Evidence-Based Medicine
Copyright ©The Author(s) 2016. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Crit Care Med. May 4, 2016; 5(2): 165-170
Published online May 4, 2016. doi: 10.5492/wjccm.v5.i2.165
Automatic quality improvement reports in the intensive care unit: One step closer toward meaningful use
Mikhail A Dziadzko, Charat Thongprayoon, Adil Ahmed, Ing C Tiong, Man Li, Daniel R Brown, Brian W Pickering, Vitaly Herasevich
Mikhail A Dziadzko, Charat Thongprayoon, Adil Ahmed, Ing C Tiong, Man Li, Daniel R Brown, Brian W Pickering, Vitaly Herasevich, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
Vitaly Herasevich, Department of Anesthesiology, Mayo Clinic, Rochester, MN 55905, United States
Author contributions: All authors contributed to this manuscript.
Conflict-of-interest statement: The authors have no conflicts of interest to disclose related to this publication.
Data sharing statement: No additional data available.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Vitaly Herasevich, MD, PhD, Department of Anesthesiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. herasevich.vitaly@mayo.edu
Telephone: +1-507-2554055 Fax: +1-507-2554267
Received: July 31, 2015
Peer-review started: July 31, 2015
First decision: October 13, 2015
Revised: October 27, 2015
Accepted: December 17, 2015
Article in press: December 18, 2015
Published online: May 4, 2016
Processing time: 269 Days and 21 Hours
Abstract

AIM: To examine the feasibility and validity of electronic generation of quality metrics in the intensive care unit (ICU).

METHODS: This minimal risk observational study was performed at an academic tertiary hospital. The Critical Care Independent Multidisciplinary Program at Mayo Clinic identified and defined 11 key quality metrics. These metrics were automatically calculated using ICU DataMart, a near-real time copy of all ICU electronic medical record (EMR) data. The automatic report was compared with data from a comprehensive EMR review by a trained investigator. Data was collected for 93 randomly selected patients admitted to the ICU during April 2012 (10% of admitted adult population). This study was approved by the Mayo Clinic Institution Review Board.

RESULTS: All types of variables needed for metric calculations were found to be available for manual and electronic abstraction, except information for availability of free beds for patient-specific time-frames. There was 100% agreement between electronic and manual data abstraction for ICU admission source, admission service, and discharge disposition. The agreement between electronic and manual data abstraction of the time of ICU admission and discharge were 99% and 89%. The time of hospital admission and discharge were similar for both the electronically and manually abstracted datasets. The specificity of the electronically-generated report was 93% and 94% for invasive and non-invasive ventilation use in the ICU. One false-positive result for each type of ventilation was present. The specificity for ICU and in-hospital mortality was 100%. Sensitivity was 100% for all metrics.

CONCLUSION: Our study demonstrates excellent accuracy of electronically-generated key ICU quality metrics. This validates the feasibility of automatic metric generation.

Keywords: Electronic medical record; Quality indicators; Critical care; Information processing; Datamart; Automatic; Intensive care; Health care

Core tip: Meaningful use of electronic healthcare records (EHRs) requires quality measures. Many administrative reporting tools provided by current EHRs are based on insufficiently accurate data and thus of limited use. We examine the feasibility and the validity of electronic generation of institutional key intensive care unit (ICU) quality metrics using ICU DataMart, a near-real time relational database.