Administrative and clinical databases: General thoracic surgery perspective on approaches and pitfalls

Abstract

Database research has become so common that it has essentially become the lingua franca of clinical research. Since the vast majority of studies using databases are observational in nature, various statistical methods are employed to control for the inherent biases in such nonrandomized data. However, even the most sophisticated causal inference technique cannot overcome limitations that are inherent to the nature of the data and how they are collected. Most databases are created with a particular purpose, which leads to limitations in the ways they can be used for research. Organizations and professional societies have created databases for national and international benchmarking and research, the archetypical example being the Society of Thoracic Surgeons (STS) cardiac surgery database, which was subsequently expanded to include general thoracic surgery and pediatric cardiac surgery. These databases all have some element of "pay to play"; they depend on subscription fees paid by the institutions that comprise the membership and contribute data. As such, they cannot be considered population-based, as they only represent the cases contributed to them. However, in general, they are clinical, outcomes-focused, high-quality, and reliable and in certain instances can be used for high-quality policy, clinical epidemiology, and quality improvement work.Thus, we posit that databases are often created to serve 1 of 2 fundamental functions: (1) research to answer questions and (2) benchmarking/quality. In this paper, we discuss various examples of different types of big clinical and administrative databases; more importantly, we discuss how the construction and nature of said databases affects the extent to which they can accomplish these fundamental functions. This paper is meant as a companion piece to that of Subramanian and colleagues, which focused on introducing the concept of clinical versus administrative data and described some of the commonly used databases. Thus, readers should refer to that paper for descriptions of commonly used databases such as National Inpatient Sample, Surveillance, Epidemiology, and End Results, and the National Cancer Database. 1