{"title":"A Pilot Study on the Collection of Adverse Event Data from the Patient Using an Electronic Platform in a Cancer Clinical Trial Unit.","authors":"Minna Grahvendy, Bena Brown, Laurelie R Wishart","doi":"10.1007/s40801-024-00461-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and objective: </strong>Accurate and robust adverse event (AE) data collection is crucial in cancer clinical trials to ensure participant safety. Frameworks have been developed to facilitate the collection of AE data and now the traditional workflows are facing renewal to include patient-reported data, improving completeness of AE data. We explored one of these workflows in a cancer clinical trial unit.</p><p><strong>Methods: </strong>The study was a single-site study conducted at a tertiary hospital located in Australia. Patients consenting to a clinical trial were eligible for inclusion in this study. Participants used an electronic platform-My Health My Way (MHMW)-to report their symptomatic data weekly for 24 weeks. A symptom list was included within the platform, along with a free text field. Data reported via the platform was compared with data recorded in the patient's medical chart. Time taken to compile data from each source was recorded, along with missing data points. Agreement between patient-reported data and data recorded in the medical notes was assessed using Kappa and Gwet's AC<sub>1</sub>; time taken to compile data and missing data points were assessed using a Wilcoxon signed rank test.</p><p><strong>Results: </strong>Low agreement was found between patient- and clinician-reported data (- 0.482 and - 0.159 by Kappa and Gwet's AC<sub>1</sub> respectively). Only 127 (30%) of the total 428 AEs were reported by both MHMW and medical notes. Patients reported higher rates of symptoms from the symptom list, while clinicians reported higher rates of symptoms outside of the symptom list. Time taken to compile the data from MHMW was significantly less than that taken to review medical notes (2.19 min versus 5.73 min respectively; P < 0.001). There were significantly less missing data points from the MHMW data compared with the medical notes (1.4 versus 7.8; P < 0.001).</p><p><strong>Conclusions: </strong>This study confirms previous reports that patient- and clinician-reported adverse event data show low agreement. This study also shows that clinical trial sites could significantly reduce the work performed by research staff in the collection of adverse event data by implementing an electronic, patient-reported platform.</p>","PeriodicalId":11282,"journal":{"name":"Drugs - Real World Outcomes","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drugs - Real World Outcomes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40801-024-00461-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
Background and objective: Accurate and robust adverse event (AE) data collection is crucial in cancer clinical trials to ensure participant safety. Frameworks have been developed to facilitate the collection of AE data and now the traditional workflows are facing renewal to include patient-reported data, improving completeness of AE data. We explored one of these workflows in a cancer clinical trial unit.
Methods: The study was a single-site study conducted at a tertiary hospital located in Australia. Patients consenting to a clinical trial were eligible for inclusion in this study. Participants used an electronic platform-My Health My Way (MHMW)-to report their symptomatic data weekly for 24 weeks. A symptom list was included within the platform, along with a free text field. Data reported via the platform was compared with data recorded in the patient's medical chart. Time taken to compile data from each source was recorded, along with missing data points. Agreement between patient-reported data and data recorded in the medical notes was assessed using Kappa and Gwet's AC1; time taken to compile data and missing data points were assessed using a Wilcoxon signed rank test.
Results: Low agreement was found between patient- and clinician-reported data (- 0.482 and - 0.159 by Kappa and Gwet's AC1 respectively). Only 127 (30%) of the total 428 AEs were reported by both MHMW and medical notes. Patients reported higher rates of symptoms from the symptom list, while clinicians reported higher rates of symptoms outside of the symptom list. Time taken to compile the data from MHMW was significantly less than that taken to review medical notes (2.19 min versus 5.73 min respectively; P < 0.001). There were significantly less missing data points from the MHMW data compared with the medical notes (1.4 versus 7.8; P < 0.001).
Conclusions: This study confirms previous reports that patient- and clinician-reported adverse event data show low agreement. This study also shows that clinical trial sites could significantly reduce the work performed by research staff in the collection of adverse event data by implementing an electronic, patient-reported platform.
期刊介绍:
Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.