Usability Testing of a Web-Based Empathy Training Portal: Mixed Methods Study.

BACKGROUND: The prepandemic period saw a rise in web-based teaching. However, web-based tools for teaching the essential clinical skill of cognitive empathy (also known as perspective taking) remain limited. More of these tools are needed and require testing for ease of use and understanding by students. OBJECTIVE: This study aimed to evaluate the usability of the In Your Shoes web-based empathy training portal application for students using quantitative and qualitative methods. METHODS: This 3-phase formative usability study used a mixed methods design. In mid-2021, we conducted a remote observation of student participants interacting with our portal application. Their qualitative reflections were captured, followed by data analysis and iterative design refinements of the application. Overall, 8 third- and fourth-year nursing students from an undergraduate baccalaureate program at a Canadian university, in the western province of Manitoba, were included in this study. Participants in phases 1 and 2 were remotely observed by 3 research personnel while engaged in predefined tasks. In phase 3, two student participants were asked to use the application as they liked in their own environments, after which a video-recorded exit interview with a think-aloud process was conducted as participants responded to the System Usability Scale. We calculated descriptive statistics and performed content analysis to analyze the results. RESULTS: This small study included 8 students with a range of technology skills. Usability themes were based on participants' comments on the application's appearance, content, navigation, and functionality. The biggest issues that participants experienced were with navigating the application's "tagging" features during video analysis and the length of educational material. We also observed variations in 2 participants' system usability scores in phase 3. This may be because of their different comfort levels with technology; however, additional research is required. We made iterative refinements to our prototype application (eg, added pop-up messages and provided a narrated video on the application's "tagging" function) based on participant feedback. CONCLUSIONS: With increasing engagement in web-based teaching, technology has become an essential medium for receiving health care education. We developed a novel prototype application as a supplemental classroom tool to foster students' self-directed learning of empathy. This study provided direction for refinements to optimize the usability of and satisfaction with this innovative application. Qualitative feedback revealed favorable input toward learning perspective taking place on the web and helpful recommendations for improving user experiences with the application. We could not fully assess the application's key functions owing to the COVID-19 protocols. Thus, our next step is to obtain feedback from a larger sample of student users, whose experiences performing "live" video capture, annotation, and analysis will be more authentic and wholesome with the refined application. We discuss our findings in relation to research on nursing education, perspective taking, and adaptive e-learning.

An mHealth App for Decision-Making Support in Wound Dressing Selection (WounDS): Protocol for a User-Centered Feasibility Study.

BACKGROUND: Primary care health professionals, especially family physicians, see a variety of wounds, and yet-despite the frequency of providing wound care-many family physicians do not feel confident in wound care management. This is partly due to a lack of formal wound education in Family Medicine programs. While there are numerous electronic wound care resources available in the UK and North America, none were identified that address the specific need in supporting clinical decision-making in wound dressing selection. At the same time, healthcare providers are increasingly using technology in personal and professional contexts, and a logical extension is to use technology for knowledge translation strategies. OBJECTIVE: This work developed a prototype mobile health software application named WounDS, designed to support clinical decision-making in selecting wound dressings. This article presents the development and evaluation plan for the WounDS app. METHODS: WounDS has been developed on the iOS platform. The primary specification included ease of use, in that one of the primary influences in user adoption would be the ability to receive a wound dressing recommendation in under 30 seconds and under 5 taps on the screen. The WounDS app guides users through a series of binary decisions for assessing the wound and provides a wound dressing recommendation. The selection algorithm is based in best practices using the Wound Bed Preparation Paradigm. RESULTS: Current work is underway to examine the implementation needs for WounDS to be most effectively utilized and to pilot test its feasibility and use in clinical care. Data will be collected through user trials, focus groups, and user metadata will be collected within the app. Optimizing these preconditions will enable a subsequent phase of study to determine effects on clinical decision-making and clinical outcomes. CONCLUSIONS: WounDS is designed for knowledge translation, use of technology in clinical decision-making, and continuity of care. The benefits of WounDS include the potential to improve healthcare providers' competency in wound management and to improve wound healing through better alignment with evidence-based best practices in wound dressing selection, consistency in care from primary to community care, and subsequent downstream impacts in quality of life for patients.

Data Preparation for West Nile Virus Agent-Based Modelling: Protocol for Processing Bird Population Estimates and Incorporating ArcMap in AnyLogic.

BACKGROUND: West Nile Virus (WNV) was first isolated in 1937. Since the 1950s, many outbreaks have occurred in various countries. The first appearance of infected birds in Manitoba, Canada was in 2002. OBJECTIVE: This paper describes the data preparation phase of setting up a geographic information system (GIS) simulation environment for WNV Agent-Based Modelling in Manitoba. METHODS: The main technology used in this protocol is based on AnyLogic and ArcGIS software. A diverse variety of topics and techniques regarding the data collection phase are presented, as modelling WNV has many disparate attributes, including landscape and weather impacts on mosquito population dynamics and birds' roosting locations, population count, and movement patterns. RESULTS: Different maps were combined to create a grid land cover map of Manitoba, Canada in a shapefile format compatible with AnyLogic, in order to modulate mosquito parameters. A significant amount of data regarding 152 bird species, along with their population estimates and locations in Manitoba, were gathered and assembled. Municipality shapefile maps were converted to built-in AnyLogic GIS regions for better compatibility with census data and initial placement of human agents. Accessing shapefiles and their databases in AnyLogic are also discussed. CONCLUSIONS: AnyLogic simulation software in combination with Esri ArcGIS provides a powerful toolbox for developers and modellers to simulate almost any GIS-based environment or process. This research should be useful to others working on a variety of mosquito-borne diseases (eg, Zika, dengue, and chikungunya) by demonstrating the importance of data relating to Manitoba and/or introducing procedures to compile such data.

A mHealth application for chronic wound care: findings of a user trial.

This paper reports on the findings of a user trial of a mHealth application for pressure ulcer (bedsore) documentation. Pressure ulcers are a leading iatrogenic cause of death in developed countries and significantly impact quality of life for those affected. Pressure ulcers will be an increasing public health concern as the population ages. Electronic information systems are being explored to improve consistency and accuracy of documentation, improve patient and caregiver experience and ultimately improve patient outcomes. A software application was developed for Android Smartphones and tablets and was trialed in a personal care home in Western Canada. The software application provides an electronic medical record for chronic wounds, replacing nurses' paper-based charting and is positioned for integration with facility's larger eHealth framework. The mHealth application offers three intended benefits over paper-based charting of chronic wounds, including: (1) the capacity for remote consultation (telehealth between facilities, practitioners, and/or remote communities), (2) data organization and analysis, including built-in alerts, automatically-generated text-based and graph-based wound histories including wound images, and (3) tutorial support for non-specialized caregivers. The user trial yielded insights regarding the software application's design and functionality in the clinical setting, and highlighted the key role of wound photographs in enhancing patient and caregiver experiences, enhancing communication between multiple healthcare professionals, and leveraging the software's telehealth capacities.

An agent based model for simulating the spread of sexually transmitted infections.

This work uses agent-based modelling (ABM) to simulate sexually transmitted infection (STIs) spread within a population of 1000 agents over a 10-year period, as a preliminary investigation of the suitability of ABM methodology to simulate STI spread. The work contrasts compartmentalized mathematical models that fail to account for individual agents, and ABMs commonly applied to simulate the spread of respiratory infections. The model was developed in C++ using the Boost 1.47.0 libraries for the normal distribution and OpenGL for visualization. Sixteen agent parameters interact individually and in combination to govern agent profiles and behaviours relative to infection probabilities. The simulation results provide qualitative comparisons of STI mitigation strategies, including the impact of condom use, promiscuity, the form of the friend network, and mandatory STI testing. Individual and population-wide impacts were explored, with individual risk being impacted much more dramatically by population-level behaviour changes as compared to individual behaviour changes.

Agent-based modeling of the spread of influenza-like illness in an emergency department: a simulation study.

The objective of this paper was to develop an agent-based modeling framework in order to simulate the spread of influenza virus infection on a layout based on a representative hospital emergency department in Winnipeg, Canada. In doing so, the study complements mathematical modeling techniques for disease spread, as well as modeling applications focused on the spread of antibiotic-resistant nosocomial infections in hospitals. Twenty different emergency department scenarios were simulated, with further simulation of four infection control strategies. The agent-based modeling approach represents systems modeling, in which the emergency department was modeled as a collection of agents (patients and healthcare workers) and their individual characteristics, behaviors, and interactions. The framework was coded in C++ using Qt4 libraries running under the Linux operating system. A simple ordinary least squares (OLS) regression was used to analyze the data, in which the percentage of patients that became infected in one day within the simulation was the dependent variable. The results suggest that within the given instance context, patient-oriented infection control policies (alternate treatment streams, masking symptomatic patients) tend to have a larger effect than policies that target healthcare workers. The agent-based modeling framework is a flexible tool that can be made to reflect any given environment; it is also a decision support tool for practitioners and policymakers to assess the relative impact of infection control strategies. The framework illuminates scenarios worthy of further investigation, as well as counterintuitive findings.

Improving Agent Based Models and Validation through Data Fusion.

This work is contextualized in research in modeling and simulation of infection spread within a community or population, with the objective to provide a public health and policy tool in assessing the dynamics of infection spread and the qualitative impacts of public health interventions. This work uses the integration of real data sources into an Agent Based Model (ABM) to simulate respiratory infection spread within a small municipality. Novelty is derived in that the data sources are not necessarily obvious within ABM infection spread models. The ABM is a spatial-temporal model inclusive of behavioral and interaction patterns between individual agents on a real topography. The agent behaviours (movements and interactions) are fed by census / demographic data, integrated with real data from a telecommunication service provider (cellular records) and person-person contact data obtained via a custom 3G Smartphone application that logs Bluetooth connectivity between devices. Each source provides data of varying type and granularity, thereby enhancing the robustness of the model. The work demonstrates opportunities in data mining and fusion that can be used by policy and decision makers. The data become real-world inputs into individual SIR disease spread models and variants, thereby building credible and non-intrusive models to qualitatively simulate and assess public health interventions at the population level.

Load Balancing at Emergency Departments using 'Crowdinforming'.

BACKGROUND: Emergency Department (ED) overcrowding is an important healthcare issue facing increasing public and regulatory scrutiny in Canada and around the world. Many approaches to alleviate excessive waiting times and lengths of stay have been studied. In theory, optimal ED patient flow may be assisted via balancing patient loads between EDs (in essence spreading patients more evenly throughout this system). This investigation utilizes simulation to explore "Crowdinforming" as a basis for a process control strategy aimed to balance patient loads between six EDs within a mid-sized Canadian city. METHODS: Anonymous patient visit data comprising 120,000 ED patient visits over six months to six ED facilities were obtained from the region's Emergency Department Information System (EDIS) to (1) determine trends in ED visits and interactions between parameters; (2) to develop a process control strategy integrating crowdinforming; and, (3) apply and evaluate the model in a simulated environment to explore the potential impact on patient self-redirection and load balancing between EDs. RESULTS: As in reality, the data available and subsequent model demonstrated that there are many factors that impact ED patient flow. Initial results suggest that for this particular data set used, ED arrival rates were the most useful metric for ED 'busyness' in a process control strategy, and that Emergency Department performance may benefit from load balancing efforts. CONCLUSIONS: The simulation supports the use of crowdinforming as a potential tool when used in a process control strategy to balance the patient loads between EDs. The work also revealed that the value of several parameters intuitively expected to be meaningful metrics of ED 'busyness' was not evident, highlighting the importance of finding parameters meaningful within one's particular data set. The information provided in the crowdinforming model is already available in a local context at some ED sites. The extension to a wider dissemination of information via an Internet web service accessible by smart phones is readily achievable and not a technological obstacle. Similarly, the system could be extended to help direct patients by including future estimates or predictions in the crowdinformed data. The contribution of the simulation is to allow for effective policy evaluation to better inform the public of ED 'busyness' as part of their decision making process in attending an emergency department. In effect, this is a means of providing additional decision support insights garnered from a simulation, prior to a real world implementation.

Agent based modeling of "crowdinforming" as a means of load balancing at emergency departments.

This work extends ongoing development of a framework for modeling the spread of contact-transmission infectious diseases. The framework is built upon Agent Based Modeling (ABM), with emphasis on urban scale modelling integrated with institutional models of hospital emergency departments. The method presented here includes ABM modeling an outbreak of influenza-like illness (ILI) with concomitant surges at hospital emergency departments, and illustrates the preliminary modeling of 'crowdinforming' as an intervention. 'Crowdinforming', a component of 'crowdsourcing', is characterized as the dissemination of collected and processed information back to the 'crowd' via public access. The objective of the simulation is to allow for effective policy evaluation to better inform the public of expected wait times as part of their decision making process in attending an emergency department or clinic. In effect, this is a means of providing additional decision support garnered from a simulation, prior to real world implementation. The conjecture is that more optimal service delivery can be achieved under balanced patient loads, compared to situations where some emergency departments are overextended while others are underutilized. Load balancing optimization is a common notion in many operations, and the simulation illustrates that 'crowdinforming' is a potential tool when used as a process control parameter to balance the load at emergency departments as well as serving as an effective means to direct patients during an ILI outbreak with temporary clinics deployed. The information provided in the 'crowdinforming' model is readily available in a local context, although it requires thoughtful consideration in its interpretation. The extension to a wider dissemination of information via a web service is readily achievable and presents no technical obstacles, although political obstacles may be present. The 'crowdinforming' simulation is not limited to arrivals of patients at emergency departments due to ILI; it applies equally to any scenarios where patients arrive in any arrival pattern that may cause disparity in the waiting times at multiple facilities.

Models of emergency departments for reducing patient waiting times.

In this paper, we apply both agent-based models and queuing models to investigate patient access and patient flow through emergency departments. The objective of this work is to gain insights into the comparative contributions and limitations of these complementary techniques, in their ability to contribute empirical input into healthcare policy and practice guidelines. The models were developed independently, with a view to compare their suitability to emergency department simulation. The current models implement relatively simple general scenarios, and rely on a combination of simulated and real data to simulate patient flow in a single emergency department or in multiple interacting emergency departments. In addition, several concepts from telecommunications engineering are translated into this modeling context. The framework of multiple-priority queue systems and the genetic programming paradigm of evolutionary machine learning are applied as a means of forecasting patient wait times and as a means of evolving healthcare policy, respectively. The models' utility lies in their ability to provide qualitative insights into the relative sensitivities and impacts of model input parameters, to illuminate scenarios worthy of more complex investigation, and to iteratively validate the models as they continue to be refined and extended. The paper discusses future efforts to refine, extend, and validate the models with more data and real data relative to physical (spatial-topographical) and social inputs (staffing, patient care models, etc.). Real data obtained through proximity location and tracking system technologies is one example discussed.