Clinical Decision Support System Analysis

Analyze the requirements of the system and how this DSS is reducing medical errors and improving clinical practice.

Identify key features related to their use of the CDSS in terms of: the type of CDSS they use (knowledge based, or analytics, or a combination of both), its usability (ease of use), utility (perceived usefulness), how they incorporate it into their own workflow, what are some of its features, its overall impact on any patient outcomes and any challenges they have experienced while using the CDSS.

For the electronic health record to be considered as a true clinical decision support system, it must be possible to access and integrate patients’ clinical information that is collected throughout their lives, guaranteeing up-to-date, safe and congruent information, immediately accessible at the place of care. Moreover, because of the considerable increase in the capacity to develop and manufacture systems that employ smart components highly integrated and miniaturized, wearable devices facilitate the home monitoring of patients with chronic diseases and their information should be integrated in existing electronic health records. Therefore, the interoperability is an essential requirement of eHealth to allow the integration of care into a

In the United State, hospitals, less than two-thirds of hospitals have any type of CDS. (Byrne et al., n.d.). We might be wondering why the use of CDC is low among health care provider, based on my study the following are some of the barriers in implantation of CDS: Usability issues (Byrne et al., n.d.). This include rate of the process of information, user what the information that he or she wanted as fasted rate as possible, it graphical User Interface is not as friendly as it should be. Secondary ,the CDS Alarms functionality include lists of possible diagnoses, drug interaction alerts, or preventive care reminders and the overused of theses alarm may lead to the clinician not paying too much attention to them as it would be seen as normal sound (Bolch, 2012).These are some of the way, in which these barrier can be remove, need for better user friendly design , if the interface is well design and easy to use more physician who like to use it in making clinical decision, There is needs for the vendor and researcher to address this design failure ("Clinical Decision Support (CDS) Initiative | AHRQ National Resource Center; Health Information Technology: Best Practices Transforming Quality, Safety, and Efficiency," 2014). It is imperative to customize CDS after it is implemented failure to do this can lead to failure. For example, disabling some of the alarm system that is redundant or lack practical value, and constantly training of clinician to ensure important reminder do receive necessary attention (Bolch,

Systems without a knowledge base, on the other hand, rely on machine learning to analyze clinical data. There are pros and cons to implementing clinical decision support systems. The foremost challenge is that a CDSS must be integrated with a healthcare organization 's clinical workflow, which is often already complex. Some clinical decision support systems are standalone products that lack interoperability with reporting and electronic health record (EHR) software. Furthermore, incorporating large amounts of data into existing systems places significant strain on application and infrastructure maintenance. CDSS is "a process for enhancing health-related decisions and actions with clinical knowledge and patient information to improve health and healthcare delivery.

Effectively managing the health of the population requires tremendous efforts and strategies of a healthcare organization. Leveraging the use of health information technology such as enabling the use of clinical decision support (CDS) at the point care is one of the strategies for an effective population health management (PHM). Rush-Copley Medical Center has successfully demonstrated the effectiveness of CDS at the point of care in managing a TB outbreak, which has resulted in containing the disease and further outbreak in the general population. Such an example has shown the optimization of the systems and the value of health information technology

Computerized Physician Order Entry (CPOE) is a process that both improves the quality and safety of care by using a standardized method. In combination with even the most rudimentary clinical decision support systems (CDSS) will decrease the number of adverse events simply by eliminating legibility issues in translation alone, while improving care. More importantly, an interactive system that responds to inputs of conditions, medications, and patient history will improve the diagnostic skills of the user by actively searching a vast knowledge base for the most current information and providing it to the provider in real time. When presented with the information, the provider can then use the advice or proceed in another direction, in either scenario the provider has information to make the best clinical decision. Of course, the goal would be to present recommended guidelines that the practitioner can use in specific disease states to improve clinical performance and patient outcomes.

Clinical decision support system (CDSS) has a potential to enhance health care if used and adopted. The delayed implementation and acceptance of CDSS is due to the poor usability of the system (Nair et al., 2015). Usability testing can highlight barriers of poor usability in a system. Therefore, the aim of this study was to conduct a literature search on the effect of iterative usability testing on improving the usability and favorability scores of the clinical decision system. I searched the literature using PubMed and Google Scholar. Six articles were used for this paper. Five were iterative studies. These articles were reviewed and two variables were measured in this study. The initial usability and favorability scores of the system were

Clinical decision-support systems (CDSS) apply best-known medical knowledge to patient data for the purpose of generating case-specific decision-support advice. CDSS forms the cornerstone of health informatics research and practice. It is an embedded concept in almost all major clinical information systems and plays an instrumental role in helping health care achieve its ultimate goal: providing high quality patient care while, at the same time, assuring patient safety and reducing costs. This computer based systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made. If used properly, CDSS have the potential to change the way medicine has been taught and

Health Informatics created two main categories such as clinical and administrative information systems to meet the needs of one or more department within the health care organization. For the clinical information system, it is set to meet the needs in improving patient care. Therefore, the clinical information system (CIS) categories provide nurses information systems (NIS) that support the way nurses documents the care that given to the patients. However, to improve the delivery of nursing care, the healthcare organization must adopt a computer system that can successfully incorporate tools that will benefit nursing. There is two vendors’ software that implies these characteristics for the

Nursing is an exciting, challenging, satisfying and rewarding profession. Nurses are required to have a wide and an extraordinary range of skills and talent in managing their clinical duty and responsibilities. Nurses are urged to deliver the highest achievable of quality care in a compassionate approach. In order to achieve these nurses need to access and evaluate extensive clinical information and integrate it to improve clinical decision making. Nurses are seen as lifelong learners, having the ability to reflect on, evaluate and modify their clinical practice based on new knowledge and thoughts. Nurses are advised to become producers of new knowledge via nursing research (Fink, et. al. 2005).

Furthermore, after achieving a computerized system in the documentation of patient information, it is important to stabilize and re-evaluate the change process. It is important the nurse leader, and the super users provide the staff with updates and ongoing education to keep abreast with the changes in the information technology platform. In other words to ensure success, information technology experts must be available at any deemed moment to address any issues resulting from the EMR system. Furthermore, clinical educators should also be availed to provide necessary training to new nurses and staffs. Still, the system must be in regulation to set standards and policies thus in evaluation compliance to set regulations as the HIPAA must be checked and necessary improvement made concerning information and data security.

Clinical decision support (CDS) systems offers health care providers pertinent knowledge about patients and their illnesses/health. They provide suggestions and alerts to enable physicians to treat their patients more holistically and efficiently. EHR systems comprising CDS are available in three basic varieties. According to Musen, Middleton, and Greenes (2014), “1) they may use information about the current clinical context to retrieve highly relevant online documents, as with so-called “infobuttons”; 2) they may provide patient-specific, situation-specific alerts, reminders, physician order sets, or other recommendations for direct action; or 3) they may organize and present information in a way that facilitates problem solving and decision making, as in dashboards, graphical displays, documentation templates, structured reports, and order sets”.

APACHE stands for Acute Physiology and Chronic Health Evaluation and the first system was developed in 1981. The newest APACHE system was developed in 1991. The APACHE III system was designed to predict an individual 's risk of dying in a hospital. It compares each individual 's medical profile against nearly 18,000 cases in its memory before reaching a prognosis that is, on average, 95 percent accurate (Open Clinical website, 2005). This system can be used in the Intensive Care Unit setting and it can predict the patient’s risk of dying in the Intensive Care Unit. This system takes into account several variables such as, diagnosis and psychical conditions upon admission to the Intensive Care Unit, age, pre-existing medical disorders and other variables. A physician gives the system facts, and based upon the facts the system can predict the patient’s risk of passing away. This system does not spit out an answer and healthcare workers have to follow it, it is rather a tool to help healthcare providers make a decision on whether to discontinue “heroic” measures and it allows them to ponder these issues in a realistic way. The Clinical Decision Support System is a system that uses patient data to generate specific advice relevant to the patient’s case. Clinical DSSs are typically designed to integrate a medical knowledge base, patient data and an inference engine to generate case specific advice. (Open Clinical website, 2006). Overall, this system can be helpful when

Health care information systems and EHRs track every detail of a patient's interactions with health care providers. These systems are often focused on recording/writing down what happens in manual health care processes-in order to meet legal/law-based and (following the law/doing as you're told) needed things-rather than providing the highest quality patient care. To improve patient care standards in a time of (old/allowing to get old/getting older) (groups of people/animals/things), increasing costs, payment reform, quickly changing best practices, and a data explosion, it's no longer (able to be done) to depend on manual processes; Health care processes must be automated wherever possible. To this end, business process management systems-which