Clinical Decision Support System: Examples, Tools and More

Posted in Healthcare Management

Executive Summary – Clinical Decision Support System Examples

This comprehensive blog discusses the details of Clinical Decision Support Systems (CDSS), which are reshaping the healthcare industry. Implementing a Clinical Decision Support System (CDSS) is a big step towards adopting modern healthcare and elevating overall patient care. Clinical Decision Support System examples can provide valuable insights into a healthcare organization’s implementation journey. We will move into real-world successes and potential downfalls to better understand the concept.

What Is a Clinical Decision Support System?

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

A clinical decision support system (CDSS) improves healthcare delivery by providing evidence-based insights, reducing medical errors, and streamlining workflows. It is important to understand that the CDSS isn’t restricted to healthcare practitioners (doctors, surgeons, etc.) but is meant to assist paramedic staff, patients, and caregivers as well.

Over the years, clinical support tools in the CDSS have quickly become one of the most efficient features for digital health solutions. These solutions require an automated and specialized tool to manage large volumes of data and deliver high-value and efficient medical services to patients.

The most significant of the clinical decision support examples is that it assists healthcare practitioners in accessing and filtering out person-specific detailed patient information. The system comes with various clinical support 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 history, discharge summaries, and various other high-utility tools. 

The CDSS can be developed through various methods. Most use machine learning algorithms, whereas others incorporate precise case-based knowledge to analyze and filter patients’ data for healthcare practitioners.

What Is CDSS In Healthcare Administration?

A clinical decision support system (CDSS) is a computer-based system that analyzes data to help healthcare professionals make informed decisions and improve patient care by delivering on-time alerts to assist healthcare facilities. Among the examples of CDSS, its use is being lauded in the field of cardiovascular disease. It can send alerts flagging elevated blood pressure and other risk factors, aiding healthcare providers in identifying at-risk patients and timely interventions.

Additionally, CDSS can provide recommendations for treatment protocols, medication warnings, and personalized healthcare suggestions. By analyzing patient data, CDSS delivers timely alerts to healthcare providers, improving their care and decision-making. Fundamentally, CDSS leverages knowledge management to provide healthcare and clinical advice.

Multiple types of CDSS can optimize integrated workflows while assisting with healthcare services. Data mining and clinical decision support systems (CDSS) are combined to transform healthcare by analyzing patient data to predict diseases and improve diagnoses and treatment plans. Data mining can extract hidden patterns and trends to assist in early disease detection and drug discovery. While CDSS aids healthcare providers with real-time alerts, evidence-based guidelines, and decision-making clinical support tools. All of these lead to reduced medical errors and better patient outcomes. 

5 Ways Clinical Decision Support System Examples Improve Patient Outcomes

What Are the Examples of Decision Support Systems in Healthcare Organizations?

The decision support system has existed since the 1950s and 1960s, but proper execution was implied in the 1980s. It was implemented as EIS, ODSS, and GDSS.

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

The DSS tends to leverage a combination of healthcare models, raw information, personal data, and documents. Its data sources include EHRs, relational information sources, resource forecasts, and sales forecasts. 

Modern healthcare facilities are focusing on data-backed decision-making, and DSS is unlocking the potential of these systems. 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 in supporting healthcare decisions.

DSS support systems have five primary categories: model-driven, data-driven, document-driven, knowledge-driven, and communication-driven. Still, if we consider the primary focus, DSS is responsible for diagnosing patients and promising more efficient healthcare treatments. 

The decision support system has three primary components: 

• The database
• Software system
• User interface

Combining these components creates a full-spectrum system that drives value and optimizes the quality of healthcare.

What Are the Benefits of Clinical Decision Support?

Just like any other system, CDSS also has its set of pros and cons. 

Some of the benefits of a clinical decision support system (CDSS) include:

• Minimal chances of medication errors
• A central repository for all information
• Low risks of misdiagnosis
• Reliable and consistent information exchange among the team
• Enhanced efficiency of healthcare practitioners by automating repeated tasks
• Improved quality of healthcare services

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

Clinical decision support tools can be used in several ways for patient care. From identifying viral infections by analyzing patient symptoms and lab results to devising cancer therapies with genetic profiling and tumor characteristics, CDSS increases overall patient satisfaction. 

Here are some promising clinical decision support examples in real life: 

By implementing the computerized surveillance algorithm, a hospital in Alabama reduced the sepsis mortality rate by over 53%. The high-quality and real-time analytics offered by the system benefit healthcare practitioners by allowing them to make a timely diagnosis. 

Mayo Clinic is implementing a specialized clinical decision support system for nurses. Its purpose is to deliver nurses precise and detailed phone screenings of patients seeking advice or appointments. The system uses a series of standardized questions to ensure nurses do not miss any important information about patients’ health.

In collaboration with Unity Health-White Country Medical Center, Harding University concluded that implementing the CDSS combined with genetic testing data will lower emergency department visits by over 42% and hospital readmissions by 52%.

Yale and Mayo Clinic are more examples of clinical decision support system applications. They developed a specialized CDSS application for patients with head injuries. The application utilized industry guidelines to advise patients with head injuries to do the following by evaluating the severity of trauma: 

1. Seek immediate medical attention
2. Monitor symptoms at home
3. Return to normal activities 

The CDS system significantly reduced the number of unnecessary CT scans by providing real-time, evidence-based recommendations.

 Clinical decision support tools implemented at the Department of Veteran Affairs site in Indiana helped reduce unnecessary lab tests by over 11%, which saved patients as much as $150,000 without lowering the quality of 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 condition 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 quickly becoming an 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 and deliver precise results for healthcare practitioners and patients alike. 

Machine learning is increasingly being used as the preferred technology in precision medicine and other analytical fields, such as the Internet of Things or automated image analysis. 

An ML-powered clinical decision support system (CDSS) implemented at the University of Pennsylvania lowered the sepsis detection time by 12 hours. This is a huge achievement with life-saving potential for patients suffering from sepsis. 

In another study conducted by MT, a deep learning tool was used to generate hourly predictions for ICU patients. The system’s input was clinical notes, bedside monitors, and other supplementary data. The system was meant to improve the delivery of healthcare services by predicting the patients’ conditions through CDSS. 

The system doesn’t just predict the patient’s health; it also produces the rationales 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 a central role in delivering high-quality healthcare services, an ever-increasing number of CDS systems are being built using advanced analytics techniques. These examples prove the benefits of clinical decision-support systems and solidify that they must be used in medicine.

How To Build A Clinical Decision Support System?

Clinical decision support systems (CDSS) have become essential for healthcare facilities and doctors because they offer high data volumes, leading to value-oriented care. CDSS reduces repetitive testing and improves patient safety by avoiding errors and complications that result in student re-admissions. Some other examples of CDSS in healthcare and CDSS tools include the fact that this tool is designed to sort out digital information to provide on-time alerts and treatments.

CDSS can outline potential errors in healthcare systems and treatment. Since hospitals have too much data to handle, integrating CDSS helps healthcare facilities 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 defines rules for extracting data for rule evaluation, while the second uses machine learning and artificial intelligence.

This system’s scope of functions is pretty extensive because it helps with drug prescription, diagnostics, disease treatment, notification systems, and drug dispensing. These functions can manifest in reports and workflow tools. In simpler words, a clinical decision support system can help with patient safety, reduce the costs of treatment and healthcare facilities, improve clinical management, and streamline administrative activities.

When it comes to building the clinical decision support system, various aspects need to be considered. The patient data and medical knowledge are 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 issues. All in all, these are just some of the clinical decision support tools examples.

Implementation Checklist of Clinical Decision Support System

Even with a sound technical foundation, the effectiveness of clinical decision-support systems can quickly become a curse for healthcare practitioners if they are not implemented correctly.

Integrating new technology into existing systems and processes is tricky for all types of organizations. It becomes even more complicated for healthcare organizations with large, complex systems and massive volumes of data. Therefore, 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 a study, the complexity of the EHR workflow is taking its toll on doctors and physicians. They are now spending more time finding and analyzing patients’ information than they are spending with them.

In such a bleak situation, organizations must understand the importance of implementing a CDS system that can reduce “physician burnout” by minimizing physicians’ interactions with the EHR. This will not only help ease physicians’ pressure but also enable them to offer patients better and more efficient healthcare services.

Here’s a step-wise plan to implement a clinical decision-support system in a healthcare organization.

1. Building the Team

The CDSS implementation starts with assessing the organization’s readiness to accept and adapt to the new system. It should not begin until you have a core team understanding of how the CDSS works. 

Many doctors and physicians may not immediately accept the new system and may have negative reactions to its implementation. In such a situation, build a general consensus amongst the healthcare practitioners by educating them about the system’s effectiveness and usability and making them aware of other organizations benefiting from implementing it. By educating them about the benefits of computer analytics power, you will be in a better position to dilute the resistance and soften their negative reaction to the system.

A successful CDSS implementation stems from a collaborative approach involving a multidisciplinary team of physicians, nurses, IT professionals, and clinical workers.

The IT team has a critical role in assessing the organization’s technological infrastructure and identifying potential integration points. By presenting potential benefits and addressing concerns, the IT team can foster a positive attitude toward the CDSS within the organization.

Clinical workers can significantly influence the adoption of the CDSS. By actively engaging with their peers and hands-on training along with valuable insights they contribute to a smooth and successful implementation.

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

• Does the organization have the internal resources to implement the clinical decision support system?
• Is there any resistance from the administrative stakeholders to implementing CDSS? If yes, how can that resistance be addressed?
• Can the organization educate the team about the effectiveness of the new system? Has it addressed the stakeholders’ concerns and taken their feedback for the system?
• Can the organization 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 clinical decision support systems are complex, multifaceted technologies with sophisticated workflows. Most clinics and healthcare institutions lack the adequate IT expertise to understand these workflows, let alone the expertise required to develop and implement a CDS system.

Also, healthcare organizations often need unique functionalities that may not be available with off-the-shelf CDS systems and may require fine-tuning and customizing the system. This means that most of the time, healthcare institutions would be required to hire the 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.

Ensuring the CDSS integrates smoothly with existing electronic health record (EHR) systems is crucial for workflow efficiency. This may involve detailed planning around data migration and interoperability standards like HL7 or FHIR.

Future Trends and Predictions in CDSS

Advancements in Clinical Decision Support Systems (CDSS) are set to positively impact healthcare delivery. Using the latest technology, CDSS has the potential to optimize clinical decision-making and improve patient outcomes.

AI and Machine Learning Integration

The integration of AI and machine learning is significantly advancing clinical decision support system software. AI/ML are among the technologies that enable more accurate predictions and recommendations within clinical decision support systems, improving overall decision-making processes.

Advanced Decision Support Tools

New clinical decision support tools are emerging, and old ones are advancing. CDSS is equipped with advanced algorithms that analyze data swiftly and provide more refined insights compared to traditional decision support systems in healthcare. These tools are designed to enhance the efficiency and effectiveness of clinical decision-making.

Integration with EHR Systems

CDSS software is widely integrated with electronic health records (EHR) systems. This integration ensures a seamless flow of data and real-time updates, greatly advancing the CDSS healthcare data by improving operational efficiency and decision accuracy.

Personalized Healthcare Solutions

Personalized CDSS healthcare solutions are in demand as they offer customizable solutions, utilizing patient-specific data to offer tailored recommendations and treatment plans. This technology trend highlights the relevance of clinical decision support system examples in healthcare in providing more individualized care.

Role of Wearable Devices

Wearable devices are becoming integral in clinical decision support tools, providing continuous data that facilitates real-time decision-making. This integration enhances the precision and timeliness CDSS tools and contributes to better patient monitoring.

Expanding Integration with Health Technologies

As CDSS continues to make strides in healthcare, caregivers and researchers are expanding their integration with other health technologies. This trend aims to create more intuitive and impactful clinical decision support tools examples, enhancing the overall effectiveness of healthcare decision-making.

Best Practices Of A Clinical Decision Support System

The clinical decision support software is a healthcare information technology system that provides patients, clinicians, and staff with patient-specific data and knowledge. It can filter the information to present 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. The software can create different summaries and reports for the patients. There are various document templates, and diagnostic support is offered as well. According to medical experts, clinical decision support software is designed to link healthcare knowledge and observations to positively influence healthcare choices.

Various best practices must be implemented to ensure you can yield these positive outcomes. Some of the best practices regarding clinical decision support systems are as follows:

• Calculate the Burden—When it comes to implementing the CDSS, healthcare organizations need to determine the burden with their software vendor. This 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—When designing and implementing the clinical decision support system, ensure that it is appropriately structured to streamline the data flow and is governed at all times to fix errors in a timely manner. 
• Choose a system that offers tailored solutions to your organization’s needs. Customization ensures that the system aligns with specific workflows and clinical requirements.
• CDSS should have a scalable architecture to accommodate future growth and changes. Scalability helps the system evolve and adapt to new challenges and opportunities.
• Updated Data—The implementation of CDSS will only be effective when the data is regularly updated. To this end, the system can be integrated with different data sources to ensure accurate and on-time data updates. 
• Adherence to regulatory compliance requirements, such as HIPAA, protects patient data. Compliance is non-negotiable and critical to maintaining trust and legal standing.
• Implementing audit trails to track access and modifications to patient information to enhance transparency and accountability. 

Proper planning and execution are crucial for developing a clinical decision support system’s success. 

Keep Track of the Success and Make Consistent Updation

Lastly, successful implementation of the clinical decision support system isn’t the end for healthcare organizations; it is just the beginning. It’s important to keep close track of the system’s performance and deliverance as a performance evaluation. 

To do this, the organizations would be required to collect continuous feedback from all concerned stakeholders about the new system’s impact. Assess the system’s positive impact on the patient’s safety, improved healthcare services, and reduced 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 IT services. You will need a team that is strong in technology development and understands the financial, regulatory, and administrative complexities involved in developing healthcare tools to ensure patients’ safety and 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 have the resources you need to successfully develop a completely customized CDSS for your organization.

Medical CDSS

We assist healthcare organizations in developing customized CDSS to reduce pressure on doctors, physicians, and other healthcare providers and improve their healthcare services.

Robust & Optimal Control

Irrespective of your organization’s size and needs, we have the experience and expertise to deliver customized, scalable, and robust infrastructure for CDSS that meets those needs.

Conclusion 

Clinical Decision Support Systems (CDSS) are revamping healthcare by empowering clinicians with real-time insights for better patient outcomes and streamlining workflows. By leveraging advanced technologies like artificial intelligence and machine learning, CDSS can analyze vast amounts of data to provide timely and accurate recommendations. However, successful CDSS implementation requires careful planning and strong collaboration between IT and clinical teams for improved overall healthcare outcomes. 

Frequently Asked Questions

What are the advantages and disadvantages of the clinical decision support system?

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

• Utilizing a clinical decision support system reduces the chances of medical errors and benefits patients needing emergency healthcare. Similarly, dosage calculation can be challenging in emergencies, but CDSS can provide accurate medication dosage calculation according to the patient’s symptoms and indications.
• Implementing CDSS reduces the chances of misdiagnoses 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 that it provides the healthcare team with reliable and consistent data and information. These systems can outline evidence-backed information, especially in an emergency. Also, the information provided will be quick and accurate, promising better healthcare decision-making.
• CDSS can improve efficiency by providing accurate diagnosis and dosage, saving lives. 

Disadvantages

• 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?

Some clinical decision support system examples include:

• First Databank
• Medispan
• Allscripts
• Cerner
• Elsevier
• Truven Health Analytics
• Zynx Health

What is CDS In Healthcare?

Clinical decision support (CDS) provides prompt patient information, usually at the point of care, which helps inform decisions about the patient’s care.

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