What is T3 research? Timothy Huerta, PhD, MS, takes a look
According to the National Institutes of Health (NIH), T3 research is focused on the knowledge translation interface between what we know in clinical science and what we do in clinical care. To that end, CATALYST has adopted four pillars that serve to provide a structure upon which health services and implementation science research can grow at The Ohio State University: 1) open science, 2) health information technology, 3) comparative effectiveness research, 4) the learning health care system.
Open science: rigor, reproducibility and transparency
CATALYST seeks to explore the opportunities for research associated with the ongoing discussions at the National Institutes of Health (NIH) related to open science, rigor, reproducibility and transparency. In both the design of our studies and the focus of our science, we seek to speak with a voice, both locally and nationally, on how we might move these issues forward in T3 translational science.
Health information technology (HIT)
Health information technology (HIT) is a potential force multiplier for the delivery of health services. CATALYST seeks to leverage the leadership that Ohio State has in this domain, particularly in the areas of clinical decision support and patient engagement.
Comparative effectiveness research (CER)
As a component of the science associated with exploring variation in health services delivery, there is a concomitant issue of heterogeneity of treatment effect. What works for one person or group may not work for another, or they may experience different outcomes. CATALYST has a focus on exploring these differences in experience using mixed methods research designs.
The learning health care system
Funding agencies see the need for programmatic activity that is engaged in design and systems engineering efforts to build better care. CATALYST serves as a laboratory that enables multiple develop-test-revise iterations of promising design features and subsystems of the sort that could normally be found in larger-scale engineering projects. It does so based on a systems model grounded in how feedback of information can be used to shape the development of robust practices that could lead to improved outcomes across the care continuum.