Objective: To evaluate the feasibility of an electronic symptom-tracking platform for patients recovering from ambulatory surgery.
Method: We assessed user response to an electronic system designed to self-report symptoms. Endpoints included compliance, postoperative symptoms, patient satisfaction. An 8-item symptom inventory (pain, nausea, vomiting, shortness of breath, fever, swelling, discharge, redness) was developed and made available on postoperative days (POD) 2-6. Responses exceeding defined thresholds of severity triggered alerts to healthcare providers. Symptoms, alerts, actions taken, urgent care center (UCC) visits, hospital admissions were tracked until POD 30. Patient satisfaction was evaluated on POD 7. A patient was defined as "responder" if at least 5/8 items on at least 3 PODs were completed. The assessment method was deemed successful if 64/100 patients responded.
Results: 97/102 patients were evaluable; 65 met "responder" criteria (67% responder rate; 95% CI 57-76%). 321 surveys were completed (median 4/patient), 248 (77%) in ≤2 min. Involving caregivers and allowing additional symptom-reporting improved the responder rate to 72% (95% CI 58-84%). Most commonly-reported moderate, severe, very severe symptoms were pain, nausea, swelling; 71% reported moderate to very severe pain on POD 2. Phone calls and adjustment of medications adequately addressed most symptoms. Two patients (2%) presented at UCC before, 6 (6%) after, POD 6; 1 (1%) was admitted. Most agreed or strongly agreed that electronic symptom-tracking was helpful, easy to use, and would recommend it to others.
Conclusion: Electronic symptom-tracking is feasible for patients undergoing ambulatory gynecologic cancer surgery. Symptom burden is high in the early postoperative period. Addressing patient-reported symptoms in a timely, automated manner may prevent severe downstream adverse events, reduce UCC visits and admission rates, and improve outcomes.
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http://dx.doi.org/10.1016/j.ygyno.2020.07.004 | DOI Listing |
JMIR Form Res
October 2024
Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom.
Background: Mobile health devices are increasingly available, presenting exciting opportunities to remotely collect high-frequency, electronic patient-generated health data (ePGHD). This novel data type may provide detailed insights into disease activity outside usual clinical settings. Assessing treatment responses, which can be hampered by the infrequency of appointments and recall bias, is a promising, novel application of ePGHD.
View Article and Find Full Text PDFJCO Clin Cancer Inform
October 2024
Medical Oncology Department, Centre Léon Bérard, Lyon, France.
Trials
October 2024
Centre for Health Informatics, Division of Informatics, Imaging and Data Science, University of Manchester, Manchester Academic Health Science Centre, Vaughan House, Portsmouth Street, Manchester, M13 9GB, UK.
Background: Management of rheumatoid arthritis (RA) relies on symptoms reported by patients during infrequent outpatient clinic visits. These reports are often incomplete and inaccurate due to poor recall, leading to suboptimal treatment decisions and outcomes. Asking people to track symptoms in-between visits and integrating the data into clinical pathways may improve this.
View Article and Find Full Text PDFJ Eval Clin Pract
October 2024
School of Health Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
Int J Med Inform
December 2024
Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, Australia.
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