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ObjectivesUsing the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes.SettingA regional cancer centre in Australia.ParticipantsDisease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data.Primary and secondary outcome measuresSurvival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC).ResultsThe ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours.ConclusionsMachine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems.

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Introduction: Cancer-related mortality rates are higher in rural areas compared with urban regions. Whether there are corresponding geographical variations in radiotherapy utilisation rates (RURs) is the subject of this study. Methods: RURs for the regional centre of Geelong and rural areas of the Barwon South Western Region were calculated using a population-based database (2009). Results: Lower RURs were observed for rural patients compared with the Geelong region for prostate cancer (15.7% vs 25.8%, P = 0.02), rectal cancer (32.8% vs 44.7%, P = 0.11), lymphoma (9.4% vs 26.2%, P = 0.05), and all cancers overall (25.6% vs 28.9%, P = 0.06). This lower rate was significant in men (rural, 19.9%; Geelong, 28.3%; P = 0.00) but not in women (rural, 33.6%; Geelong, 29.7%; P = 0.88). Time from diagnosis to radiotherapy was not significantly different for patients from the two regions. Tumour staging within the rural and Geelong regions was not significantly different for the major tumour streams of rectal, prostate and lung cancer (P = 0.61, P = 0.79, P = 0.43, respectively). A higher proportion of tumours were unstaged or unstageable in the rural region for lung (44% vs 18%, P < 0.01) and prostate (73% vs 57%, P < 0.01) cancer. Conclusion: Lower RURs were observed in our rural region. Differences found within tumour streams and in men suggest a complexity of relationships that will require further study.

Reasons for presentation to ED would be multifactorial and include complex cases with coexisting symptoms making diagnosis difficult. The general public appear to have a low level of awareness of alternative primary care services or difficulty accessing such information. Some of the changes towards reducing the number of patients presenting to ED will include patient education.