Rapid-learning system for cancer care.

By A. Abernethy, L. Etheredge, Patricia A. Ganz, Paul Wallace, R. German, Chalapathy Neti, P. Bach, Sharon B. Murphy. Journal of Clinical Oncology, 2010

344 citations recorded by the source corpus.

Compelling public interest is propelling national efforts to advance the evidence base for cancer treatment and control measures and to transform the way in which evidence is aggregated and applied. Substantial investments in health information technology, comparative effectiveness research, health care quality and value, and personalized medicine support these efforts and have resulted in considerable progress to date. An emerging initiative, and one that integrates these converging approaches to improving health care, is "rapid-learning health care." In this framework, routinely collected real-time clinical data drive the process of scientific discovery, which becomes a natural outgrowth of patient care. To better understand the state of the rapid-learning health care model and its potential implications for oncology, the National Cancer Policy Forum of the Institute of Medicine held a workshop entitled "A Foundation for Evidence-Driven Practice: A Rapid-Learning System for Cancer Care" in October 2009. Participants examined the elements of a rapid-learning system for cancer, including registries and databases, emerging information technology, patient-centered and -driven clinical decision support, patient engagement, culture change, clinical practice guidelines, point-of-care needs in clinical oncology, and federal policy issues and implications. This Special Article reviews the activities of the workshop and sets the stage to move from vision to action.

Subfields: Biomedical Engineering, Health Information Management, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/01ab9a7c4235bea765eab8148a4adf7e895fae7e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Dianbo Liu

In Health Information Management

1. Dmitriy Dligach
2. Timothy Miller
3. Dianbo Liu

In Molecular Biology

1. Timothy Miller
2. Philippe Lambin
3. Dmitriy Dligach
4. Dianbo Liu

Citations in the corpus

• Radiomics: the bridge between medical imaging and personalized medicine (2017, neutral): “For the immediate future, the field of radiomics will focus on the creation of suitable infrastructures for powerful RLHC networks that will facilitate the development and validation of models.”
• Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept. (2016, background): “The use of historical data from routine clinical practice for decisions concerning new patients or to test new hypotheses is known as rapid learning [4,43].”
• Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries (2017, neutral): “To capture and use the knowledge contained in large volumes of patient data, predictive models are essential for clinical decision making (1-8).”
• Two-stage Federated Phenotyping and Patient Representation Learning (2019, background): “Without the needs of data movement, the speed of information flow can approach real time and make a rapid learning healthcare system possible (Slut-sky, 2007; Friedman et al., 2014; Abernethy et al., 2010).”