Introduction
Clinical decision support refers to the process of providing patients, medical practitioners, staff, and administrators in healthcare institutions with evidence-based information to guide their practice. Thus, clinical support systems assist medical service workers to implement effective clinical guidelines to limit errors and achieve better outcomes. Therefore, the following essay discusses various concepts of clinical support systems in medical institutions, including the various types of clinical support, their basic functions, and their essence in quality healthcare delivery.
Types of Clinical Decision Support Systems
Clinical decision-making support systems encompass a variety of tools used by medical practitioners to make medical decisions. However, they are broadly categorized into knowledge-based support systems and non-knowledge-based clinical support systems. Knowledge-based clinical support systems obtain information from compiled data and inference engines, which is communicated to practitioners. On the other hand, non-knowledge-based clinical decision support systems adopt artificial intelligence through machine learning, allowing them to identify patterns in clinical data sets (Mahadevaiah et al., 2020). However, some clinical decision-making support systems leverage both data input and machine learning to guide clinical decisions. Clinical decision support systems are further differentiated into categories based on their specific purposes in healthcare settings.
Different Types of Clinical Support Systems used in Medical Settings
Clinical decision support systems have several functions in medical institutions. As a result, they are diversified and dedicated to particular functions in monitoring, patient safety, cost analysis, and resource management. For example, a diagnostic support system assists medical practitioners to make more accurate diagnoses based on a patient’s symptoms and medical records. On the other hand, patient management systems are used in a range of functions including admission, reporting, and medication administration (Musen et al., 2021). Subsequently, computerized alert and notification systems send messages to practitioners to inform them of medical errors, safety hazards emergencies, and treatment routines. However, clinical decision support systems are integrated to produce the best results.
Triggers That Initiate Criteria for Clinical Decision Support
Triggers refer to the events that obligate the adoption of particular approaches to limit adversities. On that account, medical errors in institutions are triggers that invoke the use of clinical support systems during diagnosis to allow clinicians to make accurate decisions. Moreover, work clutter and unstructured workflow trigger the use of patient management systems to help medical practitioners work more efficiently (Mahadevaiah et al., 2020). Finally, safety and the control of hazards in medical institutions have gained significant attention over the past years. As a result, they have led to the adoption of computerized alert and notification systems to keep medical practitioners aware of all events in the hospital.
Difference between Clinical Decision Support and Medical Management Tools
Clinical decision support is a technique used to share information with staff and medical practitioners in institutions to guide decision-making. On the other hand, workflow analysis refers to steps taken to assess and evaluate the factors that interfere with workflows in institutions, while modeling is designing a particular strategy for achieving outcomes (Musen et al., 2021). Reducing data entry errors may comprise a range of techniques used to limit mistakes during data input into health information systems. Finally, usability testing for end-user experience is a solution adopted to examine the reliability and effectiveness of a particular application system in delivering its intended purpose.
Conclusion
Medical practitioners are exposed to a wealth of information, which might introduce issues in management and following up with patients. Moreover, some disease manifestations can be similar or coincide, thus limiting them from making the appropriate decisions regarding treatment. However, clinical decision support systems are solutions designed to aid in decision-making by enhancing communication, knowledge sharing, and the use of evidence-based data. As a result, they are crucial in streamlining workflows, minimizing medical errors, and ensuring quality healthcare services.
References
Mahadevaiah, G., Rv, P., Bermejo, I., Jaffray, D., Dekker, A., & Wee, L. (2020). Artificial intelligence‐based clinical decision support in modern medical physics: selection, acceptance, commissioning, and quality assurance. Medical Physics, 47(5), e228-e235.
Musen, M. A., Middleton, B., & Greenes, R. A. (2021). Clinical decision-support systems. In Biomedical informatics (pp. 795-840). Springer, Cham.