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Abstract Background Mobile Clinics represent an untapped resource for our healthcare system. The COVID-19 pandemic has exacerbated its limitations. Mobile health clinic programs in the US already play important, albeit under-appreciated roles in the healthcare system. They provide access to healthcare especially for displaced or isolated individuals; they offer versatility in the setting of a damaged or inadequate healthcare infrastructure; and, as a longstanding community-based service delivery model, they fill gaps in the healthcare safety-net, reaching social-economically underserved populations in both urban and rural areas. Despite an increasing body of evidence of the unique value of this highly adaptable model of care, mobile clinics are not widely supported. This has resulted in a missed opportunity to deploy mobile clinics during national emergencies such as the COVID-19 pandemic, as well as using these already existing, and trusted programs to overcome barriers to access that are experienced by under-resourced communities. Main text In March, the Mobile Healthcare Association and Mobile Health Map, a program of Harvard Medical School’s Family Van, hosted a webinar of over 300 mobile health providers, sharing their experiences, challenges and best practices of responding to COVID 19. They demonstrated the untapped potential of this sector of the healthcare system in responding to healthcare crises. A Call to Action: The flexibility and adaptability of mobile clinics make them ideal partners in responding to pandemics, such as COVID-19. In this commentary we propose three approaches to support further expansion and integration of mobile health clinics into the healthcare system: First, demonstrate the economic contribution of mobile clinics to the healthcare system. Second, expand the number of mobile clinic programs and integrate them into the healthcare infrastructure and emergency preparedness. Third, expand their use of technology to facilitate this integration. Conclusions Understanding the economic and social impact that mobile clinics are having in our communities should provide the evidence to justify policies that will enable expansion and optimal integration of mobile clinics into our healthcare delivery system, and help us address current and future health crises.

Abstract Background Mobile health clinics serve an important role in the health care system, providing care to some of the most vulnerable populations. Mobile Health Map is the only comprehensive database of mobile clinics in the United States. Members of this collaborative research network and learning community supply information about their location, services, target populations, and costs. They also have access to tools to measure, improve, and communicate their impact. Methods We analyzed data from 811 clinics that participated in Mobile Health Map between 2007 and 2017 to describe the demographics of the clients these clinics serve, the services they provide, and mobile clinics’ affiliated institutions and funding sources. Results Mobile clinics provide a median number of 3491 visits annually. More than half of their clients are women (55%) and racial/ethnic minorities (59%). Of the 146 clinics that reported insurance data, 41% of clients were uninsured while 44% had some form of public insurance. The most common service models were primary care (41%) and prevention (47%). With regards to organizational affiliations, they vary from independent (33%) to university affiliated (24%), while some (29%) are part of a hospital or health care system. Most mobile clinics receive some financial support from philanthropy (52%), while slightly less than half (45%) receive federal funds. Conclusion Mobile health care delivery is an innovative model of health services delivery that provides a wide variety of services to vulnerable populations. The clinics vary in service mix, patient demographics, and relationships with the fixed health system. Although access to care has increased in recent years through the Affordable Care Act, barriers continue to persist, particularly among populations living in resource-limited areas. Mobile clinics can improve access by serving as a vital link between the community and clinical facilities. Additional work is needed to advance availability of this important resource.

Abstract Background There are approximately 2000 mobile health clinics operating in the United States. While researchers have established that mobile health clinics can be cost effective and improve outcomes, there is scant research examining the healthcare experience on a mobile health clinic from patients’ perspectives. Methods Data were gathered from interviews with 25 clients receiving care on a Boston-based mobile health clinic and analyzed using grounded theory methodology. Results Emerging patterns in the data revealed three relational and three structural factors most significant to participants’ experience of care on The Family Van. Relational factors include providers who 1) Communicate understandably, 2) Create a culture of respect and inclusivity, and 3) Are diverse with knowledge of the community. Structural factors include 1) A focus on preventative health and managing chronic disease, 2) Expeditious, free, and multiple services, and 3) Location. Conclusions The participant accounts in this report serve to expand on prior research exploring mobile health clinics’ role in patients’ healthcare, to more clearly define the most salient aspects of the mobile health clinic model for the patients they serve, and to give voice to patients too seldom heard in the academic literature.

Abstract As the U.S. healthcare system transforms its care delivery model to increase healthcare accessibility and improve health outcomes, it is undergoing changes in the context of ever-increasing chronic disease burdens and healthcare costs. Many illnesses disproportionately affect certain populations, due to disparities in healthcare access and social determinants of health. These disparities represent a key area to target in order to better our nation’s overall health and decrease healthcare expenditures. It is thus imperative for policymakers and health professionals to develop innovative interventions that sustainably manage chronic diseases, promote preventative health, and improve outcomes among communities disenfranchised from traditional healthcare as well as among the general population. This article examines the available literature on Mobile Health Clinics (MHCs) and the role that they currently play in the U.S. healthcare system. Based on a search in the PubMed database and data from the online collaborative research network of mobile clinics MobileHealthMap.org , the authors evaluated 51 articles with evidence on the strengths and weaknesses of the mobile health sector in the United States. Current literature supports that MHCs are successful in reaching vulnerable populations, by delivering services directly at the curbside in communities of need and flexibly adapting their services based on the changing needs of the target community. As a link between clinical and community settings, MHCs address both medical and social determinants of health, tackling health issues on a community-wide level. Furthermore, evidence suggest that MHCs produce significant cost savings and represent a cost-effective care delivery model that improves health outcomes in underserved groups. Even though MHCs can fulfill many goals and mandates in alignment with our national priorities and have the potential to help combat some of the largest healthcare challenges of this era, there are limitations and challenges to this healthcare delivery model that must be addressed and overcome before they can be more broadly integrated into our healthcare system.

Abstract In additive manufacturing of metal parts, the ability to accurately predict the extremely variable temperature field in detail, and relate it quantitatively to structure and properties, is a key step in predicting part performance and optimizing process design. In this work, a finite element simulation of the directed energy deposition (DED) process is used to predict the space- and time-dependent temperature field during the multi-layer build process for Inconel 718 walls. The thermal model results show good agreement with dynamic infrared images captured in situ during the DED builds. The relationship between predicted cooling rate, microstructural features, and mechanical properties is examined, and cooling rate alone is found to be insufficient in giving quantitative property predictions. Because machine learning offers an efficient way to identify important features from series data, we apply a 1D convolutional neural network data-driven framework to automatically extract the dominant predictive features from simulated temperature history. Very good predictions of material properties, especially ultimate tensile strength, are obtained using simulated thermal history data. To further interpret the convolutional neural network predictions, we visualize the extracted features produced on each convolutional layer and compare the convolutional neural network detected features of thermal histories for high and low ultimate tensile strength cases. A key result is the determination that thermal histories in both high and moderate temperature regimes affect material properties.

Abstract Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the materials design process. To this end, we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperature sequences, i.e., thermal histories. The framework enables multiresolution analysis and importance analysis to reveal dominant mechanistic features underlying the additive manufacturing process, such as critical temperature ranges and fundamental thermal frequencies. We systematically compare the developed approach with other machine learning methods. The results demonstrate that the developed approach achieves reasonably good predictive capability using a small amount of noisy experimental data. It provides a concrete foundation for a revolutionary methodology that predicts spatial and temporal evolution of mechanical properties leveraging domain-specific knowledge and cutting-edge machine and deep learning technologies.

Mitochondrial methionyl-tRNA formyltransferase (MTFMT) is a nuclear-encoded gene that produces a protein involved in mitochondrial translation. MTFMT formylates a portion of Met-tRNAMet, which allows for translation initiation of mitochondrial mRNA. Mutations in this gene have been shown to result in decreased mitochondrial translation with reduction function of the electron transport chain complexes I, III, IV, and V, thus affecting cellular energy production. Our patient presented with severe lactic acidosis in the neonatal period, and was found to be homozygous for the pathogenic mutation c.994C > T, p.(Arg332*). Her blood lactate levels normalized and her cardiomyopathy reversed after initiation of dichloroacetate (30 mg/kg/day). After two years of follow-up, she continues to show long-term lactate stability, continues to make developmental gains, and is in overall good general health. This is the first report using dichloroacetate in a patient with MTFMT deficiency, which may be a potential therapeutic option that warrants further study.