Prescient Profiling – AI Driven Volunteer Selection within a Volunteer Notification System

Elsner, Jesko; Meisen, Philipp; Ewert, Daniel; Schilberg, Daniel; Jeschke, Sabina

doi:10.1007/978-3-319-08816-7_46

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Profiling is “the process of generating profiles from obtained data, associated to one or multiple subjects” [ 49 ]. Profiling of people is widely used in several areas, such as targeted advertising [ 50 ], donation solicitation [ 51 ], and volunteer recruitment [ 52 ].…”

Section: Introductionmentioning

confidence: 99%

“…Profiling of people is widely used in several areas, such as targeted advertising [ 50 ], donation solicitation [ 51 ], and volunteer recruitment [ 52 ]. Elsner et al [ 22 , 49 ] proposed to use the profiling of volunteers in dispatch algorithms to enhance the prediction of the volunteers’ position, trajectory, and constraints. In this study, we used classification techniques to generate different behavioral profiles of volunteers that serve as independent variables for predicting responses to alerts.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Dispatch of Volunteer First Responders: Algorithm Development and Validation

Khalemsky,

Lankenau

et al. 2023

JMIR Mhealth Uhealth

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Background Smartphone-based emergency response apps are increasingly being used to identify and dispatch volunteer first responders (VFRs) to medical emergencies to provide faster first aid, which is associated with better prognoses. Volunteers’ availability and willingness to respond are uncertain, leading in recent studies to response rates of 17% to 47%. Dispatch algorithms that select volunteers based on their estimated time of arrival (ETA) without considering the likelihood of response may be suboptimal due to a large percentage of alerts wasted on VFRs with shorter ETA but a low likelihood of response, resulting in delays until a volunteer who will actually respond can be dispatched. Objective This study aims to improve the decision-making process of human emergency medical services dispatchers and autonomous dispatch algorithms by presenting a novel approach for predicting whether a VFR will respond to or ignore a given alert. Methods We developed and compared 4 analytical models to predict VFRs’ response behaviors based on emergency event characteristics, volunteers’ demographic data and previous experience, and condition-specific parameters. We tested these 4 models using 4 different algorithms applied on actual demographic and response data from a 12-month study of 112 VFRs who received 993 alerts to respond to 188 opioid overdose emergencies. Model 4 used an additional dynamically updated synthetic dichotomous variable, frequent responder, which reflects the responder’s previous behavior. Results The highest accuracy (260/329, 79.1%) of prediction that a VFR will ignore an alert was achieved by 2 models that used events data, VFRs’ demographic data, and their previous response experience, with slightly better overall accuracy (248/329, 75.4%) for model 4, which used the frequent responder indicator. Another model that used events data and VFRs’ previous experience but did not use demographic data provided a high-accuracy prediction (277/329, 84.2%) of ignored alerts but a low-accuracy prediction (153/329, 46.5%) of responded alerts. The accuracy of the model that used events data only was unacceptably low. The J48 decision tree algorithm provided the best accuracy. Conclusions VFR dispatch has evolved in the last decades, thanks to technological advances and a better understanding of VFR management. The dispatch of substitute responders is a common approach in VFR systems. Predicting the response behavior of candidate responders in advance of dispatch can allow any VFR system to choose the best possible response candidates based not only on ETA but also on the probability of actual response. The integration of the probability to respond into the dispatch algorithm constitutes a new generation of individual dispatch, making this one of the first studies to harness the power of predictive analytics for VFR dispatch. Our findings can help VFR network administrators in their continual efforts to improve the response times of their networks and to save lives.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictive Dispatch of Volunteer First Responders: Algorithm Development and Validation

Khalemsky,

Lankenau

et al. 2023

JMIR Mhealth Uhealth

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“…Of particular interest is the category of mHealth apps which facilitate spontaneous communication between stakeholders in response to an emergency event. Smartphone applications can support the connection of app users to real-world medical emergency events and are being studied in diverse situations such as the mapping and response-use of AEDs (Automatic Electronic Defibrillators) (Folke et al, 2009;Merchant et al, 2013;Peberdy et al, 2006;Rea, Blackwood, Damon, Phelps, & Eisenberg, 2011;Sakai et al, 2011), volunteer emergency response (Cappato et al, 2006;Elsner, Meisen, Ewert, Schilberg, & Jeschke, 2014;Elsner, Meisen, Thelen, Schilberg, & Jeschke, 2013;Elsner, Schneiders, Haberstroh, Schilberg, & Jeschke, 2013;Groh et al, 2007;Marshall et al, 2006), the physical proximity of diabetes patients (D. Johnson, 2014) in need of glucose or monitors, and Emergency Response Communities (ERC) for anaphylaxis events (Schwartz, Bellou, Garcia-Castrillo, Muraro & Papadopoulos, 2014). Such systems have the potential to enable a new form of emergency medical response in which the social network-based connection between individuals creates a lay-layer of support which can augment or potentially modify current forms of EMS (Emergency Medical Services).…”

Section: Introductionmentioning

confidence: 99%

Exploring mHealth Participation for Emergency Response Communities

Schwartz

Bellou

Garcı́a-Castrillo

et al. 2017

AJIS

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We explore the challenges of participation by members of emergency response communities who share a similar condition and treatment, and are called upon to participate in emergency events experienced by fellow members. Smartphones and location-based social networking technologies present an opportunity to re-engineer certain aspects of emergency medical response. Life-saving prescription medication extended in an emergency by one individual to another occurs on a micro level, anecdotally documented. We illustrate the issues and our approach through the example of an app to support patients prone to anaphylaxis and prescribed to carry epinephrine auto-injectors. We address unique participation challenges in an mHealth environment in which interventions are primarily short-term interactions which require clear and precise decision-making and constant tracking of potential participants in responding to an emergency medical event. The conflicting effects of diffused responsibility and shared identity are identified as key factors in modelling participation.

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Compound–protein interaction prediction by deep learning: Databases, descriptors and models

Qin

Jiang

et al. 2022

Drug Discovery Today

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Prescient Profiling – AI Driven Volunteer Selection within a Volunteer Notification System

Cited by 3 publications

References 16 publications

Predictive Dispatch of Volunteer First Responders: Algorithm Development and Validation

Predictive Dispatch of Volunteer First Responders: Algorithm Development and Validation

Exploring mHealth Participation for Emergency Response Communities

Compound–protein interaction prediction by deep learning: Databases, descriptors and models

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