Techonology and Decision Making Paper Hcs 482

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Running head: TECHNOLOGY AND DECISION MAKING Technology and Decision Making University of Phoenix Healthcare Informatics HCS/482 Richard Ong November 15, 2008 Technology and Decision Making Technology, decision-making processes, and data accessibility have changed dramatically in recent years. This paper will discuss systems and informatics theories. The paper will confer on the Data, Information, and Knowledge (DIK) Model. The role of expert system in nursing care and medicine will be provided. Decision aids and decision support systems are used everyday providing focus, leadership and direction within technology and will be examined.
The use of technology for patient and client management will be explored. An analysis of the impact of technology on healthcare and health status will be investigated. Systems and Informatics Theories Systems are “a group of interacting, interrelated, or interdependent elements forming a complex whole” (Systems, n. d. , Definition). Systems describe healthcare, schools, computers, 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 designed to allow third party products to plug in or interoperate with the system.
Neither system interacts with the environment. Open systems consist of three characteristics; purpose, functions, and structure (Englebardt and Nelson, 2002). Systems can have more than one purpose based on the needs of the user. Functions that the system will need to carry out need to be identified for the system to achieve 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 used to conceptualize the structure of a system; hierarchical and web (Englebardt & Nelson, 2002).



Some examples of system applications are; institution wide, specialty support, documentation, administrations, operations, expert, stand alone information, and decision support. The study of healthcare informatics incorporates theories from information Nursing science, computer science, cognitive science, along with other sciences used in the healthcare delivery (Englebardt & Nelson, 2002). Three models that represent the informatics theories are; Shannon and Weaver’s information-communication model, Blum’s model and The Nelson data to wisdom continuum.
Shannon and Weaver’s model states that a message starts with the sender and is converted to a code 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 receiver. “Bruce L. Blum developed a definition of information from an analysis of the accomplishments in medical computing” (Englebardt & Nelson, 2002, p. 12).
According to Blum the three types of healthcare computing applications are; data, information and knowledge (Englebardt & Nelson, 2002). Data is information that is not 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 Wisdom Continuum states the four types of healthcare 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 further organizing and interpreting the message. Knowledge occurs when the message is interpreted, integrated and understood. Wisdom is the ability to understand 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 patient 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 have data is not meaningful to decision making. The second level is information which is organized, interpreted and communicated data between machines or humans. Characteristics of quality information are: complete and clear in its descriptions, accurate, measurable, preferably by measurable objective means such as numbers, variable by independent observers, promptly entered, rapidly and easily available when needed, objective, rather than subjective, comprehensive, including all necessary information, appropriate to each user’s needs, clear and unambiguous, reliable, easy and convenient form to interpret, classify, store, retrieve and update” (Theoretical issues, 1998, Concepts).
Knowledge is the third level of the model and is the collection 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 useful 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 using compassion” (Theoretical issues, 1998, Concepts). “Information consists of data, but data is not necessarily 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 understanding 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). Expert Systems in Nursing Care and Medicine Medical artificial intelligence is primarily concerned with the structure of Artificial Intelligence (AI) programs that perform diagnosis and make 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 methods, medical AI programs are based on symbolic models of disease entities and their relationship to patient 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 benefits.
They hold medical knowledge containing specifically defined tasks and are able to reason with data from individual patients responding with reasoned conclusions. The advantages of an expert system over a doctor are: 1. A large 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 forever not lost with death or retirement. 4. The computer can make contact with specialist knowledge that a doctor may not have. . The ES may shorten time to make the correct diagnosis and reduce diagnostic errors. 6. Countries with a large number of population and have physicians are limited can receive medical knowledge leading to prompt care. ES’s 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 Support 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, critical lab value alerts, help with diagnosis and advice for clinicians on what medications to use. According to the British Medical Journal, “Clinical decision support systems do not always improve clinical practice, however.
In a recent systematic review 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 individual’s 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 promising new technological innovation in health care, however, like any new innovation, their widespread adoption needs to be preceded by a careful evaluation of their 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 patient’s beliefs, 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 internet or onsite wireless devices, using decision aids and decision support systems, Telemonitoring 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 future holds many technological changes that will affect healthcare directly and help shape our already powerful profession.
Technological advances will dramatically change healthcare provider’s roles and the healthcare delivery systems. Computers are not unusual for a patient to use to surf the Internet to find information related to the diagnosis. Patients may also browse the Internet and find 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 emerging 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 hemispheres (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 pregnancy 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, suction 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, some patients may endure injury secondary to misuse of the product (Cohen, Furst, Keil & Keil, 2006). The company may also incur unexpected expenses if the equipment becomes damaged and need to be replaced. Similarly, new computer systems present many learning difficulties for healthcare providers. Many computer systems are not user friendly. Computer systems designers are notorious for supplying computers with numerous advanced but obscure functions, but these systems often lack the ability to make daily tasks easier t accomplish. Millions of dollars have een wasted 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 remain three basic reasons for the continued increase in healthcare costs: 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 interdisciplinary 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. advanceweb. 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/topic/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.

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