Risk-stratified follow-up for endometrial cancer (EC) is being introduced in many cancer centres; however, there appears to be diversity in the structure and availability of schemes across the UK. This study aimed to investigate clinicians’ and clinical specialist nurses’ (CNS) experiences of follow-up schemes for EC, including patient-initiated follow-up (PIFU), telephone follow-up (TFU) and clinician-led hospital follow-up (HFU). A mixed-methods study was conducted, consisting of an online questionnaire to CNSs, an audience survey of participants attending a national “Personalising Endometrial Cancer Follow-up” educational meeting, and qualitative semi-structured telephone interviews with clinicians involved in the follow-up of EC. Thematic analysis identified three main themes to describe clinicians’ views: appropriate patient selection; changing from HFU to PIFU schemes; and the future of EC follow-up schemes. Many participants reported that the COVID-19 pandemic impacted EC follow-up by accelerating the transition to PIFU/TFU. Overall, there was increasing support for non-HFU schemes for patients who have completed primary treatment of EC; however, barriers were identified for non-English-speaking patients and those who had communication challenges. Given the good long-term outcome associated with EC, greater focus is needed to develop resources to support patients post-treatment and individualise follow-up according to patients’ personal needs and preferences.
Intelligence method was developed to support the clinical staff in their decision with regards to tumor staging and to help them identifying the most complex cases where deeper analysis and discussion were required (e. g. conflicting information from different exams). Result(s)* In the first round, the system has been used to retrieve all the eHR for the 96 patients with LACC. This was the training set of the study, with validated 2009 FIGO staging classification ranging from I B2 to IV A as output. For these patients, available eHR included MR, EUA, and PET-CT diagnostic reports. The system has been able to classify all patients belonging to the training set and -through the NLP procedures -the clinical features were analyzed and classified for each patient. A second important result was the setup of a predictive model to evaluate the patient's staging. Our approach has led to predict patient's staging within an accuracy of 94%. Lastly we created a user-oriented operational tool targeting the MTB who are confronted with the challenge of large volumes of patients to be diagnosed in the most accurate way. The resulting decision support system is summarized in figure 1. Furthermore, the MTB Smart DA was tested in a 13 LACC patients validation cohort showing an accuracy of 93%, in line with the training set performances. Conclusion* This is the first proof of concept concerning the possibility of creating a smart virtual assistant for the MTB. A significant benefit could come from the integration of these automated methods in the collaborative, crucial decision stages.
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