Examples Of Clinical Decision Support

Standing (2011), defines clinical decision-making as a complex process that involves observation, gathering information, critical thinking, evaluating evidence, applying necessary knowledge, reflection and problem-solving skills. Every day nurses make important clinical decisions and these decisions have important implications for patient outcomes and deserve serious consideration. Therefore, it is important for nurses to have a better insight of the decision-making process, be able to deliver holistic care and meet essential and complex physical and mental health needs of the patient.

UHN in Toronto is a major community care network that reaches out to and provides care to the masses. However in order to provide this kind of care they must have a very powerful decision support system. UHN utilizes an advanced CPR to support computerized physician order entry (CPOE). (Wu, Perters, & Morgan, 2002) A CPR system is a computer-based patient record system. A CPR system must provide a comprehensive clinical decision support it must include both a patient focus and a population focus. The physical computer system that is installed on the computers at UHN is called Patient 1® which is a clinical information system developed by Atlanta Based Per-Se

http://mdm.sagepub.com/ Do Patient Decision Aids Meet Effectiveness Criteria of the International Patient Decision Aid Standards Collaboration? A Systematic Review and Meta-analysis

I believe that the clinical decision making process associated with the actions to be taken in Jack and Jacqueline’s care were quite similar between the Canadian and the Rwandan perspectives. This is because both perspectives took on a holistic view of Jack and Jacqueline’s situation and thereby worked to develop goals that would not only address Jack’s physical health, but also the emotional and mental health of Jack, Jacqueline, and even Charles. These holistic based goals included, but were not limited to the utilization of various community supports to foster Jack’s cognitive development as well as the formation and implementation of diverse coping strategies for Jacqueline and Charles to use during times of stress.

Next, stage 2 consist of seventeen core criteria and six specified core objectives of which three must be picked to be reported on by an eligible provider (Wright et al., 2013). Likewise, hospitals must meet sixteen core objectives and three of six menu objectives in order to demonstrate meaningful use under the stage 2 objectives. Additionally, this stage implemented two new core objectives that must be utilized by the provider which include, the use of protected electronic messaging to communicate pertinent health information to patients and, the tracking of medication through utilization of electronic medication administration by way of assistive technological devices. As a result of implementing the two new criteria, positive patient outcomes and safety will be achieved. This stage focuses on the clinicians exchange of medical information, and enhanced care management of patients. At this stage patients are given the ability to retrieve and fill out their medical information from home and submit it to the provider within an allotted time frame. Stage 2 is a program that is geared more toward nurses and their ability to achieve better patient outcomes with the use of evidence-based practice.

The value of Clinical Decision Support Systems is having additional avenues monitoring patient’s data input. The Clinical Decision Support Systems are offering notifications of patient’s record data to specified department or medical personal.

The purpose of this study is to develop and assess the usability and effect of a problem focused clinical decision support tool designed to improve medical decision-making. The overall goal is to design an effective dashboard that will support both rapid pattern recognition and slower, more deliberative reasoning and analysis of information, when managing the care of complex patients. The dashboard design may support cognition by simplifying and grouping information to enhance pattern recognition and prompt situational awareness. It is anticipated that by lowering the effort required to obtain needed information, greater efforts in managing the problem will occur. Specifically, we want to know if the dashboard enhances knowledge of patients baseline cognitive status & delirium risk, if confidence in this knowledge is increased by providing supportive information, and if more appropriate risk prevention strategies will be ordered, as compared to the regular presentation of information.

There are many areas in the health care system which are well suited for decision support driven by health informatics. However, as with the saying “a coin always has two sides”, within the same areas, there are situations where health informatics approaches have limited value in decision making as well. The two health care areas discussed below are clinical practice and public health.

The data associated with the utilization of the Diagnotes Platform have been provided. We intend to critically analyze the data, and establish relationships between different parameters in Diagnotes Platform. The analysis will be directed towards measuring/improving the quality and outcome of patient care which will increase patient’s acceptability toward Diagnotes and indirectly will improve the financial and operational performance of health organizations.Diagnotes, is a software platform that provide common communication platform that connects healthcare providers, physicians and patients in a secured way. Some of the features include HIPAA compliant communication, care coordination, referrals and consult, transitions of care, call center

Health IT will provide the necessary tools to achieve this vision through an interoperable infrastructure and supportive building blocks such as: Rapid translation of research, evidence, or best practices into electronically actionable guidelines that enable decision support, quality measurement and reporting; Capturing data once and reusing the data many times; Standardization of CDEs for the capturing or generation of health and clinical information, will enabling the improved use of data within the normal flow of care—which allows for information relevant to eCQMs, CDS and federal reporting to occur more seamlessly, reducing data reentry; Timely, relevant, precise, valid and interoperable decision support for providers, patients, and consumers. (“Health IT Enables Quality Improvement,” 2013).

Providers need access to information to aid in their decision making; these tools are called clinical decision support tools (Sittig et al., 2010). Two keys that are necessary in CDS tools are an external location for the data either a dedicated application interface or a web-based interface and enterprise-wide standards for terminology and concepts (Sittig et al., 2010). External locations for CDS tools are necessary because embedded code in existing programs is expensive and time-consuming to keep current, especially with the speed that knowledge is being shared and updated in healthcare. Enterprise-wide standards for terminology and concepts is absolutely vital for CDS tools and algorithms to function. Some of the standards that should be considered are SNOMED, LOINC, and ICD-10 (Sittig et al., 2010). The use of these standards improves the use of CDS tools as well as data sharing within health information exchanges (HIEs) due to the standardization of terms and

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

Clinical decision support system (CDSS) is gaining increased recognition in healthcare organizations. 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 assessments and evidence-based treatment recommendations for healthcare providers to consider2,3”. Patient data can be input by digital entry, queried from other clinical information systems or transmitted from medical devices. Patient data are compared against a knowledge-base and made sense of by an inference mechanism. The knowledge base can be procured commercially or developed in-house. The inference mechanism can be highly variable in sophistication ranging from a simple ‘yes ‘no and ‘if ‘then statement to Bayesian prediction techniques and/or fuzzy logic. The output can also take a number of forms and can be delivered to a number of destinations at any time before, during or post-interaction with the patient4.

Clinical decision support provides clinical staff, patients and other individuals with person-specific information. The first computerized clinical support systems used in clinical practice was developed in the 1970s. There has been an outstanding number of clinical successes since their development, but their clinical practice routine is not as sustainable. Clinical decision support help to aid the clinician with decision making using relevant content at the point of computerized prescriber order entry (“American Journal of Health-System Pharmacy”, 2017). Using the decision system for clinical informational outcomes is a way you can promote health quality in the work place. In a way this can shorten the time that you must wait to see a provider. In this analysis we will identify the strengths and weaknesses of clinical support systems. What are the different challenges that support systems have yet to conquer? Why does clinical support matter in healthcare?

Artificial intelligence in Medicine (AIM) [‎18,‎19] frameworks are expected to help healthcare laborers in the typical course of their obligations, supporting assignments that depend on the control of data and information. An AI framework could keep running inside an electronic therapeutic record system, and alarm a clinician when it identifies a contraindication to an arranged treatment. It could likewise alarm the clinician when it distinguishes situations in clinical information that recommend recognizable changes in a patient 's state.