BACKGROUND: "Rapid learning healthcare" presents a new infrastructure to support comparative effectiveness research. By leveraging heterogeneous datasets (eg, clinical, administrative, genomic, registry, and research), health information technology, and sophisticated iterative analyses, rapid learning healthcare provides a real-time framework in which clinical studies can evaluate the relative impact of therapeutic approaches on a diverse array of measures. PURPOSE: This article describes an effort, at 1 academic medical center, to demonstrate what rapid learning healthcare might look like in operation. The article describes the process of developing and testing the components of this new model of integrated clinical/research function, with the pilot site being an academic oncology clinic and with electronic patient-reported outcomes (ePROs) being the foundational dataset. RESEARCH DESIGN: Steps included: feasibility study of the ePRO system; validation study of ePRO collection across 3 cancers; linking ePRO and other datasets; implementation; stakeholder alignment and buy in, and; demonstration through use cases. SUBJECTS: Two use cases are presented; participants were metastatic breast cancer (n = 65) and gastrointestinal cancer (n = 113) patients at 2 academic medical centers. RESULTS: (1) Patient-reported symptom data were collected with tablet computers; patients with breast and gastrointestinal cancer indicated high levels of sexual distress, which prompted multidisciplinary response, design of an intervention, and successful application for funding to study the intervention's impact. (2) The system evaluated the longitudinal impact of a psychosocial care program provided to patients with breast cancer. Participants used tablet computers to complete PRO surveys; data indicated significant impact on psychosocial outcomes, notably distress and despair, despite advanced disease. Results return to the clinic, allowing iterative update and evaluation. CONCLUSIONS: An ePRO-based rapid learning cancer clinic is feasible, providing real-time research-quality data to support comparative effectiveness research.
BACKGROUND: "Rapid learning healthcare" presents a new infrastructure to support comparative effectiveness research. By leveraging heterogeneous datasets (eg, clinical, administrative, genomic, registry, and research), health information technology, and sophisticated iterative analyses, rapid learning healthcare provides a real-time framework in which clinical studies can evaluate the relative impact of therapeutic approaches on a diverse array of measures. PURPOSE: This article describes an effort, at 1 academic medical center, to demonstrate what rapid learning healthcare might look like in operation. The article describes the process of developing and testing the components of this new model of integrated clinical/research function, with the pilot site being an academic oncology clinic and with electronic patient-reported outcomes (ePROs) being the foundational dataset. RESEARCH DESIGN: Steps included: feasibility study of the ePRO system; validation study of ePRO collection across 3 cancers; linking ePRO and other datasets; implementation; stakeholder alignment and buy in, and; demonstration through use cases. SUBJECTS: Two use cases are presented; participants were metastatic breast cancer (n = 65) and gastrointestinal cancer (n = 113) patients at 2 academic medical centers. RESULTS: (1) Patient-reported symptom data were collected with tablet computers; patients with breast and gastrointestinal cancer indicated high levels of sexual distress, which prompted multidisciplinary response, design of an intervention, and successful application for funding to study the intervention's impact. (2) The system evaluated the longitudinal impact of a psychosocial care program provided to patients with breast cancer. Participants used tablet computers to complete PRO surveys; data indicated significant impact on psychosocial outcomes, notably distress and despair, despite advanced disease. Results return to the clinic, allowing iterative update and evaluation. CONCLUSIONS: An ePRO-based rapid learning cancer clinic is feasible, providing real-time research-quality data to support comparative effectiveness research.
Authors: Arif H Kamal; David C Currow; Christine Ritchie; Janet Bull; Jane L Wheeler; Amy P Abernethy Journal: Curr Oncol Rep Date: 2011-08 Impact factor: 5.075
Authors: Jaxon J Vallely; Kathryn E Hudson; Susan C Locke; Steven P Wolf; Gregory P Samsa; Amy P Abernethy; Thomas W LeBlanc Journal: Support Care Cancer Date: 2019-02-14 Impact factor: 3.603
Authors: Arif H Kamal; Janet Bull; Dio Kavalieratos; Donald H Taylor; William Downey; Amy P Abernethy Journal: J Oncol Pract Date: 2011-11 Impact factor: 3.840
Authors: Brian Shiner; Jiang Gui; Christine Leonard Westgate; Paula P Schnurr; Bradley V Watts; Sarah L Cornelius; Shira Maguen Journal: J Eval Clin Pract Date: 2019-05-21 Impact factor: 2.431