IT-CARES: an interactive tool for case-crossover analyses of electronic medical records for patient safety

Abstract

Background: The significant risk of adverse events following medical procedures supports a clinical epidemiological approach based on the analyses of collections of electronic medical records. Data analytical tools might help clinical epidemiologists develop more appropriate case-crossover designs for monitoring patient safety.Objective: To develop and assess the methodological quality of an interactive tool for use by clinical epidemiologists to systematically design case-crossover analyses of large electronic medical records databases.Material and Methods: We developed IT-CARES, an analytical tool implementing case-crossover design, to explore the association between exposures and outcomes. The exposures and outcomes are defined by clinical epidemiologists via lists of codes entered via a user interface screen. We tested IT-CARES on data from the French national inpatient stay database, which documents diagnoses and medical procedures for 170 million inpatient stays between 2007 and 2013. We compared the results of our analysis with reference data from the literature on thromboembolic risk after delivery and bleeding risk after total hip replacement.Results: IT-CARES provides a user interface with 3 columns: (i) the outcome criteria in the left-hand column, (ii) the exposure criteria in the right-hand column, and (iii) the estimated risk (odds ratios, presented in both graphical and tabular formats) in the middle column. The estimated odds ratios were consistent with the reference literature data.Discussion: IT-CARES may enhance patient safety by facilitating clinical epidemiological studies of adverse events following medical procedures. The tool’s usability must be evaluated and improved in further research.

A Systematic Review of Knowledge Visualization Approaches Using Big Data Methodology for Clinical Decision Support

“…The PRISMA flow diagram (Figure 1) summarizes the screening process. The 17 studies remaining were included in the qualitative synthesis [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] that follows.…”

“…We identified four main groups of users of big data visualization applications for clinical decision support: (1) academicians (clinical researchers and clinical epidemiologists, nurse educators) [11,12,17,18,26], (2) administrators (hospital administrators and managers) [12,17,25], ancillary staff (caretakers, lab managers, pharmacists) [11,13,25]; (3) health care providers (clinicians, nurses, physicians) [11][12][13][14][15][16][18][19][20][21][22][23][24][25][26][27] and (4) patients [11,14,22,23]. More than 94% of the studies (n = 16) were developed to support clinical healthcare provider decision-making.…”

“…Seven studies had developed data analysis platform/tools to help the target audience (healthcare providers, academicians, etc.) [12,16,17,21,[24][25][26]. Three studies developed and used mobile care coordination [14,15,20], four studies designed web portals for healthcare providers [11,13,22,23] and patients [11].…”

“…One study [21] used only one visualization type (tabular), five studies (29%) presented two different visualization types (multidimensional and tabular, tabular and temporal, etc.) [15,17,18,20,24].…”

“…More than 70% of the studies (n = 12) used multi-dimensional visualization techniques such as area charts (color grading/without color grading) [14,19,26], bar graphs [11,13,14,16,26], bipartite graphs [25], box plots [17], bubble charts [12], causal network visualizations and heatmaps [13], key performance indicators (KPIs) [12,16], line graphs [11,19,24], pie charts [11,14,20] and scatter plots [23].…”