Optimizing multi-center perfusion data.
Optimizing may be defined as making "as perfect, effective, or (as) functional as possible" (1). One would expect that each perfusionist strives to optimize his or her clinical practices on a daily basis. Some might argue that describing how to optimize multicenter perfusion data may not be terribly relevant to the average perfusionist. Why not begin by describing the utility of collecting perfusion data, i.e., what is the reward for doing more than one already does. The next logical step might be to describe how to use these data to inform clinical decision making. To follow this argument one step further, one would end by describing how to develop a multi-center perfusion registry and how to maximize its value and utility. The aim of this paper is to present and discuss some of the principles regarding the optimization of a multi-center perfusion dataset with specific focus on the PerfusionDownunderCollaboration Research Database (PDUCRD; http://www.perfusiondownunder. com/). Such datasets are powerful tools for identifying variation in the delivery and consequence of care. Through this lens, organizations may rapidly improve the delivery of care through targeted interventions. This manuscript addresses the following principles: the quality of the datasets, the use of the data to inform practice, and developing and maintaining social capital.
Likosky
Donald S
DS
The Dartmouth Institute for Health Policy and Clinical Practice, Hanover, New Hampshire, USA. donald.likosky@Dartmouth.edu
D002315:Cardiopulmonary Bypass / Q000706:statistics & numerical data*; D019992:Databases as Topic; D003657:Decision Making; D006801:Humans; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data*; D009513:New Hampshire; D010477:Perfusion / Q000706:statistics & numerical data*; D011787:Quality of Health Care / Q000706:statistics & numerical data*; D012042:Registries