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Clinical Decision Support System Examples – Benefits, Tools And More

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    Posted in Healthcare Management

    Last Updated | February 14, 2024

    Executive Summary – Clinical Decision Support System Examples

    This article will provide a cutting-edge review of the use of clinical decision support systems in healthcare, covering the many forms, current use cases with proven efficacy, typical errors, and potential downsides; all based on providing recommendations for reducing risk in CDSS design, deployment, evaluation, and maintenance. The article will also discuss the clinical decision support system advantages and disadvantages.

    What Are Clinical Decision Support System Examples?

    Clinical Decision Support System (CDSS) is a specialized software developed to assist healthcare practitioners in analyzing the patients’ records and making well-informed decisions. Some CDSS examples are discussed further ahead.

    It is important to understand that the utility of the clinical decision support system isn’t just restricted to the healthcare practitioners (doctors, surgeons, etc.), rather it is also meant to assist paramedic staff, as well as, patients and caregivers.

    Over the years, the CDSS has quickly become one of the most efficient and leading tools from digital health solutions, who are looking for an automated and specialized tool to manage large volumes of data and be able to deliver high-value and efficient medical services to patients.

    One of the major uses and an example of clinical decision support system is that the clinical decision support system assists healthcare practitioners to access person-specific detailed information about patients, which can easily be filtered. The system comes with a variety of powerful tools to enhance the decision-making process on behalf of healthcare practitioners. The tools offered by the clinical decision support systems include alerts, reminders, patient’s history, discharge summaries, and various other high-utility tools. The CDSS can be developed through various methods. Most of them leverage the machine learning algorithms, whereas, others may incorporate a precise knowledge base to analyze and filter patients’ data for healthcare practitioners.

    What Is CDSS In Healthcare Administration?

    CDSS is a short-term for the clinical decision support system and is known as the computer-based system for analyzing the data within the electronic health records to deliver on-time alerts and prompts to assist healthcare facilities. It helps the healthcare providers to implement the healthcare and clinical guidelines for providing care. CDSS is highly recommended for CVD (cardiovascular disease) prevention. It gives alerts regarding flagging blood pressure and other risk factors.

    In addition, CDSS can provide information regarding protocols of treatment. CDSS also offers warning questions regarding medication. Moreover, it provides personalized recommendations for healthcare changes. It does everything by analyzing the data to deliver the prompts and alerts, promising high-end patient care and improved decision-making. A clinical decision support system focuses on utilizing knowledge management to offer healthcare and clinical support in the form of advice.

    It can optimize the integrated workflows while assisting with healthcare services. With the usage of CDSS, data mining can be used for examining the patient’s medical background with research to predict the potential of diseases. As far as the purpose of CDCC is concerned, it is basically responsible for assisting the healthcare providers that allow patient data analysis. The data about the patient can be used for designing the diagnosis.

    It wouldn’t be wrong to say that healthcare providers will improve healthcare quality. In addition, it offers reminders regarding preventive medical care and also provides alerts regarding drug issues. CDSS also alerts the clinicians on time which helps reduce the healthcare costs while improving the efficiency standards. Not to forget, the patients can be flagged regarding improper diagnosis. Also, the errors can be appropriately reported to enhance healthcare services.

    Still, healthcare professionals ask why they should imply CDSS systems.  Therefore, the following mentioned are clinical decision support system examples in healthcare. In this regard, healthcare providers can utilize these systems to diagnose and improve healthcare quality because it reduces the need for excessive testing. Consequently, it will improve the patient’s security by keeping them secure from health complications.

    5 Ways Clinical Decision Support Systems Examples Can Improve Patient Outcomes

    5 Ways Clinical Decision Support Systems Can Improve Patient Outcomes

    What Are Decision Support System Examples In Healthcare Organizations?

    The decision support system in healthcare organizations is defined as the interactive information solution. It is responsible for analyzing an extensive amount of data to make result-oriented and value-based healthcare decisions. A decision support system (DSS) can help with various healthcare operations, including operations, management, and organizational planning. It can be used for assessing the severity of tradeoffs and ambiguities.

    The DSS tends to leverage the combination of healthcare models, raw information, personal data, and documents. The data sources utilized by the decision support system also include the EHRs, relational information source, revenue forecast, and sales forecasts. The decision support system has been around since the 1950s and 1960s, but proper execution was implied in the 1980s. It was implemented in the form of EIS, ODSS, and GDSS.

    Modern healthcare facilities are focusing on data-backed decision-making, and DSS is unlocking the potential of these systems. To illustrate, DSS can integrate managerial science, social science design science, and data science to reduce the need for efforts to make top-notch decisions. It wouldn’t be wrong to say that decision support systems have become a common part of BI systems. However, DSS tends to be mission or purpose-oriented for supporting healthcare decisions.

    When we are talking about the decision support systems, there are actually five primary categories of the systems: model-driven, data-driven, document-driven, knowledge-driven, and communication-driven. Still, if we consider the primary focus, DSS is responsible for diagnosing the patients and promising more efficient healthcare treatments. Also, when it comes down to the decision support system, there are three primary components.

    First of all, DSS includes the database, software system, and user interface. Combining these components will create a full-spectrum system that drives value and optimizes the quality of healthcare.

    What are the Benefits of Clinical Decision Support?

    One may wonder about the clinical decision support system pros and cons, as everything in this world has advantages and disadvantages. Some of the benefits of clinical decision support system (CDSS) includes:

    • Minimize chances of medication errors
    • A central repository for all information
    • Lower risks of misdiagnosis
    • Delivering reliable and consistent information to the entire team
    • Enhancing the efficiency of healthcare practitioners
    • Improving the quality of healthcare services

    What are the Use Cases and Industry Requirements for Clinical Decision Support?

    Clinical decision support tools can be put to use in several ways for patients’ care. From virus detection to personalized cancer therapies, the system can be used to enhance the quality of medical treatment offered by healthcare practitioners in a number of ways. Mentioned in this article are some clinical decision support tools examples. Below we have come up with some of the promising clinical decision support examples where the system has been put to use by different institutions:

    By implementing the computerized surveillance algorithm, a hospital in Alabama was able to reduce the sepsis mortality rate by over 50%. The high-quality and real-time analytics offered by the system was able to alert healthcare practitioners to make a timely diagnosis for sepsis, as well as, give timely reminders for the best treatment options for deadly conditions.

    Mayo Clinic is in the process of implementing a specialized clinical decision support system for nurses. The system is meant to deliver nurses precise and detailed phone screenings of patients who are looking for advice or seeking appointments. Using a series of standardized questions, the system enables nurses to ensure they don’t miss any important piece of information about patients’ health.

    Harding University in collaboration with the Unity health-White Country Medical Center came to the conclusion that implementing the CDSS in combination with the genetic testing data will lower the emergency department visits by over 40%, and lower the hospital readmissions by over 50%.

    Some people may wonder what are examples of clinical decision support system applications? And the answer is right here. Yale and Mayo Clinic developed a specialized CDSS application for patients with head injuries. The application utilized the industry guidelines to advise patients with head injuries, by evaluating the severity of the injury. The CDS system implemented was able to significantly reduce the number of unnecessary CT scans, by carefully detailing the patients with the treatments for the head injury.

    Clinical decision support tools implemented at the Department of Veteran Affairs site in Indiana helped reduce the unnecessary lab tests by over 11%, which save as much as $150,000 to patients, without lowering the quality of the healthcare.

    Apart from the above-mentioned successful clinical decision support examples, the system may offer various other benefits including:

    • Performing calculations for drug dosing
    • Identification of the reportable conditioned by analyzing the inputs from HER
    • Evaluating the guidelines for drug formulations
    • Initiating automated reminders for medication or appointments
    • Analyzing the severity index for different diseases to suggest treatment.

    These are just some of the benefits of CDSS.

    By leveraging powerful data analytics techniques like machine learning and artificial intelligence, the CDSS is fast becoming an ever-more efficient and effective analytical tool for the healthcare industry. The power of machine learning and artificial intelligence enables these systems to store and analyze the massive volume of data to identify hidden patterns, as well as, deliver precise results for healthcare practitioners and patients alike. Machine learning is increasingly being used as the preferred technology in precision medicines, as well as, in various other analytical fields like the Internet of Things or automated image analysis.

    A machine learning powered clinical decision support system (CDSS) implemented at the University of Pennsylvania was able to lower the sepsis detection time by 12 hours. This is a huge achievement with life-saving potential for patients suffering from sepsis. The machine learning algorithm used in the system was trained for the sample size of 160,000 patients’ samples, whereas, the validation was completed for another 10,000 samples.

    In another study conducted by MT, a deep learning tool was used to generate hourly predictions for ICU patients. The input for the system was the clinical notes, bedside monitors, and other supplementary data. The system was meant to improve the deliverance of healthcare services by predicting the patients’ conditions through CDSS. The system doesn’t just predict the patients’ health, it also produces the rationales which are critical to offering trust and reliability in machine learning systems. These are just some of the examples of CDSS.

    Today, as machine learning and artificial intelligence have taken the central position towards the deliverance of high-quality healthcare services, an ever-increasing number of CDS systems are being built using advanced analytics techniques. As a result, all of these examples prove the benefits of clinical decision support systems and solidify the fact that this must be used in the world of medicine.

    How To Build A Clinical Decision Support System?

    Clinical decision support systems have become essential for healthcare facilities and doctors. This is because CDSS offers high data volume, which leads to value-oriented care. It wouldn’t be wrong to say that CDSS reduces repetitive testing and improves patient safety. Not to forget, it can avoid errors and complications that eventually result in re-admissions of the students. Some other examples of CDSS in healthcare and CDSS tools include the fact that this tool is designed to sort out the digital information for providing on-time alerts and treatments.

    CDSS can outline the potential errors in the healthcare systems and treatment. Since hospitals have too much data to handle, the integration of CDSS helps healthcare facilities to streamline data management and accessibility for effective diagnosis and better healthcare services. There are different types of CDSS systems, but the broad categories are knowledge systems and non-knowledge-based systems. The first one defines rules for extracting data for rule evaluation, while the second one uses machine learning and artificial intelligence.

    When it comes down to the scope of functions of this system, it’s pretty extensive because it helps with a drug prescription, diagnostics, disease treatment, notification system, and drug dispensing. These functions can manifest into reports and workflow tools. In simpler words, a clinical decision support system can help with patient safety, reduces the costs of treatment and healthcare facilities, clinical management, and streamlines administrative activities.

    Now, when it comes to building the clinical decision support system, various aspects need to be considered. To begin with, the patient data and medical knowledge is used and stored in the interference engine. As a result, when the information is needed, the system provides case-specific information and suggestions. It can be designed into a desktop app or web app. Some healthcare facilities have experimented by making it a mobile app (it works fine either way). The CDSS solutions can be integrated into the current IT infrastructure without any issue. All in all, these are just some of the examples of clinical decision support tools.


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    Checklist Before Clinical Decision Support System Implementation

    Irrespective of the technical foundation, the effectiveness of the clinical decision support systems can quickly turn into a curse for healthcare practitioners, if not implemented correctly.

    Integrating new technology into the existing systems and processes is always a tricky proposition for all sorts of organizations, and it becomes even more complicated for healthcare organizations with large complex systems and massive volumes of data. Thereby, it is important for organizations or healthcare practitioners to carefully detail an implementation strategy for integrating CDSS into their existing systems and processes.

    According to one study from AMA, the complexity of the EHR workflow is taking its toll on doctors and physicians, who are now spending more time finding and analyzing the patients’ information than the time they spend with the patients.

    In such a bleak situation, it is important for organizations to understand the importance of implementing a CDS system that can reduce the “physician burnout” by minimizing their interactions with the EHR. This won’t just help ease the pressure from physicians, but also enable them to offer better and more efficient healthcare services to patients.

    Here we have come up with some steps to plan the implementation of a clinical decision support system in a healthcare organization.

    1. Building the Team

    The CDSS implementation should start by assessing the organization’s readiness to accept and adapt to the new system. Until you have a core team that understands the working of the CDSS and is ready to adapt to the new system, the implementation shouldn’t start.

    It is noteworthy to understand that many doctors and physicians may not be immediately ready to adopt the system, and may, in fact, come up with the negative reactions to the implementation of the system, it is important to build a general consensus amongst the healthcare practitioners by educating them about the effectiveness and usability of the system, as well as, making them aware of other organizations that are benefiting by implementing the CDS system. By educating them with the established benefits of the computer analytics power, you will be in a better position to dilute the resistance and soften their negative reaction to the system.

    The key is to take a collaborative approach and initiate group conversations with the doctors, physicians, nurses, and all other concerned staff about the decision. It is recommended to take on-board the IT team and have them present the potential impacts of the CDSS implementation to other staff.

    Also, consider taking on-board the clinical champions, to have a more receptive environment during the group discussions. The clinical champions in this case would be the tech-geek staff with an understanding of the IT and technicalities involved in the process. They would be in a better position to educate their fellows about the effectiveness of the technology and create a more positive environment towards the implementation of the clinical decision support system.

    Now, apart from building up the team, it is also important to assess the organization’s readiness to adapt to the new technology. Few important considerations which organization needs to ask themselves include:

    • If the organization has the internal resources to take-up and implement the clinical decision support system?
    • If there is any resistance from the administrative stakeholders for the implementation of CDSS? If yes, how can that resistance be addressed?
    • Is the organization able to educate the team about the effectiveness of the new system to be implemented? If it has adequately addressed the stakeholders’ concerns and taken their feedback for the system?
    • If the organization is able to determine precise and well-defined roles for the team members required to implement the system?

    2. Work with Professional IT Vendors

    It is important to understand that the clinical decision support system is a complex and multi-faceted technology with a sophisticated workflow. Most of the time clinics and other healthcare institutions don’t have the adequate IT expertise to understand the complex workflows, let alone the expertise required to develop and implement a CDS system.

    Also, many times, healthcare organizations would need unique functionalities that may not be available with off-the-shelf CDS systems and may require fine-tuning and customizations of the system.All of this means that most of the time healthcare institutions would require to hire services of professional IT firms specializing in developing IT tools for the medical industry. This won’t just help the organizations to implement the CDS system effectively and integrate it with the existing systems and processes, but also gives an opportunity to train the internal resources with the complexities and technicalities involved in developing, implementing, and maintaining the clinical decision support system.

    What Is A Clinical Decision Support System Best Practices?

    The clinical decision support software is basically a healthcare information technology that provides patients, clinicians, and staff with patient-specific data and knowledge. It can filter the data to present it at the right time, promising improved healthcare. CDS software uses a wide range of tools to offer high-end decision-making while optimizing the clinical workflow. For instance, these tools include reminders and tools for patients as well as doctors.

    The clinical guidelines and order sets are integrated into the software to ensure that the reports can be designed without compromising effectiveness. It can create different summaries and reports for the patients. There are various templates for documents, and diagnostic support is offered as well. According to medical experts, clinical decision support software is designed to link healthcare knowledge and healthcare observations for positively influencing healthcare choices.

    To ensure you can yield these positive outcomes, various best practices must be implicated. In the section below, we are sharing the best practices regarding clinical decision support systems, such as;

    • Calculate The Burden – When it comes down to implementing the CDSS, healthcare organizations need to determine the burden with their software vendor. It is essential because it helps determine the necessary IT resources, so functionality is appropriately supported. Also, you must assess the clinical expertise that software vendors have to ensure they can customize the systems correctly.
    • Structured System – While you are designing and implementing the clinical decision support system, make sure that the system is appropriately structured to streamline the data flow. In addition, it must be governed at all times for timely error fixing.
    • Updated Data – The implementation of CDSS will only be effective when the data is regularly updated. For this purpose, the system can be integrated with different data sources to ensure accurate and on-time data updates.

    Keep Track of the Success and Make Consistent Updation

    Lastly, successful implementation of the clinical decision support system isn’t the end of the road for healthcare organizations, rather it is just the beginning. It’s important to keep close track of the performance and deliverance of the system as a performance evaluation. To be able to do this, the organizations would require to collect continuous feedback from all concerned stakeholders about the impact of the new system. Assess the positive impact the system has on the patients’ safety, improvement in the healthcare services, and reducing physicians’ burnout rate.

    Folio3 Digital Health is your Best Technology Partner for Clinical Decision Support System

    Developing and implementing a clinical decision support system requires specialized expertise in the field of IT telemedicine services. You will need an IT team that’s not only strong in technology development but also has a strong understanding of the financial, regulatory, and administrative complexities involved in the development of healthcare tools to ensure patients’ safety, as well as, protect crucial information from cyber breaches.

    At Folio3, we have the expertise and experience to develop robust and completely customized telemedicine software for healthcare organizations, to enhance their internal controls, efficiency, and healthcare services. As the leading medical software development company, we got the resources you need to successfully develop a completely customized CDSS for your organization.

    • Medical CDSS

    We assist healthcare organizations to develop customized CDSS to reduce the pressure on doctors, physicians and other healthcare providers, as well as, to improve the healthcare services of the organizations.

    • Robust & Optimal Control

    Irrespective of the size and need of your organization, we have the experience and expertise to deliver customized, scalable and robust infrastructure for CDSS, which meets the needs of your organization.


    What Are The Clinical Decision Support System – Advantages And Disadvantages?

    The clinical decision support systems have become essential for the majority of healthcare facilities around the globe. It wouldn’t be wrong to say that patients suffering from acute diseases keep increasing, putting immense pressure on emergency departments and trauma centers. That being said, this high pressure on the medical facilities leaves space open for medical errors. For this reason, using CDSS seems like a better option but considering its pros and cons is essential, such as;

    Advantages Of CDSS

    • The utilization of a clinical decision support system reduces the chances of medical errors. To be honest, medical errors can lead to severe outcomes for patients who need emergency healthcare and for infants and kids. Similarly, dosage calculation can be challenging in such emergencies, but CDSS can be used for accurate medication dosage calculation. The best thing is that CDSS calculates the medication dosage according to the patient’s symptoms and indications.
    • The implementation of CDSS reduces the chances of misdiagnoses. According to research, 10% to 30% of medical errors account for diagnosis errors. With the utilization of CDSS, the chances of misdiagnoses will be reduced, and patients will get the best care and medications. In addition, there are multiple diagnosis support tools available.
    • The third benefit of using the CDSS is providing reliable and consistent data and information to the healthcare team. These systems can outline the evidence-backed information, especially if there is an emergency. Also, the information provided will be quick and accurate, promising better healthcare decision-making.
    • CDSS can enhance the throughput of patients, which eventually improves efficiency. This is because these systems offer accurate diagnosis and dosage, so time is saved.


    • Not all alerts made by CDSS will be correct, which means healthcare providers might struggle with alert-related fatigue. In addition, there are always some chances of low-value prompts and alerts.
    • If CDSS isn’t implemented correctly, it might not be able to identify medication errors, which puts patients at risk.

    What are the two main types of clinical decision support systems?

    There are two main types of clinical decision support systems including:
    Knowledge-based Clinical Decision Support System
    Non Knowledge-Based Clinical Decision Support System

    What are the examples of clinical decision support systems?

    First Databank
    Truven Health Analytics
    Zynx Health

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