Understanding Clinical Decision Support System

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

Alert fatigue and using a clinical decision support system (CDS) in an electronic health record is a growing concern in health care. Although alerts and warnings in an EHR are well intended, the volume of alerts EHR end-users receive is surprising. The Agency for Healthcare Research and Quality (AHRQ) estimates that in some EHR end-users have the potential to receive over 100 CDS alerts per day (Agency for Healthcare Research and Quality [AHRQ], 2015, p. 1). This cause’s alert fatigue when the end-users become desentized to the alerts and even the most important alerts become meaningless.

Clinical Decision Support System (CDSS) has potential chances to enhance general security, quality what's more, cost-adequacy of human services. The CDSS has existed for over four decades, yet its selection rate by therapeutic groups is not empowering even in the nations that have been a pioneer in creating them. At numerous locales, it was hazardous, slowed down in the arranging stages or never at any point endeavored. To date, CDSS is considered as an incompletely effective framework. A few current difficulties have not been enough tended to amid the improvement of CDSS. According to most recent research, the arrangements of difficulties are: enhance the human-PC interface,

Martin Wiesner,Daniel Pfeifer[7] proposed a recom- mender system that supply patients a friendly infor- mation to comprehend their health status. The sugges- tions done froma a health recommendation system is done from a individualized health data documented in personal record. Data entries in a PHR database constitute the medical history of PHR owner. HRS will search relevant items of interest for the tar- get user. Such items originate from health knowledge base repositories and displayed online while he/she in- spects.

According to Ferdinand Velasco, M.D., physician resistance to clinical decision support is a result of hasty rollouts and organizations are stuck with the technology they have while trying to make improvements and fix what is not working in addition to moving forward with optimization. The initial first generation of clinical decision support tools was a disappointment and HCO’s will have to incorporate physicians’ suggestions and ideas to get buy-in for second generation

In 1999 the Institute of Medicine (IOM) released a report that shook the world. “To Err is human” warned and informed the healthcare industry that a need for change is necessary. The lack of consistency in the delivery of the quality of care that is provided to the US population. In response, Clinical decision support systems (CDSS) have become a resolution for such an issue. The objective of this paper is to highlight the benefits of implementing a CDSS as well as the challenges faced today when implementing such a system. Furthermore, study will be conducted to explore what can be done to further research and recommendations will be given as to how CDSS implementation can improve health outcomes. Clinicians, staff as well as patients are

Despite their potential to significantly improve health care, advanced clinical decision support (CDS) capabilities are not widely available in the clinical setting. An important reason for this limited availability of CDS capabilities is the application-specific and institution-specific nature of most current CDS implementations. There is a critical need to enable CDS capabilities on a much larger scale is the development and adoption of standards that enable current and emerging CDS resources to be more effectively leveraged across multiple applications and care settings. Standards required for such effective scaling of CDS include (i) standard terminologies and information models to represent and communicate about health care data; (ii) standard approaches to representing clinical knowledge in both human-readable and machine-executable formats; and (iii) standard approaches for

The two examples of the clinical decision support applications are DXplain and Problem Knowledge Couplers.

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

There are a lot of problems and challenges involved in implementing a clinical decision support system. It is important for medical staff (doctors, administrators etc.) to be an integral part in the implementation and development of CDSS. Our limited research concluded that acceptance of such technology is not easy amongst physicians. One of the main reasons for non acceptance is that the physicians want to be a standalone entity. We plan to investigate many challenges such as lack of technical expertise, cost, integration, misdiagnosis, speed etc involved in implementation of different types of CDSS in the health care industry today. Our research paper will focus on the different decision problems involved in these challenges.

The major components of Clinical Decision-Support System (CDSS) was generated to manage daily work flow in a more effective manner in regards to clinicians cognitive thing. This would also help aid in “clinical data banks and algorithms, analytic or pathophysiologic models, clinical decision theoretical models, statistical pat-terns recognition methods, symbolic reasoning, and clinical expert knowledge bases....” (Tan, 1998 pp.220) The goal of CDSS was to improve patient care and assist in the patients’ quality of life with the help of the Clinical Reminder System (CRS).

Since the 1970s, Clinical decision support systems (CDSS) have been implemented, tested and evaluated in different health environments. From ‘De Dombal’s system for acute abdominal pain’ to ‘’IBM Watson Health’’, this health information technology has been a key for the perfection and improvement of health care systems around the world. In a perfect world, Clinical decision support systems seem to be the answer to human errors made by physicians and improvement of quality of care for patients. But, what have we learn in the past 50 years of having these devices? Do they actually deliver all the wonderful promises that they make? Are they actually worth the cost and effort put in the past years and present? In this paper, we will talk about

Clinical decision support system (CDSS) is gaining increased recognition in healthcare organization. This is due to an increasing recognition that a stronger CDSS is crucial to achieve a high quality of patients care and safety1,2. CDSS is a class of computerized information system that supports decision-making activities2. It uses patient data to provide tailored patient

Clinical decision support, is a great tool for of health information technology. The Clinical decision support can increase quality of care, enhances health outcomes for population and individuals. CDS can helps to avoid errors improves efficiency and reduces costs.7 computerized clinical decision support shown to decrease the volume of imaging orders in certain clinical settings as well as has increase the “appropriateness of imaging” in clinical settings.9 Computerized clinical decision support systems showed improvement in providing advice for the diagnosis of acute health conditions, provided medication dosing assistants and Computerized clinical decision support systems generated recommendations for the management of