Overlaying multiple sources of data to identify bottlenecks in clinical workflow.

The pursuit of increased efficiency and quality of clinical care based on the analysis of workflow has seen the introduction of several modern technologies into medical environments. Electronic health records (EHRs) remain central to analysis of workflow, owing to their wide-ranging impact on clinical processes. The two most common interventions to facilitate EHR-related workflow analysis are automated location tracking using sensor-based technologies and EHR usage data logs. However, to maximize the potential of these technologies, and especially to facilitate workflow redesign, it is necessary to overlay these quantitative findings on the contextual data from qualitative methods such as ethnography. Such a complementary approach promises to yield more precise measures of clinical workflow that provide insights into how redesign could address inefficiencies. In this paper, we categorize clinical workflow in the Emergency Department (ED) into three types (perceived, real and ideal) to create a structured approach to workflow redesign using the available data. We use diverse data sources: sensor-based location tracking through Radio-Frequency Identification (RFID), summary EHR usage data logs, and data from physician interviews augmented by direct observations (through clinician shadowing). Our goal is to discover inefficiencies and bottlenecks that can be addressed to achieve a more ideal workflow state relative to its real and perceived state. We thereby seek to demonstrate a novel data-driven approach toward iterative workflow redesign that generalizes for use in a variety of settings. We also propose types of targeted support or adjustments to offset some of the inefficiencies we noted.

A method for the analysis and visualization of clinical workflow in dynamic environments.

The analysis of clinical workflow offers many challenges, especially in settings characterized by rapid dynamic change. Typically, some combination of approaches drawn from ethnography and grounded theory-based qualitative methods are used to develop relevant metrics. Medical institutions have recently attempted to introduce technological interventions to develop quantifiable quality metrics to supplement existing purely qualitative analyses. These interventions range from automated location tracking to repositories of clinical data (e.g., electronics health record (EHR) data, medical equipment logs). Our goal in this paper is to present a cohesive framework that combines a set of analytic techniques that can potentially complement traditional human observations to derive a deeper understanding of clinical workflow and thereby to enhance the quality, safety, and efficiency of care offered in that environment. We present a series of theoretically-guided techniques to perform analysis and visualization of data developed using location tracking, with illustrations using the Emergency Department (ED) as an example. Our framework is divided into three modules: (i) transformation, (ii) analysis, and (iii) visualization. We describe the methods used in each of these modules, and provide a series of visualizations developed using location-tracking data collected at the Mayo Clinic ED (Phoenix, AZ). Our innovative analytics go beyond qualitative study, and includes user data collected from a relatively modern but increasingly ubiquitous technique of location tracking, with the goal of creating quantitative workflow metrics. Although we believe that the methods we have developed will generalize well to other settings, additional work will be required to demonstrate their broad utility beyond our single study environment.

A mobile application to support collection and analytics of real-time critical care data.

BACKGROUND AND OBJECTIVES: Data collection, in high intensity environments, poses several challenges including the ability to observe multiple streams of information. These problems are especially evident in critical care, where monitoring of the Advanced Trauma Life Support (ATLS) protocol provides an excellent opportunity to study the efficacy of applications that allow for the rapid capture of event information, providing theoretically-driven feedback using the data. Our goal was, (a) to design and implement a way to capture data on deviation from the standard practice based on the theoretical foundation of error classification from our past research, (b) to provide a means to meaningfully visualize the collected data, and (c) to provide a proof-of-concept for this implementation, using some understanding of user experience in clinical practice. METHODS: We present the design and development of a web application designed to be used primarily on mobile devices and a summary data viewer to allow clinicians to, (a) track their activities, (b) provide real-time feedback of deviations from guidelines and protocols, and (c) provide summary feedback highlighting decisions made. We used a framework previously developed to classify activities in trauma as the theoretical foundation of the rules designed to do the same algorithmically, in our application. Attending physicians at a Level 1 trauma center used the application in the clinical setting and provided feedback for iterative development. Informal interviews and surveys were used to gain some deeper understanding of the user experience using this application in-situ. RESULTS: Activity visualizations were created highlighting decisions made during a trauma code as well as classification of tasks per the theoretical framework. The attendings reviewed the efficacy of the data visualizations as part of their interviews. We also conducted a proof-of-concept evaluation by way of usability questionnaire. Two attendings rated 4 out of the usability 6 categories highly (inter-rater reliability: R = 0.87; weighted kappa = 0.59). This could be attributed to the fact that they were able to fit the use of the application into their regular workflow during a trauma code relatively seamlessly. A deeper evaluation is required to answer explain this further. CONCLUSIONS: Our application can be used to capture and present data to provide an accurate reflection of work activities in real-time in complex critical care environments, without any significant interruptions to workflow.

Collaborative virtual reality based advanced cardiac life support training simulator using virtual reality principles.

BACKGROUND: Advanced Cardiac Life Support (ACLS) is a series of team-based, sequential and time constrained interventions, requiring effective communication and coordination of activities that are performed by the care provider team on a patient undergoing cardiac arrest or respiratory failure. The state-of-the-art ACLS training is conducted in a face-to-face environment under expert supervision and suffers from several drawbacks including conflicting care provider schedules and high cost of training equipment. OBJECTIVE: The major objective of the study is to describe, including the design, implementation, and evaluation of a novel approach of delivering ACLS training to care providers using the proposed virtual reality simulator that can overcome the challenges and drawbacks imposed by the traditional face-to-face training method. METHODS: We compare the efficacy and performance outcomes associated with traditional ACLS training with the proposed novel approach of using a virtual reality (VR) based ACLS training simulator. One hundred and forty-eight (148) ACLS certified clinicians, translating into 26 care provider teams, were enrolled for this study. Each team was randomly assigned to one of the three treatment groups: control (traditional ACLS training), persuasive (VR ACLS training with comprehensive feedback components), or minimally persuasive (VR ACLS training with limited feedback components). The teams were tested across two different ACLS procedures that vary in the degree of task complexity: ventricular fibrillation or tachycardia (VFib/VTach) and pulseless electric activity (PEA). RESULTS: The difference in performance between control and persuasive groups was not statistically significant (P=.37 for PEA and P=.1 for VFib/VTach). However, the difference in performance between control and minimally persuasive groups was significant (P=.05 for PEA and P=.02 for VFib/VTach). The pre-post comparison of performances of the groups showed that control (P=.017 for PEA, P=.01 for VFib/VTach) and persuasive (P=.02 for PEA, P=.048 for VFib/VTach) groups improved their performances significantly, whereas minimally persuasive group did not (P=.45 for PEA, P=.46 for VFib/VTach). Results also suggest that the benefit of persuasiveness is constrained by the potentially interruptive nature of these features. CONCLUSIONS: Our results indicate that the VR-based ACLS training with proper feedback components can provide a learning experience similar to face-to-face training, and therefore could serve as a more easily accessed supplementary training tool to the traditional ACLS training. Our findings also suggest that the degree of persuasive features in VR environments have to be designed considering the interruptive nature of the feedback elements.

Design and development of a virtual reality simulator for advanced cardiac life support training.

The use of virtual reality (VR) training tools for medical education could lead to improvements in the skills of clinicians while providing economic incentives for healthcare institutions. The use of VR tools can also mitigate some of the drawbacks currently associated with providing medical training in a traditional clinical environment such as scheduling conflicts and the need for specialized equipment (e.g., high-fidelity manikins). This paper presents the details of the framework and the development methodology associated with a VR-based training simulator for advanced cardiac life support, a time critical, team-based medical scenario. In addition, we also report the key findings of a usability study conducted to assess the efficacy of various features of this VR simulator through a postuse questionnaire administered to various care providers. The usability questionnaires were completed by two groups that used two different versions of the VR simulator. One version consisted of the VR trainer with it all its features and a minified version with certain immersive features disabled. We found an increase in usability scores from the minified group to the full VR group.