Clinical Decision-Support System
The Clinical Decision-Support System is a comprehensive medical knowledge based system that would enables easier creation and execution of physician orders relative to the patient’s electronic medical record or EMR regarding what procedures, testing and medications are needed by the patient. It has the same scope as the CPOE or computerized physician order entry and virtually designed to perform the same function. The way the system works is to consolidate the required steps in the patient’s entire treatment process. An approach developed to be a supporting tool infrastructure that will offer a more precise clinical decision and process management.
What did they do?
They have used evidence based guidelines in managing clinical difficulties that became the standards of practice. Since guidelines are also protocols they effectively customized those protocols for deployment. The approach employed on this system is supported by the idea that allows protocol models to be customized according to per-patient basis with customized treatment management through TMC or treatment management console. Domain specific modeling language or DSML had to be developed because of the absence of visual language to capture treatment protocols and generic software similar to UML which is designed to represent medical knowledge. By doing so it offered several benefits and advantages in managing protocols for patient treatment. In order to realize the reliability of clinical decision-support system, they sought out a common clinical paradigm that has sufficient evidence based treatment protocol and one of the best candidates for modeling is Sepsis.
The medical condition is a severe reaction to infection that manifests in inflammatory responses in the body. Managing this medical condition requires the use of several hospital responses, making it one of the most expensive yet common health cases in the United States. Sepsis treatment is proven to be complex and involves extremely sensitive process being performed in ICU’s. Considering the large scope required addressing in treating this condition it is then a perfect model for comparing and testing diagnosis and treatment management processes needed to the integrated into the system being developed.
How it relates to Information System
At some degree clinical decision-support system or simply CDSS is similar to information system in such a way that CDSS has been coined as knowledge systems that uses two or more patient’s data to be able to create a case based advice. This is correlated to the definition of information system being a combination of infrastructure consists of hardware and software that facilitates planning, control, coordination and decision making to aid an organization.
In information system, a decision can only be made by analyzing comparable data derived from a pool of knowledgebase source within the system. CDSS works similarly because when patient’s results have indicated further review it will alert the health team. If there are anomalies in the patient’s physiological parameters the physician will activate the decision support and the system will interact with the TMC or treatment management console to asses the patient’s health status. The decision support will then refer to the evidence based guidelines to actuate decision. In comparison to a common information system being used in other organization, calculations will be made by the system based on available data in the database to examine variability and will return with data suggestions.
Advantages and Benefits
Several benefits have been provided by using model integrated techniques. One is that protocol model was able to capture the medical knowledge precisely while avoiding ambiguity. The model was easily comprehended by the medical professionals and it has eliminated the need for IT personnel to consolidate the computer and medical data perspectives. It also allowed an effective transfer of knowledge because of the fact that it was based on the best available practices. In return expert knowledge was learned by medical students and resident practitioners in actual practice.
Furthermore, when new knowledge emerged in the new medical literatures the models can be easily updated regularly and even facilitates protocol execution tracking as well as improving the protocols enabling outcome analysis. Apart from effectively capturing medical knowledge, CSDSS also represents several advantages as far as perspectives in software development are concern. The software architecture was also designed to work perfectly on other illnesses aside from sepsis.
In terms of medical language, MIC came out soundly and a disciplined foundation of verification and validation process was created. This is important in checking if the generated decision-support guideline would correspond to the expectations of the clinician. The modeling language plays a crucial role in this process and therefore justifies the importance of DSML and its other benefit which is formally verifying established criteria against the domain model.
Conclusion
In my opinion, the use of Clinical Decision-Support System will greatly improve the healthcare delivery system, imposing efficiency in serving patients and accurately monitoring their conditions not by mere analysis of paper works but with the help of software that can validate data for a more effective decision making. However further evaluation of CDSS in order to assess how efficient the use of the system in an actual practice and the impacts on the user. Any flaws no matter how small will greatly affect the decisions of the clinician and will result to incorrect assessment of the patient’s treatment management.
Although there is an increase in recognition of CDSS, from the looks of the presented structure it could still use a little design improvement to make it more user-friendly. That way, when implemented would be able to deliver impressive results and bigger possibilities for efficiency that will also result to healthcare cost reduction. The careful consideration of its purpose is important in understanding the required complex intervention of such system not to replace clinicians but to aid better decisions and identifying its benefits and limitations.
Clinical Decision-Support System holds a lot of potential and opportunities in addressing the increasing demand for better healthcare service quality at a larger scale. It would also be beneficial on the part of clinicians in assessing the patient’s health condition and mapping the needed steps in the treatment process. The integration of healthcare and information technology is a beneficial collaboration that would hold promise of a healthier future for the people and lesser burden for the government in addressing the issues of healthcare. By using CDSS there would also be less cases of wrong treatment and clinical procedures done on patients and therefore eliminating probability of human error.
