No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Digital Health Solutions Transforming Long-Term Care and Rehabilitation
Abstract
Emerging digital healthcare solutions (DHS) have opened wide range of opportunities for tele-monitoring and improvements in health behavior. These solutions not only help monitor health status, but also aid towards diagnosis, prevention and better management of health conditions. DHS have a broad scope in long-term care, disease management as well as addressing psychological and social needs of patients. In this chapter we discuss tele-monitoring solutions for long-term care and solutions for rehabilitation. Long-term care includes a wide range of care services for patients of varied age groups with chronic conditions or functional disabilities. Their requirements can vary from minimal help for conducting daily activities to complete care. Tele-monitoring assistance can aid self-monitoring for such patients while also being digitally connected with their health care providers. The scope of these solutions for long-term care includes addressing issues such as fatigue and anxiety, quality of life, nutrition, sleep, physical activity, etc. The advancements in rehabilitation technologies are increasingly enhancing the role of rehabilitation in building and maintaining the self-dependence and quality of life of patients. The field of rehabilitation often requires complex technologies, such as virtual reality, robotics and haptic devices. The healthcare application of these technologies revolves around providing solutions for efficient home rehabilitation, multimodal approaches for recovery, to support activities of daily living and to enhance clinical assessment. Thus, the use of emerging technologies can aid family members of apparently healthy older adults and also detect mild symptoms while relying on a user-friendly solution.
Real-time monitoring is the cornerstone of proactive healthcare. By continuously collecting data from wearable sensors and connected devices, healthcare practitioners gain unprecedented insights into patients' physiological parameters, allowing them to track vital signs, detect anomalies, and identify trends in real time. This level of vigilance empowers clinicians to promptly intervene and make informed decisions, preventing potential complications and ensuring that patients receive timely interventions that can significantly impact outcomes. In the realm of disease management, real-time monitoring assumes a transformative role. For patients with chronic conditions, such as diabetes or cardiovascular diseases, the ability to monitor glucose levels, heart rates, and other critical metrics in real time allows for personalized treatment adjustments. This chapter focuses on enhancing the patient's quality of life but also enables healthcare providers to tailor treatment plans dynamically, leading to better disease control and improved patient compliance.
The healthcare landscape is undergoing a profound transformation with the integration of cutting-edge technologies such as Augmented Reality (AR), Virtual Reality (VR), Smart Wearables and Intelligent Systems. Autonomous Robotics is reshaping healthcare delivery by introducing automation into various aspects of patient care. Robotic systems are being used to assist or even perform complex procedures with precision and minimal invasiveness. This continuous data stream enhances early detection of health issues and supports proactive interventions. The incorporation of Augmented and Virtual Reality, Smart Wearables and Intelligent Systems which is coupled with the rise of Autonomous Robotics, heralds a new era in healthcare. This futuristic healthcare ecosystem is characterized by precision, personalization and autonomy, where technology seamlessly integrates with patient care, diagnostics and treatment. This chapter explores the junction of these technologies which emphasizing the role of autonomous robotics in shaping the future of healthcare.
The incorporation of ubiquitous Sensors with healthcare system creating an environmentally aware system for patient management, focusing on patient-centric applications, emphasizes the transformative impact of remote care. Elder care and fall detection are feasible to detect falls in elderly people by using wireless sensors. These sensors can notify emergency services or caretakers in the event of a fall since they are made to recognize sudden movements or changes in posture. Monitoring patients' adherence to prescribed drug regimens can be tracked by integrating wireless sensors into pill dispensers or medication management packages. Remote Diagnostics With the use of wireless sensors, medical experts may evaluate a patient's status without having to see them in person. Thus, this chapter focuses on the wireless sensor technology being incorporated into prosthetics and smart implants to improve functionality and offer real-time information on patient health and device usage.
Machine learning (ML) in health and fitness has a huge impact on religious, social as well as legal fields. From the religious viewpoint, use of ML in health can induce ethical arguments and dialogues especially around dilemma between ‘hand of Man' vs. ‘creativity' (science/AI). Religious groups could voice apprehensions about the possible dehumanizing of care or that ML could reduce traditional healing practice. Socially, ML in well-being can transform access to healthcare eliminating the issues concerning geographical and economic disparities. ML and their connections with religious, social and legal domain on the health maintenance as both physical & mental wellbeing becomes part of humanist outlook; but also how junctions are invited where it need to be careful that transformational values of what an auto-tuner can offer across all sect within the society in an ethical way.
AI is a broad category of technologies that includes machine learning, natural language processing and predictive analytics, all of which are being applied into healthcare systems. These tools are improving clinical workflows, helping to make more accurate diagnoses and providing data-driven insights that can inform better decisions made by healthcare professionals. The artificial intelligence programs being employed as diagnostic tools are helping to catch diseases at early stages and improving treatment options & results. AI, being the major disruptive force in healthcare for delivering faster results and better experiences to patients as well as a radical change from traditional mechanisms. This chapter goes deep into how Artificial Intelligence is shaping the healthcare outcomes such as improving patient experiences and many more. It also explores the wide variety of applications for AI in healthcare, which continues to grow medio uses cases as a part of its broader shift towards enterprise IoT.
The advent of augmented reality (AR) and virtual reality (VR) technology has completely changed health expansions and medical training. The in-depth analysis and compilation of these technologies noteworthy contributions shows how they have the ability to significantly increase student performance and engagement as well as educational outcomes. The use of haptic feedback technology is an important advancement in medical education. With providing a very lifelike simulation of clinical operations, haptic interfaces, which enable tactile engagement in virtual environments, are redefining the acquisition of surgical skills. AR and VR technologies are changing education by fusing interactive experiences with real-world applications in ways that conventional lecture-based techniques are unable to do that. This chapter focuses on how immersive VR and AR technologies, in medical education and training contexts, enhance certain competencies in healthcare professionals when compared to no intervention or traditional teaching techniques.
Artificial Intelligence encompasses a range of technologies which includes machine learning, natural language processing and predictive analytics and all of which are being integrated into healthcare systems. These applications are optimizing clinical workflows, aiding in more accurate diagnoses and providing data-driven insights that enhance decision-making for healthcare professionals. The diagnostic tools powered by AI are proving instrumental in early detection of diseases ultimately leading to improved treatment plans and outcomes. With accelerating outcomes, improving patient experiences and transforming traditional approaches, AI is reshaping the healthcare industry in profound ways. This chapter provides in-depth exploration into the transformative impact of Artificial Intelligence on healthcare outcomes, specifically focusing on enhancing the patient experience in the digital era. It also explores the multifaceted ways in which AI is accelerating healthcare outcomes and reshaping patient experiences which contributing to the overall transformation of the healthcare industry.
Population aging is accelerating globally, with the population of people over 60 expected to double by 2050, reaching 2.1 billions. This phenomenon, together with increased longevity due to advances in salud, education and reduced fertility rates, presents unique challenges and opportunities for society. Against this backdrop, the design of digital interventions that promote active and healthy aging becomes a priority. This work proposes the initial development of a web application aimed at supporting memory in older adults, applying a holistic approach that integrates knowledge from various disciplines. The application is based on principles of accessibility, usability and user-centered design, seeking not only to improve cognition, but also to offer a tool that facilitates social inclusion and improves the quality of life of older adults. By focusing on accessibility and inclusive design, this project contributes directly to technological intervention strategies in the field of aging, marking a step forward in the development of solutions that respond effectively to the needs of a growing population.
Augmented reality (AR) and virtual reality (VR) modules are emerging as revolutionary tools for improving mindfulness, emotional intelligence, and mental health. These immersive technologies provide a one-of-a-kind and engaging platform for simulating real-life settings and guiding users through a variety of experiences aimed at regulating emotions and improving mental health. These modules can teach and reinforce mindfulness practices by immersing users in virtual worlds, allowing them to get a better awareness of their emotions, manage stress, and build emotional resilience. AR and VR modules are proving to be strong tools for personal growth and well-being, whether through guided meditation, stress reduction exercises, or interactive situations aimed at increasing empathy and self-awareness. This chapter comprehensively explores the transformational potential of AR and VR modules in building mindfulness, enhancing emotional intelligence, and contributing to overall mental well-being.
Background Physical activity (PA) is important for both the prevention and management of long-term conditions (1). Previous research has identified a beneficial impact of PA on pain, function and overall health (2, 3, 4). However, levels of PA are often lower in people with a long-term condition (LTC) and decline further for those with multiple conditions (5, 6). Digital tools, such as websites, apps or wearables, show some potential for supporting engagement with PA in the short term (7, 8), but lack data on maintaining PA behaviours longer term. A search of existing reviews in this area identified only a small number of systematic reviews that included digital interventions with a focus on maintaining PA for people with a LTC (9, 10, 11). Of these, only Grimmett et al (2019) reported effective maintenance at >3 months post-intervention (9). Consequently, a scoping review methodology has been chosen to explore digital PA maintenance for people with a LTC more widely. The aims of this review are to: Identify the range and variety of digital tools and their associated theoretical foundations for supporting people with a LTC/s to maintain physical activity. Uncover the components considered to be necessary for engagement with digital tools to support maintenance of physical activity Review objectives: What is the “extent (size), range (variety) and nature (characteristics) of the evidence” (12) on digital tools to support the maintenance of physical activity for people with a LTC/s? What theoretical underpinnings are used in digital tools to promote the maintenance of physical activity? What are the experiences of people using digital tools to maintain physical activity? What are the barriers and facilitators to maintaining physical activity for people with a LTC/s using digital tools? Design This scoping review will be undertaken in accordance with PRISMA-ScR guidelines (12) and the frameworks developed by Arksey and O’Malley (13) and Levac et al., (14). Preliminary searches will be undertaken in consultation with an academic librarian to create a comprehensive search strategy. Screening of titles and abstracts will be undertaken by two independent reviewers, with conflicts resolved by an independent verifier, using Covidence software (15). Full-text screening will subsequently be undertaken following this same approach. A charting form will be developed based on the objectives of the review and refined by the research team. Data will be collated and summarised, with quantitative sources described descriptively and qualitative data analysed thematically (16). Results will be presented using summary tables and/or using pictorial/flow charts, if appropriate.
Background The dramatic increase in complexity and volume of health data has challenged traditional health systems to deliver useful information to their users. The novel coronavirus disease 2019 (COVID-19) pandemic has further exacerbated this problem and demonstrated the critical need for the 21st century approach. This approach needs to ingest relevant, diverse data sources, analyze them, and generate appropriate health intelligence products that enable users to take more effective and efficient actions for their specific challenges.
Objectives This article characterizes the Health Intelligence Atlas (HI-Atlas) development and implementation to produce Public Health Intelligence (PHI) that supports identifying and prioritizing high-risk communities by public health authorities. The HI-Atlas moves from post hoc observations to a proactive model-based approach for preplanning COVID-19 vaccine preparedness, distribution, and assessing the effectiveness of those plans.
Results Details are presented on how the HI-Atlas merged traditional surveillance data with social intelligence multidimensional data streams to produce the next level of health intelligence. Two-model use cases in a large county demonstrate how the HI-Atlas produced relevant PHI to inform public health decision makers to (1) support identification and prioritization of vulnerable communities at risk for COVID-19 spread and vaccine hesitancy, and (2) support the implementation of a generic model for planning equitable COVID-19 vaccine preparedness and distribution.
Conclusion The scalable models of data sources, analyses, and smart hybrid data layer visualizations implemented in the HI-Atlas are the Health Intelligence tools designed to support real-time proactive planning and monitoring for COVID-19 vaccine preparedness and distribution in counties and states.
Aims and objectives
To examine demographic and work characteristics of interdisciplinary healthcare professionals associated with higher burnout and to examine whether the four domains of moral resilience contribute to burnout over and above work and demographic variables.
Background
Healthcare professionals experience complex ethical challenges on a daily basis leading to burnout and moral distress. Measurement of moral resilience is a new and vital step in creating tailored interventions that will foster moral resilience at the bedside.
Design
Cross-sectional descriptive design.
Methods
Healthcare professionals in the eastern USA were recruited weekly via email for 3 weeks in this cross-sectional study. Online questionnaires were used to conduct the study. The STROBE checklist was used to report the results.
Results
Work and demographic factors, such as religious preference, years worked in a healthcare profession, practice location, race, patient age, profession and education level, have unique relationships with burnout subscales and turnover intention, with the four subscales of moral resilience demonstrating a protective relationship with outcomes above and beyond the variance explained by work and demographic characteristics.
Conclusions
Higher moral resilience is related to lower burnout and turnover intentions, with multiple work demographic correlates allowing for potential areas of intervention to deal with an increase in morally distressing situations occurring at the bedside. Additionally, patterns of significant and non-significant relationships between the moral resilience subscales and burnout subscales indicate that these subscales represent unique constructs.
Relevance to clinical practice
Understanding the everyday, pre-pandemic correlations of moral resilience and burnout among interdisciplinary clinicians allows us to see changes that may exist. Measuring and understanding moral resilience in healthcare professionals is vital for creating ways to build healthier, more sustainable clinical work environments and enhanced patient care delivery.
Technology has become an integral part of everyday lives. Recent years have witnessed advancement in technology with a wide range of applications in healthcare. However, the use of the Internet of Things (IoT) and robotics are yet to see substantial growth in terms of its acceptability in healthcare applications. The current study has discussed the role of the aforesaid technology in transforming healthcare services. The study also presented various functionalities of the ideal IoT-aided robotic systems and their importance in healthcare applications. Furthermore, the study focused on the application of the IoT and robotics in providing healthcare services such as rehabilitation, assistive surgery, elderly care, and prosthetics. Recent developments, current status, limitations, and challenges in the aforesaid area have been presented in detail. The study also discusses the role and applications of the aforementioned technology in managing the current pandemic of COVID-19. A comprehensive knowledge has been provided on the prospect of the functionality, application, challenges, and future scope of the IoT-aided robotic system in healthcare services. This will help the future researcher to make an inclusive idea on the use of the said technology in improving the healthcare services in the future.
Background
COVID-19 has put extraordinary stress on healthcare workers. Few studies have evaluated stress by worker role, or focused on experiences of women and people of color.
Methods
The “Coping with COVID” survey assessed US healthcare worker stress. A stress summary score (SSS) incorporated stress, fear of exposure, anxiety/depression and workload (Omega 0.78). Differences from mean were expressed as Cohen's d Effect Sizes (ESs). Regression analyses tested associations with stress and burnout.
Findings
Between May 28 and October 1, 2020, 20,947 healthcare workers responded from 42 organizations (median response rate 20%, Interquartile range 7% to 35%). Sixty one percent reported fear of exposure or transmission, 38% reported anxiety/depression, 43% suffered work overload, and 49% had burnout. Stress scores were highest among nursing assistants, medical assistants, and social workers (small to moderate ESs, p < 0.001), inpatient vs outpatient workers (small ES, p < 0.001), women vs men (small ES, p < 0.001), and in Black and Latinx workers vs Whites (small ESs, p < 0.001). Fear of exposure was prevalent among nursing assistants and Black and Latinx workers, while housekeepers and Black and Latinx workers most often experienced enhanced meaning and purpose. In multilevel models, odds of burnout were 40% lower in those feeling valued by their organizations (odds ratio 0.60, 95% CIs [0.58, 0.63], p< 0.001).
Interpretation
Stress is higher among nursing assistants, medical assistants, social workers, inpatient workers, women and persons of color, is related to workload and mental health, and is lower when feeling valued.
The COVID-19 pandemic has created multiple opportunities to deploy artificial intelligence (AI)-driven tools and applied interventions to understand, mitigate, and manage the pandemic and its consequences. The disproportionate impact of COVID-19 on racial/ethnic minority and socially disadvantaged populations underscores the need to anticipate and address social inequalities and health disparities in AI development and application. Before the pandemic, there was growing optimism about AI's role in addressing inequities and enhancing personalized care. Unfortunately, ethical and social issues that are encountered in scaling, developing, and applying advanced technologies in health care settings have intensified during the rapidly evolving public health crisis. Critical voices concerned with the disruptive potentials and risk for engineered inequities have called for reexamining ethical guidelines in the development and application of AI. This paper proposes a framework to incorporate ethical AI principles into the development process in ways that intentionally promote racial health equity and social justice. Without centering on equity, justice, and ethical AI, these tools may exacerbate structural inequities that can lead to disparate health outcomes.
The COVID-19 Vaccines Global Access Facility (COVAX) represents an unprecedented global collaboration facilitating the development and distribution of vaccines for COVID-19. COVAX pools and channels funds from state and non-state actors to promising vaccine candidates, and has started to distribute successful candidates to participating states. The WHO, one of the leaders of COVAX, recognised vaccine doses would initially be scarce, and therefore, prepared a two-staged allocation mechanism they considered fair. In the first stage, vaccine doses are distributed equally among participating countries, while in the second stage vaccine doses will be allocated according to a country’s need. Ethicists have questioned whether this is the fairest distribution—they argue a country’s need should be taken into account from the start and correspondingly, have proposed a framework that treats individuals with equal moral concern, aims to minimise harm and gives priority to the worst-off. In this paper, we seek to explore these concerns by comparing COVAX’s allocation mechanism to a targeted allocation based on need. We consider which distribution would more likely maximise well-being and align with principles of equity. We conclude that although in theory, a targeted distribution in proportion to a country’s need would be more morally justifiable, when political realities are taken into account, an equal distribution seems more likely to avert a greater number of deaths and reduce disparities.
Background:
Coronavirus disease (COVID19) has challenged the resilience of the healthcare information system (HIS), which has affected the ability to achieve the global sustainable goal of health and wellbeing. This research is motivated by the recent cyber-attacks that have happened to the hospitals, pharmaceutical companies, the US Department of Health and Human Services, the World Health Organization (WHO) and its partners, etc.
Objective:
The aim of this review was to identify the key cyber security challenges, cyber security solutions adopted by the health sector and the areas to be improved in order to counteract the heightened cyber-attacks such as phishing campaigns and ransomware attacks which have been adapted to exploit vulnerabilities in technology and people introduced through changes to working practices dealing with the current COVID19 pandemic.
Methods:
A scoping review was conducted through the searches of two major scientific databases (PubMed and Scopus) using the terms "(covid or healthcare) and cybersecurity". Reports, news articles, industrial white papers were also included only when they are related directly to previously published work, or they were the only available sources at the moment of manuscript preparation. Only articles in English in the last decade were included, i.e. 2011-2020, in order to focus on the current issues, challenges and solutions.
Results:
This scoping review identified 9 main challenges in cyber security, 11 key solutions that the healthcare organisations adopted to address these challenges, and 4 key areas that require to be strengthened in terms of the cyber security capacity in health sector. We also found that the most prominent and significant methods of cyber-attacks happened during COVID19 are related to phishing, ransomware, distributed denial of service attack and malware.
Conclusions:
This scoping review identified the most prominent and significant methods of cyber-attacks that impacted the health sector initially during the COVID 19 pandemic, the cyber security challenges, solutions as well as the areas that require further efforts in the community. This provides useful insights to the health sector to address their cybersecurity issues during the COVID 19 pandemic as well as other epidemics or pandemics that may materialise in the future.
Background:
Large health organizations often struggle to build complex health information technology (HIT) solutions and are faced with ever-growing pressure to continuously innovate their information systems. Limited research has been conducted that explores the relationship between organizations’ innovative capabilities and HIT quality in the sense of achieving high-quality support for patient care processes.
Objective:
The aim of this study is to explain how core constructs of organizational innovation capabilities are linked to HIT quality based on a conceptual sociotechnical model on innovation and quality of HIT, called the IQHIT model, to help determine how better information provision in health organizations can be achieved.
Methods:
We designed a survey to assess various domains of HIT quality, innovation capabilities of health organizations, and context variables and administered it to hospital chief information officers across Austria, Germany, and Switzerland. Data from 232 hospitals were used to empirically fit the model using partial least squares structural equation modeling to reveal associations and mediating and moderating effects.
Results:
The resulting empirical IQHIT model reveals several associations between the analyzed constructs, which can be summarized in 2 main insights. First, it illustrates the linkage between the constructs measuring HIT quality by showing that the professionalism of information management explains the degree of HIT workflow support (R²=0.56), which in turn explains the perceived HIT quality (R²=0.53). Second, the model shows that HIT quality was positively influenced by innovation capabilities related to the top management team, the information technology department, and the organization at large. The assessment of the model’s statistical quality criteria indicated valid model specifications, including sufficient convergent and discriminant validity for measuring the latent constructs that underlie the measures of HIT quality and innovation capabilities.
Conclusions:
The proposed sociotechnical IQHIT model points to the key role of professional information management for HIT workflow support in patient care and perceived HIT quality from the viewpoint of hospital chief information officers. Furthermore, it highlights that organizational innovation capabilities, particularly with respect to the top management team, facilitate HIT quality and suggests that health organizations establish this link by applying professional information management practices. The model may serve to stimulate further scientific work in the field of HIT adoption and diffusion and to provide practical guidance to managers, policy makers, and educators on how to achieve better patient care using HIT.
Objective:
A systematic review of cost-utility and cost-effectiveness research works of telemedicine, electronic health (e-health), and mobile health (m-health) systems in the literature is presented.
Materials and methods:
Academic databases and systems such as PubMed, Scopus, ISI Web of Science, and IEEE Xplore were searched, using different combinations of terms such as "cost-utility" OR "cost utility" AND "telemedicine," "cost-effectiveness" OR "cost effectiveness" AND "mobile health," etc. In the articles searched, there were no limitations in the publication date.
Results:
The search identified 35 relevant works. Many of the articles were reviews of different studies. Seventy-nine percent concerned the cost-effectiveness of telemedicine systems in different specialties such as teleophthalmology, telecardiology, teledermatology, etc. More articles were found between 2000 and 2013. Cost-utility studies were done only for telemedicine systems.
Conclusions:
There are few cost-utility and cost-effectiveness studies for e-health and m-health systems in the literature. Some cost-effectiveness studies demonstrate that telemedicine can reduce the costs, but not all. Among the main limitations of the economic evaluations of telemedicine systems are the lack of randomized control trials, small sample sizes, and the absence of quality data and appropriate measures.
Purpose
To determine how hospitals across the United States determined allocation criteria for remdesivir, approved in May 2020 for treatment of coronavirus disease 2019 (COVID-19) through an emergency use authorization, while maintaining fair and ethical distribution when patient needs exceeded supply.
Methods
A electronic survey inquiring as to how institutions determined remdesivir allocation was developed. On June 17, 2020, an invitation with a link to the survey was posted on the Vizient Pharmacy Network Community pages and via email to the American College of Clinical Pharmacy’s Infectious Disease Practice and Research Network listserver.
Results
66 institutions representing 28 states responded to the survey. The results showed that 98% of surveyed institutions used a multidisciplinary team to develop remdesivir allocation criteria. A majority of those teams included clinical pharmacists (indicated by 97% of respondents), adult infectious diseases physicians (94%), and/or adult intensivists (69%). Many teams included adult hospitalists (49.2%) and/or ethicists (35.4%). Of the surveyed institutions, 59% indicated that all patients with COVID-19 were evaluated for treatment, and 50% delegated initial patient identification for potential remdesivir use to treating physicians. Prioritization of remdesivir allocation was often determined on a “first come, first served” basis (47% of respondents), according to a patient’s respiratory status (28.8%) and/or clinical course (24.2%), and/or by random lottery (22.7%). Laboratory parameters (10.6%), comorbidities (4.5%), and essential worker status (4.5%) were rarely included in allocation criteria; no respondents reported consideration of socioeconomic disadvantage or use of a validated scoring system.
Conclusion
The COVID-19 pandemic has exposed the inconsistencies of US medical centers’ methods for allocating a limited pharmacotherapy resource that required rapid, fair, ethical and equitable distribution. The medical community, with citizen participation, needs to develop systems to continuously reevaluate criteria for treatment allocation as additional guidance and data emerge.
[This corrects the article DOI: 10.2196/12782.].
Vaccines, when available, will prove to be crucial in the fight against Covid-19. All societies will face acute dilemmas in allocating scarce lifesaving resources in the form of vaccines for Covid-19. The author proposes The Value of Lives Principle as a just and workable plan for equitable and efficient access. After describing what the principle entails, the author contrasts the advantage of this approach with other current proposals such as the Fair Priority Model.
Objective: Symptom burden remains a distressing problem for survivors with non-small-cell lung cancer (stages I-IIIa). This pilot study evaluated feasibility and preliminary effects of a tailored mindfulness-based intervention, Breathe Easier, which encompasses meditation, 2 levels of mindful hatha yoga, breathing exercises, and participant interaction.
Methods:
Participants were recruited from 2 cancer programs in the US Southeast. A family member was required for participation. Sixty-two participants enrolled (20% recruitment) and 49 completed the intervention (79% retention). Participants chose level 1 yoga (basic) or level 2 (more advanced). Of the completers, survivors were 39% male and 65% Black. A community-based participatory research framework helped identify the specific needs and interests of potential participants and foreseeable barriers to implementation. A 2-month prospective, 1-group, pre-post design evaluated feasibility. Intervention dosage was measured using written protocols. Attendance and completion of daily home assignments measured adherence. Acceptability was assessed using a 10-item questionnaire, completed at three time points. Preliminary outcome data collected pre- and post-intervention tested the hypothesis that participants who received the 8-week intervention Breathe Easier would, post-intervention, demonstrate (a) less dyspnea, (b) less fatigue, (c) less stress, (d) improved sleep, (e) improved anxiety and depression, and (f) improved functional exercise capacity. Exit interviews were conducted, transcribed verbatim, and analyzed for content using descriptive statistics.
Results:
Quantitative and qualitative measures indicated strong feasibility. Over time, level 1 participants had statistically less dyspnea, fatigue and improved exercise capacity, as well as improved sleep, and stress scores. Level 2 participants experienced slightly increased dyspnea and fatigue but improved sleep, stress, and exercise capacity. All participants experienced anxiety and depression within normal limits pre- and post-intervention. Five major themes emerged out of exit interviews: Learning to Breathe Easier; Interacting with Others as a Personal Benefit; Stretching, Releasing Tension, and Feeling Energized; Enhancing Closeness with Committed Partners; Refocusing on Living; and Sustaining New Skills as a Decision.
Conclusions:
The study offers insight into the feasibility of an 8-week in-person mindfulness-based intervention with a unique subset of understudied survivors of lung cancer and family members. Outcome data interpretation is limited by the 1-group design and sample size.
Objective
The study sought to describe the contributions of clinical informatics (CI) fellows to their institutions’ coronavirus disease 2019 (COVID-19) response.
Materials and Methods
We designed a survey to capture key domains of health informatics and perceptions regarding fellows’ application of their CI skills. We also conducted detailed interviews with select fellows and described their specific projects in a brief case series.
Results
Forty-one of the 99 CI fellows responded to our survey. Seventy-five percent agreed that they were “able to apply clinical informatics training and interest to the COVID-19 response.” The most common project types were telemedicine (63%), reporting and analytics (49%), and electronic health record builds and governance (32%). Telehealth projects included training providers on existing telehealth tools, building entirely new virtual clinics for video triage of COVID-19 patients, and pioneering workflows and implementation of brand-new emergency department and inpatient video visit types. Analytics projects included reports and dashboards for institutional leadership, as well as developing digital contact tracing tools. For electronic health record builds, fellows directly contributed to note templates with embedded screening and testing guidance, adding COVID-19 tests to order sets, and validating clinical triage workflows.
Discussion
Fellows were engaged in projects that span the breadth of the CI specialty and were able to make system-wide contributions in line with their educational milestones.
Conclusions
CI fellows contributed meaningfully and rapidly to their institutions’ response to the COVID-19 pandemic.
Background
Myofunctional therapy has demonstrated efficacy in treating sleep-disordered breathing. We assessed the clinical use of a new mobile health (mHealth) app that uses a smartphone to teach patients with severe obstructive sleep apnea–hypopnea syndrome (OSAHS) to perform oropharyngeal exercises.
Objective
We conducted a pilot randomized trial to evaluate the effects of the app in patients with severe OSAHS.
Methods
Forty patients with severe OSAHS (apnea–hypoxia index [AHI]>30) were enrolled prospectively and randomized into an intervention group that used the app for 90 sessions or a control group. Anthropometric measures, Epworth Sleepiness Scale (0-24), Pittsburgh Sleep Quality Index (0-21), Iowa Oral Performance Instrument (IOPI) scores, and oxygen desaturation index were measured before and after the intervention.
Results
After the intervention, 28 patients remained. No significant changes were observed in the control group; however, the intervention group showed significant improvements in most metrics. AHI decreased by 53.4% from 44.7 (range 33.8-55.6) to 20.88 (14.02-27.7) events/hour (P<.001). The oxygen desaturation index decreased by 46.5% from 36.31 (27.19-43.43) to 19.4 (12.9-25.98) events/hour (P=.003). The IOPI maximum tongue score increased from 39.83 (35.32-45.2) to 59.06 (54.74-64.00) kPa (P<.001), and the IOPI maximum lip score increased from 27.89 (24.16-32.47) to 44.11 (39.5-48.8) kPa (P<.001). The AHI correlated significantly with IOPI tongue and lip improvements (Pearson correlation coefficient −0.56 and −0.46, respectively; both P<.001). The Epworth Sleepiness Scale score decreased from 10.33 (8.71-12.24) to 5.37 (3.45-7.28) in the app group (P<.001), but the Pittsburgh Sleep Quality Index did not change significantly.
Conclusions
Orofacial exercises performed using an mHealth app reduced OSAHS severity and symptoms, and represent a promising treatment for OSAHS.
Trial Registration
Spanish Registry of Clinical Studies AWGAPN-2019-01, ClinicalTrials.gov NCT04438785; https://clinicaltrials.gov/ct2/show/NCT04438785
Cancer patients are often not sufficiently oriented to manage side effects at home. Sending text messages with self-care guidelines aimed managing side effects is the main objective of this randomized controlled trial. Patients who started outpatient chemotherapy treatment between March and December 2017 at a hospital in southern Brazil were invited to participate in this study and were allocated to the intervention or control group (ratio 1: 1). Each patient in the intervention group received a daily SMS (short message service) with some guidance on management or prevention of side effects. All text messages were sent to the intervention group patients in an automated and tailored way by our app called cHEmotHErApp. Side effects experienced by patients were verified using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core-30 (EORTC QLQ-C30). Results showed intervention group patients experienced fewer side effects compared to the control group in cycle 1 (p < 0.05), in general. In addition, intervention group experienced less nausea in relation to the control group, in the cycle 1 and cycle 2 (p < 0.05). This study indicate text messaging may be a tool for supporting side effect management in patients receiving chemotherapy. This study was enrolled in ClinicalTrials.gov with the identification number NCT03087422. This research was performed in accordance with the Declaration of Helsinki.
The Internet of Things (IoT) is a system of wireless, interrelated, and connected digital devices that can collect, send, and store data over a network without requiring human-to-human or human-to-computer interaction. The IoT promises many benefits to streamlining and enhancing health care delivery to proactively predict health issues and diagnose, treat, and monitor patients both in and out of the hospital. Worldwide, government leaders and decision makers are implementing policies to deliver health care services using technology and more so in response to the novel COVID-19 pandemic. It is now becoming increasingly important to understand how established and emerging IoT technologies can support health systems to deliver safe and effective care. The aim of this viewpoint paper is to provide an overview of the current IoT technology in health care, outline how IoT devices are improving health service delivery, and outline how IoT technology can affect and disrupt global health care in the next decade. The potential of IoT-based health care is expanded upon to theorize how IoT can improve the accessibility of preventative public health services and transition our current secondary and tertiary health care to be a more proactive, continuous, and coordinated system. Finally, this paper will deal with the potential issues that IoT-based health care generates, barriers to market adoption from health care professionals and patients alike, confidence and acceptability, privacy and security, interoperability, standardization and remuneration, data storage, and control and ownership. Corresponding enablers of IoT in current health care will rely on policy support, cybersecurity-focused guidelines, careful strategic planning, and transparent policies within health care organizations. IoT-based health care has great potential to improve the efficiency of the health system and improve population health. ©Jaimon T Kelly, Katrina L Campbell, Enying Gong, Paul Scuffham.
Importance:
A stay-at-home social distancing mandate is a key nonpharmacological measure to reduce the transmission rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), but a high rate of adherence is needed.
Objective:
To examine the association between the rate of human mobility changes and the rate of confirmed cases of SARS-CoV-2 infection.
Design, setting, and participants:
This cross-sectional study used daily travel distance and home dwell time derived from millions of anonymous mobile phone location data from March 11 to April 10, 2020, provided by the Descartes Labs and SafeGraph to quantify the degree to which social distancing mandates were followed in the 50 US states and District of Columbia and the association of mobility changes with rates of coronavirus disease 2019 (COVID-19) cases.
Exposure:
State-level stay-at-home orders during the COVID-19 pandemic.
Main outcomes and measures:
The main outcome was the association of state-specific rates of COVID-19 confirmed cases with the change rates of median travel distance and median home dwell time of anonymous mobile phone users. The increase rates are measured by the exponent in curve fitting of the COVID-19 cumulative confirmed cases, while the mobility change (increase or decrease) rates were measured by the slope coefficient in curve fitting of median travel distance and median home dwell time for each state.
Results:
Data from more than 45 million anonymous mobile phone devices were analyzed. The correlation between the COVID-19 increase rate and travel distance decrease rate was -0.586 (95% CI, -0.742 to -0.370) and the correlation between COVID-19 increase rate and home dwell time increase rate was 0.526 (95% CI, 0.293 to 0.700). Increases in state-specific doubling time of total cases ranged from 1.0 to 6.9 days (median [interquartile range], 2.7 [2.3-3.3] days) before stay-at-home orders were enacted to 3.7 to 30.3 days (median [interquartile range], 6.0 [4.8-7.1] days) after stay-at-home social distancing orders were put in place, consistent with pandemic modeling results.
Conclusions and relevance:
These findings suggest that stay-at-home social distancing mandates, when they were followed by measurable mobility changes, were associated with reduction in COVID-19 spread. These results come at a particularly critical period when US states are beginning to relax social distancing policies and reopen their economies. These findings support the efficacy of social distancing and could help inform future implementation of social distancing policies should they need to be reinstated during later periods of COVID-19 reemergence.
The Fair Priority Model offers a practical way to fulfill pledges to distribute vaccine fairly and equitably.
• The United States is in the midst of a 40-year-long population health crisis. Life expectancy has declined since 2014, an unprecedented event that has followed on the heels of a decades-long slowing in secular gains in longevity in the US relative to peer countries. These adverse population health trends appear to be primarily driven by worsening health among working-age individuals of lower socioeconomic status. • A growing body of research suggests that worsening economic outcomes-e.g., fading employment opportunities and increasing economic insecurity-may be a primary causal driver of adverse health trends among low-income and less-educated working-age US residents. • Evidence-based public policies to address widening gaps in economic and health outcomes include expanding early childhood health and educational investments, increasing the scope of programs that assist displaced workers in developing new skills and finding new jobs, reinforcing the social safety net, and improving the reach of public health efforts to help moderate the health consequences of adverse economic shocks. • Policymakers will also need to consider and rigorously evaluate new approaches, such as basic income grants, investments to direct automation toward complementing rather than replacing the work force, or job guarantee programs. • The size and scope of the population health challenges that have arisen with the changing economy highlight the importance of new data sources and evidence-based engagement by policymakers. © 2020 Venkataramani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background
Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care.
Objective
This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application.
Methods
We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms “conversational agents,” “conversational AI,” “chatbots,” and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis.
Results
The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education.
Conclusions
The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence–driven, and smartphone app–delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents’ formats, focusing on their acceptability, safety, and effectiveness.
Background
Information technologies have been vital during the COVID-19 pandemic. Telehealth and telemedicine services, especially, fulfilled their promise by allowing patients to receive advice and care at a distance, making it safer for all concerned. Over the preceding years, professional societies, governments, and scholars examined ethical, legal, and social issues (ELSI) related to telemedicine and telehealth. Primary concerns evident from reviewing this literature have been quality of care, access, consent, and privacy.
Objectives
To identify and summarize ethical, legal, and social issues related to information technology in healthcare, as exemplified by telehealth and telemedicine. To expand on prior analyses and address gaps illuminated by the COVID-19 experience. To propose future research directions.
Methods
Literature was identified through searches, forward and backward citation chaining, and the author’s knowledge of scholars and works in the area. EU and professional organizations’ guidelines, and nineteen scholarly papers were examined and categories created to identify ethical, legal, and social issues they addressed. A synthesis matrix was developed to categorize issues addressed by each source.
Results
A synthesis matrix was developed and issues categorized as: quality of care, consent and autonomy, access to care and technology, legal and regulatory, clinician responsibilities, patient responsibilities, changed relationships, commercialization, policy, information needs, and evaluation, with subcategories that fleshed out each category. The literature primarily addressed quality of care, access, consent, and privacy. Other identified considerations were little discussed. These and newer concerns include: usability, tailoring services to each patient, curriculum and training, implementation, commercialization, and licensing and liability. The need for interoperability, data availability, cybersecurity, and informatics infrastructure also is more apparent. These issues are applicable to other information technologies in healthcare.
Conclusions
Clinicians and organizations need updated guidelines for ethical use of telemedicine and telehealth care, and decision- and policy-makers need evidence to inform decisions. The variety of newly implemented telemedicine services is an on-going natural experiment presenting an unparalleled opportunity to develop an evidence-based way forward. The paper recommends evaluation using an applied ethics, context-sensitive approach that explores interactions among multiple factors and considerations. It suggests evaluation questions to investigate ethical, social, and legal issues through multi-method, sociotechnical, interpretive and ethnographic, and interactionist evaluation approaches. Such evaluation can help telehealth, and other information technologies, be integrated into healthcare ethically and effectively.
Background:
One of the main objectives of Electronic Health Records (EHRs) is to enhance collaboration among healthcare professionals. However, our knowledge of how EHRs actually affect collaborative practices is limited. This study examines how an EHR facilitates and constrains collaboration in five outpatient clinics.
Methods:
We conducted an embedded case study at five outpatient clinics of a Dutch hospital that had implemented an organization-wide EHR. Data were collected through interviews with representatives of medical specialties, administration, nursing, and management. Documents were analyzed to contextualize these data. We examined the following collaborative affordances of EHRs: (1) portability, (2) co-located access, (3) shared overviews, (4) mutual awareness, (5) messaging, and (6) orchestrating.
Results:
Our findings demonstrate how an EHR will both facilitate and constrain collaboration among specialties and disciplines. Affordances that were inscribed in the system for collaboration purposes were not fully actualized in the hospital because: (a) The EHR helps health professionals coordinate patient care on an informed basis at any time and in any place but only allows asynchronous patient record use. (b) The comprehensive patient file affords joint clinical decision-making based on shared data, but specialty- and discipline-specific user-interfaces constrain mutual understanding of that data. Moreover, not all relevant information can be easily shared across specialties and outside the hospital. (c) The reduced necessity for face-to-face communication saves time but is experienced as hindering collective responsibility for a smooth workflow. (d) The EHR affords registration at the source and registration of activities through orders, but the heightened administrative burden for physicians and the strict authorization rules on inputting data constrain the flexible, multidisciplinary collaboration. (e) While the EHR affords a complete overview, information overload occurs due to the parallel generation of individually owned notes and the high frequency of asynchronous communication through messages of varying clinical priority.
Conclusions:
For the optimal actualization of EHRs' collaborative affordances in hospitals, coordinated use of these affordances by health professionals is a prerequisite. Such coordinated use requires organizational, technical, and behavioral adaptations. Suggestions for hospital-wide policies to enhance trust in both the EHR and in its coordinated use for effective collaboration are offered.
The dichotomy index (I < O), a quantitative estimate of the circadian regulation of daytime activity and sleep, predicted overall cancer survival and emergency hospitalization, supporting its integration in a mHealth platform. Modifiable causes of I < O deterioration below 97.5%—(I < O)low—were sought in 25 gastrointestinal cancer patients and 33 age- and sex-stratified controls. Rest-activity and temperature were tele-monitored with a wireless chest sensor, while daily activities, meals, and sleep were self-reported for one week. Salivary cortisol rhythm and dim light melatonin onset (DLMO) were determined. Circadian parameters were estimated using Hidden Markov modelling, and spectral analysis. Actionable predictors of (I < O)low were identified through correlation and regression analyses. Median compliance with protocol exceeded 95%. Circadian disruption—(I < O)low—was identified in 13 (52%) patients and four (12%) controls (p = 0.002). Cancer patients with (I < O)low had lower median activity counts, worse fragmented sleep, and an abnormal or no circadian temperature rhythm compared to patients with I < O exceeding 97.5%—(I < O)high—(p < 0.012). Six (I < O)low patients had newly-diagnosed sleep conditions. Altered circadian coordination of rest-activity and chest surface temperature, physical inactivity, and irregular sleep were identified as modifiable determinants of (I < O)low. Circadian rhythm and sleep tele-monitoring results support the design of specific interventions to improve outcomes within a patient-centered systems approach to health care.
Motivation:
Recent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input variables of survival model, is larger than the amount of available cancer patient samples, deep-learning models are prone to overfitting. To address the issue, we introduce a new deep-learning architecture called VAECox. VAECox uses transfer learning and fine tuning.
Results:
We pre-trained a variational autoencoder on all RNA-seq data in 20 TCGA datasets and transferred the trained weights to our survival prediction model. Then we fine-tuned the transferred weights during training the survival model on each dataset. Results show that our model outperformed other previous models such as Cox Proportional Hazard with LASSO and ridge penalty and Cox-nnet on the 7 of 10 TCGA datasets in terms of C-index. The results signify that the transferred information obtained from entire cancer transcriptome data helped our survival prediction model reduce overfitting and show robust performance in unseen cancer patient samples.
Availability and implementation:
Our implementation of VAECox is available at https://github.com/dmis-lab/VAECox.
Supplementary information:
Supplementary data are available at Bioinformatics online.
Great technology alone is rarely sufficient to ensure a product’s success. Scenario-Focused Engineering is a customer-centric, iterative approach used to design and deliver the seamless experiences and emotional engagement customers demand in new products. In this book, you’ll discover the proven practices and lessons learned from real-world implementations of this approach, including why delight matters, what it means to be customer-focused, and how to iterate effectively using the Fast Feedback Cycle. In an engineering environment traditionally rooted in strong analytics, the ideas and practices for Scenario-Focused Engineering may seem counter-intuitive. Learn how to change your team’s mindset from deciding what a product, service, or device will do and solving technical problems to discovering and building what customers actually want.
With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.
Objectives
This operational study aims to investigate the barriers in communicating medication changes at hospital discharge, and to inform design requirements of the CancelRx functionality to better support the communication.
Methods
We conducted seven semi-structured interviews with inpatient prescribers at an urban academic medical center. The interview protocol was framed from a human factors perspective, specifically the work system design approach. We took notes of the interviews and identified the initial themes of system barriers that may impact patient safety.
Results
Medication changes need to be communicated to multiple stakeholders. We identified two initial themes of the system barriers: the lack of an information flow that connects all the involved stakeholders, and the difficulties to communicate key pieces of information. We identified three key pieces of information that are difficult to communicate: the discontinuation reasons, the notification urgency, and the duration of changes.
Conclusions
While the CancelRx functionality can facilitate the communication (e.g. prescribers no longer need to call pharmacists when a medication is discontinued), enhancements are needed to address the system barriers. We proposed enhanced design requirements of the CancelRx functionality, e.g., to allow users to specify a reason for a medication discontinuation and transmit the reasons to other stakeholders, to indicate the urgency of notification, to specify the duration of a change, and to receive system status feedback .
The Covid-19 pandemic is having a significant impact on the well-being of nurses and has exacerbated long-standing issues of stress and burnout. Expecting or hoping that nurses will recover quickly or bounce back from the stress and deep trauma of the pandemic is not realistic. Each nurse has a story, and while these stories may have similar themes, they are all different. It is important to reflect on our stories, identify the myriad of emotions we are experiencing, and find ways to work through our feelings. Ignoring, denying, or suppressing feelings does not serve us well in the long run. Stifling negative emotions does not make them go away. A Call to Action is needed to address the impact of the pandemic, clinician burnout, and systemic racism on health-care organizations and educational institutions. Strategies are identified that will support personal and organizational well-being.
Health and biomedical informatics graduate-level degree programs have proliferated across the United States in the last 10 years. To help inform programs on practices in teaching and learning, a survey of master's programs in health and biomedical informatics in the United States was conducted to determine the national landscape of culminating experiences including capstone projects, research theses, internships, and practicums. Almost all respondents reported that their programs required a culminating experience (97%). A paper (not a formal thesis), an oral presentation, a formal course, and an internship were required by ≥50% programs. The most commonly reported purposes for the culminating experience were to help students extend and apply the learning and as a bridge to the workplace. The biggest challenges were students' maturity, difficulty in synthesizing information into a coherent paper, and ability to generate research ideas. The results provide students and program leaders with a summary of pedagogical methods across programs.
Rationing of scarce health-care resources is distressing. Clinicians therefore require clear guidance, which should be developed systematically and transparently through multi-stakeholder engagement. Rationing is seldom required in high-income settings but is often necessary in low-income settings. Global solidarity and health system strengthening are required to reduce the need for rationing.
We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazards of the underlying survival distribution, as required by the Cox-proportional hazard model. By jointly learning deep nonlinear representations of the input covariates, we demonstrate the benefits of our approach when used to estimate survival risks through extensive experimentation on multiple real world datasets with different levels of censoring. We further demonstrate advantages of our model in the competing risks scenario. To the best of our knowledge, this is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.
Significance
COVID-19 has generated a huge mortality toll in the United States, with a disproportionate number of deaths occurring among the Black and Latino populations. Measures of life expectancy quantify these disparities in an easily interpretable way. We project that COVID-19 will reduce US life expectancy in 2020 by 1.13 y. Estimated reductions for the Black and Latino populations are 3 to 4 times that for Whites. Consequently, COVID-19 is expected to reverse over 10 y of progress made in closing the Black−White gap in life expectancy and reduce the previous Latino mortality advantage by over 70%. Some reduction in life expectancy may persist beyond 2020 because of continued COVID-19 mortality and long-term health, social, and economic impacts of the pandemic.
Board certified clinical informaticians provide expertise in leveraging health IT (HIT) and health data for patient care and quality improvement. Clinical Informatics experts possess the requisite skills and competencies to make systems-level improvements in care delivery using HIT, workflow and data analytics, knowledge acquisition, clinical decision support, data visualization, and related informatics tools. However, these physicians lack structured and sustained funding because they have no billing codes. The sustainability and growth of this new and promising medical subspecialty is threatened by outdated and inconsistent funding models that fail to support the education and professional growth of clinical informaticians. The Clinical Informatics Program Directors' Community is calling upon the Centers for Medicare and Medicaid Services to consider novel funding structures and programs through its Innovation Center for Clinical Informatics Fellowship training. Only through structural and sustained funding for Clinical Informatics fellows will be able to fully develop the potential of electronic health records to improve the quality, safety, and cost of clinical care.
This heavily revised second edition defines the current state of the art for informatics education in medicine and healthcare. This field has continued to undergo considerable changes as the field of informatics continues to evolve. The book features extensively revised chapters addressing the latest developments in areas including relevant informatics concepts for those who work in health information technology and those teaching informatics courses in clinical settings, techniques for teaching informatics with limited resources, and the use of online modalities in bioinformatics research education. New topics covered include how to get appropriate accreditation for an informatics program, data science and bioinformatics education, and undergraduate health informatics education.
Informatics Education in Healthcare: Lessons Learned addresses the broad range of informatics education programs and available techniques for teaching informatics. It therefore provides a valuable reference for all involved in informatics education.
Background:
Providing adequate psychiatry consultation capacity on a 24/7 basis is an intrinsic challenge throughout many multihospital health care systems. At present, implementation research has not adequately defined the effectiveness and feasibility of a centralized telepsychiatry consultation service within a multihospital health care system.
Objective:
To demonstrate feasibility of a hub and spoke model for provision of inpatient consult telepsychiatry service from an academic medical center to 2 affiliated regional hospital sites, to reduce patient wait time, and to develop best practice guidelines for telepsychiatry consultations to the acutely medically ill.
Methods:
The implementation, interprofessional workflow, process of triage, and provider satisfaction were described from the first 13 months of the service.
Results:
This pilot study resulted in 557 completed telepsychiatry consults over the course of 13 months from 2018 to 2019. A range of psychiatric conditions commonly encountered by consultation-liaison services were diagnosed and treated through the teleconferencing modality. The most common barriers to successful use of telepsychiatry were defined for the 20% of consult requests that were retriaged to face-to-face evaluation. The average patient wait time from consult request to initial consultation was reduced from >24 hours to 92 minutes.
Conclusions:
This study demonstrated the feasibility of a centralized telepsychiatry hub to improve delivery of psychiatry consultation within a multihospital system with an overall reduction in patient wait time. This work may serve as a model for further design innovation across many health care settings and new patient subpopulations.
Background:
The growing complexity of data systems in health care has precipitated increasing demand for clinical informatics subspecialists. The first board certification exam for the clinical informatics subspecialty was offered in 2013. Characterizing trends in this novel workforce is important to inform its development.
Methods:
We conducted an exploratory analysis of American Board of Medical Specialties data on individuals certified in clinical informatics from 2013 to 2019 to review trends and demographic characteristics of current subspecialists.
Results:
2018 physicians were certified in clinical informatics from 2013 to 2019. The annual number of awarded certifications declined after 2016. The majority of primary certifications held by clinical informaticians were in broad-based medical specialties relative to primarily procedural specialties.
Conclusions:
Disparities may exist within the clinical informatics physician workforce with respect to primary specialty certifications and geographic distribution. There remains a need for the creation of fellowship programs to sustain the growth of this workforce.
Study objectives:
Telemonitoring system is a promising wireless technology which possibly enhances the adherence to CPAP therapy. The study aimed to determine the effect of telemonitoring system on CPAP therapy adherence among Asians with moderate-to-severe OSA.
Methods:
A prospective randomized controlled trial enrolled 60 Asian adults with moderate-to-severe OSA. Thirty patients each were randomized to CPAP with telemonitoring system (TM group) or CPAP with usual care (UC group). Telemonitoring system functions by transferring CPAP-usage data via cellular network. When there were any triggers occurring 2 nights consecutively (usage hour<4 hours per night, leakage>27 L/min or AHI>5/hour), the investigator contacted the patients. The primary outcome was the 4-week CPAP usage hour per night. The secondary outcome included % good adherence (defined as a 4-week period of therapy with CPAP usage >4 hours/night on >70% of total days), median leakage per night, adverse events from CPAP therapy, sleep quality improvement, and daytime sleepiness reduction.
Results:
Seventy percent of the participants were male and mean AHI was 50.3/hour. The mean 4-week CPAP usage hour per night was insignificantly higher in TM group (5.16 ±1.47 hour/night vs 4.42 ±1.91 hour/night, p-value = 0.18). However, % good adherence was significantly higher in TM group (64.2% vs 34.4%, p-value = 0.024). Median leakage per night was also significantly lower in TM group. Furthermore, significant sleep quality improvement was observed in TM group. Overall adverse events and daytime sleepiness reduction were not different.
Conclusions:
The telemonitoring system implementation demonstrated a trend towards increasing in CPAP nightly hour usage and significantly improved adherence as well as sleep quality among Asian moderate-to-severe OSA.
Objectives-This report presents 2018 infant mortality statistics by age at death, maternal race and Hispanic origin, maternal age, gestational age, leading causes of death, and maternal state of residence. Trends in infant mortality are also examined. Methods-Descriptive tabulations of data are presented and interpreted for infant deaths and infant mortality rates using the 2018 period linked birth/infant death file; the linked birth/infant death file is based on birth and death certificates registered in all states and the District of Columbia. Results-A total of 21,498 infant deaths were reported in the United States in 2018. The U.S. infant mortality rate was 5.67 infant deaths per 1,000 live births, lower than the rate of 5.79 in 2017 and an historic low in the country. The neonatal and post neonatal mortality rates for 2018 (3.78 and 1.89, respectively) demonstrated a nonsignificant decline compared with 2017 (3.85 and 1.94, respectively). The 2018 mortality rate declined for infants of Hispanic women compared with the 2017 rate; changes in rates for other race and Hispanic-origin groups were not statistically significant. The 2018 infant mortality rate for infants of non-Hispanic black women (10.75) was more than twice as high as that for infants of non-Hispanic white (4.63), non-Hispanic Asian (3.63), and Hispanic women (4.86). Infants born very preterm (less than 28 weeks of gestation) had the highest mortality rate (382.20), 186 times as high as that for infants born at term (37-41 weeks of gestation) (2.05). The five leading causes of infant death in 2018 were the same as in 2017; cause-of-death rankings and mortality rates varied by maternal race and Hispanic origin. Infant mortality rates by state for 2018 ranged from a low of 3.50 in New Hampshire to a high of 8.41 in Mississippi.
Big data, high-performance computing, and (deep) machine learning are increasingly becoming key to precision medicine—from identifying disease risks and taking preventive measures, to making diagnoses and personalizing treatment for individuals. Precision medicine, however, is not only about predicting risks and outcomes, but also about weighing interventions. Interventional clinical predictive models require the correct specification of cause and effect, and the calculation of so-called counterfactuals, that is, alternative scenarios. In biomedical research, observational studies are commonly affected by confounding and selection bias. Without robust assumptions, often requiring a priori domain knowledge, causal inference is not feasible. Data-driven prediction models are often mistakenly used to draw causal effects, but neither their parameters nor their predictions necessarily have a causal interpretation. Therefore, the premise that data-driven prediction models lead to trustable decisions/interventions for precision medicine is questionable. When pursuing intervention modelling, the bio-health informatics community needs to employ causal approaches and learn causal structures. Here we discuss how target trials (algorithmic emulation of randomized studies), transportability (the licence to transfer causal effects from one population to another) and prediction invariance (where a true causal model is contained in the set of all prediction models whose accuracy does not vary across different settings) are linchpins to developing and testing intervention models. Machine learning models are commonly used to predict risks and outcomes in biomedical research. But healthcare often requires information about cause–effect relations and alternative scenarios, that is, counterfactuals. Prosperi et al. discuss the importance of interventional and counterfactual models, as opposed to purely predictive models, in the context of precision medicine.