Technological advancement is an inevitable aspect that should be adopted by every individual and organization that aims at achieving success and prosperity in activities undertaken. Information is an incredibly essential tool in an organization and its utilization affects its performance greatly. Health care information systems entail a set of electronic devices aimed at handling information in an effective and productive manner (Berlin et al. 656). A clinical decision support system on the other hand entails any computer program or knowledge system that is designed to assist health care professionals in making clinical related decisions. This could be possible through the provision of clinical data and knowledge that enhances decision-making (Berner 3). This piece of work will look at health care information systems with much focus being on the emerging development in clinical decision support systems.
Just like any other field, the medical field has been at the forefront in adopting technology. One concept that has been characterized by a lot of developments and advancements is the clinical decision support systems. Some of the current trends that have been experienced in this sector include global mandate on reducing errors, quality improvement as well as lowering of the cost incurred in the provision and access of health care facilities and services. There is also an issue of personalizing the concept of medicine. This is through the integration of clinical molecular, social-economic, imaging as well as genomic information in a systematic manner in an effort to enhance the health care services. Clinical decision support has also improved with various strategies being implemented through major global initiatives (Englebardt and Nelson 82).
Some of the notable emerging developments include the implementation of national and regional infrastructure including regulatory and evaluation protocols, interoperable standards, tools, and interfaces that allow for sharing as well as broad-based education and training. There is also the concept of solution-based technology architecture that entails aspects such as clinical research and industrial collaboration, an open-source working in conjunction with proprietary systems, and advanced communication and computational technologies among others (Musen 231).
Other emerging technologies in regard to clinical decision support systems include a combination of techniques. They include new modeling, analytic, and learning algorithms. This entails image-based reasoning, probabilistic graphical networks as well as natural language processing. There are also some hybrid techniques that have been designed to support analytic tasks in the clinical field. They include those that are involved in data mining, diagnosis and prediction of a condition, its optimization as well as discrimination based on some facts (Goor and Christensen 182). Various Modeling and analytic models are being developed to enhance decision-making. Machine learning techniques also enhance clinical practices. Others include user modeling and business intelligence systems (Leong, Kaiser and Miksch 75).
According to Greenes (346), the emerging developments in clinical decision support systems have been instrumental in bringing about efficiency and effectiveness in the medical field. This is through enhancing the functions of the clinical decision support systems. For instance, the administrative function; that of supporting clinical coding, authorization of practices, making of referrals as well as documentation. Decision support has also been enhanced greatly; technological advancements have assisted in clinical diagnosis and treatment practices through provision of strategic guidelines. The function of managing clinical complexity and details has also been enhanced. This has been made possible through reinforcement of concepts such as chemotherapy and research protocols and facilitating the processes of order tracking, making referrals and follow ups as well as provision of preventive and long term health care (Musen, Shahar and Shortliffe 700).
Regardless of any development that is done in the clinical field, it is advisable that clinical decision support system be cost effective particularly in addressing a patient’s condition and preferences as well as the clinician’s workflow and any possible technical challenges. This has brought about changes in views from technology centric view where the main focus was how to solve a technical problem effectively to socio-technical view that deals with how to support a clinician’s workflow tasks to the emerging perspective; the patient centric view. This is extremely crucial and aims at managing a patient’s conditions and preferences in the most cost effective manner (Yu and Kacprzyk 161).
Tan and Sheps (380) assert that the developments that have been witnessed in the health care field are significant and have led to major improvements in regard to the provision of health care services and handling of different cases that are presented by different patients. For this reason, they cannot be underemphasized.
From the above discussion, it is evident that clinical decision support systems are extremely crucial in the medical field. They form a considerable part of the field of clinical management technologies. This is so because they support various clinical processes, through use of knowledge, all through from diagnosis and investigation of a given health condition to its treatment and care. It is also clear that there have been some developments in clinical support systems, all aimed at enhancing the practices carried out in the clinical field especially through bringing about efficiency, effectiveness and economy, an aspect that is crucial in the achievement of success in any activity or practice.
Berlin et al. A Taxonomic Description Of Computer-Based Clinical Decision Support Systems. Journal of Biomedical Informatics 2006, 39(6): 656-667.
Berner, Eta. Clinical Decision Support Systems: Theory and Practice. New York: Springer, 2007
Englebardt Sheila and Nelson Ramona. Health Care Informatics: An Interdisciplinary Approach. 5th ed. Michigan: the University of Michigan, 2008.
Goor van Noothoven and Christensen Pihlkjaer Jens. Advances in medical informatics: results of the AIM exploratory action. UK: IOS Press, 1992
Greenes Robert. Clinical Decision Support: The Road Ahead. New York: Academic Press, 2007
Leong, T. Y., K. Kaiser and Miksch S. Free and open source enabling technologies for patient-centric, guideline-based clinical decision support: A survey. Methods of information in Medicine, 46, no. Suppl 1 (IMIA Yearbook of Medical Informatics) (2007): 74-86.
Musen, Mark. Scalable Software Architectures For Decision Support. Methods of Information in Medicine, 2000, 38:229–238.
Musen, Mark, Shahar Yuval and Shortliffe Edward. Clinical Decision-Support Systems. Springer: 2006, 698-736.
Tan Joseph and Sheps Barry Samuel. Health Decision Support Systems. UK: Jones & Bartlett Learning, 1998
Yu Hou Xing and Kacprzyk Janusz. Applied Decision Support With Soft Computing. New York: Springer, 2003