Automatic generation of minimum dataset and quality indicators from data collected routinely by the clinical information system in an intensive care unit

Identificador:  imarina:9138889

Handle: https://hdl.handle.net/20.500.11797/imarina9138889

Autors:  Bodi, Maria; Claverias, Laura; Esteban, Federico; Sirgo, Gonzalo; De Haro, Lluis; Guardiola, Juan Jose; Gracia, Rafael; Rodriguez, Alejandro; Gomez, Josep

Resum:
© 2020 Elsevier B.V. Background: Quality indicators (QIs) are being increasingly used in medicine to compare and improve the quality of care delivered. The feasibility of data collection is an important prerequisite for QIs. Information technology can improve efforts to measure processes and outcomes. In intensive care units (ICU), QIs can be automatically measured by exploiting data from clinical information systems (CIS). Objective: To describe the development and application of a tool to automatically generate a minimum dataset (MDS) and a set of ICU quality metrics from CIS data. Methods: We used the definitions for MDS and QIs proposed by the Spanish Society of Critical Care Medicine and Coronary Units. Our tool uses an extraction, transform, and load process implemented with Python to extract data stored in various tables in the CIS database and create a new associative database. This new database is uploaded to Qlik Sense, which constructs the MDS and calculates the QIs by applying the required metrics. The tool was tested using data from patients attended in a 30-bed polyvalent ICU during a six-year period. Results: We describe the definitions and metrics, and we report the MDS and QI measurements obtained through the analysis of 4546 admissions. The results show that our ICU's performance on the QIs analyzed meets the standards proposed by our national scientific society. Conclusions: This is the first step toward using a tool to automatically obtain a set of actionable QIs to monitor and improve the quality of care in ICUs, eliminating the need for professionals to enter data manually, thus saving time and ensuring data quality.