Ivan Marsic

Introducing RFID technology in dynamic and time-critical medical settings

We describe the process of introducing RFID technology in the trauma bay of a trauma center to support fast-paced and complex teamwork during resuscitation. We analyzed trauma resuscitation tasks, photographs of medical tools, and videos of simulated resuscitations to gain insight into resuscitation tasks, work practices and procedures. Based on these data, we discuss strategies for placing RFID tags on medical tools and for placing antennas in the environment for optimal tracking and activity recognition. Results from our preliminary RFID deployment in the trauma bay show the feasibility of our approach for tracking tools and for recognizing trauma team activities. We conclude by discussing implications for and challenges to introducing RFID technology in other similar settings characterized by dynamic and collocated collaboration.

Leadership structures in emergency care settings: A study of two trauma centers

BACKGROUND Trauma resuscitation involves multidisciplinary teams under surgical leadership in most US trauma centers. Because many trauma centers have also incorporated emergency department (ED) physicians, shared and cross-disciplinary leadership structures often occur. Our study identifies leadership structures and examines the effects of cross-disciplinary leadership on trauma teamwork. METHODS We conducted an ethnographic study at two US Level-1 trauma centers, one of which is a dedicated pediatric trauma center. We used observation, videotaping and interviews to contextualize and classify leadership structures in trauma resuscitation. Leadership structures were evaluated based on three dimensions of team performance: defined leadership, likelihood of conflict in decision making, and appropriate care. FINDINGS We identified five common leadership structures, grouped under two broad leadership categories: solo decision-making and intervening models within intra-disciplinary leadership; intervening, parallel, and collaborative models within cross-disciplinary leadership. CONCLUSION Most important weaknesses of different leadership structures are manifested in inefficient teamwork or inappropriate patient care. These inefficiencies are particularly problematic when leadership is shared between physicians from different disciplines with different levels of experience, which often leads to conflict, reduces teamwork efficiency and lowers the quality of care. We discuss practical implications for technology design.

Teamwork Errors in Trauma Resuscitation

Human errors in trauma resuscitation can have cascading effects leading to poor patient outcomes. To determine the nature of teamwork errors, we conducted an observational study in a trauma center over a two-year period. While eventually successful in treating the patients, trauma teams had problems tracking and integrating information in a longitudinal trajectory, which resulted in inefficiencies and near-miss errors. As an initial step in system design to support trauma teams, we proposed a model of teamwork and a novel classification of team errors. Four types of team errors emerged from our analysis: communication errors, vigilance errors, interpretation errors, and management errors. Based on these findings, we identified key information structures to support team cognition and decision making. We believe that displaying these information structures will support distributed cognition of trauma teams. Our findings have broader applicability to other collaborative and dynamic work settings that are prone to human error.

Non-intrusive localization of passive RFID tagged objects in an indoor workplace

This paper presents our work on localizing a passive UHF RFID tagged object in an indoor workplace. We focus on uncontrolled settings with random orientations of the target object, dynamically moving people in the environment and cluttered rooms with many furniture items. Multiple fixed antennas are used to handle random tag orientations and human body effects. The antennas are placed in a way to minimize the obstruction for human activities and the effect of human presence and movement on the localization system. We use zone-based and exact localization methods incorporating probabilistic and deterministic machine learning techniques. We also propose a combined coarse-to-fine approach to improve accuracy and increase speed. Experimental results show that our system is able to localize an object with an error of 37 cm for exact localization and with an accuracy of 92% for zone-based classification. Experiments in challenging conditions showed that our overall design is robust to human body effects, even exploits the destructive effects of human body on UHF RFID sensing.

Detecting Object Motion Using Passive RFID: A Trauma Resuscitation Case Study

We studied object motion detection in an indoor environment using RFID technology. Unlike prior work, we focus on dynamic scenarios, such as emergency medical situations, subject to signal interference by people and many RFID tags. We build a realistic trauma resuscitation setting and record a dataset of around 14000 detection instances. We find that factors affecting radio signal, such as tag motion, have different statistical fingerprints, making them discernible using statistical methods. Our method for object motion detection extracts descriptive features of the received signal strength and classifies them using machine-learning techniques. We report experimental results obtained with several statistical features and classifiers, and provide guidelines for feature and classifier selection in different environments. Experimental results show that object motion could be detected with an accuracy of 80% in complex scenarios and 90% on average. The motion type, on the other hand, could not be identified with such high accuracy using currently available passive RFID technology.

Design and Evaluation of RFID Deployments in a Trauma Resuscitation Bay

We examined configuring a radio frequency identification (RFID) equipment for the best object use detection in a trauma bay. Unlike prior work on RFID, we 1) optimized the accuracy of object use detection rather than just object detection; and 2) quantitatively assessed antenna placement while addressing issues specific to tag placement likely to occur in a trauma bay. Our design started with an analysis of the environment requirements and constraints. We designed several antenna setups with different number of components (RFID tags or antennas) and their orientations. Setups were evaluated under scenarios simulating a dynamic medical setting. We used three metrics with increasing complexity and bias: read rate, received signal strength indication distribution distance, and target application performance. Our experiments showed that antennas above the regions with high object density are most suitable for detecting object use. We explored tagging strategies for challenging objects so that sufficient readout rates are obtained for computing evaluation metrics. Among the metrics, distribution distance was correlated with target application performance, and also less biased and simpler to calculate, which made it an excellent metric for context-aware applications. We present experimental results obtained in the real trauma bay to validate our findings.

Quantifying adaptation parameters for information support of trauma teams

Trauma centers are stressful, noisy and dynamic environments, with many people performing complex tasks, and with little in the way of information support. Information must be prioritized and filtered to avoid overload or loss. This work quantifies the information-selection parameters that will guide adaptive user interfaces for trauma teams.

Monitoring Interactions with RFID Tagged Objects Using RSSI

In this paper, we present SVM and HMM-based methods for monitoring interactions with passive RFID tagged objects. We continuously track the motion status of an object and declare the status as standing still, randomly moving or linearly moving. Inspired by phone transition modeling in speech processing, each interaction type is represented with two sub-states to handle transitions and continuity. Experiments were designed to simulate our target application: monitoring interactions with medical equipment during trauma resuscitation. Our system identified interaction status with 85% accuracy using an HMM. The most useful feature for discrimination was the difference between the average RSSI of two consecutive windows.