Harnessing the Power of Health IT in a New Era of Translational Research

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In the final years of the 20th century and the first decade of the 21st century, tremendous progress has been made toward bridging a recognized chasm between science and the real world, and specifically in medicine, between biomedical research and its application in healthcare.  Three identified “blocks” to translation have impeded the use of research findings to better the lot of our patients: T1, the translation of laboratory findings to clinical care, T2, the application of best evidence identified during T2 to everyday clinical care; and T3, wider generalization of research findings to improve the health of the community and, more broadly, the public.  The recent deluge of funding for comparative effectiveness research (CER) represents, in large part, an attempt to conquer T2 and T3.  T1, however, persists and presents a fundamental impediment to personalized medicine.

To overcome T1, and transfer T1 knowledge to T2, will require true integration of the clinical and research spheres – an integration that necessitates bidirectional information flow from the patient and physician in the clinic to the research scientist and back again, in an iterative cycle of hypothesis, question, answer, and testing of that answer in the real world setting.  To support this sort of information exchange, we will need: new coordinated health information technology (HIT) systems that span former “silos” in the biomedical community, and that can collect and manage large volumes of disparate and heterogeneous data; culture change that engages clinicians and researchers in a common mission of inquiry to improve care; communication channels that fuel hypothesis generation, and that support the translation of research findings into change in clinical practice, and; decision support mechanisms that help clinicians leverage the power of large-scale aggregated data to improve care for the individual patient.  In short, we need a new model of care, one that harnesses the potential of HIT and integrated clinical/research data to dismantle the T1 block. The purpose, fundamentally, of such a model will be to enable personalized medicine.

In advancing “rapid learning healthcare,” the Institute of Medicine has spearheaded the development of a new healthcare paradigm in which personalized medicine could become a reality.  Efforts are underway to develop this paradigm and its prerequisites.  As one such example, the Cancer Biomedical Informatics Grid (caBIG®) championed by the National Cancer Institute has tackled the development of an infrastructure promoting large-scale data interoperability spanning the data type boundaries from the basic sciences to clinical care and the patient report.  As we seek to match novel therapeutics and trials to patients, and to personalize care using individually relevant information, critical steps will be: (1) providing access to data, (2) generating data, and (3) making sense of the data.  Making sense of data needs to be facilitated at the levels of basic science (to guide translation of in silico research results into clinical practice change and further discovery), the population (to allow CER to guide health services decisions and policy), and the individual patient (to enable personalized medicine).  New data generated in any of these steps should be reinvested in the system to iteratively update the knowledge base.

The caBIG® experience has taught us that just having access to better HIT does not, in itself, advance personalized medicine.  Though a powerful tool, HIT alone is not enough to bulldoze the translation blocks.  Why?  Because healthcare is not a purely technical matter; rather, it is a human system, fundamentally dependent upon human understanding, acceptance, and behavior.  All of these must change in order to transform information flow through HIT, implement a new data-driven model of healthcare, and thus realize the vision of personalized medicine.  The individual stakeholders in medicine need to be aligned behind the new vision – through incentives to participation that speak to each one.  First, the new model needs to be structured, and to function, so that the HIT makes sense to real human beings using the system (clinicians, staff, patients, administrators, clinical researchers, basic scientists); HIT must represent “value added” to the existing system from the perspective of each stakeholder.  Second, interoperable data must be generated, so that the system has “grist for the mill” of inquiry; we have to start somewhere and someone needs to be encouraged to put their first big toe in the water — there is nothing like a “big story” to bring along the naysayers.  Third, to build confidence in the approach, we must make sure that privacy, confidentiality and the sanctity of personal health information are preserved. And fourth, novel ways to make sense of ever-growing databanks need to be developed; these methods may include new approaches for visualization, decision support systems, Bayesian and other branched analytic approaches, CER, and in silico research.  Current efforts focus on generating interoperable data (the middle step), but neglect to create systems that make sense of the data and promote its use, or that provide a structure and an engine to produce the data.

Finally, and, in my mind, most importantly, a critical next step is to define a reorganization of medicine at the point of care.  The new model must fully utilize available data and linked datasets, and must help clinicians understand the data and apply it in tailoring care to their individual patients.  If it makes sense to them, clinicians and patients will drive this.  In the background, interdigitated with the growing body of clinical experiences captured in linked clinical/research databases, will be the robust evidence base comprising published results of basic science, clinical research, and translational studies.  The resulting combination creates a system in which each patient’s care is guided by personal history and characteristics, the experiences of similar patients included in local and massive national longitudinal datasets, and the historical evidence base constituting an up-to-date state of the science.  Our challenge, today, is to develop this system beginning at the point of care, with the patient.

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