Clinical Decision Support Systems

The clinical decision systems such as the DXplain are beneficial because they offer recommendations for a care plan, avail assistance during the care period, and enable integrated workflows (American Nurses Association, 2015). The systems that assist the healthcare organizations are still evolving and therefore now and then they are subject to improvements based on the feedback from the patients, nurses, and physicians. The working of expert systems such as the DXplain in that it carries out an analysis of the patient’s medical history by data mining. It is through such analysis that it is possible to identify disease symptoms and predict potential events like drug interactions. Information is the foundation of the expert systems, therefore aiding in the development of a diagnosis (Addington, Shah, Liu, & Addington, 2014).

On the other hand, an illustration of a non-knowledge-based system is an artificially intelligent neural network. The neural network is designed in such a way that it considers certain examples in an attempt to perform specific tasks. The difference with this system from conventional knowledge-based systems is that it is not programmed using if-then protocols. The neural networks are more concerned with patterns in the sense that it carries out an analysis of patient data thus determining relationships between diagnosis and other symptoms (Mithani, Salsburg, & Rao, 2018). The DXplain System The first design of the DXplain system was actualized in 1984. It holds information about 4800 clinical findings such as radiologic or laboratory findings, epidemiologic data as well as signs and symptoms. The KB also includes knowledge of up to 2241 diseases (Nair, et al.

In an attempt to describe diseases approximately 52. 8 findings are typically presented. On average, the conclusions presented always range from 10 to over 100 depending on the information fed into the system. The order used to list the conditions is in order of the degree of support about the findings (American Nurses Association, 2015). The diseases that have the closest relationship to the results are listed first while those that minimally relate to the results listed last. Furthermore, the system has a feature that flags the disease that is supported moderately and very well. The design of the system is in such a way that a user has the liberty to click on the disease suggestions. The user can compare the findings available for the disease with the signs and symptoms that were input.

As much as it is not necessary to cover the period of symptoms, a considerable number of the users are keen on including this detail. The next step involves the inclusion of narrative text to type clinical findings in the blank text box that is presented (Mithani, Salsburg, & Rao, 2018). After the user has finished submitting the demographic data as well as the initial findings, a list of possible diseases that relates to the condition is displayed. The state presented must refer to one or more of the results. Additional features displayed at this step of the inquiry process includes a text-entry box that allows for the input of the additional conclusions about the disease as well as four questions selected by the system about the case (Liu, Lu, Ma, Chen, & Qin, 2016).

The human brain is incapable of remembering all the details regarding a particular patient’s complicated information. Nonetheless, clinicians have a responsibility to recognize all the crucial elements about the patient data as well as the conclusions about the patient. The provision of the DXplain systems that gives information regarding the disease based on findings makes the clinicians lazy to utilize their data. In such cases, it will lead to the clinician prescribing wrong medication based on a wrong diagnosis. Clinicians must consider that the systems are meant to supplement their knowledge. Schizophrenia Research, 153(1 -3), 64-67. American Nurses Association. Scope and standards of nursing informatics. Washington, DC. Brennan, P. Mithani, M. , Salsburg, M. , & Rao, S. A decision support system for moving workloads to public clouds.