Revolutionizing Infectious Diseases Surveillance using Big Data - a Web based Dashboard

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Abstract Summary

Introduction

The emergence and spread of multidrug-resistant organisms (MDROs) are causing a global crisis. Among S. aureus isolates identified in the hospitals of Hospital Authority, 43.1% were MRSA; 55% and 8.6% of Acinetobacter species were carbapenem-resistant and multi-drug resistant, respectively (unpublished data). In continuing the response to this serious public health issue, World Health Organization (WHO) stated the priority area is improving surveillance of antimicrobial resistance. To stop the chain of transmission, early identification and monitoring of patients with MDROs are essential. In the Kowloon West Cluster (KWC), for culture isolates which are multiple drug resistant, laboratory staff would manually add infection control team as the printing location of the reports. Infection control personnel would then review the laboratory reports and the patients’ movements to recognize the source of acquisition or association; and the unusual clustering of MDROs at ward units within a time period. As the burden of MDROs is ever increasing, and the complexity of tracking patients’ journeys, early identification of potential MDRO outbreak is extremely difficult, if not possible. Improved efficiency and automation is thus urgently needed to cope with the heavy burden. Reference: 1. World Health Organization. Global Action Plan on Antimicrobial Resistance. [Internet]. Switzerland: World Health Organization; 2015 p. 1–28. Available from: http://www.who.int/iris/bitstream/10665/193736/1/9789241509763_eng.pdf?ua=1

Objectives

1) To pilot a web-based infectious disease dashboard surveillance information system developed by HA Information Technology & Health Informatics (HA IT&HI) in the KWC hospitals 2) To evaluate the system performance – by accuracy of MDRO detection and classification of specimen reports, and early clustering detection

Methodology

This is a prospective comparative study on the system performance of a web-based infectious disease dashboard https://dccmdro.home/Login.aspx to capture patients with MDROs and their respective hospital movements over a 3 months period. The online system retrieves microbiology culture and screening results from The Laboratory Information System (LIS) and the corresponding patients’ movements from the Patient Admission Information System (PAIS) at every midnight. Patients with history of MDROs are identified, with respective to all the locations in the hospital for the same admission. History of admission to HA hospitals is also displayed for up to 12 weeks. The distribution and the density of MDROs cases within the hospital are also visually displayed in a 3 dimensional floor plan, under Geographical View. A list of MDROs patients (the bed number, transfer-in time, and the duration between transfer-in and isolation of MDROs) residing in a particular ward can be viewed. The statistical view shows the number of newly reported MDROs acquired in different wards, which is defined as detection of MDROs after 48 hours of transfer-in or within 48 hours of transfer-out, in a defined period. Potential clustering can be detected at an early stage; and it also captures the positive specimens collected in other HA hospital. Patient tracing for each type of MDROs within a defined time frame, which is used to be performed manually, can be automated. The image can be exported for communication with frontline managers.

Results & Outcome

The system generated data of newly acquired MDROs at ward units is highly concordant to the data manually defined by infection control personnals. Besides, the automated patient tracing in a graphical presentation allows ICT to alert frontline managers for ward acquired MDROs cases, on a routine basis; which is impossible if performed manually. Last but not least, the system identified additional MDROs cases as laboratory staff may miss adding ICT as the printing location.

 

 

Abstract ID :
HAC965
Submission Type
Authors (including presenting author) :
Luk S(1)(2), To WK(3), Tang WY(4), Tong Anna(4), Lau KFE(1), Lee WM(3), Lam HSB(5), Chang Esther(5), Leung WCC(2), Cheung NT(4)
Affiliation :
(1)Infection Control Team, Caritus Medical Centre, (2)Infection Control Team, Yan Chai Hospital, (3)Infection Control Team, Princess Margaret Hospital, (4)Head Office, Information Technology and Health Informatics, (5)Infection Control Team, North Lantau Hospital

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