Nurse Informatics – Clinical Decision Support Research Paper
Nurse Informatics – Clinical Decision Support Research Paper
Nurse Informatics – Clinical Decision Support
Clinical decision support (CDS) is important in providing timely information regarding the point of care in order to help in making clinical decisions about patients’ care. Using clinical decision support it becomes easy to improve patient care and enhance high-quality healthcare (Jankovic, 2020). The clinical decision support tools and systems are crucial in providing clinical teams with warnings about possible problems, routine tests, and in making suggestions about patients care. For instance, CDS tools can include recommendations, patients, conditions, and even databases that help in giving information that is helpful in diverse clinical conditions. The main benefits of CDS include avoiding or reducing cases of adverse medical events, boosting employee efficiency, preventing clinical mistakes, improve patients’ quality care, and strengthening favorable outcomes among others (Sutton et al., 2020). Important types of clinical decision support include clinical decision support system (CDSS) architecture, knowledge-based CDSS, and non-knowledge-based CDSS Nurse Informatics – Clinical Decision Support Research Paper.
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CDSS architecture is a type of clinical decision support that contains three elements including the processing layer or interface engine, data management, and user interface. The processing layer uses the algorithm rules and the datasets available from patients that are a source of the knowledge base. The data management layer is involved with the storage of information, and patient data and also contains a knowledge base about machine learning models (Jankovic, 2020). Additionally, the user interface displays results using the web, EHR system, desktop application, or mobile text alerts.
Knowledge-based CDSS is a type of clinical decision support that possesses a system built on a knowledge base where every piece of data is modeled in form of computer learning. For example, in an instance where a blood test is done and placed in the system then another similar test is done then there is a likelihood of results duplication. The system in this case uses knowledge in acquiring patient history and other diagnostic data to help solve the current issue (Dramburg et al., 2020). The outcomes are presented in form of reminders, alerts, treatment options, diagnostic suggestions, and possible solutions are ranked in order of efficacy (Muhiyaddin et al., 2020). However, the final decision is left to the physician or the human expert.
Non-knowledge-based CDSS is a type of clinical decision support that uses machine learning models. This model does not consult with the library of information like the knowledge-based CDSS but learns from experiences and gets the patterns of patient historical data (Jankovic, 2020). Important methods used in this type include genetic algorithms and artificial neural network techniques. The non-knowledge-based systems help in reducing the healthcare costs and reduce the pressure from the medical experts (Muhiyaddin et al., 2020). However, the model is time-consuming and computer-intensive in processing huge datasets for accuracy.
The triggers for clinical decision support should be on systems that are a cost-friendly and ones that are helpful to the medical experts. In this regard, the non-knowledge-based CDSS is the best in that it helps relieve the pressure of huge workflow from medical experts and also it saves the healthcare sector a lot of costs.
Clinical decision support involves making quality improvement, efficiency, and safety in healthcare using support systems. However, clinical workflow analysis involves a thorough assessment of healthcare organization systems of processes in delivering care. On the other hand, modeling involves a way through which clinicians seek to understand the unique model of every patient and their significance (Sutton et al., 2020). Clinical decision support is bigger and more complex than reducing data entry errors in the clinician setting and includes all the systems for reducing data entry errors including automated systems, templates and evidence-based order sets. In addition, clinical decision support includes part of usability testing in enhancing the end-user experience. This is whereby the systems observe as the users interact with diverse sites in receiving their concerns and needs Nurse Informatics – Clinical Decision Support Research Paper.
Clinical decision support is about using the right trigger, to the right person, with the right instructions, with the intent to ensure that the person is making the right decision. The purpose of this assignment is to describe the different types of clinical decision support and determine the outcomes of applied clinical decision support.
Using the topic Resources and your own research, write a 500-750-word paper addressing the following:
What are the different types of clinical decision support?
Describe at least three different types of clinical decision support that could be used in a health care setting or provide a personal workplace example.
Using the examples you have provided (above), identify the triggers that would initiate the criteria for clinical decision support.
Differentiate clinical decision support and clinical workflow analysis, modeling, reducing data entry errors, and usability testing for improving end-user experience.
References
Dramburg, S., Marchante Fernández, M., Potapova, E., & Matricardi, P. M. (2020). The Potential of Clinical Decision Support Systems for Prevention, Diagnosis, and Monitoring of Allergic Diseases. Frontiers in immunology, 11, 2116. https://doi.org/10.3389/fimmu.2020.02116
Jankovic, I., & Chen, J. H. (2020). Clinical Decision Support and Implications for the Clinician Burnout Crisis. Yearbook of medical informatics, 29(1), 145–154. https://doi.org/10.1055/s-0040-1701986
Muhiyaddin R, Abd-Alrazaq AA, Househ M, Alam T, Shah Z. (2020). The Impact of Clinical Decision Support Systems (CDSS) on Physicians: A Scoping Review. Stud Health Technol Inform. 272:470-473. doi: 10.3233/SHTI200597. PMID: 32604704.
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ digital medicine, 3, 17. https://doi.org/10.1038/s41746-020-0221-y Nurse Informatics – Clinical Decision Support Research Paper