A patient-centered symptom monitoring and reporting system for children and young adults with cancer (SyMon-SAYS)

  • Lai J
  • Yount S
  • Beaumont J
 et al. 
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Abstract

BACKGROUND: This study evaluated the feasibility of implementing a patient-centered, technology-based symptom monitoring and reporting system (SyMon-SAYS) in pediatric oncology clinics using fatigue as a prototypic symptom. Timely identification of symptoms related to multi-modal therapy for children with cancer is fundamental to the overall success of cancer treatment. SyMon-SAYS was developed to address this need. PROCEDURE: Patients with a cancer diagnosis, ages 7-21 years, currently on treatment, or off treatment within 6 months, were eligible. Patients/parents completed weekly fatigue assessments over 8 weeks via the internet or interactive voice response (IVR) by phone. Alert emails were generated when pre-defined fatigue score thresholds were met, and fatigue reports were forwarded to clinicians accordingly. Clinicians and parents/patients received cumulative graphic reports of fatigue scores prior to clinic visits at 4 and 8 weeks post-baseline to facilitate discussion. Parents/patients completed an exit survey at their last visit. RESULTS: Fifty-seven patients/parents completed the study. The majority of patients (93%) and parents (78%) felt it was very/extremely easy to complete SyMon-SAYS; 95% of parents were satisfied with the system; 60% reported it helped deal with their child's fatigue; 70% reported that clinicians didn't discuss fatigue with them; 81% would be willing to use SyMon-SAYS to manage fatigue and other symptoms. Clinicians reported insufficient time to review reports, yet 71% were willing to receive the report on a monthly basis. CONCLUSION: SyMon-SAYS is feasible and acceptable to patients and parents. Future efforts should focus on better integrating the system into the clinical workflow to improve clinicians' acceptance.

Author-supplied keywords

  • Cancer
  • Fatigue
  • Pediatrics
  • Symptom monitoring

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Authors

  • Jin Shei Lai

  • Susan Yount

  • Jennifer L. Beaumont

  • Jacquie Toia

  • Stewart Goldman

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