26 Sep feedback
CLINICAL DECISION SUPPORT SYSTEMS 2
Alvares
How do you see clinical decision support systems evolving in the future?
Over the past few decades, Clinical Decision Support Systems (CDSSs) have evolved to become both strategic and tactical tools that offer strategic benefit to healthcare organizations. By using a predefined set of rules, such systems intelligently filter knowledge from complex data and present at an appropriate time. They are designed to assist clinicians and other healthcare professionals in diagnosis as well as decision-making. By adopting CDSS, healthcare can become more accessible to large populations. They are used in almost all aspects of healthcare delivery, from the clinical decision-making process to medication and lab order monitoring to quality assurance and cost control.
Implementation and clinical utilization of CDSS are on the rise (Kashani, Dalili, Carter, Kellum, & Mehta, 2019). Several uses of CDSS have evolved. One of the important roles of CDSS is to monitor user’s input, evaluate them and trigger events associated with these inputs. CDSS can be integrated with computerized physician order entry (CPOE) systems to alert physicians if they provide a medication overdose, or if the patient is allergic to some drug. Future applications of CDSS will optimize drug and dosage selection using low-cost genomic analysis to predict drug metabolism based on individual patient genetic makeup (Castaneda, Nalley, Mannion, Bhattacharyya, Blake, Pecora, & 2015). CDSS can be integrated with electronic health records (EHR) to remind physicians of necessary lab tests or prescription renewals. The use of CDSS, especially for alarming physicians about possible adverse effects of overdoses, can play an important role in enhancing quality and reducing medication errors. CDSS promise to alleviate the ever-intensifying time demands upon physicians. CDSS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years (Middleton, Sittig, & Wright, 2016). CDS has evolved from medical data processing tools to complex decision support frameworks and infrastructures for clinical knowledge management.
References
Brown, G. D., Pasupathy, K. S., Patrick, T. B. Health Informatics (2018). A Systems
Perspective, Second Edition. [Textbooks.com]. Retrieved from
https://bookshelf.textbooks.com/#/books/9781640550056/
Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A., … & Suh, K. S.
(2015). Clinical decision support systems for improving diagnostic accuracy and achieving precision
medicine. Journal of clinical bioinformatics, 5(1), 4. Retrieved on September 23, 2019 from
https://jclinbioinformatics.biomedcentral.com/articles/10.1186/s13336-015-0019-3.
Kashani, K., Dalili, N., Carter, R. E., Kellum, J. A., & Mehta, R. L. (2019). Using Clinical
Decision Support Systems for Acute Kidney Injury Pragmatic Trials. Journal of Translational Critical Care Medicine, 1(1), 28. Retrieved on September 23, 2019 from
Marcus
How do you see clinical decision support systems evolving in the future?
Throughout human history there has always been a want to make things that will help simplify our lives. Examples are the washing machine, the automobile, sewing machine, etc., these inventions helped to improve our lives and helped us to free up time and to do other things. The clinical decision support systems (CDSSs) are right under this category of improving the lives of humans. Just as washing machines, cars and many others get updates and improvements made to them every year the same will be for CDSSs. In the hospital I am currently working for they are updating and re-configuring there CPOE and CDSSs systems because a faster and more streamlined update has come out for these systems. This was not the first time this has happened and it will definitely not be the last. Most health organizations need money to run the organization and in order for them to do that they need good outcomes. Health information technology gives health organizations the possibility “to improve the quality of care provided to patients, increase provider performance and patient engagement, produce cost savings for individuals and institutions, prevent errors, reduce adverse events, and enhance patient health overall” (Brown, Pasupathy & Patrick, 2019, p.121). In order for these things to happen there is a need for CDSSs to be mixed in. For this reason I see CDSSs continuing to evolve as time goes on. Those aspects are too important for health organizations so there will be time and money invested into CDSSs. CDSSs will become more streamlined and conform to the individual patient more than it does already. It will be able to alert doctors to specific things like if a patients is in need of a device replacement. It would also be tailored to the doctors individually taking into account their different practices, education, experiences and preferences (Baum, 2012). Overall, I see CDSSs becoming more integrated into HIT and being used at home to monitor patients more closely. It will evolve to a point where it will become like how phones are to us today, where it is personalized with all of our information. For physicians it will help them to gain more accurate information on the patient saving lives, and money for all in the process. CDSSs will continue to grow and become intertwined with healthcare for years to come.
References
Baum, S. (2012). How will EMRs use clinical decision support tools in the future? Retrieved from https://medcitynews.com/2012/07/how-will-emrs-of-the-future-use-clinical-decision-support-tools/
Brown, G., Pasupathy, K., & Patrick, T. (2019). Predictive Analytics In Knowledge Management. In Health informatics: a systems perspective (p. 121). Chicago, IL: Health Administration Press.
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