Techonology and Decision Making Paper Hcs 482

Running head TECHNOLOGY AND DECISION MAKING engine room and ratiocination qualification University of Phoenix wellness assistance In fermentatics HCS/482 Richard Ong November 15, 2008 Technology and finale Making Technology, stopping point-making processes, and data accessibility consider adjustmentd dramatically in recent years. This authorship leave behind discuss governing bodys and informatics theories. The paper will confer on the selective selective selective information, Information, and Knowledge (DIK) Model. The fibre of expert organisation in nursing cargon and medicine will be provided. Decision aid and finis endure systems be white plagued every sidereal day providing focus, becomeership and direction within engine room and will be examined.The use of technology for forbearing and client oversight will be explored. An analysis of the impact of technology on health c ar and health status will be investigated. Systems and Informatics Theories Systems argon a group of interacting, interrelated, or interdependent elements forming a complex whole (Systems, n. d. , Definition). Systems take up health compassionate, schools, figurers, and a person. The systems are either open or closed. Closed systems are inoperable to function with others third party products and open systems are intentional to include third party products to plug in or interoperate with the system. uncomplete system interacts with the environment. Open systems consist of three characteristics purpose, functions, and structure (Englebardt and Nelson, 2002). Systems can conduct much than one purpose based on the needs of the user. Functions that the system will need to carry out need to be set for the system to chance on its purpose. The systems are structured in ways that allow them to perform their functions (Englebardt & Nelson, 2002, p. 6). The two types of models utilise to conceptualize the structure of a system hierarchical and web (Englebardt & Nel son, 2002).Some examples of system applications are institution wide, potency financial backing, documentation, administrations, operations, expert, stand alone information, and decision support. The study of health sell informatics incorporates theories from information Nursing science, computer science, cognitive science, along with other sciences employ in the health care tar (Englebardt & Nelson, 2002). Three models that represent the informatics theories are Shannon and weaver finchs information-communication model, Blums model and The Nelson data to lore continuum.Shannon and Weavers model states that a message starts with the sender and is converted to a codification by the encoder. The converted message can be letters, words, music, symbols or a computer code (Englebardt & Nelson, 2002). The message is carried by a channel and along with the message noise is transmitted in the space to the decoder where the message is converted to a format that is understood by the re ceiver. Bruce L. Blum developed a exposition of information from an analysis of the accomplishments in medical checkup computing (Englebardt & Nelson, 2002, p. 12). check to Blum the three types of health care computing applications are data, information and cognition (Englebardt & Nelson, 2002). information is information that is non interpreted. Data that is processed and displayed is categorized as information and when the data and information are combined and formalized knowledge results (Englebardt & Nelson, 2002). A knowledge base includes the interrelationship between the data and information (Englebardt & Nelson, 2002, p. 13). The Nelson Data to firmness Continuum states the quaternary types of health care computing applications are data, information, knowledge and wisdom.The four overlap at all times. Data is the naming, collecting and organizing the message. Information is except organizing and interpreting the message. Knowledge occurs when the message is interp reted, integrated and understood. Wisdom is the ability to recognize and apply the message with compassion. Data, Information and Knowledge Model Nursing informatics, as defined by the American Nurses Association(ANA), is a specialty that integrates nursing science, computer science and information science to manage and communicate data, information and knowledge in nursing practice (Newbold, 2008, para. 1).Decision making by healthcare professionals is based on the assimilation of data, information and knowledge to support uncomplaining care. Organizing data, information and knowledge for the processing by computers is accomplished through the use of information technology and information structures (Newbold, 2008). The first level is data which are recorded (captured and stored) symbols and signal readings (Liew, 2007, Definitions). Data is bits of information though to just restrain data is not meaningful to decision making. The second level is information which is organized, interpreted and communicated data between machines or homophiles. Characteristics of quality information are complete and clear in its descriptions, accurate, measurable, preferably by measurable heading means much(prenominal) as numbers, variable by independent observers, promptly entered, rapidly and easily available when needed, objective, rather than subjective, comprehensive, including all necessary information, distract to each users needs, clear and unambiguous, reliable, easy and convenient form to interpret, classify, store, return and up interlocking (Theoretical issues, 1998, Concepts).Knowledge is the third level of the model and is the hookup of information that is obtained from several sources to produce a concept used to achieve a basis for logical decision-making. The information needs to be usable and applied to be known as knowledge. The final level is Wisdom which is the highest level of being able to understand and apply knowledge use compassion (Theoret ical issues, 1998, Concepts). Information consists of data, unless data is not inescapably information. Also, wisdom is knowledge, which in turn is information, which in turn is data, but, for example, knowledge is not necessarily wisdom.So wisdom is a subset of knowledge, which is a subset of information, which is a subset of data (Steyn, 2001, para. 2). Without an sagaciousness of the source of data and information which is based on activities and situations, the relationship between data, information, and knowledge will not be understood (Liew, 2007). effective Systems in Nursing Care and Medicine Medical artificial intelligence is generally concerned with the structure of Artificial discussion (AI) programs that perform diagnosis and draw therapy recommendations. Unlike medical applications based on other programming methods, such as purely statistical and probabilistic methods, medical AI programs are based on symbolic models, such as statistical and probabilistic meth ods, medical AI programs are based on symbolic models of disease entities and their relationship to diligent factors and clinical manifestations as defined by Clancey and Shortliffe (1984). Expert systems (ES) in nursing care and medicine fill an appropriate role with intelligent programs offering significant make headways.They hold medical knowledge containing specifically defined tasks and are able to reason with data from soul forbearings responding with well-grounded conclusions. The advantages of an expert system over a doctor are 1. A king-size database of knowledge can be added and kept up to date with the ability of a large amount to be stored. 2. The system does not forget or get facts wrong. 3. The continued existence of the knowledge is unendingly not lost with death or retirement. 4. The computer can confound contact with specialist knowledge that a doctor may not keep. . The ES may shorten time to make the correct diagnosis and reduce diagnostic errors. 6. Co untries with a large number of population and have physicians are limited can receive medical knowledge leaders to prompt care. ESs are not replacing doctors or nurses but are being used by them stimulating an interrogated large database of knowledge of a human expert. Decision Aids and Decision bear Systems Decision support systems (DSS) are systems that model and provide support for human decision-making processes in clinical situations.They are advanced technologies that support clinical decision making by interfacing evidence-based clinical knowledge at the point of care with real-time clinical data at significant clinical decision points(Gregory, 2006, p. 21). Decision support systems offer various methods of decision support, including recommendations for diagnostic testing, exact lab pry alerts, suffice with diagnosis and advice for clinicians on what medications to use. According to the British Medical journal, Clinical decision support systems do not forever and a day improve clinical practice, however.In a recent systematic come off of computer based systems, most (66%) significantly improved clinical practice, but 34% did not (Kawanoto, Houlihan, Balas, & Lobach, 2005, p. 769). Decision support systems can improve patient outcomes however more studies are needed to develop better systems. Decisions by their very nature are uncertain, medical decisions have the added complexity of involving an individuals values and beliefs as related to the risk-benefit profiles or uncertain outcomes of medical treatment. The goal of using a decision aid is to help the patient make informed decisions based on his or her belief and value system.Limited and conflicting research on the use of decision aids makes it impossible to determine if having patients use a decision aid would benefit him or her. According to an article published in the Medical Decision Making Journal Decision aids are a assure new technological innovation in health care, however, like al l new innovation, their widespread adoption needs to be preceded by a careful evaluation of their electric potential harms, rather than an uncritical promotion of their potential benefits (Nelson, Han, Fagerlin, Stefanek, & Ubel, 2007, p. 617).Decision aids can be an important addition to promoting shared decision making between the physicians and patient however, decision aids may send the wrong message to patients about the goals of decision making, or lead patients to believe that they can reduce or eliminate uncertainty when confronting decisions (Nelson, Han, Fagerlin, Stefanek, & Ubel, 2007, p. 618) Technology for Patient and Client Management Technology can be used in many areas of patient and client management. Technology is said to have the potential to bring the patient and healthcare providers together creating patient-centered care.The goal of patient-centered care is to empower the patients, give patients choices and tailor treatment decisions based on the patients be liefs, values, cultural traditions, their family situations and their lifestyles. Technology impacts this concept when healthcare providers use clinical information systems such as enhanced patient registration systems which uses the mesh or onsite wireless devices, using decision aids and decision support systems, Teleproctoring Devices, and the electronic health record.New technology will help healthcare providers with patient management by increasing the ability of healthcare providers to retrieve and apply accurate information about their patients quickly and allow patients to acquire information to improve control of their diagnosis and or treatments and to talk with their healthcare providers. Technology on Healthcare and Health Status Analysis The rising holds many technological changes that will affect healthcare directly and help shape our already powerful profession.Technological advances will dramatically change healthcare providers roles and the healthcare delivery sys tems. Computers are not unusual for a patient to use to surf the Internet to breakthrough information related to the diagnosis. Patients may also browse the Internet and queue conditions here the symptoms are closely related to what he or she is experiencing. He reads all he can find, and when he goes to the doctor he may be informed, misinformed, or over-informed, regarding the possible diagnosis of his problem. Technology presents to the healthcare consumer a tremendous resource of information regarding his healthcare.Computers, biosensors, implants, genetic therapies, and imaging devices are examples of the acclivitous technologies of the 21st century. Medical artificial intelligence in contexts such as computer-assisted surgery, electrocardiography and fetal monitoring interpretation, clinical diagnosis, and genetic counseling will have a major impact on our future. Telemedicine currently ranges from radiographic consultations across cities to telebiotic surgeries across hemi spheres (Cohen, Furst, Keil & Keil, 2006). Interactive disks already assist patients to make more independent medical decisions regarding their care.Devices for home use can help monitor blood pressure and blood glucose or perform a motherhood test. Technology also helps assist patients with finding information regarding a diagnosis. Although technology is very beneficial to healthcare other concerns continue to exist. Every day healthcare providers use complex machinery, including many types of monitors, ventilators, intravenous pumps, feeding pumps, sucking devices, electronic beds and scales, lift equipment, and assistive devices. The directions for use of many of these machines are not self-evident and may be highly complicated.As a result, near patients may endure injury secondary to misuse of the product (Cohen, Furst, Keil & Keil, 2006). The come with may also incur unexpected expenses if the equipment becomes damaged and need to be replaced. Similarly, new computer system s present many learning difficulties for healthcare providers. galore(postnominal) computer systems are not user friendly. Computer systems designers are ill-famed for supplying computers with numerous advanced but obscure functions, but these systems a lot lack the ability to make daily tasks easier t accomplish. Millions of dollars have een small on computer systems that are not used or are underused because the user needs were not assessed before the systems were designed (Thielst, 2007). There watch three basic reasons for the continued increase in healthcare cost inflation, increased demand for services as a result of federal programs such as Medicare and Medicaid, and expensive technological advances in medicine. Conclusion In conclusion, significant economic and social trends are dramatically altering the forms of healthcare delivery in the United States and the roles played by healthcare providers.Advances in technology, globalization of culture and communication, ever -widening computer applications, aging of the population, and dynamic changes in the healthcare industry are among major developments (Thielst, 2007). To cope with and to contribute to the future of healthcare, the healthcare team must understand how computers are now being used in healthcare, and they must be able to work with computers in a cost-effective manner in their healthcare practice.No matter what delivery system is in place in a particular institution, healthcare providers will find that each is vitally involved with ensuring quality and in discovering measurable ways of monitoring quality. References W. J. Clancey and E. H. Shortliffe, eds. (1984). Readings in Medical Artificial Intelligence First Decade. Reading, Massachusetts Addison-Wesley. Cohen, T. , First, E. , Keil, O. & Wang, B. (2006). Medical equipment management strategies. Biomedical Instrumentation & Technology, 40(3), 233-238.Englebardt, S. P. , & Nelson, R. (2002). Health care informatics An interdisciplin ary approach. St. Louis, MO Mosby Elsevier. Gregory, A. (2006, January/March). Issues of Trust and Ethics in Computerized Clinical Decision Support Systems. Nursing Administration Quarterly, 30(1), Pp. 21-29. Kawanoto, K. , Houlihan, C. , Balas, A. , & Lobach, D. (2005, April 2). Improving clinical practice by using clinical decision support systems A systematic review of trials to identify features critical to success. BMJ, 330, P. 765-700. Liew, A. (2007, June).Understanding data, information, knowledge and their relationship. Retrieved November 10, 2008, from Journal of Knowledge Management Practice http//www. tlainc. com/article 134. htm Nelson, W. , Han, P. , Fagerlin, A. , Stefanek, M. , & Ubel, P. (2007, October 1, 2007). Rethinking the Objectives of Decision Aids A Call for Conceptual Clarity. Medical Decision Making, 27(5), Pp. 609-618. Newbold, S. (2008). A new definition for nursing informatics. Retrieved November 10, 2008, from Advance for Nurses http//nursing. advancewe b. com/Article/A-New-Definition-for-Nursing-Informatics. spx Steyn, J. (2001). Data, information, knowledge and wisdom. Retrieved November 12, 2008, from Knowsystem http//knowsystems. com/km/definition. html System. (n. d. ). Retrieved November 11, 2008, from Answers. com http//www. answers. com/ payoff/system Theoretical Issues. (1998). Retrieved November 10, 2008, from University of Texas at Tyler http//www. uttyler. edu/nursing/ckilmon/ni/theory. htm Thielst, C. (2007). The future of healthcare technology. Journal of Healthcare Management, 52(1), 7-10. Retrieved from ProQuest database on November 11, 2008.