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\"In The Patient Will See You Now, Eric Topol, one of the nation's top physicians, examines what he calls medicine's \"Gutenberg moment.\" Much as the printing press liberated knowledge from the control of an elite class, new technology--from the smartphone to machine learning--is poised to democratize medicine. In this new era, patients will control their data and be emancipated from a paternalistic medical regime in which \"the doctor knows best.\" Mobile phones, apps, and attachments will literally put the lab and the ICU in our pockets. Computers will replace physicians for many diagnostic tasks, and enormous data sets will give us new means to attack conditions that have long been incurable. In spite of these benefits, the path forward will be complicated: some in the medical establishment will resist these changes, and digitized medicine will raise serious issues surrounding privacy. Nevertheless, the result--better, cheaper, and more humane health care for all--will be worth it. The Patient Will See You Now is essential reading for anyone who thinks they deserve better health care. That is, for all of us.\"-- Provided by publisher.

A trip to the doctor is almost a guarantee of misery. You'll make an appointment months in advance. You'll probably wait for several hours until you hear \"the doctor will see you now\"—but only for fifteen minutes! Then you'll wait even longer for lab tests, the results of which you'll likely never see, unless they indicate further (and more invasive) tests, most of which will probably prove unnecessary (much like physicals themselves). And your bill will be astronomical. In The Patient Will See You Now, Eric Topol, one of the nation's top physicians, shows why medicine does not have to be that way. Instead, you could use your smartphone to get rapid test results from one drop of blood, monitor your vital signs both day and night, and use an artificially intelligent algorithm to receive a diagnosis without having to see a doctor, all at a small fraction of the cost imposed by our modern healthcare system. The change is powered by what Topol calls medicine's \"Gutenberg moment.\" Much as the printing press took learning out of the hands of a priestly class, the mobile internet is doing the same for medicine, giving us unprecedented control over our healthcare. With smartphones in hand, we are no longer beholden to an impersonal and paternalistic system in which \"doctor knows best.\" Medicine has been digitized, Topol argues; now it will be democratized. Computers will replace physicians for many diagnostic tasks, citizen science will give rise to citizen medicine, and enormous data sets will give us new means to attack conditions that have long been incurable. Massive, open, online medicine, where diagnostics are done by Facebook-like comparisons of medical profiles, will enable real-time, real-world research on massive populations. There's no doubt the path forward will be complicated: the medical establishment will resist these changes, and digitized medicine inevitably raises serious issues surrounding privacy. Nevertheless, the result—better, cheaper, and more human health care—will be worth it. Provocative and engrossing, The Patient Will See You Now is essential reading for anyone who thinks they deserve better health care. That is, for all of us.

Background Transdiagnostic processes confer risk for multiple types of psychopathology and explain the co-occurrence of different disorders. For this reason, transdiagnostic processes provide ideal targets for early intervention and treatment. Childhood trauma exposure is associated with elevated risk for virtually all commonly occurring forms of psychopathology. We articulate a transdiagnostic model of the developmental mechanisms that explain the strong links between childhood trauma and psychopathology as well as protective factors that promote resilience against multiple forms of psychopathology. Main body We present a model of transdiagnostic mechanisms spanning three broad domains: social information processing, emotional processing, and accelerated biological aging. Changes in social information processing that prioritize threat-related information—such as heightened perceptual sensitivity to threat, misclassification of negative and neutral emotions as anger, and attention biases towards threat-related cues—have been consistently observed in children who have experienced trauma. Patterns of emotional processing common in children exposed to trauma include elevated emotional reactivity to threat-related stimuli, low emotional awareness, and difficulties with emotional learning and emotion regulation. More recently, a pattern of accelerated aging across multiple biological metrics, including pubertal development and cellular aging, has been found in trauma-exposed children. Although these changes in social information processing, emotional responding, and the pace of biological aging reflect developmental adaptations that may promote safety and provide other benefits for children raised in dangerous environments, they have been consistently associated with the emergence of multiple forms of internalizing and externalizing psychopathology and explain the link between childhood trauma exposure and transdiagnostic psychopathology. Children with higher levels of social support, particularly from caregivers, are less likely to develop psychopathology following trauma exposure. Caregiver buffering of threat-related processing may be one mechanism explaining this protective effect. Conclusion Childhood trauma exposure is a powerful transdiagnostic risk factor associated with elevated risk for multiple forms of psychopathology across development. Changes in threat-related social and emotional processing and accelerated biological aging serve as transdiagnostic mechanisms linking childhood trauma with psychopathology. These transdiagnostic mechanisms represent critical targets for early interventions aimed at preventing the emergence of psychopathology in children who have experienced trauma.

Background The suspension and/or termination of many programmes funded through the United States Agency for International Development (USAID) by the new US administration has severe short- and long-term negative impacts on the health of people worldwide. We draw attention to the termination of the Demographic and Health Surveys (DHS) Program, which includes nationally representative surveys of households, DHS, Malaria Indicator Surveys [MIS]) and health facilities (Service Provision Assessments [SPA]) in over 90 low- and middle-income countries. USAID co-funding and provision of technical support for these surveys has been shut down. Main body The impact of these disruptions will reverberate across local, regional, national, and global levels and severely impact the ability to understand the levels and changes in population health outcomes and behaviours. We highlight three key impacts on (1) ongoing data collection and data processing activities; (2) future data collection and consequent lack of population-level health indicators; and (3) access to existing data and lack of support for its use. Conclusions We call for immediate action on multiple fronts. In the short term, universal access to existing data and survey materials should be restored, and surveys which were planned or in progress should be completed. In the long term, this crisis should serve as a tipping point for transforming these vital surveys. We call on national governments, regional organisations, and international partners to develop sustainable alternatives that preserve the principles (standardised questionnaires, backward compatibility, open access data with rigorous documentation) which made the DHS Program an invaluable global health resource.

Background Older adults are at risk for loneliness, and interventions to promote social connectedness are needed to directly address this problem. The nature of interventions aimed to affect the distinct, subjective concepts of loneliness/social connectedness has not been clearly described. The purpose of this review was to map the literature on interventions and strategies to affect loneliness/social connectedness for older adults. Methods A comprehensive scoping review was conducted. Six electronic databases were searched from inception in July 2015, resulting in 5530 unique records. Standardized inclusion/exclusion criteria were applied, resulting in a set of 44 studies (reported in 54 articles) for further analysis. Data were extracted to describe the interventions and strategies, and the context of the included studies. Analytic techniques included calculating frequencies, manifest content analysis and meta-summary. Results Interventions were described or evaluated in 39 studies, and five studies described strategies to affect loneliness/social connectedness of older adults or their caregivers in a qualitative descriptive study. The studies were often conducted in the United States (38.6%) among community dwelling (54.5%), cognitively intact (31.8%), and female-majority (86.4%) samples. Few focused on non-white participants (4.5%). Strategies described most often were engaging in purposeful activity and maintaining contact with one’s social network. Of nine intervention types identified, the most frequently described were One-to-One Personal Contact and Group Activity. Authors held divergent views of why the same type of intervention might impact social connectedness, but social contact was the most frequently conceptualized influencing factor targeted, both within and across intervention types. Conclusions Research to test the divergent theories of why interventions work is needed to advance understanding of intervention mechanisms. Innovative conceptualizations of intervention targets are needed, such as purposeful activity, that move beyond the current focus on the objective social network as a way to promote social connectedness for older adults.

Large-scale collection of data could help curb the COVID-19 pandemic, but it should not neglect privacy and public trust. Best practices should be identified to maintain responsible data-collection and data-processing standards at a global scale.

Background Photovoice is a visual research methodology with the intention to foster social change. Photovoice has been used to investigate change in empowerment in vulnerable communities, However, the individual experience of participants involved in Photovoice projects is seldom scrutinized. Our aim was to explore and describe the individual experiences of the female individuals who participated in a previous Photovoice project. We analyzed a change in the women’s empowerment in terms of: 1) gain in knowledge and skills, 2) change in self-perception, and 3) access to and use of resources. Methods This qualitative study took place in the low-income District of Villaverde (Madrid, Spain), from January-June 2016. We conducted 10 semi-structured interviews with the female residents who had participated in the previous Photovoice project. We also collected field notes. We analyzed these data through a direct qualitative content analysis. The three outlined dimensions of empowerment provided guidance for the analysis of the results. Results We found positive changes in the three dimensions of empowerment: 1) participants acquired new knowledge and developed critical awareness of their community; 2) the social recognition participants received transformed their self-perception; and 3) the project allowed them to expand their social networks and to build new links with different actors (research partners, local decision makers, media and the wider public). Conclusions Photovoice projects entail the opportunity for empowering participants. Future research using Photovoice should assess the influence it has on participants’ empowerment changes and how to sustain these individual and social changes.

The rapid integration of Artificial Intelligence (AI) into the healthcare field has occurred with little communication between computer scientists and doctors. The impact of AI on health outcomes and inequalities calls for health professionals and data scientists to make a collaborative effort to ensure historic health disparities are not encoded into the future. We present a study that evaluates bias in existing Natural Language Processing (NLP) models used in psychiatry and discuss how these biases may widen health inequalities. Our approach systematically evaluates each stage of model development to explore how biases arise from a clinical, data science and linguistic perspective. A literature review of the uses of NLP in mental health was carried out across multiple disciplinary databases with defined Mesh terms and keywords. Our primary analysis evaluated biases within 'GloVe' and 'Word2Vec' word embeddings. Euclidean distances were measured to assess relationships between psychiatric terms and demographic labels, and vector similarity functions were used to solve analogy questions relating to mental health. Our primary analysis of mental health terminology in GloVe and Word2Vec embeddings demonstrated significant biases with respect to religion, race, gender, nationality, sexuality and age. Our literature review returned 52 papers, of which none addressed all the areas of possible bias that we identify in model development. In addition, only one article existed on more than one research database, demonstrating the isolation of research within disciplinary silos and inhibiting cross-disciplinary collaboration or communication. Our findings are relevant to professionals who wish to minimize the health inequalities that may arise as a result of AI and data-driven algorithms. We offer primary research identifying biases within these technologies and provide recommendations for avoiding these harms in the future.

Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases. The COVID-19 pandemic has resulted in an accelerated development of applications for digital health, including symptom monitoring and contact tracing. Their potential is wide ranging and must be integrated into conventional approaches to public health for best effect.

The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including \"updates about confirmed cases,\" \"COVID-19 related death,\" \"cases outside China (worldwide),\" \"COVID-19 outbreak in South Korea,\" \"early signs of the outbreak in New York,\" \"Diamond Princess cruise,\" \"economic impact,\" \"Preventive measures,\" \"authorities,\" and \"supply chain.\" Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.