Search

BackgroundStudent mental health in higher education has been an increasing concern. The COVID-19 pandemic situation has brought this vulnerable population into renewed focus.ObjectiveOur study aims to conduct a timely assessment of the effects of the COVID-19 pandemic on the mental health of college students.MethodsWe conducted interview surveys with 195 students at a large public university in the United States to understand the effects of the pandemic on their mental health and well-being. The data were analyzed through quantitative and qualitative methods.ResultsOf the 195 students, 138 (71%) indicated increased stress and anxiety due to the COVID-19 outbreak. Multiple stressors were identified that contributed to the increased levels of stress, anxiety, and depressive thoughts among students. These included fear and worry about their own health and of their loved ones (177/195, 91% reported negative impacts of the pandemic), difficulty in concentrating (173/195, 89%), disruptions to sleeping patterns (168/195, 86%), decreased social interactions due to physical distancing (167/195, 86%), and increased concerns on academic performance (159/195, 82%). To cope with stress and anxiety, participants have sought support from others and helped themselves by adopting either negative or positive coping mechanisms.ConclusionsDue to the long-lasting pandemic situation and onerous measures such as lockdown and stay-at-home orders, the COVID-19 pandemic brings negative impacts on higher education. The findings of our study highlight the urgent need to develop interventions and preventive strategies to address the mental health of college students.

Aim: This study sought to investigate architectural and space design considerations for veterans with post-traumatic stress disorder (PTSD). Background: Anecdotal evidence suggests that urban design features could have a positive impact on the mental well-being of individuals suffering from PTSD. However, evidence-based architectural and space design guidelines for PTSD are largely absent. Methods: Semi-structured interviews were conducted with 17 veterans diagnosed with PTSD to gain insights into their personal experiences with physical indoor and outdoor spaces, and to inquire about their needs and expectations for future architectural design. Transcripts were analyzed thematically. Results: Architectural design features including windows, entrances and exits, walkways and hallways, open space, defensible space, and green space; interior design features including furnishings and color; and ambient features including light, air quality, and noise levels were identified as most influential design features. Conclusions: Our results underscore the first important step to developing comprehensive architectural and space design guidelines for veterans with PTSD. Work is in progress to solicit more feedback from veterans.

BACKGROUND The COVID-19 pandemic has necessitated a rapid increase of space in highly infectious disease intensive care units (ICUs). At Houston Methodist Hospital (HMH), a virtual intensive care unit (vICU) was used amid the COVID-19 outbreak. OBJECTIVE The aim of this paper was to detail the novel adaptations and rapid expansion of the vICU that were applied to achieve patient-centric solutions while protecting staff and patients’ families during the pandemic. METHODS The planned vICU implementation was redirected to meet the emerging needs of conversion of COVID-19 ICUs, including alterations to staged rollout timing, virtual and in-person staffing, and scope of application. With the majority of the hospital critical care physician workforce redirected to rapidly expanded COVID-19 ICUs, the non–COVID-19 ICUs were managed by cardiovascular surgeons, cardiologists, neurosurgeons, and acute care surgeons. HMH expanded the vICU program to fill the newly depleted critical care expertise in the non–COVID-19 units to provide urgent, emergent, and code blue support to all ICUs. RESULTS Virtual family visitation via the Consultant Bridge application, palliative care delivery, and specialist consultation for patients with COVID-19 exemplify the successful adaptation of the vICU implementation. Patients with COVID-19, who were isolated and separated from their families to prevent the spread of infection, were able to virtually see and hear their loved ones, which bolstered the mental and emotional status of those patients. Many families expressed gratitude for the ability to see and speak with their loved ones. The vICU also protected medical staff and specialists assigned to COVID-19 units, reducing exposure and conserving personal protective equipment. CONCLUSIONS Telecritical care has been established as an advantageous mechanism for the delivery of critical care expertise during the expedited rollout of the vICU at Houston Methodist Hospital. Overall responses from patients, families, and physicians are in favor of continued vICU care; however, further research is required to examine the impact of innovative applications of telecritical care in the treatment of critically ill patients.

BACKGROUND Hypoglycemia, or low blood sugar levels, in people with diabetes can be a serious life-threatening condition, and serious outcomes can be avoided if low levels of blood sugar are proactively detected. Although technologies exist to detect the onset of hypoglycemia, they are invasive or costly or exhibit a high incidence of false alarms. Tremors are commonly reported symptoms of hypoglycemia and may be used to detect hypoglycemic events, yet their onset is not well researched or understood. OBJECTIVE This study aimed to understand diabetic patients’ perceptions of hypoglycemic tremors, as well as their user experiences with technology to manage diabetes, and expectations from a self-management tool to ultimately inform the design of a noninvasive and cost-effective technology that detects tremors associated with hypoglycemia. METHODS A cross-sectional internet panel survey was administered to adult patients with type 1 diabetes using the Qualtrics platform in May 2019. The questions focused on 3 main constructs: (1) perceived experiences of hypoglycemia, (2) experiences and expectations about a diabetes management device and mobile app, and (3) beliefs and attitudes regarding intention to use a diabetes management device. The analysis in this paper focuses on the first two constructs. Nonparametric tests were used to analyze the Likert scale data, with a Mann-Whitney U test, Kruskal-Wallis test, and Games-Howell post hoc test as applicable, for subgroup comparisons to highlight differences in perceived frequency, severity, and noticeability of hypoglycemic tremors across age, gender, years living with diabetes, and physical activity. RESULTS Data from 212 respondents (129 [60.8%] females) revealed statistically significant differences in perceived noticeability of tremors by gender, whereby males noticed their tremors more (PP CONCLUSIONS Our findings support the need for cost-efficient and noninvasive continuous monitoring technologies. Although hypoglycemic tremors were perceived to occur frequently, such tremors were not found to be severe compared with other symptoms such as sweating, which was the highest rated symptom in our study. Using a combination of tremor and galvanic skin response sensors may show promise in detecting the onset of hypoglycemic events.

Preventable adverse events in modern healthcare settings are the leading cause of deaths in the United States. The field of human factors and ergonomics has investigated the physiological, psychological, and psychosocial stressors that affect healthcare personnel performance and contribute to these preventable events, deteriorated healthcare worker’s quality of life, and employee burnout. Five of these stressors—workflow, shift work, staffing, working with electronic health records (EHRs), and patient handling—are discussed as relevant to clinical engineering. These stressors are presented through the frameworks of human performance and information processing theories, resource allocation theories, physiological adaptation, and effort-reward systems to derive recommendations for designing tasks, tools, technologies, training, and other engineering considerations for alleviating these stressors.

Resilience is considered an essential capability of an Incident Management Team (IMT) in planning for and responding to disasters and catastrophes. While IMTs have been studied as a decision-making unit, few attempts were made to view them from a Joint Cognitive System (JCS) perspective that highlights the interplay among humans and technical agents and demands imposed by the incident. To that end, this paper presents a JCS model of the IMT grounded in findings from the existing literature and naturalistic observations of simulated IMT's incident action planning, which functions in a cyclic manner across multiple scales. Using this model, three measures for resilience of the IMT, recovery time, resource status, and interactions, are discussed. To effectively represent the resilient performance incorporating these measures, a novel adoption of the Interactive Episode Analysis method is utilized. By providing a few examples of the analysis method, this study provides proof-of-concept for objective assessment of the resilience characteristics of the IMT. The proposed JCS-based IMT model can be used for descriptive modeling of similar systems to investigate resilient performance.

Diabetes mellitus in adults is a global health burden affecting 382 million people and costing over $612 billion worldwide. Remote patient monitoring is often considered to be a technological solution to the chal-lenges in healthcare delivery, yet many studies have shown mixed results or no effect on patient outcomes. A narrative review of literature was conducted to contribute to the field of technology-driven home healthcare delivery by analyzing the systems in context with the monitoring and intervention technologies. This review analyzed papers with home telemonitoring and intervention systems for adults with type 1 or type 2 diabetes. Technologies used were differentiated into four categories: telephones, mobile devices, computers, and other Internet-connected devices. Our findings suggest no clear association between the type of technology used and the outcomes of the participants. Frequency of monitoring and intervention were distinguishable by diabetic outcome metrics.

Nurses are the last line of defense to reduce preventable medical errors; however, they suffer from poor systems design and human factors issues (e.g., long shifts, dynamic workload, stressful situations, and fatigue), contributing to a reduced quality of care. A smart nursing system based on physiological monitoring is being designed to help nurses and their managers to efficiently communicate, reduce interruptions that affect critical task performance, and monitor acute stress and fatigue levels. This paper documents the systematic process of deriving information requirements through a group-participatory usability study, conducted with nurses working in various Southeastern Texas hospitals. Information requirements derived from these studies include: a need for accessing patients’ vital signs as well as laboratory results, memory aid tools for various critical nursing tasks, and options to call for help and to reduce interruptions for critical tasks. The system shows promise to meet these requirements.

Socio-technical systems, such as those in oil and gas, or the petrochemical and energy industries, are escalating in complexity, a consequence of increasingly advanced technologies, organizational constructs, and business functions that interact and depend on one another. These dynamic social and technological elements, coupled with the high risk inherent in these systems, have generated conditions that can bring about catastrophic failure and the tragic loss of human life, such as the disaster in Bhopal, India (1984) or the explosion in the Houston Ship Channel near Pasadena, Texas (1989). Historically, the perception of such complexities and the struggle to minimize catastrophic failures observed within the petrochemical industry have been attributed to the inherent variability in people. Therefore, process safety regulations associated with the Clean Air Act Amendments of 1990 and the Occupational Safety and Health Administration (OSHA) require employers to develop written process safety information or “procedures” which aim for consistency in plant operations and to help workers at the “sharp-end” of the system cope with unexpected events (OSHA, 2000). However, investigation reports since, such as the BP Texas City incident of 2005, suggest “outdated and ineffective procedures” as significant contributing factors to failure. Evidence from other studies suggest that procedures in complex environments are sometimes misunderstood, outdated, or simply not used (Bullemer & Hajdukiewicz, 2004). While there have been studies on procedural deviations and safety violations (Alper & Karsh, 2009; Jamison & Miller, 2000), employers continue to report a high rate of procedural breakdown as root causes for incidents (Bates & Holroyd, 2012). This warrants a contemporary, systems-oriented inquiry into process safety and behavior surrounding the use of the documents at different individual (e.g. cognitive), task, cultural, organizational, and environmental levels. This perspective appreciates the interdependent nature of these interrelated socio-technical elements and should provide insight into the effectiveness of current procedure systems, thereby informing future work in creating and empirically testing mitigation methods to address potential barriers. This research documents one part of a three part, large-scale project that investigates the issues with procedure forms, usage, adoption, and challenges in a wide range of high-risk industries. As such, the method was framed around first understanding the extent to which these challenges could be generalized between various locations. A grounded theory approach in qualitative data analysis, influenced by the Strauss & Corbin and Charmaz approaches (Bryman 2015) and facilitated by the analysis software MAXQDA-12 was used to examine 72 semi-structured interviews with operators of varying roles and experiences across 6 countries and an offshore drilling vessel. Findings reaffirm previous research, suggesting that the effectiveness of written procedures is limited by an abundance of outdated procedures plagued by information overload. New findings suggest that frequency of the task and the experience level of the worker would impact workers’ procedure use, with participants commenting that the perceived importance of these documents decreases significantly after initial training periods. Other unintended consequences associated with written procedural systems range from complications in using the documents around personal protective equipment (PPE) requirements and harsh weather, reactive organizational behavior surrounding changing procedures, and a general disconnect between the users and the writers of these documents. This is only exacerbated as management imposes pressure to use procedures on personnel despite the issues encountered with the documents, inhibiting valuable feedback within their organizations as personnel withhold information for fear of job security and potential punishment (in the form of 20-day suspension programs or termination). Moving forward, research is in-progress to identify the interdependencies between environmental, cultural, organizational, task, and personal factors unique to each location. This will provide insight regarding the extent to which procedures may not be generalized, after which a holistic view of procedure use in the industry will be offered. The resulting insight will point to recommendations for the future redesign of procedures’ role in promoting safe operations within petrochemical systems. Finally, the third part of this research project will demonstrate the efficacy of using visualizations as tools and methods in qualitative research for modeling complexity in socio- technical systems.

Task complexity plays an important role in performance and procedure adherence. While studies have attempted to assess the contribution of different aspects of task complexity and their relationship to workers’ performance and procedure adherence, only a few have focused on application-specific measurement of task complexity. Further, generalizable methods of operationalizing task complexity that are used to both write and evaluate a wide range of routine or non-routine procedures is largely absent. This paper introduces a novel framework to quantify the step-level complexity of written procedures based on attributes such as decision complexity, need for judgment, interdependency of instructions, multiplicity of instructions, and excess information. This framework was incorporated with natural language processing and artificial intelligence to create a tool for procedure writers for identifying complex elements in procedures steps. The proposed technique has been illustrated through examples as well as an application to a tool for procedure writers. This method can be used both to support writers when constructing procedures as well as to examine the complexity of existing procedures. Further, the complexity index is applicable across several high-risk industries in which written procedures are prevalent, improving the linguistic complexity of the procedures and thus reducing the likelihood of human errors with procedures associated with complexity.

There has been an increase in the usage of electronic health records by healthcare providers as a result of recent federal incentive programs. One benefit of electronic systems is providing patients remote access to their records and an online interface with their healthcare provider via a patient portal. Patients, however, have been slow to adopt these systems and many studies have been conducted in attempts to investigate such slow adoption. It is not clear if the users were involved in the design of such systems and if the functionality meets user expectation. This paper aims to address this issue by documenting a comprehensive and systematically-derived set of functional and information requirements for patient portals based on a review of literature and interviews with patients.

Post-traumatic stress disorder (PTSD) is a common mental health disorder that can affect those who have experienced a traumatic event. Despite the availability of different treatment options for PTSD, there are several barriers that prevent some patients from receiving treatment. To overcome these barriers, mobile health (mHealth) apps have been developed to allow access to therapeutic and self-assessment tools outside the clinic. Our review of literature shows that the three mostly used apps (PTSD Coach, PE Coach, and CPT Coach) are not empirically evaluated and very little information is available for the process used in design and development of these tools. This paper documents a usability study of the most popular PTSD mHealth app; PTSD Coach. Findings indicate that the learning component of the app provides useful information, the assessment is effective in keeping track of the symptoms, and that some of the tools provided can help mitigate some of the symptoms. However, the color scheme, lack of personalization options, and lack of clarity on the mitigation techniques was deemed to affect usability.

Social media usage is widespread among young adults. While the effects of social media usage on depression have been documented, studies to investigate the effects on sleep quality are largely absent. As part of the requirements for a graduate course at Texas A&M University, a user study was conducted to investigate whether usage of social media before bed time would result in sleep disturbance and diminished sleep quality. Ten participants were asked to not use social media before bed (baseline) for one week and use several popular applications for three weeks. While the effects were not statistically significant, social media usage before sleep might still negatively affect sleep quality. Further research with an increased sample size conducted over a longer period is warranted to investigate such effects.

Post-traumatic stress disorder (PTSD) is a prevalent disorder affecting veterans, first responders, children and others who have experienced traumatic events. Heart rate measures like resting heart rates along with heart rate accelerations are important measures to monitor the changes in the state of PTSD and other related disorders. A scoping review was conducted to investigate the efficacy of using heart rate (HR) measurements as reliable indicator for assessment of PTSD and other anxiety-related disorders. Findings show that while Heart Rate Variability (HRV) may provide a more robust measurement of PTSD and other anxiety-related symptoms, heart rate accelerations in response to traumatic reminders are reliable indicators. Changes in resting or basal heart rates show promise in long-term monitoring of PTSD symptoms.

Stress and fatigue are being interchangeably used in the nursing literature resulting in operationalization and measurement issues. A scoping review was conducted to identify different definitions and operationalization of these constructs. Findings can be used to develop tools to measure stress and fatigue as different constructs to aid nursing administrators to gain a new perspective into nursing schedule, workload, morale and well-being of nurses. The findings show that there is a research gap in measurement of acute stress in nursing, physiological measures are currently not used for continuous assessment of stress or fatigue for nurses, and lack of stress and fatigue definitions in terms of physiological measures for assessment purposes. Continuous monitoring and physiological measures such as heart rate can be used to measure and differentiate between the constructs of stress and fatigue

Mathematics has been long known to be a complicated subject, requiring an advanced set of skills. Each skill learned in mathematics requires different level of effort and goes through a different path of processing when working memory is utilized. Being a leading indicator of children’s academic achievement, mathematical cognition is of vital importance and is monitored from a young age, where low performance could be a sign of a learning disability that needs to be addressed. A scoping review of literature was conducted to investigate the fortifying evidence on the importance of working memory in children’s mathematical performance, and present the common measures for which future research in this field would need to account. One of the main findings of this research is the strong contribution of working memory to problem solving, whether it is a single- or multi-digit arithmetic in consideration. Verbal working memory will have greater contribution for orally presented problems in contrast to written ones that would interact with visual-spatial sketchpad. A number of measures were agreed on to be good contributors for working memory performance, however some limitations apply on the modeling of working memory which was found to be alleviating some implications; at the same time, providing rich soil for cultivating new research.

Distracted driving while gaming is a serious hazard especially for young drivers. Increasing popularity of augmented reality games may increase distracted driving incidents. The main purpose of this study is to test the feasibility of an identification test to differentiate between drivers and passengers to prevent drivers from playing augmented reality games while driving to reduce distracted driving incidents. We hypothesized that increased cognitive burden will reduce vigilance on the secondary task which will reduce attention and increase the risk associated with the secondary task. An experiment with a driving simulator tested university students’ performance to evaluate the efficacy of the distinguishing test. The results show that the test shows promise to distinguish between drivers and passengers. Such tests can be used in applications when users perform two tasks that require the same modality leading to decreased performance in both tasks.

The EPIC MyChart Bedside iPad application aims to make patient-provider communication more personalized and efficient and to provide patients with access to medical education information and electronic health records during their hospital stay. The purpose of this on-going study is to learn the impacts of the application on healthcare provider workflow and satisfaction. Data presented here is from the first phase of data collection which was collected before the implementation of the Bedside application. Observational and survey data was collected from 32 registered nurses (RNs) and personal care assistants (PCAs) (mean age = 33; sd = 12) before the implementation of the Bedside application in three inpatient hospital units of a large hospital in Southeastern Texas. Results highlighted three main issues in provider workflow in regards to patient-initiated communication: 1) providers have insufficient information about the requested task at the time of request, 2) there is no running task list for the provider to refer to and 3) the incorrect care provider is often notified to perform a task. It is hypothesized that the implementation of the Bedside application will mitigate these issues by directly notifying the correct care provider and by populating specific tasks to a task list that the providers can refer to from their workstations.

In this paper, we estimate the effect of fatigue on physiological tremors in adults suffering from diabetes. We used a simple, wearable accelerometer to collect the acceleration data from 5 diabetic subjects with varying physical activity levels. Fatigue was induced via an intermittent submaximal isometric handgrip protocol, normalized for individual grip strength, until voluntary exhaustion. The overall results presented here show that the physiologic tremors in the range of 10-14 Hz are most noticeable under fatigue.

BACKGROUND Advances in technology engender the investigation of technological solutions to opioid use disorder (OUD). However, in comparison to chronic disease management, the application of mobile health (mHealth) to OUD has been limited. OBJECTIVE The overarching aim of our research was to design OUD management technologies that utilize wearable sensors to provide continuous monitoring capabilities. The objectives of this study were to (1) document the currently available opioid-related mHealth apps, (2) review past and existing technology solutions that address OUD, and (3) discuss opportunities for technological withdrawal management solutions. METHODS We used a two-phase parallel search approach: (1) an app search to determine the availability of opioid-related mHealth apps and (2) a scoping review of relevant literature to identify relevant technologies and mHealth apps used to address OUD. RESULTS The app search revealed a steady rise in app development, with most apps being clinician-facing. Most of the apps were designed to aid in opioid dose conversion. Despite the availability of these apps, the scoping review found no study that investigated the efficacy of mHealth apps to address OUD. CONCLUSIONS Our findings highlight a general gap in technological solutions of OUD management and the potential for mHealth apps and wearable sensors to address OUD.

Background A common challenge with all opioid use disorder treatment paths is withdrawal management. When withdrawal symptoms are not effectively monitored and managed, they lead to relapse which often leads to deadly overdose. A prerequisite for effective opioid withdrawal management is early identification and assessment of withdrawal symptoms. Objective The objective of this research was to describe the type and content of opioid withdrawal monitoring methods, including surveys, scales and technology, to identify gaps in research and practice that could inform the design and development of novel withdrawal management technologies. Methods A scoping review of literature was conducted. PubMed, EMBASE and Google Scholar were searched using a combination of search terms. Results Withdrawal scales are the main method of assessing and quantifying opioid withdrawal intensity. The search yielded 18 different opioid withdrawal scales used within the last 80 years. While traditional opioid withdrawal scales for patient monitoring are commonly used, most scales rely heavily on patients’ self-report and frequent observations, and generally suffer from lack of consensus on the criteria used for evaluation, mode of administration, type of reporting (e.g., scales used), frequency of administration, and assessment window. Conclusions It is timely to investigate how opioid withdrawal scales can be complemented or replaced with reliable monitoring technologies. Use of noninvasive wearable sensors to continuously monitor physiologic changes associated with opioid withdrawal represents a potential to extend monitoring outside clinical setting.

BACKGROUND The development, review, and approval process of therapeutic biological products in the United States presents two primary challenges: time and cost. Advancing a biotherapeutic from concept to market may take an average of 12 years, with costs exceeding US $1 billion, and the product may still fail the US Food and Drug Administration (FDA) approval process. Despite the FDA’s practices to expedite the approval of new therapies, seeking FDA approval remains a long, costly, and risky process. OBJECTIVE The objective of this paper is to explore the factors and gaps related to the FDA review and approval process that contribute to process inefficiencies and complexities as well as proposed methods and solutions to address such gaps. This paper also aims to investigate the available modeling efforts for the FDA approval process of therapeutic biological products. METHODS A narrative review of literature was conducted to understand the scope of published knowledge about challenges, opportunities, and specific methods to address the factors and gaps related to the review and approval of new drugs, including therapeutic biological products. Relevant peer-reviewed journal articles, conference proceedings, book chapters, official reports from public policy professional centers, and official reports and guidelines from the FDA were reviewed. RESULTS Of the 23 articles identified in this narrative literature review, none modeled the current FDA review and approval process structure to address issues related to the robustness, reliability, and efficiency of its operations from an external point of view. Although several studies summarize the FDA approval process with clarity, in addition to bringing to light the problems and challenges faced by the regulatory agency, only a few attempts have been made to provide solutions for the problems and challenges identified. In addition, although several reform models have been discussed, these models lack the application of scientific methodologies and modeling techniques in understanding FDA as a complex sociotechnical system. Furthermore, tools and methods to assess the efficacy of the models before implementation are largely absent. CONCLUSIONS The findings suggest the efficacy of model-based systems engineering approaches for identifying opportunities for significant improvements to the FDA review and approval process. Using this holistic approach will serve several investigative purposes: identify influential sources of variability that cause major delays, including individual, team, and organizational decision making; identify the human-system bottlenecks; identify areas of opportunity for design-driven improvements; study the effect of induced changes in the system; and assess the robustness of the structure of the FDA approval process in terms of enforcement and information symmetry. CLINICALTRIAL

There is a growing need for resilience in dealing with unexpected events during disasters. The purpose of this review was to summarize and synthesize the literature that examined resilience in the context of emergency management (EM). Four groups of findings were synthesized: definitions, key dimensions, technical tools, and research settings employed in the research. First, definitions of resilience, improvisation, and adaptation were summarized and critically evaluated. Second, four key dimensions of EM resilience were identified: collective sensemaking, team decision making, harmonizing work-as-imagined and work-as-done, and interaction and coordination. Third, this review identified five prevalent technical tools used to enhance resilience in EM: mapmaking, event history logging, mobile communication applications, integrated information management system, and decision support tools. Fourth, two major design features of emergency simulations, incident scenarios and participant roles, are evaluated. For each finding, directions for future research efforts to improve resilience in EM are proposed.

BACKGROUND Electronic Health Record Systems have become ubiquitous in the delivery of patient care. While the implementation has brought safety and efficiency boosts to the industry, it has also exposed patients and their data to new risks in the form of downtime. Downtimes are any period where the computer systems are unavailable and these periods occur for updates or upgrades, but can also be triggered by deliberate cyber-attack. During an unexpected downtime, healthcare workers are forced to fall back to rarely practiced paper-based methods for healthcare delivery, while at the same time, patient data is potentially exposed to parties seeking to profit from its sale. OBJECTIVE We sought to provide a foundational perspective of the current state of downtime readiness in light of the growing cyber-attack threat on healthcare data and hospital networks. METHODS A search of technical news media related to healthcare informatics and a scoping review of research literature were conducted. Following the ENTEREQ framework, 1,651 records were retrieved, of which 16 were included in the final review. RESULTS 164 US-based hospitals experienced a total of 670 days of downtime in 41 events between 2012 and 2018. Almost half (48.8%) of the published downtime events involved some form of cyber-attack. 1,651 studies matching downtime search strings were found, 16 of which were found to meet inclusion criteria. Few research studies have a downtime emphasis; those that do are predominantly focused on a top-down approach. They were found to have a range of focus from the theoretical exploration of downtime to direct empirical comparison of downtime versus normal operation. CONCLUSIONS Downtime contingency planning is still predominantly considered in abstract or top-down organizational focus. It is proposed that a bottom-up approach to comprehending and addressing downtime will be beneficial due to the complicated nature of patient care and computer downtime events. A bottom-up approach would involve the front-line clinical staff responsible for executing the downtime procedure and directly caring for the patients. EHR downtime events will continue to be a complication to hospital and healthcare operations. Significant new research support for the development of contingency plans will be needed as the cyber-attack threat continues to grow.

Background The COVID-19 pandemic has exacerbated the challenges of meaningful health care digitization. The need for rapid yet validated decision-making requires robust data infrastructure. Organizations with a focus on learning health care (LHC) systems tend to adapt better to rapidly evolving data needs. Few studies have demonstrated a successful implementation of data digitization principles in an LHC context across health care systems during the COVID-19 pandemic. Objective We share our experience and provide a framework for assembling and organizing multidisciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of health care digitization, in the context of LHC systems across a complex health care organization. Methods Houston Methodist (HM) comprises eight tertiary care hospitals and an expansive primary care network across Greater Houston, Texas. During the early phase of the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and established the retrospective research task force (RRTF). We describe an account of the structure, functioning, and productivity of the RRTF. We further elucidate the technical and structural details of a comprehensive data repository—the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard health care digitization principles in the LHC context. Results The HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. The RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research; to date, it has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR, which in itself was IRB-approved in April 2020. CURATOR is a relational structured query language database that is directly populated with data from electronic health records, via largely automated extract, transform, and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 cases and controls before and after COVID-19 testing. CURATOR has been set up following the SSoT principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. It currently hosts deeply phenotyped sociodemographic, clinical, and outcomes data of approximately 200,000 individuals tested for COVID-19. It supports more than 30 IRB-approved protocols across several clinical domains and has generated numerous publications from its core and associated data sources. Conclusions A data-driven decision-making strategy is paramount to the success of health care organizations. Investment in cross-disciplinary expertise, health care technology, and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future health care catastrophes by providing timely and validated decision support.

Background Health coaching is an intervention process for driving behavior change through goal-setting, education, encouragement, and feedback on health-related behaviors. Telehealth systems that include health coaching and remote monitoring are making inroads in managing chronic conditions and may be especially suited for older populations. Objective This literature review aimed to investigate the current status of health coaching interventions incorporating telehealth technology and the associated effectiveness of this intervention to deliver health care with an emphasis on older adults (aged 65 and older). Methods A literature review was conducted to identify the research conducted on health coaching combined with remote monitoring for delivering health care to older adults. The Ovid MEDLINE and CINAHL databases were queried using a combination of relevant search terms (including middle aged, aged, older adult, elderly, health coaching, and wellness coaching). The search retrieved 196 papers published from January 2010 to September 2019 in English. Following a systematic review process, the titles and abstracts of the papers retrieved were screened for applicability to health coaching for older adults to define a subset for further review. Papers were excluded if the studied population did not include older adults. The full text of the 42 papers in this subset was then reviewed, and 13 papers related to health coaching combined with remote monitoring for older adults were included in this review. Results Of the 13 studies reviewed, 10 found coaching supported by telehealth technology to provide effective outcomes. Effectiveness outcomes assessed in the studies included hospital admissions/re-admissions, mortality, hemoglobin A1c (HbA1c) level, body weight, blood pressure, physical activity level, fatigue, quality of life, and user acceptance of the coaching program and technology. Conclusions Telehealth systems that include health coaching have been implemented in older populations as a viable intervention method for managing chronic conditions with mixed results. Health coaching combined with telehealth may be an effective solution for providing health care to older adults. However, health coaching is predominantly performed by human coaches with limited use of technology to augment or replace the human coach. The opportunity exists to expand health coaching to include automated coaching.

Background Drowsy driving following the night shift is persistent among nurses resulting in elevated rates of vehicle crashes and crash-related injuries and deaths. While considerable effort has been dedicated to the development of countermeasures, implementation of these countermeasures in nursing has lagged behind other shift work oriented industries. Developing effective countermeasures for drowsy driving in nurses requires a thorough characterization of nurse's perceptions of drowsy driving and potential mitigations. Objective The objective of this research was to elicit night shift nurses’ perceptions of drowsy driving, countermeasures, and educational and technological interventions. Design Perceptions were elicited through a semi-structured interview protocol. The protocol design was driven by previously identified research gaps. Questions focused on four topics: perceptions of drowsy driving, current practices and methods to mitigate drowsiness during the shift and commute, preferences and expectations for training on drowsiness management, and, preferences and expectations for technological mitigations. Setting The data collection took place at a large urban hospital in Texas, USA. Participants Thirty night-shift nurses were recruited with voluntary sampling. No nurses declined to participate after initially consenting. The participants were male and female nurses who currently worked a 12 hour night shift. The nurses had between 1 and more than 20 years of experience and worked in a variety of units. Method The interview recordings were transcribed by the research team and entered into a qualitative data analysis software. Transcripts were analyzed by two independent coders with a grounded theory approach to identify common themes and subthemes across participants. Findings Feelings of drowsiness typically manifested immediately following the shift or during the post work commute. Nurses responded to drowsiness by engaging in multiple ineffective countermeasures (e.g., listening to music) and effective countermeasures (e.g., naps) were used sparingly. Experiences and mitigation methods traversed through the nurses’ social network although they did not always alter behavior. Nurses were uncertain but enthusiastic about educational and technological interventions preferring practical training and auditory interactive alerts. Conclusions The findings suggest a strong need for real time drowsiness interventions during or immediately prior to nurses’ post work commutes. Nurses’ enthusiasm for training and technology to prevent drowsy driving suggests high levels of readiness and acceptance for such interventions. Future work should focus on the development and implementation of practical training and technological interventions for drowsy driving in nurses.

Background Evidence suggests that the COVID-19 pandemic has generally increased levels of stress and depression among the public. However, the impact on college students in the United States has not been well-documented. Objective This paper surveys the mental health status and severity of depression and anxiety of college students in a large university system in the United States during the COVID-19 pandemic. Methods An online survey was conducted among undergraduate and graduate students recruited from Texas A&M University via email. The survey consisted of two standardized scales—the Patient Health Questionnaire-9 and the General Anxiety Disorder-7—for depression and anxiety, and additional multiple-choice and open-ended questions regarding stressors and coping mechanisms specific to COVID-19. Results Among the 2031 participants, 48.14% (n=960) showed a moderate-to-severe level of depression, 38.48% (n=775) showed a moderate-to-severe level of anxiety, and 18.04% (n=366) had suicidal thoughts. A majority of participants (n=1443, 71.26%) indicated that their stress/anxiety levels had increased during the pandemic. Less than half of the participants (n=882, 43.25%) indicated that they were able to cope adequately with the stress related to the current situation. Conclusions The proportion of respondents showing depression, anxiety, and/or suicidal thoughts is alarming. Respondents reported academic-, health-, and lifestyle-related concerns caused by the pandemic. Given the unexpected length and severity of the outbreak, these concerns need to be further understood and addressed.

Issues related to procedural systems have been found to contribute to incidents in many high-risk industries such as petrochemical, oil and gas, etc. While previous research has focused on understanding issues with procedural systems from the perspective of the workers (who are the end-users of procedures), most of this research suffers from samples that only include companies with programs focused on improving safety by improving procedures. These companies may have inherent differences in their safety practices and thus the experiences of these workers may not completely represent all workers’ experiences in this domain. The purpose of this study is to gain insights into the thoughts and perceptions from a representative and broad sample of workers concerning procedure use and purpose. To improve the generalizability of previous findings, interviews were conducted with workers from a broad range of high-risk process industries to investigate issues related to procedure adherence that may be present in companies not currently implementing. Findings from a qualitative data analysis provide support for the generalizability of issues previously discovered, such as: more experience workers being more likely to deviate; procedure quality being inconsistent; and the procedure revision process being problematic. However additional prominent issues were found as well. Most importantly, this study found that adherence to procedures is often motivated by potential liability issues instead of genuine concerns for safety in organizations and many deviations from procedures were due to pressure from immediate supervisors. These findings suggest a relationship between the effectiveness/quality of procedural systems and the safety climate of the organization or work unit.

Background Hypoglycemia, or low blood sugar levels, in people with diabetes can be a serious life-threatening condition, and serious outcomes can be avoided if low levels of blood sugar are proactively detected. Although technologies exist to detect the onset of hypoglycemia, they are invasive or costly or exhibit a high incidence of false alarms. Tremors are commonly reported symptoms of hypoglycemia and may be used to detect hypoglycemic events, yet their onset is not well researched or understood. Objective This study aimed to understand diabetic patients’ perceptions of hypoglycemic tremors, as well as their user experiences with technology to manage diabetes, and expectations from a self-management tool to ultimately inform the design of a noninvasive and cost-effective technology that detects tremors associated with hypoglycemia. Methods A cross-sectional internet panel survey was administered to adult patients with type 1 diabetes using the Qualtrics platform in May 2019. The questions focused on 3 main constructs: (1) perceived experiences of hypoglycemia, (2) experiences and expectations about a diabetes management device and mobile app, and (3) beliefs and attitudes regarding intention to use a diabetes management device. The analysis in this paper focuses on the first two constructs. Nonparametric tests were used to analyze the Likert scale data, with a Mann-Whitney U test, Kruskal-Wallis test, and Games-Howell post hoc test as applicable, for subgroup comparisons to highlight differences in perceived frequency, severity, and noticeability of hypoglycemic tremors across age, gender, years living with diabetes, and physical activity. Results Data from 212 respondents (129 [60.8%] females) revealed statistically significant differences in perceived noticeability of tremors by gender, whereby males noticed their tremors more (P Conclusions Our findings support the need for cost-efficient and noninvasive continuous monitoring technologies. Although hypoglycemic tremors were perceived to occur frequently, such tremors were not found to be severe compared with other symptoms such as sweating, which was the highest rated symptom in our study. Using a combination of tremor and galvanic skin response sensors may show promise in detecting the onset of hypoglycemic events.

Background Advances in technology engender the investigation of technological solutions to opioid use disorder (OUD). However, in comparison to chronic disease management, the application of mobile health (mHealth) to OUD has been limited. Objective The overarching aim of our research was to design OUD management technologies that utilize wearable sensors to provide continuous monitoring capabilities. The objectives of this study were to (1) document the currently available opioid-related mHealth apps, (2) review past and existing technology solutions that address OUD, and (3) discuss opportunities for technological withdrawal management solutions. Methods We used a two-phase parallel search approach: (1) an app search to determine the availability of opioid-related mHealth apps and (2) a scoping review of relevant literature to identify relevant technologies and mHealth apps used to address OUD. Results The app search revealed a steady rise in app development, with most apps being clinician-facing. Most of the apps were designed to aid in opioid dose conversion. Despite the availability of these apps, the scoping review found no study that investigated the efficacy of mHealth apps to address OUD. Conclusions Our findings highlight a general gap in technological solutions of OUD management and the potential for mHealth apps and wearable sensors to address OUD.

Multidisciplinary incident management teams (IMTs) are required to operate in resilient ways as emergency situations unfold unexpectedly. Although resilience in emergency management has been widely studied in many emergency contexts, the development of a new method to investigate actual resilient performance of the IMTs under realistic settings has been limited. To address such gap, this paper first introduces Interaction Episode Analysis (IEA), a novel approach to capture and describe emergent team performance. As an exploratory observation study, we apply the IEA to an information management aspect of the IMTs in two emergency exercises carried out in a high-fidelity environment. As a result, the IEA provides comparable sets of episodes as instances of work-as-done, rendering opportunities to further analyze essential elements of interactions between team members as well as information management activities. Moreover, the IEA enables comparisons between the observations and identification of challenges faced by the team in managing incident information and adaptive behaviors used to address the challenges. By gathering more evidences as well as addressing limitations identified in this study, the IEA is expected to serve as a method that facilitates the analysis of work-as-done of complex team work and the reconciliation between work-as-done and work-as-imagined to promote resilience in emergency management.

Severe hypoglycemia leverages complication in diabetes patients: e.g., it increases death rate by a six-fold. Therefore, early detection and prediction of hypoglycemic events are of utmost importance. This publication presents a prototype of a wearable hand-tremor system that detects the onset of hypoglycemic events. The results show the prototype is capable of simulating anticipated frequency and amplitude of the tremor relevant for hypoglycemic events. The initial functional performance-tests demonstrate a maximum error of 4.75% in the detecting the tremor frequency.

Introduction: Hypoglycemia is a prevalent condition where diabetic patients experience low blood glucose. There are approximately 300 million diabetic patients in the world; at least 30 million of those are in the United States. Diabetic patients facing hypoglycemia continue to grow but the condition worsens as patients lose awareness the more episodes that occur. Mild hypoglycemia is characterized as blood glucose below 70mg/dl and severe as blood glucose below 40mg/dl (Kalra et al., 2013). Severe hypoglycemia may cause loss of patient's awareness and in some cases fatality. Loss of awareness leads to a 6-fold increase in the risk of death. While in most cases hypoglycemia is treated through medication and diet (e.g. fast-acting carbohydrates), a popular technology among diabetic patients; especially those with hypoglycemia unawareness; is continuous glucose monitors (CGM). While CGMs have shown promise, these devices are expensive, invasive, and not always accurate throughout the day (Bay et al., 2013). Hypoglycemic events are associated with several signs and symptoms such as fatigue, sweating, and pale skin, but tremors seem to be a prevalent symptom as well. One study has reported that around 20% of hypoglycemic patients stated that trembling is the first symptom they notice indicating low blood sugar (Muhlhauser, 1991), and another showed that 77.5% of diabetic patients who are aware of their symptoms experience tremors (Berlin, 2005). Our research aims at investigating the efficacy of using hand (or leg) tremor to predict the early onset of hypoglycemic events. Work is in progress to design and develop a non-invasive sensor-based wearable system that detects hypoglycemic tremor with high sensitivity and specificity. Method: A systematic review of literature is in progress to search variety of medical and engineering databases to identify research related to hypoglycemia detection, wearable sensors, and tremor. The studies considered are those pertaining to diabetic patients who have been assessed for the symptoms of hypoglycemia and the sensors used in order to predict a hypoglycemic episode. One of the main objectives of the study is to understand hypoglycemia and its symptoms, and to document technological interventions, related detection methods and sensors, as well as their shortcomings and opportunities. We hypothesize that tremor has not been used for hypoglycemia detection. The research is motivated by the preliminary review evidence that suggests current sensor-based technologies to address diabetes and hypoglycemia suffer from low patient engagement and satisfaction due to intrusiveness, low accuracy, and price. Results: While the review is in progress, we expect to have completed the search by January and plan to present the comprehensive findings at the conference. Our preliminary findings suggest a large research gap in non-intrusive technologies and methods to detect hypoglycemic events. While a frequency band of 10-14 Hz in the wrist for hypoglycemic tremors has been suggested in the literature (Rana & Chou, 2015), there is very little research done on utilizing this phenomenon to predict hypoglycemia. Most research relies on other symptoms such as skin conductance and body temperature which have been shown to cause inaccurate predictions (Howsmon & Bequette, 2015). In understanding the tremors, it is important to consider at the time the tremors start to occur in relation with particular activities, food consumption, and blood sugar; as well as the location of these tremors and their prevalence. The current review found studies that assess and promote user-centered design, and the usability of wearable sensors. This includes usability studies, human factors and FDA requirements, and other recommendations to be incorporated into developing a wearable sensor. Wide range of factors related to patient comfort and usability were identified, they include optimizing between battery life and data processing, size, location on the body. These findings are in line with other studies such as those done on wearable sensors for detection of Parkinson's disease (Rigas et al., 2012). Despite these preliminary findings, comprehensive guidelines to design for wearability had not been offered. Based on these and upcoming findings a taxonomy for design criteria for wearability will be presented. Conclusion: Diabetes is a growing epidemic and the occurrence of hypoglycemia for diabetic patients is prevalent and potentially fatal. Detecting the onset of hypoglycemia is vital and tremors seem to be a viable symptom. Our preliminary findings from a systematic literature review suggest a general gap in technological interventions for early detection and recognition of hypoglycemic events. In particular, our current findings show that tremor detection technologies have not been utilized in this domain. Sensors assessing tremor frequency could be an alternative approach to current sensors in the market and may contribute to non-intrusive, high accuracy and low cost solutions. This review will inform a taxonomy of design considerations for non-intrusive wearable technologies. Our future work aims at addressing this gap by utilizing a user-centered design of a wearable device to detect the early onsets of hypoglycemic events by measuring tremor.