The health service bus: An architecture and case study in achieving interoperability in healthcare

Interoperability in healthcare is a requirement for effective communication between entities, to ensure timely access to up to-date patient information and medical knowledge, and thus facilitate consistent patient care. An interoperability framework called the Health Service Bus (HSB), based on the Enterprise Service Bus (ESB) middleware software architecture is presented here as a solution to all three levels of interoperability as defined by the HL7 EHR Interoperability Work group in their definitive white paper “Coming to Terms”. A prototype HSB system was implemented based on the Mule Open-Source ESB and is outlined and discussed, followed by a clinically-based example

The health service bus: An architecture and case study in achieving interoperability in healthcare

Interoperability in healthcare is a requirement for effective communication between entities, to ensure timely access to up to-date patient information and medical knowledge, and thus facilitate consistent patient care. An interoperability framework called the Health Service Bus (HSB), based on the Enterprise Service Bus (ESB) middleware software architecture is presented here as a solution to all three levels of interoperability as defined by the HL7 EHR Interoperability Work group in their definitive white paper “Coming to Terms”. A prototype HSB system was implemented based on the Mule Open-Source ESB and is outlined and discussed, followed by a clinically-based example

Sentiment analysis of health care tweets: review of the methods used.

BACKGROUND: Twitter is a microblogging service where users can send and read short 140-character messages called "tweets." There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. OBJECTIVE: The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. METHODS: A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. RESULTS: A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study's final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. CONCLUSIONS: Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting-specific corpus of manually annotated tweets first

City of Ideas: Reinventing Boston's Innovation Economy: The Boston Indicators Report 2012

Analyzes indicators of the city's economic, social, and technological progress; potential for creating innovative solutions to global and national challenges; and complexities, disparities, and weaknesses in the indicators and innovation economy paradigm

Harnessing Openness to Transform American Health Care

The Digital Connections Council (DCC) of the Committee for Economic Development (CED) has been developing the concept of openness in a series of reports. It has analyzed information and processes to determine their openness based on qualities of "accessibility" and "responsiveness." If information is not available or available only under restrictive conditions it is less accessible and therefore less "open." If information can be modified, repurposed, and redistributed freely it is more responsive, and therefore more "open." This report looks at how "openness" is being or might usefully be employed in the healthcare arena. This area, which now constitutes approximately 16-17 percent of GDP, has long frustrated policymakers, practitioners, and patients. Bringing greater openness to different parts of the healthcare production chain can lead to substantial benefits by stimulating innovation, lowering costs, reducing errors, and closing the gap between discovery and treatment delivery. The report is not exhaustive; it focuses on biomedical research and the disclosure of research findings, processes of evaluating drugs and devices, the emergence of electronic health records, the development and implementation of treatment regimes by caregivers and patients, and the interdependence of the global public health system and data sharing and worldwide collaboration

Contrasting Community Building in Sponsored and Community Founded Open Source Projects

Prior characterizations of open source projects have been based on the model of a community-founded project. More recently, a second model has emerged, where organizations spinout internally developed code to a public forum. Based on field work on open source projects, we compare the lifecycle differences between these two models. We identify problems unique to spinout projects, particularly in attracting and building an external community. We illustrate these issues with a feasibility analysis of a proposed open source project based on VistA, the primary healthcare information system of the U.S. Department of Veterans Affairs. This example illuminates the complexities of building a community after a code base has been developed and suggests that open source software can be used to transfer technology to the private sector