Patients receiving pelvic radiotherapy can experience long term GI side effects post-radiotherapy. The Trigger project identifies patients experiencing symptoms of radiation-related bowel toxicity using the ALERT-B questionnaire, and directs them to the appropriate clinician. Trigger is a service evaluation project, aiming to prove the utility of electronic PROMs, and to demonstrate the feasibility of a low-resource project as a model for collecting PROMs. It is a collaboration between Macmillan Cancer Support, the Royal College of Radiologists, and three NHS Trusts: Velindre, Imperial College Healthcare and Brighton and Sussex University Hospitals.Patients register on the Trigger website, hosted by My Clinical Outcomes, and receive periodic emails to complete the short ALERT-B questionnaire electronically, to screen for longterm bowel symptoms which could have been caused by pelvic radiotherapy. If answering 'yes' to any of the questions, patients are directed to appropriate services. 6 months following the completion of their radiotherapy, patients are sent a separate questionnaire to evaluate the utility of the project. 336 patients registered in first the 9 months across the 3 sites. Patients with a range of different cancers signed up: anal (2%), bladder (1%), prostate (87%), rectal (4%) and gynaecological (6%). 43 patients (/65 (uptake 66%)) have answered their 6-month post treatment questionnaire, and 72% answered 'yes' to at least one of the ALERT-B questions. 85% of responding patients reported they found the Trigger project helpful.These promising results show that electronic PROMS can be introduced in radiotherapy departments using a low resource model. The Trigger project works as a feasibility model, showing patients engage with electronic PROMs projects, and find them useful. PROMs for other tumour types could be collected in a similar manner, based on the lowresource model used here, using site-specific PROMs based on the ALERT-B tool.Introduction It is not uncommon for a patient coming to the NHS to present with investigations and treatments done in EU countries and outside the EU for various medical and surgical conditions. Many times, reports are written in languages other than English and therefore does not to contribute to medical consultations.Another issue with the patients is that they feel helpless and frustrated when they can't understand what the reports mean for them. To resolve these problems, we developed a technology called Medisense is a medical report image capture, data extraction, validation and interpretation technology. By using this technology, doctors and patients can take a snap of the picture of any report and get an instant translation, interpretation and review of the report.Study design In the current study, we carried out 97 cases of breast cancer histopathology reports of various patients. The test was carried out using a specified protocol in a test environment. ResultsThe study results show that in all the 97 cases, the image capture was successful. However, the data...
Background: At the start of the coronavirus disease 2019 (COVID-19) pandemic there was widespread concern about potentially overwhelming demand for intensive care and the need for intensive care unit (ICU) triage. In March 2020, a draft United Kingdom (UK) guideline proposed a decision-support tool (DST). We sought to evaluate the accuracy of the tool in patients with COVID-19. Methods: We retrospectively identified patients in two groups: referred and not referred to intensive care in a single UK national health service (NHS) trust in April 2020. Age, Clinical Frailty Scale score (CFS), and co-morbidities were collected from patients’ records and recorded, along with ceilings of treatment and outcome. We compared the DST, CFS, and age alone as predictors of mortality, and treatment ceiling decisions. Results: In total, 151 patients were included in the analysis, with 75 in the ICU and 76 in the non-ICU-reviewed groups. Age, clinical frailty and DST score were each associated with increased mortality and higher likelihood of treatment limitation (p-values all <.001). A DST cut-off score of >8 had 65% (95% confidence interval (CI) 51%-79%) sensitivity and 63% (95% CI 54%-72%) specificity for predicting mortality. It had a sensitivity of 80% (70%-88%) and specificity of 96% (95% CI 90%-100%) for predicting treatment limitation. The DST was more discriminative than age alone (p<0.001), and potentially more discriminative than CFS (p=0.08) for predicting treatment ceiling decisions. Conclusions: During the first wave of the COVID-19 pandemic, in a hospital without severe resource limitations, a hypothetical decision support tool was limited in its predictive value for mortality, but appeared to be sensitive and specific for predicting treatment limitation.
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