An openEHR based infection control system to support monitoring of nosocomial bacterial clusters and contacts

Abstract

Early outbreak detection, allowing rapid intervention, is essential to reduce the burden of healthcare-associated pathogen transmission, including multidrug-resistant bacteria. Digital, routine data-driven solutions are promising, but often proprietary, non-interoperable, or limited in functional scope. The open-source Smart Infection Control System (SmICS) offers automatic calculations and interactive views on patients' movement and lab data, epidemic curves, contact networks, complemented by temporal-spatial visualizations. It is an open-source software based on openEHR as an interoperability standard and was evaluated by assessing time efficiencies in performing basic infection control tasks (e.g., contact networks) and usability with the System Usability Scale (SUS). Evaluated at three sites, SmICS reduced the time needed for performing routine infection control tasks by up to 81.47% (68.5 min (95%CI [30.5-106.5])) reaching a SUS of 51.6 points. The study reveals time savings through the use of SmICS in daily tasks, but also identified usability issues and a need for minimizing query waiting times.

Fig. 1. SmICS architecture.

The underlying openEHR data models are available at: Encounter [https://ckm.highmed.org/ckm/templates/1246.169.620], Microbiology Report [https://ckm.highmed.org/ckm/templates/1246.169.69/46], Patient Stay [https://ckm.highmed.org/ckm/templates/1246.169.620].

Fig. 2. SmICS Core.

Ward Overview (top left), Patient View (top right), Contact Network (bottom left), Contact Comparison (bottom right).

Fig. 3. SmICS Visualization.

Epidemic Curve (top left), Contact Network (top right), Contact Tracing (bottom left) and Patient History (bottom right) offer distinct perspectives, allowing users to interactively analyze patient data. All four visualizations support interactive actions. Mouse-overs provide additional patient information, detailed information on contact duration and location, or microbiological test results as tooltips for each visual element. Left-clicking on visual elements of patients enables filtering them, aiding visual analysis by making filtered elements opaque. Furthermore, all three timeline visualizations—Epidemic Curve, Contact Tracing and Patient History—offer mouse interactions to change the displayed time frame. Any changes to patient and time filter settings are simultaneously applied across all open views.

Fig. 4. Comparison of time efficiency in performing exemplary tasks of IPC work in a standard procedure versus using SmICS.

Results of SmICS efficiency evaluation. Comparison of time efficiency (required time for correct completion of task) in performing exemplary tasks of IPC work in a standard procedure (blue) versus using SmICS (light blue) at three partner sites.

Fig. 5. SUS- and cSUS-scores of evaluation participants.

SUS-score (blue), cSUS-score (gray), SUS average (orange) and cSUS average (yellow) results of SmICS usability evaluation with 15 participants from three partner sites.

Fig. 6. SUS- and cSUS-scores of the evaluation focused on age and occupation.

SUS-score evaluation concerning age groups (top left), cSUS-score concerning age groups (top right), SUS-score concerning occupation (bottom left) and cSUS-score concerning occupation (bottom right).