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The enactment of the Patient Protection and Affordable Care Act is a signal achievement on the road to reform, which arguably began with the passage of the American Recovery and Reinvestment Act of 2009. That statute's Health Information Technology for Economic and Clinical Health (HITECH) provisions created an essential foundation for restructuring health care delivery and for achieving the key goals of improving health care quality; reducing costs; and increasing access through better methods of storing, analyzing, and sharing health information. This article discusses the range of initiatives under HITECH to support health reform, including proposed regulations on "meaningful use" and standards; funding of regional extension centers and Beacon communities; and support for the development and use of clinical registries and linked health outcomes research networks, all of which are critical to carrying out the comparative clinical effectiveness research that will be expanded under health reform.

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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 underappreciated 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 socialeconomically 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.

BackgroundSleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon.ObjectiveOur aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues.MethodsTwitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, “can’t sleep”, Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues.ResultsUser data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (P<.001), and fewer followers (P<.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets (P<.001), indicating a possible relationship between sleep and pyschosocial issues.ConclusionsWe have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered.