With Great Power Comes Great Responsibility: Big Data Research From the National Inpatient Sample

Abstract

The use of large administrative databases is transforming clinical cardiovascular research. These sources of big data allow the study of practices and outcomes across a spectrum of health systems, providing real-world evidence. However, these databases have peculiarities to their design that require specialized expertise and distinct analytic practices for their appropriate interpretation. We discuss these issues in the context of the National Inpatient Sample (NIS) that is one such data set used in healthcare research. Compiled by the Agency for Healthcare Research and Quality annually since 1988, it comprises a large number of inpatient discharges from US community hospitals regardless of the payer (≈8 million/y), with each observation representing a unique hospitalization.1 It has some features to its design and the content of its data that are essential to consider in the pursuit of studies with it. The NIS includes information on patient demographics, administrative codes for primary diagnosis and secondary diagnoses, procedures, survival to discharge, disposition, hospital charges, and length of stay.1 The NIS can be used to examine the use of hospital health services, practice variation, cost, and the impact of health policy interventions in the inpatient setting.1 The data are easily accessible, inexpensive, and can be analyzed using ubiquitous statistical programs. Consequently, research publications from the NIS data have grown rapidly in recent years (Figure 1). Nevertheless, researchers, as well as scientific journals and their readers, may not yet be familiar with the nuances of this complex data set and therefore be challenged to determine if the data are interpreted correctly. Figure 1. Calendar year trends in publications from the National Inpatient Sample (NIS). Number of peer-reviewed publications from the NIS have increased rapidly in recent years. Data from other Healthcare Cost and Utilization Project (HCUP) data sets are presented for comparison—Kids’ Inpatient Database (KID) and …