More on Rapid Learning Systems for Cancer: Amy Abernethy Featured in Health Affairs
Jun 30, 2010, 2:31 AM, Posted by RWJF Blog Team
Rapid Learning Cancer Care: Getting Serious About Implementation
With respect to rapid learning healthcare, it’s time to get serious about implementation. National entities, such as the Institute of Medicine and the Robert Wood Johnson Foundation, have helped shape a growing consensus that this new model can help bridge the chasm between research and clinical care, and have convened thought leaders to define it.
As a practicing oncologist and clinical researcher, I see rapid learning healthcare as starting from patient care itself, which is provided individually, patient-by-patient. In the rapid learning healthcare vision, data that are routinely collected in patient care will feed into an ever-growing databank or set of coordinated databases. The system “learns” by routinely and iteratively: (1) collecting data in a planned, strategic manner; (2) analyzing captured data; (3) generating evidence through retrospective analysis of existing data as well as data from prospective studies; (4) implementing new insights into subsequent clinical care; (5) evaluating outcomes of changes in clinical practice, and; (6) generating new hypotheses for investigation.
Discovery thus becomes a natural outgrowth of patient care. The care of the current patient is informed by all those with like characteristics who came before him/her, and this patient’s care and outcomes are reinvested into the data stream to inform the care of similar individuals in the future.
Oncology is the natural place to start developing a rapid learning health care system. The flagship demonstration of rapid learning healthcare in action should happen in oncology, given cancer’s severity, threat to life, costliness, strong patient engagement, and population-wide impact. Continuous investigation, discovery, and evidence implementation are intrinsic to cancer research. Patient-reported outcomes have been well-studied in oncology, and are widely used in clinical practice as well as research, so reliable patient-reported data can anchor the rapid learning system to patient-centered care. Cancer is ripe for a new model of integrated clinical/research function, and rapid learning healthcare provides just that.
Implemented in cancer care, a rapid learning system would expand the pace and magnitude of evidence generation for oncologists and our patients. It would enable increasingly definitive analyses of the comparative effectiveness of current and future treatment options, at both the individual and population level – better matching the right intervention with the right individual at the right time. It would facilitate and encourage system-wide learning, leveraging the experience of all cancer patients as well as that of clinical trial participants.
Thus far, substantial progress has been made in developing the cancer-focused tools and infrastructure for a top-down rapid learning strategy in cancer. These include: the National Cancer Institute’s investment in caBIG® and its related toolbox to support an interoperable data infrastructure that accommodates the complexities of cancer research, discovery and patient care; a national track record in registries and data collection to support cancer surveillance and outcomes assessment; and, commitment across the oncology community to research and best practice as evidenced by widely implemented clinical trials programs, clinical practice guidelines, and quality initiatives. This is overlaid by national investments in health information technology, comparative effectiveness research, and accountable care.
However, what we still lack is a national blueprint for building up the rapid learning healthcare system from the foundational level of daily clinical practice and research. At Duke, we have begun to implement a rapid learning model in the cancer clinic using linked datasets that coordinate clinical, patient-reported, administrative, financial, clinical research, and basic science information into a pooled resource. Iterative analyses and reinvestment of lessons learned occur within the context of what clinicians, patients, and researchers define as important questions to answer and hypotheses to test.
In order for rapid learning healthcare to be truly embedded in clinical practice, we need to understand the nuances of implementation at point-of-care, and the tools needed by the frontline provider and patients such as clinical decision support, data capture solutions, data visualization, real-time analytics, streamlined care models, patient education, and improved communication. At Duke, our demonstration in the clinic is intended to parallel, and ultimately link up with, national efforts to develop rapid learning healthcare – thereby providing a living laboratory to work through logistical and practical solutions.