cancer patients identified from a primary care retrospective cohort of 61 174 were followed from 2018 to 2023 and compared with an equivalent sample of patients from a university cancer centre identified during the same period. Outcome measures: Quality of care was assessed based on American Cancer Society standards, while quality of life was measured using the EuroQol 5 Dimensions-5 Levels survey instrument. Results: A total of 420 cancer survivors participated in the study; 208 from primary care and 212 from the cancer centre. All participants received substandard care. Patients in primary care had lower educational levels and higher rates of comorbidity. They reported a lower quality of life score (72.22 vs 78.43, p<0.001), a higher prevalence of chronic pain (37.02% vs 25.6%, p=0.016) and more severe mental health symptoms (19.89% vs 10.05%, p=0.03). Differences in educational level and cancer stage at diagnosis explained the observed disparities in chronic pain and mental health disorders between the two populations. Primary care patients received more psychosocial care (OR=2.29; 95% CI: 1.55 to 3.39), cardiovascular assessment (OR=2.66; 95% CI:2.17 to 3.26) and psychosocial evaluations (OR: 9.07; 95% CI:4.75 to 17.32). Conclusion: Cancer survivors face a significant disease burden and receive substandard care in Chile. As the primary source of care for this population, primary care is challenged to better integrate with speciality care to develop an effective shared care model for cancer survivors.

Objective: We calculate positive predictive values (PPVs) of patients presenting with unexpected weight loss (UWL) being diagnosed with cancer within 6 months, using data from a population of Australian primary care patients to replicate results from a previous UK study. Design: A diagnostic accuracy study involving calculation of the PPV for any cancer using retrospective data from routinely collected electronic healthcare records. The index date is defined as the first recorded UWL presentation and the reference standard is cancer diagnosis within 6 months of the index date. Setting: This study uses primary care data from the Patron primary care database, linked to hospital admissions data and the Victorian Cancer Registry. We include only patients who presented to their General Practitioners (GPs) at least once between 1 July 2007 and 1 February 2022. Participant: Patients were included if they were at least 18 years of age at the index date, had no previous diagnosis of cancer or previous weight loss intervention, including being prescribed medications for weight loss. 13 306 patients out of a primary care population of 1 791 051 patients were identified that met the eligibility criteria. Results: When stratified by age, sex and smoking status, we found PPVs lower than those derived in a previous UK primary care study, though still above 3% for male non-smokers over 60, female smokers over 70 and all males over 70. Patients from ages 60–79 with at least one abnormal blood test result had PPVs consistently above 3%, while overall, patients with abnormal blood test results have PPVs of up to 35%. Conclusion: We confirmed that many PPVs, while consistently below those derived in the UK study, are above clinically significant thresholds and increasing with age and the number of different abnormal blood test results.

BACKGROUND Early detection in primary care could improve pancreatic cancer survival, but diagnosis is often delayed due to the low prevalence of the disease, the nonspecific nature of early symptoms, and the broad range of conditions and volume of consultations managed by general practitioners (GPs). In Australia, improving pancreatic cancer outcomes, including via earlier diagnosis, is a priority being progressed under the National Pancreatic Cancer Roadmap developed by Cancer Australia. Computerized clinical decision support systems (CDSSs) have shown promise in aiding timely cancer diagnosis; however, barriers to adopting CDSS such as mistrust of the recommendations or not being embedded in the clinical workflow remain. Simulation techniques, which offer flexible and cost-effective ways to evaluate digital health interventions, can be used to test CDSS before real-world implementation. OBJECTIVE This study aims to assess the acceptability and feasibility of identifying patients with symptoms associated with pancreatic cancer through a CDSS within a simulated environment. METHODS We developed a CDSS that interacted with an electronic health record used in general practice to identify patients with symptoms, which may indicate pancreatic cancer (unintended weight loss or new-onset diabetes), in a simulation laboratory for digital interventions. We tested it by inviting GPs (n=11) to use the CDSS, with patient actors simulating specific clinical scenarios. We then interviewed GPs about the interaction to assess the acceptability and feasibility of the CDSS in their clinical practice. We used thematic analysis and 2 relevant frameworks to analyze the data. RESULTS GPs found the CDSS easy to use, unobstructive, and effective as a prompt to consider investigations for people with risk factors for pancreatic cancer. However, they expressed concerns about possible overtesting, financial costs, and the potential for anxiety in patients with a very low probability of having cancer. CONCLUSIONS While GPs found the tool useful and compatible with their workflow, concerns about overtesting, lack of evidence, and cost-effectiveness were identified as barriers. GPs favored a stepwise approach to investigations rather than immediate imaging. Despite the overall acceptability of the tool, additional evidence to underpin clinical recommendations is necessary before implementing a CDSS with these specific recommendations for pancreatic cancer in primary care.

Background: For patients with cancer, the pathway to diagnosis will most often begin in general practice. In the absence of strong diagnostic features or in patients with nonspecific symptoms, delays in diagnosis can occur. Initial presentations and routine blood tests are important in determining whether a patient requires further investigation. Quality improvement interventions, including auditing tools and clinical decision support (CDS), have been developed for use in general practice to support this diagnostic process. We conducted a process evaluation of a pragmatic, cluster-randomized trial that evaluated the effectiveness of a new technology, Future Health Today (FHT), implemented in general practice to assist with the appropriate follow-up of patients at risk of undiagnosed cancer. Objectives: This study aims to understand implementation gaps, explore differences between the general practices involved, provide context to the trial effectiveness outcomes, and understand the mechanisms behind the intervention successes and failures. Methods: The trial intervention consisted of the FHT tool (with CDS, audit, recall, and quality improvement components), training and educational sessions, benchmarking reports, and ongoing practice support. The 21 general practices in the intervention arm of the trial were included in the process evaluation. Process data were collected using semistructured interviews, usability and educational session surveys, engagement with intervention components, and technical logs. The Medical Research Council’s Framework for Developing and Evaluating Complex Interventions was used to analyze and interpret the data. Results: The uptake of the supporting components of the intervention (training and education sessions, benchmarking reports) was low. Most practices only used the CDS component of the tool, facilitated by active delivery, with general practitioners reporting acceptability and ease of use. Complexity, time, and resources were reported as barriers to the use of the auditing tool. Access to a study coordinator and ongoing practice support facilitated the sustained involvement of practices in the trial, while contextual factors, such as the COVID-19 pandemic and staff turnover, impacted their level of participation. The relevance of the intervention varied between practices, with some practices reporting very low numbers of patients who were flagged for further investigation. Conclusions: While some components of the intervention, such as the CDS tool, were considered to be acceptable and useful, this process evaluation highlighted barriers such as time and resources, practice differences, and considerations around the optimal amount of support needed when delivering the intervention. Addressing these in future studies may optimize the implementation process. Further work is needed to determine if a scaled-back approach, which meets the time and resource availability of a busy general practice, can effectively facilitate the implementation of CDS tools. Given the variation seen between practices, the use of the FHT cancer module may be better targeted to certain practices based on size, location, and patient demographics.