ASCO’s CancerLinQ—Building a Transformation in Cancer Care.

Rapid-learning system for cancer care

Rapid learning health care is a novel approach to health care delivery that relies on intelligent and continuous integration of clinical and research data sets to deliver personalized medicine using the most current evidence available. Results of important studies in the management of chronic respiratory disease are presented in brief; however, the focus of this review is on evidence.

Rapid-learning system for cancer care

Having demonstrated 2 critical aspects of the rapid learning cancer clinic model—feasibility of the data collection methodology and validity of data collected using the electronic data infrastructure—we piloted the use of the ePRO system to support, in a preliminary fashion, rapid learning cancer care.

Rapid-learning system for cancer care

Dr. Abernethy is an internationally recognized expert in health services research and delivery in patient-centered cancer care, especially pain, symptom management and palliative care. Under her.

Rapid-learning system for cancer care

The Athena Breast Health Network is an initiative of the five University of California medical and cancer centers to prototype this approach and to enable the development of a rapid learning system - connecting risk and outcome information from a heterogeneous patient population in real time and using new knowledge from research to continuously improve the quality of care. The Network is based.

Rapid-learning system for cancer care

The work of the Board on Health Care Services (HCS) is helping to shape the direction of health care in the United States and abroad. The Board considers the entire health care system in order to ensure the best possible care for all patients. Its activities pertain to organization, financing, effectiveness, workforce, and delivery of health care.

Rapid-learning system for cancer care

Big data and comparative effectiveness research methodologies can be applied within the framework of a rapid-learning health care system (RLHCS) to accelerate discovery and to help turn the dream of fully personalized medicine into a reality. We synthesize recent advances in genomics with trends in big data to provide a forward-looking perspective on the potential of new advances to usher in.

Rapid-learning system for cancer care

The National Cancer Policy Forum of the Institute of Medicine workshop entitled “A Foundation for Evidence-Driven Practice: A Rapid Learning System for Cancer Care”(11) examined the elements of a rapid learning system for cancer. It recommended that the elements of such a system include: regis-.

Rapid-learning system for cancer 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.

Rapid-learning system for cancer care

EMR serve as a broad platform that merges a variety of patient information and expert advice to facilitate co-ordinated cancer care. 24 They aid in personalised cancer risk management and treatment by taking into account the behavioural and social contexts. 5 An EMR-adapted healthcare system efficiently streamlines day-to-day hospital procedures, along with internal quality control checks, and.

Rapid-learning system for cancer care

The 2013 IOM report, Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis, identified contemporary challenges to the delivery of high-quality cancer care and specified.

Rapid-learning system for cancer care

Shah A, Stewart AK, Kolacevski A, et al. 2016. Building a rapid learning health care system for oncology: why CancerLinQ collects identifiable health information to achieve its vision. Journal of Clinical Oncology 34(7): 756-63; Miller RS, Wong JL. 2017. Using oncology real-world evidence for quality improvement and discovery: the case for.