INTEGRATING PPI, COAS, AND DHT-DERIVED MEASURES ACROSS CLINICAL CASE-STUDY SITES
Author(s)
Preston Long, BS, MA, MSc, PhD1, Deborah A. Marshall, PhD2, Gillian R. Currie, PhD2, Joost F. Swart, MD, PhD3, Axel Christian Mühlbacher, PhD4, Ann-Kathrin Fischer, BSc, MSc5, Esther de Bekker-Grob, PhD6, Brett Hauber, PhD7, Tanja Stamm, PhD8.
1Postdoctorate, Medical University of Vienna, Vienna, Austria, 2University of Calgary, Calgary, AB, Canada, 3UMC Utrecht, Wilhelmina Children's Hospital, Utrecht, Netherlands, 4HS Neubrandenburg, Neubrandenburg, Germany, 5Hochschule Neubrandenburg, Neubrandenburg, Germany, 6Erasmus University, Rotterdam, Netherlands, 7Pfizer, New York, NY, USA, 8Medical University of Vienna, Vienna, Austria.
1Postdoctorate, Medical University of Vienna, Vienna, Austria, 2University of Calgary, Calgary, AB, Canada, 3UMC Utrecht, Wilhelmina Children's Hospital, Utrecht, Netherlands, 4HS Neubrandenburg, Neubrandenburg, Germany, 5Hochschule Neubrandenburg, Neubrandenburg, Germany, 6Erasmus University, Rotterdam, Netherlands, 7Pfizer, New York, NY, USA, 8Medical University of Vienna, Vienna, Austria.
OBJECTIVES: The EU-funded UNIFIED project aims to integrate patient-preference information (PPI), clinical outcome assessments (COAs), and digital health technology-derived measures (DHT-derived measures) to provide a complete picture of patient-centered benefit in research and care. However, resources, measurement practices, digital infrastructure, and research capacity differ across clinical sites. Therefore, a landscape analysis was conducted to assess current practices and capacities and identify shared outcome domains and implementation requirements.
METHODS: A structured, multi-site landscape analysis was conducted across UNIFIED clinical case-study sites/teams covering Parkinson’s disease, lung cancer, pediatric radiation oncology, childhood cancer survivorship, juvenile idiopathic arthritis, and chronic cardiometabolic disease. The unit of analysis was the case-study-level landscape template, supplemented where available by multidisciplinary clinical and research teams. Five templates captured clinical workflows, PPI-related activities, COA/PROM and clinical-outcome measurement practices, DHT-derived measures, digital health infrastructure, and research capacities. Comparative synthesis identified shared domains, capabilities, and gaps relevant to implementation and data collection.
RESULTS: Results showed a shared orientation toward patient-centered, multidisciplinary care, but substantial heterogeneity in measurement and digital maturity. Reported COA/PROM domains included symptoms, physical functioning, fatigue or sleep, psychological distress, and health-related quality of life, although instruments and timing varied. PPI activities were reported or planned in some use cases, but showed the greatest variance of any assessed domain. Several centers reported DHT-derived measures, including remote symptom reporting, wearable- or sensor-derived data, and app- or platform-based capture; however, these were not uniformly defined or collected for the same purpose. Findings support a centralized data model with alignment around generic outcome domains, including standard EQ-5D collection, rather than immediate harmonization of disease-specific endpoints.
CONCLUSIONS: UNIFIED sites have heterogeneous experience with PPI, COAs, and DHT-derived measures. Next steps should clarify common domains, finalize the data flow, document collection purposes, and identify minimum data elements for cross-condition learning while preserving site-specific requirements for future harmonized implementation efforts.
METHODS: A structured, multi-site landscape analysis was conducted across UNIFIED clinical case-study sites/teams covering Parkinson’s disease, lung cancer, pediatric radiation oncology, childhood cancer survivorship, juvenile idiopathic arthritis, and chronic cardiometabolic disease. The unit of analysis was the case-study-level landscape template, supplemented where available by multidisciplinary clinical and research teams. Five templates captured clinical workflows, PPI-related activities, COA/PROM and clinical-outcome measurement practices, DHT-derived measures, digital health infrastructure, and research capacities. Comparative synthesis identified shared domains, capabilities, and gaps relevant to implementation and data collection.
RESULTS: Results showed a shared orientation toward patient-centered, multidisciplinary care, but substantial heterogeneity in measurement and digital maturity. Reported COA/PROM domains included symptoms, physical functioning, fatigue or sleep, psychological distress, and health-related quality of life, although instruments and timing varied. PPI activities were reported or planned in some use cases, but showed the greatest variance of any assessed domain. Several centers reported DHT-derived measures, including remote symptom reporting, wearable- or sensor-derived data, and app- or platform-based capture; however, these were not uniformly defined or collected for the same purpose. Findings support a centralized data model with alignment around generic outcome domains, including standard EQ-5D collection, rather than immediate harmonization of disease-specific endpoints.
CONCLUSIONS: UNIFIED sites have heterogeneous experience with PPI, COAs, and DHT-derived measures. Next steps should clarify common domains, finalize the data flow, document collection purposes, and identify minimum data elements for cross-condition learning while preserving site-specific requirements for future harmonized implementation efforts.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
PCR28
Topic
Clinical Outcomes, Health Service Delivery & Process of Care, Patient-Centered Research
Disease
Diabetes/Endocrine/Metabolic Disorders (including obesity), Musculoskeletal Disorders (Arthritis, Bone Disorders, Osteoporosis, Other Musculoskeletal), Neurological Disorders, Oncology, Pediatrics