Codifying healthcare - big data and the issue of misclassification

Karim S. Ladha, Matthias Eikermann

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

The rise of electronic medical records has led to a proliferation of large observational studies that examine the perioperative period. In contrast to randomized controlled trials, these studies have the ability to provide quick, cheap and easily obtainable information on a variety of patients and are reflective of everyday clinical practice. However, it is important to note that the data used in these studies are often generated for billing or documentation purposes such as insurance claims or the electronic anesthetic record. The reliance on codes to define diagnoses in these studies may lead to false inferences or conclusions. Researchers should specify the code assignment process and be aware of potential error sources when undertaking studies using secondary data sources. While misclassification may be a short-coming of using large databases, it does not prevent their use in conducting meaningful effectiveness research that has direct consequences on medical decision making.

Original languageEnglish (US)
Article number179
JournalBMC Anesthesiology
Volume15
Issue number1
DOIs
StatePublished - Dec 15 2015
Externally publishedYes

ASJC Scopus subject areas

  • Anesthesiology and Pain Medicine

Fingerprint

Dive into the research topics of 'Codifying healthcare - big data and the issue of misclassification'. Together they form a unique fingerprint.

Cite this