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Technology and Decision Making Essay

The quality of patient care, communication between health care staff, and the safety of patients has greatly improved since the onset of technology. Through the improvement of information technology, the ability to collect data and manage the decisions based on the data collected has enhanced in the clinical setting as well as in the business portion. Health care informatics incorporates theories from informational science, computer science, and cognitive science (Englebardt & Nelson, 2002). This information helps to gather and process it in order to make an informed decision.

Important information could be missed if the data is ignored. Some of the most recent technology which includes the internet and cell phones has made it possible to access information quickly in order to make the best decision for the patient in order to provide good quality care. Technology changes every day and it is important to keep up with these changes that will help support clinical decisions made by the caregivers. This paper on informatics will show the systems and information theories, the DIK model, and the role of the expert system in nursing care and medicine.

System and information theories

System. “A system is a set of related interacting parts enclosed in a boundary” (Englebardt & Nelson, 2002, p.5). There are many types of systems which include but are not limited to: computer systems, school systems, health care systems, and people. Systems can be living or nonliving, open or closed. Closed systems do not act with the environment whereas open system have the ability to act with the environment. Open systems can be used to understand technology and those individuals associated with its use. This type of system takes input from the environment, processes it, and then returns it back to the environment as output, which serves as feedback.

This theory can better help the individual understand the way people work with systems in the health care industry and allow for a visualization of the whole picture. A common term using in computer science is GIGO, “garbage in, garbage out”. This applies in the sense that a system is only as good as its user. If the user is inputting garbage, or poor quality data, the computer is likely to output the same. A system requires an accurate source in order for accurate material to be produced as a result. Open systems have three types of characteristics which include: purpose, functions, and structure (Englebardt & Nelson, 2002). The purpose is the reason for the existence of the system or the program and is most often stated in the organization’s mission statement. This is true for health care organizations, churches, and schools.

For example, the mission statement of the local public health department to promote health, prevent illness, and control communicable disease by providing quality services, health education, and environmental services for the community. Computer systems are often classified by their purpose and may have more than one purpose. By selecting a purpose that all individuals agree upon within the organization, a system can be chosen. It is important to take the time to identify the purpose with all those who will be using the system. Functions identify the methods in which the system will achieve its purpose. “Functions are activities that a system carries out to achieve its purpose” (Englebardt & Nelson, 2002, p.6). When a computer system is chosen a list of functional specification must be put in writing to identify each function and how it will be performed. Systems are structured to allow the functions to be carried out.

Some examples of structured systems include the nursing department. The nurse in charge will assign patients to the staff nurses with the purpose to provide care. The charge nurse will ensure that the team is functioning with the ability to provide the care the patient needs and deserves. Two different models can be used to visualize the structure of a system: hierarchical and web. In the hierarchical model, each computer is a part of the local area network (LAN) which in turn is part of a wide area network (WAN) that is connected to the mainframe computer system. The mainframe is the leader of the system or lead part. The web model functions much like that of a spider-web. It has the capability to pass information to many departments that may use it for different purposes. For example, laboratory results may be sent to the pharmacy to calculate a medication dosage and patient vitals may be sent to another department for review and use. “A system includes structural elements from both the web and hierarchical model” (Englebardt & Nelson, 2002, p.7).

Everything living or nonliving are in a constant state of change. Six concepts are helpful in understanding the change process: 1)dynamic homeostasis, 2)entropy, 3)negentropy, 4) specialization, 5)reverberation, and 6)equifinality. Dynamic homeostasis consists of maintaining an equal balance within the system. At times, increased stress can throw off the balance and cause challenges to the organization. A health care informatics specialist’s job is to decrease the stress and restore the balance within the organization. Entropy can be best described as the tendency of the system to break down into parts. This can be the loss of some data when transmitted from one department to another. All systems, living or nonliving, reach a point where they are no longer repairable. When this point is reached, a system must be replaced. Negentropy is the opposite of entropy and is best described as the system’s ability to multiply and become more complex. As the size of the health care industry grows, so do the health care information systems. Information technology. “Information technology has the potential to greatly streamline healthcare and greatly reduce the chance of human error.

However, there is a growing literature indicating that if systems are not designed adequately they may actually increase the possibility of error in the complex interaction between clinician and machine in healthcare” (Borycki, E., Kushniruk, A., Brender, J., 2010, p. 714). The term information has more than one meaning and the term information theory refers to multiple theories. The two common theoretical theories of information theories are: Shannon and Weaver’s information-communication model and Blum’s model (Englebardt & Nelson, 2002, p. 10). The information theory was presented as a formal theory in 1948 with a publication by Claude Shannon titled “A Mathematical Theory of Communication”. In this theory, the sender is the originator of the message and then the encoder converts the message into a code.

A code can be a number, symbol, letters, or words. The decoder then converts the message to a format that can be recognized by the receiver. Shannon was a telephone engineer and explained this theory in a way that the decoder was the telephone converting sound waves into a message the receiver could understand. “Warren Weaver, from the Sloan-Kettering Institute for Cancer Research, provided the interpretation for understanding the semantic meaning of a message” (Englebardt & Nelson, 2002, p. 12). He used Shannon’s works to explain the interpretational aspects of communication as each individual perceives things different from the next. Different types of circumstances may occur causing a message to be interpreted wrong.

For example, if a physician is using medical terminology that the patient cannot understand there is definitely a communication problem. If the patient cannot hear what is being said because the ear is not transmitting sound, then there is a different type of communication problem. The message must convey meaning and produce the intended result. Bruce L. Blum defined three types of health care computing applications called Blum’s Model. He grouped these applications in data, information, or knowledge. Data are those things such as height, weight, age, and name. Information is defined as data that has been processed. Knowledge is the relationship between data and information. Using these concepts, it is possible to identify different levels of computing and automated systems.

Data, Information, and Knowledge (DIK) model

Healthcare informatics can be explained using a model consisting of three parts: data, information, and knowledge (Georgiou, 2002). The three parts are demonstrated using a hierarchy pyramid. Data is the platform in the model, representing the foundation. Data is represented as facts and observations, but without supporting context, the data is irrelevant. Until the information is validated or manipulated the data is not significant, once it is manipulated, the data can provide value to the user. Information is the product of data once the data has been manipulated. The result of data and information is evidence-based knowledge. Evidence based knowledge can be used to support evidence based medicine. Some individuals feel that too much focus has been put on data, limiting the ability to practice medicine as a science. Instead, the use of data suggests that medicine is being practiced based on statistics instead of science.

Yet, the same critics will use the same hierarchy of data, information and knowledge to treat a patient that develops a fever after hip surgery. The fever alone does not provide significant information but combined with information of a recent surgery, a physician will test further for signs of infection. The end result is the knowledge of why the patient is feverish. Viewing informatics in the form of the decision-information-knowledge (DIK) model allows individuals to see the process as a whole. The data must be accurately representing what is occurring or the information will not be accurate. The statement, “dirty in, dirty out,” can be applied to the platform of the model. It is essential that clean data be entered into the system, allowing clean data and information to be produced. The product, knowledge, can then be substantiated through the evidence produced. Just as evidence is used to make clinical decisions, the DIK model is used, in conjunction with the scientific information, to build evidence based medicine. Health informatics involves spreading and distributing information as just one piece of the process of producing knowledge which is multifaceted (Georgiou, 2002).

The role of expert system in nursing care and medicine

Nurses and other health care professionals make decisions on a daily basis that affect patients’ care and treatment. Nurses and health care professionals are not expert in all areas of nursing care and medicine. Health care workers specialized in certain area or field of medicine or nursing are not always readily available to everyone. Expert systems have been developed to assist medical and health care providers with decisions about care and treatment of patient. An expert system is a knowledge-based computer program designed to “enhance the human ability to analyze, problem solve, treat, diagnose, and estimate prognosis of health-related conditions” (Englebardt& Nelson, 2002, p. 114). “Nursing expert systems can improve the overall quality of care when designed for the appropriate end-user group and based on a knowledge base reflecting nursing expertise” (Courtney, Alexander, and Demiris, 2008, P. 697).

Examples of expert systems include MYCIN, a system that advise physicians about antimicrobial selection for patients with meningitis or bacteremia and INTERNIST-1, a system that assist with diagnosing complex problems in general internal medicine (Shortliffe, 1986). Health care workers may not always have the knowledge base to diagnose and treat every condition or situation encountered. Expert systems are used to close the gap in knowledge providing effective, efficient, and accurate care. The concept of expert system is driven by the desire to improve patient care, reduce cost, and disseminate expert knowledge. Expert systems are used just as x-rays and lab values are obtained to improve the human understanding of a patient’s condition. The human memory has limitations. Expert systems can be the answer to eliminating a large number of preventable medical mistakes. This system can alert health care workers about drug interactions and allergies, and provide preferable form of treatment. Expert systems can assist in diagnostic suggestions, testing prompts, therapeutic protocols, and practice guidelines.

The utilization of expert systems has an impact on the quality of care, economy, and medical education of staff. Expert systems, when used effectively can improve patient outcomes and decrease health care costs. Fewer mistakes lead to lower financial expenditures and increased profits. Improved quality of care result in improved patient satisfaction that leads to increased reimbursement from Medicare and Medicaid. Expert systems can also decrease the variation in medical practice emphasizing standardized and evidence-based practice of care. Along with expert systems, decision aids and decision support systems are used to improve patient care.

The use of decision aids and decision support systems

Clinical decision aids help to identify solutions to clinical situations. Decision aids can be either paper-form or electronic. The electronic decision aids can be accessed via recorded media or the Internet. Decision aids are utilized to facilitate shared decisions between the patient and interdisciplinary team taking care of them. They help the patient to think about the multiple decisions they must make in the course of their treatment regimen. An example is the Ottawa Patient Decision Aid. This decision aid helps to determine whether or not patients should seek antibiotics for bronchitis. Another example is a decision aid about whether or not someone should place his or her family in a long-term care facility for Alzheimer’s disease (Englebardt & Nelson, 2002). A decision support system (DSS) is an interactive, flexible, and adaptable computer-based information system (CBIS), which was made to support decision-making as it relates to the solution of an individual problem.

“A clinical decision support system (CDSS) is an automated decision support system (DSS) that mimics human decision making and can facilitate the clinical diagnostic process, promote the use of best practices, assist with the development and adherence of guidelines, facilitate processes for improvement of care, and prevent errors” (Englebardt and Nelson, 2002, p. 116). Decision support systems utilize data and provide easy user interface that permit for the decision maker’s own insights. Four components of decision support systems are user interface, model library, model manager, and report writer. User interface makes communication between the executive and decision support system. Model library includes statistical, graphical, financial, and “what if” models. Model manager accesses available models.

Report Writer generates written output (Englebardt& Nelson, 2002). Four types of CDSS used in patient care decision-making are systems that use alerts to respond to clinical data, systems respond to decisions to alter care by critiquing decisions, systems suggest interventions at the request of care providers, and systems conduct retrospective quality assurance reviews. Examples of nursing-specific decision support systems are nursing diagnosis systems such as the Computer Aided Nursing Diagnosis and Intervention (CANDI) system, care planning systems such as the Urological Nursing Information System, symptom management systems such as the Cancer Pain Decision Support system, and nursing education systems such as the Creighton Online Multiple Modular Expert System (Courtney, Alexander, and Demiris, 2008).

The uses of technology for patient and client management

As Information Technology continues to have more presence in health care, patients, physicians, and staff are benefiting from on-demand access to information anyplace, anytime it is needed. Advances in technology provide healthcare organizations the ability to improve the quality of patient care. An ultimate goal of using technology is to improve the quality of care patients receive (Become a Meaningful User of Health IT, 2010). Technology can be found patient homes, clinics, extended care facilities, and hospitals, to name just a few. As the number of chronic diseases continues to increase technologies like telemedicine and video-conferencing can improve the quality of life of patients with chronic conditions, and reduce costs caused by these illnesses (Finkelstein & Friedman, 2000). Improving quality, access, and client management is done by enhancing the exchange of information between providers, institutions, and payers, allowing patients to receive uninterrupted continuity of care.

For the people living in rural areas, the restrictions placed on services and specialists can be improved using technology (Smith, Bensink, Armfield, Stillman, Caffery, 2005). Telecommunications in the healthcare environment can provide patients and providers an opportunity to meet and even exceed expectations clients and the community have. A physician accessing a patients’ record from his home can provide treatment and develop a plan of care without sitting in his clinic to access the patients’ chart. Caregivers are no longer at the mercy of ongoing education provided at a variety of locations and cost. Learning management systems available via the Internet allow staff to review material and participate in competency testing. Tools are available through the advances in technology, which allow training by developing simulations of patients used for assessment training in virtual environments, assessing cognitive skills of providers (McGowan, 2008).

As technologies in healthcare continue to improve, caregivers and patients will continue to experience changes in many areas. Communication, teaching, and documenting will be affected, which change the way clinicians provide care and the way clients will receive it. Analysis of the effect of technology on health care and health status Prior to computers and digital equipment seen in today’s healthcare facilities, most of what was done for patients was done manually. Manual processes could be time consuming and the opportunity for human error, which could affect the quality of care a patient received, was real. In a recent report from the Institute of medical care, it was stated that humans are inherently imperfect, and error is frequent in medical car (Patton, 2001).

Technologies affecting patient care and a person’s health status include improvements to imaging systems, documentation solutions, and scheduling systems. Modern medicine relies on technological systems coming together: the operating room, clinical laboratory, radiology department, and radiation oncology facility each incorporate interrelated networks of technologies (Patton, 2001). Surgeries that once required large incisions can be done through microscopic incisions resulting in shorter hospital stays. Early diagnosis and improved treatment plans have been inevitably affected by technology.

Although technology allows healthcare to improve access to patient

information allowing easier access that is current and up-to-date there are also disadvantages to this kind of access. Consumers and caregivers have large volumes of information, which can be accessed, not all of the information accessed will be understood or accurate. Society must be aware that not all sites will be able to monitor and ensure information being accessed is credible; it is inevitable some of the information provided and retrieved will be inaccurate.

Worse yet information which are by law confidential, may also be accessed without the consent of the patient. In addition to the ability to monitor healthcare information, technology may also make it challenging for physicians to practice under complete autonomy. With the increase in the complexity of technology, physicians must agree on how components relate to one another, also known as standards (Patton, 2001). As a result, some physicians can be seen resisting the adoption of new processes, but with ongoing development of user-friendly systems, resistance can be overcome.

Become a Meaningful User of Health IT. (2010). H&HN: Hospitals & Health Networks, 84(12), 47. Borycki, E., Kushniruk, A., & Brender, J. (2010). Theories, models and frameworks for diagnosing technology-induced error. Studies In Health Technology And Informatics, 160(Pt 1), 714-718.

Finkelstein, J. J., & Friedman, R. H. (2000). Potential Role of Telecommunication Technologies in the Management of Chronic Health Conditions. Disease Management & Health Outcomes, 8(2), 57-63. Retrieved from EBSCOhost. Courtney, K. L., Alexander, G. L., & Demiris, G. (2008). Information technology from novice to expert: implementation implications. Journal of Nursing Management, 16(6), 692-699. doi:10.1111/j.1365-2834.2007.00829.x Englebardt, S. P. & Nelson, R. (2002).Health care informatics. An interdisciplinary approach. St. Louis, MO: Mosby Elsevier. Georgiou, A. (2002). Data information and knowledge: the health informatics model and its role in evidence-based medicine. Journal Of Evaluation In Clinical Practice, 8(2), 127-130. McGowan, J. J. (2008). The Pervasiveness of Telemedicine: Adoption With or Without a Research Base. JGIM: Journal of General Internal Medicine, 23(4), 505-507.
doi:10.1007/s11606-008-0534-z Patton, G. (2001). The two-edged sword: how technology shapes medical practice. Physician Executive, 27(2), 42-49. Shortliffe, E. H. (1986). Medical Expert Systems- Knowledge Tool for Physicians. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1307157/?page=2 Smith, A., Bensink, M., Armfield, N., Stillman, J., & Caffery, L. (2005, October-December). Telemedicine and rural health care applications. Journal of Postgraduate Medicine, 51(4), 286.

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