Reasoning about Who to Notify to avoid Alarm Fatigue: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Chrystinne Fernandes ◽  
Simon Miles ◽  
Carlos José Pereira De Lucena
Keyword(s):  
Constraint Satisfaction Problem ◽  
Vital Signs ◽  
High Volume ◽  
False Alarms ◽  
Healthcare Team ◽  
Alarm Fatigue ◽  
Automatic Reasoning ◽  
Reasoning System ◽  
Future Work ◽  
Reasoning Algorithm

BACKGROUND Alarm Fatigue is a scenario experienced by an overwhelmed and fatigued healthcare team that is desensitized and slow to respond to alarms. The most common alarm-related issues that may lead to Alarm Fatigue include the excessive number of alarms, a number of alarms generated by many different types of alarm devices, and the high percentage of false alarms (80%-99%). All of these alerts have to be processed by the healthcare teams who are consistently under pressure: they should analyze the high volume of inputs they are receiving in order to answer to them quickly and correctly, by making decisions in real-time about the response to the next alarm. Under alarm fatigue conditions, the staff may ignore and/or silence alarms, putting patients in risky situations. OBJECTIVE This paper’s main goal is to propose a feasible solution for mitigating alarm fatigue by using an automatic reasoning mechanism to choose the best caregiver to be assigned to a given notification within the set of available caregivers in an Intensive Care Unit. METHODS Our main contribution in this work consists of an algorithm that decides who is the best caregiver to notify in an ICU. We formalized this problem as a Constraint-Satisfaction Problem and we present one example of how it can be solved. We designed a case study where patients’ vital signs were collected through a vital signs’ generator that also triggers alarms. We conducted five experiments to test our algorithm considering different situations for an ICU. The evaluation of our algorithm was made through the comparison between the results of the choices made by our reasoning algorithm and another strategy that we call “blind” strategy, which randomly assigns caregivers to notifications. RESULTS Experiments are used to demonstrate that providing a reasoning system we could decide who is the best caregiver to receive a notification. By comparing the choices made by our reasoning algorithm and the “blind” strategy, our reasoning algorithm achieved a better result in terms of prioritizing the assignments we wanted to make based on our defined criteria: patient’s severity, the distance between caregivers and patients, caregivers’ experience, the probability of a notification to be false, and the number of notifications caregivers have received. CONCLUSIONS The experimental results strongly suggest that this reasoning algorithm is a useful strategy for mitigating alarm fatigue. We showed, in our experiments, that caregivers with higher levels of experience received more notifications than the ones with lower levels. Our future work is to deal with resource negotiation and to evaluate the distribution of the notifications to the caregivers’ teams made by the algorithms.

scholarly journals Living and Working in a Multisensory World: From Basic Neuroscience to the Hospital

2019 ◽  
Vol 3 (1) ◽  
pp. 2 ◽  
Author(s):  
Kendall Burdick ◽  
Madison Courtney ◽  
Mark Wallace ◽  
Sarah Baum Miller ◽  
Joseph Schlesinger
Keyword(s):  
Patient Outcomes ◽  
Music Theory ◽  
Healthcare Providers ◽  
Vital Signs ◽  
Clinical Information ◽  
False Alarms ◽  
Alarm System ◽  
Potential Solution ◽  
Alarm Fatigue ◽  
Improve Patient

The intensive care unit (ICU) of a hospital is an environment subjected to ceaseless noise. Patient alarms contribute to the saturated auditory environment and often overwhelm healthcare providers with constant and false alarms. This may lead to alarm fatigue and prevent optimum patient care. In response, a multisensory alarm system developed with consideration for human neuroscience and basic music theory is proposed as a potential solution. The integration of auditory, visual, and other sensory output within an alarm system can be used to convey more meaningful clinical information about patient vital signs in the ICU and operating room to ultimately improve patient outcomes.

scholarly journals Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation (Preprint)

2019 ◽  
Author(s):  
Chrystinne Fernandes ◽  
Simon Miles ◽  
Carlos José Pereira Lucena
Keyword(s):  
Statistical Analysis ◽  
False Alarm ◽  
Improve Patient Care ◽  
False Alarm Probability ◽  
False Alarms ◽  
Context Information ◽  
Health Providers ◽  
Notification System ◽  
Alarm Fatigue ◽  
Reasoning Algorithm

BACKGROUND Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as <i>alarm fatigue</i>. OBJECTIVE This paper’s main goal is to propose a solution to mitigate <i>alarm fatigue</i> by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. METHODS We present a new approach to cope with the <i>alarm fatigue</i> problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to <i>alarm fatigue</i>. RESULTS The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid <i>alarm fatigue</i>. CONCLUSIONS Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.

scholarly journals Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation

10.2196/15407 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e15407 ◽  
Author(s):  
Chrystinne Fernandes ◽  
Simon Miles ◽  
Carlos José Pereira Lucena
Keyword(s):  
Statistical Analysis ◽  
False Alarm ◽  
Improve Patient Care ◽  
False Alarm Probability ◽  
False Alarms ◽  
Context Information ◽  
Health Providers ◽  
Notification System ◽  
Alarm Fatigue ◽  
Reasoning Algorithm

Background Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as alarm fatigue. Objective This paper’s main goal is to propose a solution to mitigate alarm fatigue by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. Methods We present a new approach to cope with the alarm fatigue problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to alarm fatigue. Results The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid alarm fatigue. Conclusions Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.

Challenges and Opportunities in Collaborative Vulnerability Research Workflows

2020 ◽  
Vol 64 (1) ◽  
pp. 420-424
Author(s):  
Ryan Mullins ◽  
Deirdre Kelliher ◽  
Ben Nargi ◽  
Mike Keeney ◽  
Nathan Schurr
Keyword(s):  
Human Performance ◽  
Collaborative Work ◽  
Research Process ◽  
Workflow Model ◽  
Human Vulnerability ◽  
Research Teams ◽  
Reasoning System ◽  
Challenges And Opportunities ◽  
New Research ◽  
Future Work

Recently, cyber reasoning systems demonstrated near-human performance characteristics when they autonomously identified, proved, and mitigated vulnerabilities in software during a competitive event. New research seeks to augment human vulnerability research teams with cyber reasoning system teammates in collaborative work environments. However, the literature lacks a concrete understanding of vulnerability research workflows and practices, limiting designers’, engineers’, and researchers’ ability to successfully integrate these artificially intelligent entities into teams. This paper contributes a general workflow model of the vulnerability research process, and identifies specific collaboration challenges and opportunities anchored in this model. Contributions were derived from a qualitative field study of work habits, behaviors, and practices of human vulnerability research teams. These contributions will inform future work in the vulnerability research domain by establishing an empirically-driven workflow model that can be adapted to specific organizational and functional constraints placed on individual and teams.

A SysML-Based Modeling Language for Mechatronic System Architecture

2015 ◽  
Author(s):  
Ruirui Chen ◽  
Yusheng Liu ◽  
Yue Cao ◽  
Jing Xu
Keyword(s):  
Systems Engineering ◽  
System Architecture ◽  
Modeling Language ◽  
Mechatronic Systems ◽  
Geometric Information ◽  
Automatic Reasoning ◽  
Control Behavior ◽  
Reasoning System ◽  
And Control ◽  
Model Based Systems Engineering

Model Based Systems Engineering (MBSE) is the mainstream methodology for the design of complex mechatronic systems. It emphasizes the application of the system architecture, which highly depends on a formalized modeling language. However, such modeling language is less researched in previous studies. This paper proposes a general modeling language for representing the system architecture, aiming for representing function, physical effect, geometric information and control behavior which the system should satisfy. It facilitates the communication of designers from different technological domains and supports a series of applications such as automatic reasoning, system simulation, etc. The language is illustrated and verified with a practical mechatronic device finally.

Progress and Prospects for the Development of Computer-Generated Actors for Military Simulation: Part 1-Introduction and Background

2003 ◽  
Vol 12 (3) ◽  
pp. 311-325 ◽  
Author(s):  
Martin R. Stytz ◽  
Sheila B. Banks
Keyword(s):  
Research Area ◽  
Background Information ◽  
Life Cycle Costs ◽  
Material Management ◽  
Reasoning System ◽  
Force Structure ◽  
The Cost ◽  
Future Work ◽  
Level Training ◽  
Made In

The development of computer-generated synthetic environments, also calleddistributed virtual environments, for military simulation relies heavily upon computer-generated actors (CGAs) to provide accurate behaviors at reasonable cost so that the synthetic environments are useful, affordable, complex, and realistic. Unfortunately, the pace of synthetic environment development and the level of desired CGA performance continue to rise at a much faster rate than CGA capability improvements. This insatiable demand for realism in CGAs for synthetic environments arises from the growing understanding of the significant role that modeling and simulation can play in a variety of venues. These uses include training, analysis, procurement decisions, mission rehearsal, doctrine development, force-level and task-level training, information assurance, cyberwarfare, force structure analysis, sustainability analysis, life cycle costs analysis, material management, infrastructure analysis, and many others. In these and other uses of military synthetic environments, computer-generated actors play a central role because they have the potential to increase the realism of the environment while also reducing the cost of operating the environment. The progress made in addressing the technical challenges that must be overcome to realize effective and realistic CGAs for military simulation environments and the technical areas that should be the focus of future work are the subject of this series of papers, which survey the technologies and progress made in the construction and use of CGAs. In this, the first installment in the series of three papers, we introduce the topic of computer-generated actors and issues related to their performance and fidelity and other background information for this research area as related to military simulation. We also discuss CGA reasoning system techniques and architectures.

Dynamic Alarm Systems for Hospitals (D.A.S.H.)

2018 ◽  
Vol 26 (4) ◽  
pp. 14-19 ◽  
Author(s):  
Jasmine M. Greer ◽  
Kendall J. Burdick ◽  
Arman R. Chowdhury ◽  
Joseph J. Schlesinger
Keyword(s):  
Health Care ◽  
Health Care Quality ◽  
Improve Patient Safety ◽  
Environmental Noise ◽  
Care Quality ◽  
False Alarms ◽  
Alarm Fatigue ◽  
Alarm Systems ◽  
Physiological Information ◽  
Improve Patient

Hospital alarms today indicate urgent clinical need, but they are seldom “true.” False alarms are contributing to the ever-increasing issue of alarm fatigue, or desensitization, among doctors and nurses. Alarm fatigue is a high-priority health care concern because of its potential to compromise health care quality and inflict harm on patients. To address this concern, we have engineered Dynamic Alarm Systems for Hospitals (D.A.S.H.), a dynamic alarm system that self-regulates alarm loudness based on the environmental noise level and incorporates differentiable and learnable alarms. D.A.S.H., with its ability to adapt to environmental noise and encode nuanced physiological information, may improve patient safety and attenuate clinician alarm fatigue.

Modular Technical Concepts for Ambulatory Monitoring of Risk Patients Based on Multiple Parameters and an Automatic Alarm Function

2016 ◽  
Vol 4 (1) ◽  
pp. 1-9
Author(s):  
Jörg Piper ◽  
Birgit Müller
Keyword(s):  
Data Acquisition ◽  
Ambulatory Monitoring ◽  
Vital Signs ◽  
Data Acquisition System ◽  
Acquisition System ◽  
False Alarms ◽  
Multiple Parameters ◽  
Risk Patients ◽  
Life Threatening ◽  
Long Term Prognosis

Technical concepts of a multi-parameter-based system are described which can be used for continuous ambulatory monitoring of several vital signs. When critical or fatal events are detected, an automatic alarm is generated including information about the patient´s position (global positioning system, GPS) and additional messages. A lot of vital parameters are continuously monitored by “bio detectors” which are connected with a mobile data acquisition system carried by the patient. This data acquisition system interacts with a mobile phone so that an alarm can immediately be sounded in cases of critical or fatal events. Other episodes relevant for the patient´s long-term prognosis without leading to life-threatening outcomes can be stored for elective analyses without generating an alarm. Moreover, patients can manually give an alarm on demand. Potential false alarms can be manually canceled. In further stages of development these technical components could interact with electronic control systems of cars so that cars could be immediately stopped if the driver becomes unconscious.

scholarly journals LO21: Consistency of CTAS scores by presenting complaint pre and post eCTAS implementation in 35 emergency departments across Ontario

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S14-S15
Author(s):  
S. McLeod ◽  
C. Thompson ◽  
B. Borgundvaag ◽  
L. Thabane ◽  
H. Ovens ◽  
...  
Keyword(s):  
Emergency Departments ◽  
Improve Patient Safety ◽  
Vital Signs ◽  
Decision Support Tool ◽  
High Volume ◽  
Population Based ◽  
Primary Objective ◽  
Absolute Difference ◽  
Altered Level ◽  
Support Tool

Introduction: eCTAS is a real time electronic triage decision-support tool designed to improve patient safety and quality of care by standardizing the application of the Canadian Triage and Acuity Scale (CTAS). The tool dynamically calculates a recommended CTAS score based on the presenting complaint, vital signs and selected clinical modifiers. The primary objective was to assess consistency of CTAS score distributions across 35 emergency departments (EDs) by 16 presenting complaints pre and post eCTAS implementation. Methods: This retrospective cohort study used population-based administrative data from January 2016 to December 2018 from all hospital EDs in Ontario that had implemented eCTAS with at least 9 months of data. Following a 3-month stabilization period, we compared data for 6 months post-eCTAS implementation to the same 6-month period the previous year (pre-implementation) to account for potential seasonal variation, patient volume and case-mix. We included triage encounters of adult (≥18 years) patients if they had one of 16 pre-specified high-volume, presenting complaints. A paired-samples t-test was used to determine consistency by estimating the absolute difference in CTAS distribution for each presenting complaint, by each hospital, pre and post eCTAS implementation, compared to the overall average of the 35 EDs. Results: There were 183,231 triage encounters in the pre-eCTAS cohort and 179,983 in the post-eCTAS cohort from 35 EDs across the province. Triage scores were more consistent with the overall average after eCTAS implementation in 6 (37.5%) presenting complaints: chest pain (cardiac features) (p < 0.001), extremity weakness/symptoms of cerebrovascular accident (p < 0.001), fever (p < 0.001), shortness of breath (p < 0.001), syncope (p = 0.02), and hyperglycemia (p = 0.03). Triage consistency was similar pre and post eCTAS implementation for the presenting complaints of altered level of consciousness, anxiety/situational crisis, confusion, depression/suicidal/deliberate self-harm, general weakness, head injury, palpitations, seizure, substance misuse/intoxication or vertigo. Conclusion: A standardized, electronic approach to performing triage assessments increased consistency in CTAS scores across many, but not all, high-volume CEDIS complaints. This does not reflect triage accuracy, as there are no known benchmarks for triage accuracy. Improvements in consistency were greatest for sentinel presenting complaints with a minimum allowable CTAS score.

Intrusion Detection of Cyber-Physical Attacks in Manufacturing Systems: A Review

2019 ◽  
Author(s):  
Mingtao Wu ◽  
Young B. Moon
Keyword(s):  
Intrusion Detection ◽  
Manufacturing Systems ◽  
High Volume ◽  
Cyber Attacks ◽  
Intrusion Detection Systems ◽  
Detection Methods ◽  
False Alarms ◽  
Alert Correlation ◽  
Acoustic Image ◽  
Detection Systems

Abstract Cyber-physical manufacturing system is the vision of future manufacturing systems where physical components are fully integrated through various networks and the Internet. The integration enables the access to computation resources that can improve efficiency, sustainability and cost-effectiveness. However, its openness and connectivity also enlarge the attack surface for cyber-attacks and cyber-physical attacks. A critical challenge in defending those attacks is that current intrusion detection methods cannot timely detect cyber-physical attacks. Studies showed that the physical detection provides a higher accuracy and a shorter respond time compared to network-based or host-based intrusion detection systems. Moreover, alert correlation and management methods help reducing the number of alerts and identifying the root cause of the attack. In this paper, the intrusion detection research relevant to cyber-physical manufacturing security is reviewed. The physical detection methods — using side-channel data, including acoustic, image, acceleration, and power consumption data to disclose attacks during the manufacturing process — are analyzed. Finally, the alert correlation methods — that manage the high volume of alerts generated from intrusion detection systems via logical relationships to reduce the data redundancy and false alarms — are reviewed. The study show that the cyber-physical attacks are existing and rising concerns in industry. Also, the increasing efforts in cyber-physical intrusion detection and correlation research can be utilized to secure the future manufacturing systems.

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