The Effects of Focus on Performance: Evidence from California Hospitals

Abstract

We use hospital-level discharge data from cardiac patients in California to estimate the effects of focus on operational performance. We examine focus at three distinct levels of the organization-at the firm level, at the operating unit level, and at the process flow level. We find that focus at each of these levels is associated with improved outcomes, namely, faster services at higher levels of quality, as indicated by lower lengths of stay (LOS) and reduced mortality rates. We then analyze the extent to which the superior operational outcome is driven by focused hospitals truly excelling in their operations or by focused hospitals simply "cherry-picking" easy-to-treat patients. To do this, we use an instrumental variables estimation strategy that effectively randomizes the assignment of patients to hospitals. After controlling for selective patient admissions, the previously observed benefits of firm level focus disappear; focused hospitals no longer demonstrate a statistically significant reduction in LOS or mortality rate. However, at more granular measures of focus within the hospital (e.g., operating unit level), we find that more focus leads to a shorter LOS, even after controlling for selective admission effects. We use hospital level discharge data from cardiac patients in California to estimate the effects of focus on operational performance. We examine focus at three distinct levels of the organization -at the firm level, at the operating unit level, and at the process flow level. We find that focus at each of these levels is associated with improved outcomes, namely faster services at higher levels of quality, as indicated by lower lengths of stay (LOS) and reduced mortality rates. We then analyze the extent to which the superior operational outcome is driven by focused hospitals truly excelling in their operations or by focused hospitals simply "cherry-picking" easy-to-treat patients. To do this, we use an instrumental variables estimation strategy that effectively randomizes the assignment of patients to hospitals. After controlling for selective patient admissions, the previously observed benefits of firm-level focus disappear; focused hospitals no longer demonstrate a statistically significant reduction in LOS or mortality rate. However, at more granular measures of focus within the hospital (e.g. operating unit level), we find that more focus leads to a shorter LOS, even after controlling for selective admission effects.

Boundaries of Focus and Volume: An Empirical Study in Neonatal Intensive Care

“…This patient segment is composed of two medically distinct clusters, with cluster 1 containing very-low birth weight (VLBW) infants and cluster 2 consisting of non-VLBW infants (we provide more details of the clustering below). Following the recent literature on volume and focus in health care organizations (Clark and Huckman 2012, KC and Terwiesch 2011, Kuntz et al 2019, McDermott and Stock 2011, we denote the absolute annual number of patients within a cluster who are admitted to the unit as cluster volume, while cluster focus is conceptualized as the unit's cluster volume as a proportion of the unit's overall annual volume. This conceptualizes focus as emphasis, that is, "the disproportionate emphasis on some service lines, while still maintaining others" (McDermott and Stock 2011, p. 618).…”

“…There is an emerging scholarly debate with respect to redesigning hospitals and the question of whether specialized units that admit a homogeneous patient cluster are preferable or whether, instead, flexible units that admit a pool of patient clusters are better (Best et al 2015). Specialized units, which admit one homogeneous patient cluster might benefit from a narrower range of treatment protocols, lower variability, and fewer conflicting or competing operational activities (Clark and Huckman 2012, Huckman and Zinner 2008, KC and Terwiesch 2011, McDermott and Stock 2011. The advantage of focusing solely on one cluster might, however, lead to the disadvantage of insufficiently achieving economies of scale and scope due to lower patient volume levels.…”

Examining the impact of clinical quality and clinical flexibility on cardiology unit performance—Does experiential quality act as a specialized complementary asset?

“…Further, given that length of stay and cost are influenced by the complexity of case-mix of patients (Gapenski, 2012), we consider patient case-mix adjusted length of stay and average cost. A focused examination of cardiology units helps in disentangling the impact of quality and flexibility capabilities on operational performance (KC and Terwiesch, 2011).…”

The Role of Industry Studies and Public Policies in Production and Operations Management

“…Three-quarters of the empirical industry studies in our sample control for firm-specific fixed factors. In addition to the traditional controls for time, geography, ownership, number of employees, patents and age, the empirical industry studies also reflect more nuanced controls such as oil price in the transportation and energy sectors (Davies and Joglekar 2013); scheduled shifts in production or planned maintenance in the automotive sector (Cachon and Olivares 2010); hospital, patient and procedural characteristics in healthcare studies (Olivares et al 2008, KC and Terwiesch 2011, Chandrasekaran et al 2012; and controlling for retailers' product categories or perishable inventory in the grocery sector Sinha 2001, Li et al 2012). An exemplar in our sample of combining disaggregation with fixed effects is the estimation of the individual learning curves for six separate airlines through interaction effects (Lapr e and Tsikriktsis 2006).…”

“…Integrative methodologies show much promise within the empiric-analytics cycles. For instance, we see a trend in the growth of structural estimation models that combine a formal model with a dataset from specific industries such as automotive distribution (Cachon and Olivares 2010) and healthcare operations (KC and Terwiesch 2011). Another integrative approach begins with detailed case studies that lead into the development of regression models though an iterative cycle (Edmondson and McManus 2007, Cui et al 2012, Tucker and Singer 2015.…”