Statewide integration of electronic patient-reported outcome measures into routine oncology care: a mixed-methods implementation study
Bibliographic record
Abstract
Background: Electronic patient-reported outcome measures (ePROMs) have emerged as a crucial tool in oncology, facilitating real-time symptom monitoring, enhancing patient-centred care, and supporting data-driven clinical decision-making. In New South Wales (NSW), Australia, the Cancer Institute New South Wales led the state-wide implementation of an electronic patient reported measures (ePRM) system to integrate ePROMs into routine cancer care. Methods: We describe the ePRM system build and implementation through the EPIS (Explore, Preparation, Implementation, Sustainment) implementation science framework. The ePRM system was iteratively developed to support real-time symptom monitoring, automated alerts, and integration with oncology information systems (ARIA, MOSAIQ). A phased roll-out strategy included system integration, clinician training, stakeholder engagement, and change management coaching. Data collection included system utilisation metrics (e.g. number of surveys completed), implementation metrics (activation timelines), and clinician and patient feedback. Descriptive statistics summarised adoption patterns, while qualitative analysis identified key implementation barriers and enablers. A statewide governance structure guided development, ensuring alignment with clinical workflows and sustainability across diverse cancer services in NSW. Findings: From 09 September 2019 to 23 July 2025, the ePRM system has been technically implemented in 55 sites (and clinically implemented in 43/55) in NSW, collecting over 30,000 PROM surveys. 29 (67%) of 43 participating services utilise three validated measures: the Edmonton Symptom Assessment Scale (ESAS), Distress Thermometer (DT), and Problem Checklist. Implementation enablers included strong leadership support, clinician engagement, and system flexibility to accommodate local workflows. Barriers included variable IT infrastructure, clinician workload concerns, and limited allied health resources. Early feedback suggests that both patients and clinicians recognise the value of ePROMs in improving symptom management and care coordination. Interpretation: The NSW ePRM system demonstrates the feasibility of integrating ePROMs into routine oncology care at scale. Key lessons include the importance of co-design and adaptive implementation strategies. Future directions include expanding the use of ePROMs data for advanced analytics and policy insights, improving interoperability with electronic medical records, and refining clinician workflows to optimise response to PROM breaches. The findings offer insights for other healthcare systems seeking to implement and sustain ePROMs in routine practice. Funding: The Cancer Institute New South Wales funded the build of the electronic Patient Reported Measures System and integration costs of the platform. No other external funds from industry partners were obtained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".