About: BACKGROUND: Increasing numbers of ovarian cancer patients are living longer and requiring regular follow-up to detect disease recurrence. New models of follow-up care are needed to meet the growing number and needs of this patient group. The potential for patient-reported outcome measures (PROMs) to capture key symptoms and online technology to facilitate long-term follow-up has been suggested. OBJECTIVES: Prior to a pilot study exploring the potential for electronic patient-reported symptom monitoring, the content of an online intervention was developed via Delphi methodology. DESIGN AND SETTING: A Delphi process was conducted aiming to obtain consensus amongst the clinicians and patients from 4 hospitals on the key aspects to monitor during follow-up after ovarian cancer treatment, and how to monitor them in an online intervention. A two round Delphi was conducted. Consensus was defined as at least 70% agreement. RESULTS: Out of 43 participants, 30 (18 patients, 12 healthcare professionals) completed round 1 and 19 (11 patients, 8 healthcare professionals) completed round 2. Consensus was reached on the key symptoms to monitor, and the importance of monitoring both duration and frequency of symptoms. Opportunity for review of psychological wellbeing and holistic needs were considered important by both groups. The frequency of online questionnaire completion, timeframe for patients to reflect on (e.g. during the past X weeks), and the choice of PROMs items to monitor symptoms did not reach the consensus threshold. CONCLUSION: It is crucial that any intervention and the selection of PROMs is fully described to ensure transparency about the development and decisions taken. In this work, a set of key symptoms and areas to monitor were agreed, which has informed the design of an online intervention and a subsequent pilot study is now underway. The proposed model of remote follow-up using electronic PROMs could be adapted and explored in other cancer sites.   Goto Sponge  NotDistinct  Permalink

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  • BACKGROUND: Increasing numbers of ovarian cancer patients are living longer and requiring regular follow-up to detect disease recurrence. New models of follow-up care are needed to meet the growing number and needs of this patient group. The potential for patient-reported outcome measures (PROMs) to capture key symptoms and online technology to facilitate long-term follow-up has been suggested. OBJECTIVES: Prior to a pilot study exploring the potential for electronic patient-reported symptom monitoring, the content of an online intervention was developed via Delphi methodology. DESIGN AND SETTING: A Delphi process was conducted aiming to obtain consensus amongst the clinicians and patients from 4 hospitals on the key aspects to monitor during follow-up after ovarian cancer treatment, and how to monitor them in an online intervention. A two round Delphi was conducted. Consensus was defined as at least 70% agreement. RESULTS: Out of 43 participants, 30 (18 patients, 12 healthcare professionals) completed round 1 and 19 (11 patients, 8 healthcare professionals) completed round 2. Consensus was reached on the key symptoms to monitor, and the importance of monitoring both duration and frequency of symptoms. Opportunity for review of psychological wellbeing and holistic needs were considered important by both groups. The frequency of online questionnaire completion, timeframe for patients to reflect on (e.g. during the past X weeks), and the choice of PROMs items to monitor symptoms did not reach the consensus threshold. CONCLUSION: It is crucial that any intervention and the selection of PROMs is fully described to ensure transparency about the development and decisions taken. In this work, a set of key symptoms and areas to monitor were agreed, which has informed the design of an online intervention and a subsequent pilot study is now underway. The proposed model of remote follow-up using electronic PROMs could be adapted and explored in other cancer sites.
Subject
  • Addiction
  • Quality of life
  • Clinical research
  • Evaluation methods
  • Clinical data management
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