Using Big Data in Otolaryngology

PROGRAM DESCRIPTION: “Big Data” databases are ubiquitous in health care today, where they offer a unique and powerful resource in understanding current practices and assessing treatment outcomes, costs, and value on a broad population level. With the increasing emphasis on evidence-based practice, value, and quality improvement, the databases available for research or quality improvement opportunities are ever-expanding, along with the types of questions that can be addressed. This panel presentation will provide an overview of some of the large databases available to researchers and clinicians, offering insight and experience from researchers who have published studies using these databases. We will discuss how to identify databases relevant to specific questions, discuss strengths and limitations, and provide practical advice in accessing and working with these datasets.

Specific databases that will be discussed:
-National Surgical Quality Improvement Program Pediatric (NSQIP): nationally validated, risk-adjusted database with data generated from patient-level chart extraction by trained clinical reviewers, measures surgical complications and 30-day post-operative outcomes
-Pediatric Health Information System (PHIS) and Kids’ Inpatient Database (KID): pediatric databases drawn from children’s hospitals across the country, including inpatient, ambulatory, and emergency department encounters (PHIS) and national estimates of hospital inpatient stays based on discharge all-payer insurance claims data (KID)
-Marketscan database: national insurance claims database with individual level claims data from employers, health plans, hospitals in inpatient, outpatient, ED settings.
-National Ambulatory Medical Care Survey (NAMCS): data sampled across the US population, based on surveys of individuals or households, providers, birth and death certificates, standardized physical exam, and medical records.
-Reg-ENT: Otolaryngology-specific clinical data registry, focused on quality improvement and patient outcomes

OUTCOME OBJECTIVE 1: Recognize commonly utilized large databases in health care and the data they can provide

OUTCOME OBJECTIVE 2: Discuss the strengths, limitations, and pitfalls of using large databases

OUTCOME OBJECTIVE 3: Understand how to interpret and apply the results of big data analysis

BACKGROUND STATEMENT: Need to understand the strengths and weaknesses of available large clinical databases, as well as best practices in utilizing such databases for quality improvement or research.