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Broad view of the technology landscape.

Broad view of the technology landscape.

The goal of this assignment is get a broad view of the technology landscape and what types of technologies are out there.
Using mind-mapping software of your choice, create a mind map of the different healthcare information technologies and parse it out into different categories.
Review the Mind Map document for an example of this assignment. You can use any of the following mind-mapping tools for your assignment or use a tool of your choice.
Then, in a Word document, complete the following:
• How has the healthcare technology landscape changed?
• Are the healthcare technologies used for the same purpose? Explain.
• How can they be used across departments?
• How do healthcare technologies impact the roles across the industry?
Guidelines for Submission: Your mind map should be created using free software of your choice. The answers to the questions should be in a Word document and should be a minimum of 1 page in length. All sources should be cited using APA style.

The patterns surfacing in technological innovation and information design landscaping advise we have been going into a period of technology change that could be as substantial since the shift from mainframe architectures to buyer/web server architectures from the 1980s. Whereas PCs and client/server software made it possible to distribute both applications and data closer to their users in the 1980s, the next-generation technology architecture will distribute even smaller units of software over the Internet directly to distant users as well as directly to devices and objects such as equipment on the factory floor, packages on store shelves or servers and hardware devices in a partner organization. Using sophisticated messaging, open-source solutions and new security protocols, data processing and information exchange will become tightly connected to business processes, facilitating new kinds of collaboration, partnering and outsourcing relationships. The individual movements that are fueling this next-generation architecture scenario have been percolating for some time. The unprecedented spread of data exchange standards like TCP/IP, XML and MP3, and broad access to nonproprietary networking and data communications infrastructure (the Internet) have supported rising technology waves and strong development undercurrents. Many experts say that the combination of new standards, distributed software and a worldwide Internet infrastructure will create a profoundly new technology architecture landscape within the next five years. We identified the rapid adoption of collaboration technologies earlier in this document. In this section we will explore four additional aspects of this technology landscape that will likely impact information creation, dissemination and management. We will conclude by providing a framework for analyzing some of the specific applications, technologies and standards that will be the building components of this new environment.

A skim in the technologies landscaping recognizes elevated purchases in technology and criteria which allow organizations to take structure to unstructured details. In the interviews OCLC staff did with 100 professionals actively engaged in the creation, management and dissemination of information, there was a clearly expressed interest in technologies and methods that will allow information professionals (and end users) to bring structure to the vast amount of unstructured data that is available in today’s Information Mall. Increased user interest in unstructured or uncataloged information such as historical photograph collections, audio clips, research notes, genealogy materials and other riches hidden in library special collections has ignited conversations of how best to create metadata and methods to ensure dynamic and meaningful links to and among these currently unstructured information objects. This drive to bring structure to unstructured data is being spurred by not only the library and information community, but by the business and government communities worldwide. It is estimated that 85 percent of the content in an enterprise is unstructured content3 and as enterprises look for new forms of competitive advantages, they are working to harness the power of this unstructured data. Two dominant technical and structural approaches have emerged: a reliance on search technologies and a trend towards automated data categorization.

Lookup technology Using the Online at 6 billion web pages and developing, and organizational info page numbers dwarfing that figure, locating everything you what if you want it may be a difficult process. This problem has dominated the technology landscape in the last several years. The “killer app” solution is “search.” Searching has become an international pastime. Over 625 million searches are conducted on the top eight search engines each day.4 Yet, even after five years of rapid growth, search engine technology is considered by many analysts to be in its early stages. The search engine arena is highly competitive, with nearly a hundred solutions on the market from companies ranging from upstarts like Endeca to the leaders Google, Yahoo! and Microsoft. The following chart provides a brief overview of the top search technologies and sample vendors.

One 2002 estimate implies that Google search engines deal with far more inquiries per day along with a 50 % than every one of the libraries from the U.S. supply every year.6 There is little question how the speedy adoption of research technologies have significantly greater the strength and efficiency of the World Wide Web. Savvy Web users have become experts at maximizing search techniques to achieve the desired output but are also beginning to demand more sophisticated (or more structured) search methodologies. A group of high school students interviewed for this scan discussed how they have learned search techniques to find the information they need for school projects.

As users become more knowledgeable and more discriminating, the shortcomings of current search solutions are surfacing. While many students had become very skilled at finding what they wanted, all focus group participants felt that easier search methods are needed. The experts agree. Finding known objects in huge search spaces, assembling top-down overviews that summarize the important points of a topic, and helping searchers decide what they really want when their initial search ideas are confused, misguided or ambiguous are casting doubts on the long-term viability of today’s search techniques.

A number of info company and outline systems and methodologies are becoming popular as strategies to street address the void. Data organization techniques that library science has utilized for decades are becoming popular and important outside the information management community. “The demand, outside the library community, for information about data organization and metadata is exploding,”9 say Gartner, Inc. technology analysts. In 2003 Gartner issued several research notes on metadata including, Enterprises Need a Metadata Integration Strategy10 and Taxonomy Creation: Bringing Order to Complexity. 11 Many data categorization techniques are being applied across the landscape including: taxonomies, semantics, natural-language recognition, autocategorization, “what’s related” functionality, data visualization, personalization and more. All techniques aim to help searchers find what they really want. Data categorization is not new. “At one time, researchers speculated that solving such search problems might require artificial intelligence: systems that simulated human thought and could behave like skilled reference librarians. […] Until recently, however, IT applications required paid humans to think up the category names, define their relationships and write the rules that channeled data into the proper boxes. As a result, the technique was limited to fields with big budgets, such as financial analysis or defense. During the past few years, however, technology development has made it much easier to automate or at least semiautomate categorization.”12 Data categorization techniques are moving from manual activities, done by librarians and other information professionals, to automated processes executed on behalf of users. “More and more information travels with a lengthening entourage of data about itself. Autocategorization software recognizes and leverages that data.”13 Information professionals have an opportunity to leverage these new technologies to bring information management methods to a large portion of today’s born-digital content.