A comparison of two structured taxonomic strategies in capturing adverse events in U.S. hospitals

Health Serv Res. 2019 Jun;54(3):613-622. doi: 10.1111/1475-6773.13090. Epub 2018 Nov 25.

Abstract

Objective: To compare the Agency for Healthcare Research and Quality's Quality and Safety Review System (QSRS) and the proposed triadic structure for the 11th version of the International Classification of Disease (ICD-11) in their ability to capture adverse events in U.S. hospitals.

Data sources/study setting: One thousand patient admissions between 2014 and 2016 from three general, acute care hospitals located in Maryland and Washington D.C.

Study design: The admissions chosen for the study were a random sample from all three hospitals.

Data collection/extraction methods: All 1000 admissions were abstracted through QSRS by one set of Certified Coding Specialists and a different set of coders assigned the draft ICD-11 codes. Previously assigned ICD-10-CM codes for 230 of the admissions were also used.

Principal findings: We found less than 20 percent agreement between QSRS and ICD-11 in identifying the same adverse event. The likelihood of a mismatch between QSRS and ICD-11 was almost twice that of a match. The findings were similar to the agreement found between QSRS and ICD-10-CM in identifying the same adverse event. When coders were provided with a list of potential adverse events, the sensitivity and negative predictive value of ICD-11 improved.

Conclusions: While ICD-11 may offer an efficient way of identifying adverse events, our analysis found that in its draft form, it has a limited ability to capture the same types of events as QSRS. Coders may require additional training on identifying adverse events in the chart if ICD-11 is going to prove its maximum benefit.

Keywords: adverse events; measurement; patient safety.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adult
  • Aged
  • District of Columbia
  • Documentation / standards*
  • Female
  • Hospital Administration / statistics & numerical data*
  • Humans
  • International Classification of Diseases / standards*
  • Male
  • Maryland
  • Middle Aged
  • Patient Harm / statistics & numerical data*
  • Patient Safety / standards
  • Safety Management / standards
  • United States
  • United States Agency for Healthcare Research and Quality / standards*
  • United States Agency for Healthcare Research and Quality / statistics & numerical data