Requirement Analysis for an Intelligent Warning System to Alarm the Rapid Response Team Prior to Patient Deterioration

Abstract

The early warning system alarms the rapid response team (RRT) for clinical deterioration monitoring and prediction. Available systems do not perform well to decrease the number of ICU transfers or death. This study aimed to address the requirement of an intelligent warning system for timely and accurate RRT activation. Methodology: A literature review was conducted in scientific databases to extract data. Then, a questionnaire was developed for experts’ views collection (N=12). The collected data were analyzed using the Content Validity Ratio (CVR). According to the Lawshe table for the corresponding number of experts, the cut-off=0.56 for items to be accepted/rejected was considered. A schematic structure was suggested. Findings: The analysis of the extracted papers (N=24) and qualitative analysis addressed 44 requirements in the frame of five involved sub-systems, including a patient monitoring system, electronic health record, clinical decision support system, remote monitoring patient, and dashboard &registries. They were confirmed by meeting the least cut-off value (CVR= 0.86). Conclusion: An integrated approach and technologies of IoT, deep and machine learning techniques, big data, advanced databases, and standards to create an intelligent EWS are required.

Initiatives to detect and prevent death from perioperative deterioration

“…Further improvement off alarm strategies to reduce total alarm rates as well as false alarm rates, and improving alarms for early detection of adverse events is an essential step in future optimization of remote monitoring systems [ 54 ▪▪ ]. Smart alarms might be created by the use of big data, machine learning, advanced databases, imputation techniques for missing data and integrated approaches of technology in the workflow of ward staff (nurses and doctors) [ 58 ▪ , 59 , 60 ].…”

“…Smart alarms might be created by the use of big data, machine learning, advanced databases, imputation techniques for missing data and integrated approaches of technology in the workflow of ward staff (nurses and doctors)[58 …”