Which of the following are essential components of a clinical decision support system?
D.Data repository, user interface and data triggersAnswer- CA.These are functions, not components of a CDSS.B.These are required, but can exist without a CDSSC.The system is dependent on the ability to apply an algorithm from a knowledge baseD.Data triggers are used, but are not sufficient by themselves for a CDSS.54.Outcome measures refer to reporting an organization’s compliance with – Show
Get answer to your question and much more 55.A healthcare provider facility has been collecting patient information in a data center forthe last 30 years. Due to budget constraints, they do not have the funds to buy newdatabase servers. Which of the following is the BEST process for removing old healthrecords from a database to allow space for storing newer records? Get answer to your question and much more 56.A CEO of a for-profit cardiac care center that performed 5000 cardiac catheterizationslast year has 20 million dollars in reserves. The organization is known for adopting state-of-the-art technology. A vendor has recently demonstrated that a new piece of cardiacequipment will reduce the risks of cardiac catheterization. The CEO should first Get answer to your question and much more A clinical decision support system (CDSS) is a type of health information technology that provides clinicians, staff, patients, and other individuals with knowledge and individual-specific information that is intelligently filtered or presented at the appropriate time to improve health and health care.
What is Clincical decision support system (CDSS)
A clinical decision support system (CDSS) is a computer programme that analyses data in order to assist healthcare providers in making decisions and improving patient care. It is a subset of the decision support system (DSS) that is frequently used in corporate management. A CDSS focuses on the use of knowledge management to obtain clinical advice based on a variety of characteristics associated with patient data. Clinical decision support systems facilitate integrated processes, provide aid during care, and make suggestions about treatment plans. When combined with appropriate clinical research, data mining can be used to investigate a patient’s medical history. Such analysis can therefore aid in the prediction of future occurrences, such as drug interactions or the identification of disease signs. Among these capabilities are automated alerts and reminders to caregivers and patients; clinical guidelines; condition-specific order sets; focused patient data reports and summaries; documentation templates; diagnostic help, and contextually appropriate reference information. Purpose
Initially, CDSSs were envisioned as being utilised to make clinical choices for the doctor. The physician would enter the data and then wait for the CDSS to generate the “correct” choice, which the doctor would then act on. However, the modern methodology of utilising CDSSs for assistance requires the clinician to interact with the CDSS, utilising both their own knowledge and that of the CDSS, in order to perform a more accurate analysis of the patient’s data than either the human or the CDSS could do on their own. Typically, a CDSS provides ideas for the clinician to consider, and the clinician is supposed to glean important information from the supplied data and disregard erroneous CDSS recommendations. CDSS ClassificationCDSS are classified into two broad categories: knowledge-based and non-knowledge-based. A diagnosis decision support system is one example of how a clinician might use a clinical decision support system. A DDSS collects some of the patient’s data and responds with a list of possible diagnoses. The physician then uses the DDSS output to assess which diagnoses are potentially relevant and which are not, and, if necessary, performs more tests to narrow down the diagnosis. A case-based reasoning (CBR) system is another type of CDSS. A CBR system may utilise prior case data to assist in determining the right number of beams and ideal beam angles for use in radiotherapy for patients with brain cancer; medical physicists and oncologists would then examine the recommended treatment plan for viability. A CDSS can also be classified according to the time period in which it is used. Physicians employ these systems at the point of care to assist them in dealing with a patient, whether pre-, during, or post-diagnosis. Pre-diagnosis CDSS systems assist physicians in preparing diagnoses. CDSS are utilised during diagnosis to assist physicians in reviewing and filtering their preliminary diagnostic choices in order to improve their final outcomes. Post-diagnosis CDSS systems are used to mine data in order to establish linkages between patients and their prior medical history, as well as to conduct clinical research in order to forecast future events. As of 2012, it was suggested that decision support would eventually begin to take the position of clinicians in routine duties. Another approach, used by the National Health Service in England, is to use a DDSS (either operated by the patient in the past or, more recently, by a non-medically trained phone operator) to triage medical emergencies outside of normal business hours by suggesting a suitable next step to the patient (e.g. call an ambulance, or see a general practitioner on the next working day). The suggestion, which may be disregarded by either the patient or the phone operator if common sense or caution dictate otherwise, is based on known information and an implicit conclusion about what the worst-case diagnosis is likely to be; it is not always disclosed to the patient, as it may be incorrect and is not based on the opinion of a medically trained person; it is only used f Knowledge-based CDSS
To express knowledge artefacts in a computable manner, an expression language such as GELLO or CQL (Clinical Quality Language) is required. For instance, if a patient has diabetes mellitus and their most recent haemoglobin A1c test result was less than 7%, they should be re-tested if it has been more than 6 months; however, if their most recent test result was greater than or equal to 7%, they should be re-tested if it has been more than 3 months. The HL7 CDS Working Group’s current objective is to expand on the Clinical Quality Language (CQL). CMS recently stated its intention to use CQL to specify eCQMs (https://ecqi.healthit.gov/cql). Non-knowledge-based CDSS
Support-vector machines, artificial neural networks, and genetic algorithms are three forms of non-knowledge-based systems as of 2012. Artificial neural networks analyse patterns observed in patient data in order to derive links between symptoms and a diagnosis. Challenges
The pharmaceutical and billing sectors of healthcare are two areas where CDSSs have had a significant impact. There are several widely used pharmacy and prescription ordering systems that currently do batch-based drug interaction checks on orders and alert the ordering professional. Another area where CDSS has had success is in billing and claim filing. Given that many hospitals rely on Medicare reimbursements to remain open, systems have been developed to assist in examining both a proposed treatment plan and the current Medicare rules in order to suggest a plan that attempts to address both the patient’s care and the institution’s financial needs. Other CDSSs geared towards diagnostic tasks have been successful, but their implementation and scope are frequently somewhat limited. The Leeds Abdominal Pain System began operation in 1971 at the University of Leeds hospital and was claimed to have a success rate of 91.8 percent, compared to 79.6 percent for physicians. Despite the numerous efforts made by institutions to develop and implement these systems, general adoption and acceptance of the majority of offers have not yet been accomplished. Historically, one significant impediment to adoption has been workflow integration. There was a tendency to focus exclusively on the CDSS’s functional decision-making core, resulting in a lack of planning for how the clinician will actually utilise the product in situ. Often, CDSSs were standalone apps that required clinicians to exit their present system, switch to the CDSS, input the relevant data (even if it had already been entered into another system), and analyse the results provided. Additional processes disrupt the clinician’s flow and consume valuable time. Technical difficulties and implementation impediments
Clinically, a significant barrier to CDSS adoption is workflow integration. Another point of criticism with many medical care systems is the enormous amount of notifications they generate. Apart from the irritation, when systems generate a high volume of warnings (especially those that do not require escalation), clinicians may pay less attention to warnings, resulting in the ignored of potentially vital signals. The large number of EHR native rule engines, each with its own approach and workflow, creates a difficult environment in which to develop scalable CDS content. Mapping across these different systems is difficult and expensive, and making content changes to reflect new knowledge is perhaps no less difficult. Implementing CDS in a cloud-based environment offers the best opportunity to achieving the desired outcome of scale and spread. Toward that end, using a standards-based, Web API approach makes sense in that it will reduce EHR vendor work, CDS content vendor work, and implementation costs. Maintenance
Nonetheless, it is more practical for a corporation to handle this centrally, if inefficiently, than for each individual physician to keep up with all published studies. Apart from being time consuming, integrating new data might also be difficult to quantify or include into the existing decision support schema, particularly in circumstances when clinical publications appear to contradict one another. Correctly resolving these types of inconsistencies frequently requires the writing of clinical papers (see meta-analysis), which might take months to complete. How Can Clinical Decision Support Be Put Into Action?CDS can be used on a variety of platforms (such as the Internet, personal computers, electronic medical record networks, handheld devices, or written materials). Planning for a new health information technology (IT) system to support electronically-based CDS includes a number of key steps, such as identifying the needs of users and what the system is expected to do, deciding whether to purchase a commercial system or build the system, designing the system for a clinic’s specific needs, planning the implementation process, and determining how to evaluate how well the system has addressed the identified needs. In the case of CDS, issues around design and implementation of the system are often interconnected. AHRQ’s CDS Initiative includes a variety of research projects and outreach efforts to develop agreement in the health care field around the use of CDS to promote safe and effective health care. Each part of the initiative attempts to engage clinicians, provider organizations, guideline and quality measurement developers, and IT professionals in the ongoing work to improve making health care decisions using CDS systems. Types of CDSSThere are different ways by which CDSS can be applied in an EPR application. CDSS can even be applied without an EPR application, to provide stand-alone decision support services. These are as below: Evaluation
The evaluation criterion for a CDSS is determined by the system’s objective: for instance, a diagnostic decision support system may be graded on the consistency and accuracy of its disease classification (as compared to physicians or other decision support systems). A system of evidence-based medicine may be graded on the basis of its high rate of patient improvement or higher financial compensation for care providers. Benefits of Clinical Decision Support SystemOrganizations that use clinical decision support systems get more responses from the end-users. They are effective, provide the right treatment, and the best healthcare plan for the patients. Here are a few benefits of CDSS and how it is helping healthcare professionals. Reduce the Risk of ErrorsFinding accurate information and doses given to any patient is challenging, especially when he/she is in critical condition. It is reported that over 35% of pediatric medication errors are caused by improper dosage. Clinical Decision Support could give physicians the idea of dosage according to the patient’s weight, height, and disease. If accurate medication is given, there would be fewer errors. Improves EfficiencyDeciding on a treatment plan and assessing a patient is a complex task. This requires experience and a lot of effort in collecting accurate information. It is reported that around 25 billion dollars are spent in correcting the mistakes as a result of misdiagnosis. This affects the patient’s health outcome. With CDSS, physicians have greater chances of delivering accurate outcomes and also avoiding mistakes. This solves the problem before showing error. Information at FingertipsBy using CDS, the organization is assured of getting reliable information about their specific patient. This information helps them to treat the patient accordingly without wasting time on the research. Also, the CDS system could easily be updated and validated. The data here is stored in the central location, so there is no need for multiple logins. It also reduces care costs by avoiding unnecessary tests. It delivers the right information at the right time. For any organization, the system must provide meaningful support. Examples of CDSSResearch into the use of artificial intelligence in medicine started in the early 1970’s and produced a number of experimental systems.
Characteristics of CDSS
An effective CDSS involves six levels of decision making: alerting, interpreting, critiquing, assisting, diagnosing and managing . Alerts are a vital component of a CDSS. Automated clinical alerts remain an important part of current error reduction strategies that seek to affect the cost, quality, and safety of health care delivery. The embedded knowledge component in a CDSS combines patient data and generates meaningful interpretations that aid clinical decision making . An effective CDSS also summarizes the outcomes, appraises and criticizes the caring plans, assists clinicians in ordering necessary medications or diagnostic tests, and initiates a disease management plan after a specific disease is identified. Priorities for accelerating CDS Progress
Conclusion
Related posts:What are three types of clinical decision support systems?Examples of various types of clinical decision support systems include diagnostic support such as MYCIN and QMR, alerts and reminders based on the Arden Syntax, and patient management systems that use computer representations of patient care guidelines.
What are the key functions of a clinical decision support system?Clinical decision support systems (CDSS) are computer-based programs that analyze data within EHRs to provide prompts and reminders to assist health care providers in implementing evidence-based clinical guidelines at the point of care.
What are the principles of clinical decisionClinical decision making is a balance of experience, awareness, knowledge and information gathering, using appropriate assessment tools, your colleagues and evidence-based practice to guide you. Good decisions = safe care. Good, effective clinical decision making requires a combination of experience and skills.
Which of the following are the major applications of CDSS?Functions and advantages of CDSS. Patient safety. Strategies to reduce medication errors commonly make use of CDSS (Table 1). ... . Clinical management. Studies have shown CDSS can increase adherence to clinical guidelines. ... . Cost containment. ... . Administrative functions. ... . Diagnostics support. ... . Patient-facing decision support.. |