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06.04.2018 | Review | Ausgabe 6/2018

International Journal of Colorectal Disease 6/2018

What have we learned in minimally invasive colorectal surgery from NSQIP and NIS large databases? A systematic review

Zeitschrift:
International Journal of Colorectal Disease > Ausgabe 6/2018
Autoren:
Gabriela Batista Rodríguez, Andrea Balla, Santiago Corradetti, Carmen Martinez, Pilar Hernández, Jesús Bollo, Eduard M. Targarona

Abstract

Background

“Big data” refers to large amount of dataset. Those large databases are useful in many areas, including healthcare. The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) and the National Inpatient Sample (NIS) are big databases that were developed in the USA in order to record surgical outcomes. The aim of the present systematic review is to evaluate the type and clinical impact of the information retrieved through NISQP and NIS big database articles focused on laparoscopic colorectal surgery.

Methods

A systematic review was conducted using The Meta-Analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. The research was carried out on PubMed database and revealed 350 published papers. Outcomes of articles in which laparoscopic colorectal surgery was the primary aim were analyzed.

Results

Fifty-five studies, published between 2007 and February 2017, were included. Articles included were categorized in groups according to the main topic as: outcomes related to surgical technique comparisons, morbidity and perioperatory results, specific disease-related outcomes, sociodemographic disparities, and academic training impact.

Conclusions

NSQIP and NIS databases are just the tip of the iceberg for the potential application of Big Data technology and analysis in MIS. Information obtained through big data is useful and could be considered as external validation in those situations where a significant evidence-based medicine exists; also, those databases establish benchmarks to measure the quality of patient care. Data retrieved helps to inform decision-making and improve healthcare delivery.

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