There is a long-standing interest in the relationship between surgical procedural volume and patient outcomes dating back to early work by Luft, Bunker, and Enthoven (1979) and Luft (1980). Meta-analysis of earlier studies suggests there is a positive association between volume and outcomes in a variety of surgical and treatment settings (Halm, Lee, and Chassin 2002). As a result of this literature, there is a push to use surgical volume as a measure of provider quality, particularly for prevalent or high-risk procedures, including coronary artery bypass grafting surgery (CABG) (Peterson et al. 2004). Researchers, policy makers, hospital managers, physicians, and patients have continued interest in whether volume should be used as a quality indicator for major surgery, including CABG (Birkmeyer 2000; Birkmeyer, Finlayson, and Birkmeyer 2001; Birkmeyer et al. 2002; Epstein 2002; Shahian and Normand 2003; Birkmeyer and Dimick 2004; Shahian 2004;). Given that this procedure is still performed more than 100,000 times each year in the United States and an estimated 800,000 times per year globally and is invasive and expensive, this interest and efforts to address the extent to which volume continues to impact the outcomes of CABG surgery in different settings are warranted. Over time, a variety of methodological and empirical concerns have been raised in the volume–outcomes literature (Sfekas 2009). Some have suggested that further probing the mechanisms through which volume affects mortality would be of additional value (Huesch and Sakakibara 2009). Previously stated and investigated concerns about the observed relationship between volume and outcomes range from issues of patient selection and the causality of the observed relationship (Luft, Hunt, and Maerki 1987) to whether administrative data are detailed enough to account for differences in patient severity of illness (Hannan et al. 1992, 1997; Ho 2005; Tsai et al. 2006) to which modeling methods are appropriate in estimating this relationship (Austin, Tu, and Alter 2003; Hannan et al. 2005; Sfekas 2009;) and whether superior processes of care, which are generally unmeasured by researchers, may be correlated with volume and are at the root of the volume–outcome relationship (Shahian 2004). In spite of these concerns, studies using modern statistical analysis has found a relationship between both hospital (Sfekas 2009) and surgeon volume and outcomes (Hannan et al. 2003), though there is evidence these associations may have grown weaker over time in the United States (Peterson et al. 2004; Marcin et al. 2008; Ricciardi et al. 2008; Boudourakis et al. 2009;). In this study we reiterate the original question, “Does provider volume impact patient health outcomes?” We address this question by first clarifying what it means for volume to impact outcomes. Better technology and strategic efforts to improve immediate postoperative care reduce the likelihood of short-term mortality following CABG, meaning that measurable differences in mortality outcomes may not occur within 30 days, but at a later time point. Rather than simply estimate the impact of volume on a binary mortality outcome of whether a patient survives to a particular time point shortly after surgery, we consider what the impact of volume is on the postoperative hazard of mortality. We show that only looking at binary short-term mortality outcomes may not provide a complete picture of the volume–outcome relationship for CABG, particularly as it applies to Taiwan. We also address an important methodological issue with our choice of models; we consider whether unobserved heterogeneity of the patients or providers could be impacting the estimated relationship between volume and outcomes. We use a model rarely applied in this literature to address the issue of unobserved heterogeneity. While this estimation technique does not allow us to separate the impacts of the unobserved characteristics from one another (i.e., we cannot separate referral effects from organizational process effects, for example), it yields estimates of the impact of provider (hospital and surgeon) volume net of this unobserved heterogeneity on the hazard of mortality. We obtain estimates of the impact of surgeon and hospital volume on the hazard to mortality that are more statistically robust than those that would be derived from standard hazard estimation using administrative claims data in a study such as this.