Automatic generation of minimum dataset and quality indicators from data collected routinely by the clinical information system in an intensive care unit. (January 2021)

• Record Type: Journal Article
• Authors: Bodí, María; Claverias, Laura; Esteban, Federico; Sirgo, Gonzalo; De Haro, Lluis; Guardiola, Juan José; Gracia, Rafael; Rodríguez, Alejandro; Gómez, Josep
• Is Part Of: International journal of medical informatics. Volume 145(2021)
• Publication Date: 2021-01
• Subjects: ICU Intensive care medicine -- CIS Clinical information systems -- SEMICYUC Spanish Society of Critical Care Medicine and Coronary Units; Clinical information system -- Quality indicators -- Critical care -- Data quality; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2020.104327
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Automatic generation of MDS information and QIs in daily practice from quality electronic data reuse is possible. It will make feasible to develop a benchmarking model not based on manual registries and unsustainable human efforts. Abstract: 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.