Abstract 84P
Background
Oncology clinical trials provide an opportunity to assess the value of utilizing active and passive clinical outcome assessments (COAs) to capture diverse perspectives of patient experience and quality of life in their investigational cancer treatment. These insights may contribute to the data that informs clinical research outcomes and clinical care for these patients. To add value for patients, regulators and clinicians, the selection of endpoints demands a practical approach that balances traditionally accepted patient-reported outcomes with innovative sensor-derived data. The proposed novel framework was initially developed as a conceptual model, but can be adapted to oncology providing a comprehensive view of patient outcomes. This approach may be leveraged for a more nuanced understanding of treatment impacts, ultimately leading to enhanced patient care in oncology.
Methods
The framework utilizes a collaborative methodology by aligning the ontologies of standard survey-based COAs with connected sensor-based COAs. This approach involves selecting relevant oncology quality of life questionnaires and extracting key themes, or domains, and ontology components for alignment. By employing a visual display for decision-making support focusing on whether survey-based COAs, sensor-COAs, or a combination of both are most effective, it will be possible to further determine treatment impact on patients' lives.
Results
Anticipated output of this methodology will include an applied COA framework aligning the ontologies of COAs, including a comparison of domains of interest. It is expected that the investigations into the utility of sensor-based patient monitoring for safety and functional status will elucidate the potential to influence patient care during and post-trial.
Conclusions
The hypothesis supports the proposed framework's potential in advancing oncology clinical trials by fusing survey and sensor based COAs for a richer, quantifiable understanding of patient experiences. Anticipated benefits include efficiencies in data capture, enhanced care quality and support for adoption of digital health technologies by regulators. Collaborators: C. Manta Campbell, C. Webster, R. Sherafat-Kazemzadeh, J. Braid, T. Switzer, M. Alves Favaro, C. Sassano, A. Coravos.
Clinical trial identification
Editorial acknowledgement
Alice Miller, Syneos Health.
Legal entity responsible for the study
Syneos Health.
Funding
Has not received any funding.
Disclosure
M. Parisi, J.K. Bentley, W. Harb: Financial Interests, Institutional, Full or part-time Employment: Syneos Health.