Clinical Decision Support Systems: Key Facts, Applications, and Challenges

Out of the existing healthcare technologies, a clinical decision support system (CDSS) is one of the most revolutionary, as it directly aids healthcare professionals in their complex decision-making process and takes a share of cognitive burden from them.

With a compound 10.5% annual growth rate from 2022 to 2030, clinical decision support systems are developing at an unprecedented pace. However, there are still risks and limitations of CDSS related to how their complex algorithms translate to reality.

This article contains an overview of decision support systems in healthcare, applications, and corresponding challenges. It also compares CDSS to RPM and demonstrates how they can work together. After reading, you will be one step closer to choosing the right CDSS for your organization.

Clinical decision support systems basics

A Clinical Decision Support System (CDSS) is a health information technology system that provides healthcare professionals with patient-specific advice to aid clinical decision-making.

Tasks of clinical decision support systems

Common tasks of clinical decision support systems are:

• Drug selection: Includes dosage guidelines, drug allergy checking, and drug interaction analysis.
• Diagnostic Support: Offering suggestions or guidance on potential diagnoses based on patient data and symptoms. Diagnostic imaging is an element of this component.
• Clinical/Workflow Management: Managing and streamlining clinical processes and workflows. Can also involve prompting newcomers and alerting doctors about patients not following their treatments.
• Order Entry: Ensuring that the patient's test orders are accurate and safe.
• Predictive Analytics: Using patient data to predict potential health risks or outcomes.

Types of clinical decision support systems

Even though there are different ways to categorize the types of clinical decision support systems, two basic types are knowledge-based CDSS and non-knowledge-based CDSS.

Knowledge-based CDSS have a knowledge base. They analyze the existing data repository to analyze and arrive at a solution. In contrast, non-knowledge-based CDSS uses machine learning to analyze clinical data. It observes the patterns of how basic tasks are performed and learns from examples.

Knowledge-based clinical decision support applications are best for scenarios with established medical guidelines. They are also the best for determining drug interactions.

Non-knowledge-based decision support solutions are best for complex data analysis and predictive modeling cases when explicit knowledge is lacking. Some applications include image interpretation and personalized treatment recommendations.

The architecture of a clinical decision support system

The basic architecture of a clinical decision support system in healthcare consists of three main elements:

• Database/data management: The place where all relevant information is stored. This includes patient information, medical knowledge, clinical practice guidelines, and protocols.
• Inference engine: The system component that analyzes the information and makes decisions.
• User interface: This is the part of the system that the users (healthcare providers) interact with. It includes different features, including alerts and reminders.

Applications of CDSS

CDSSs have many applications depending on the specific type of software. Let's look into the most common ones:

Reminders and alerts

Clinical decision support systems generate alerts about drug interactions, health maintenance reminders, and suggestions for diagnostic tests. Alerts also notify about critical results of laboratory tests and risky stats. With alerts, it is possible to prevent medical errors.

Clinical guidelines and protocols

CDSSs provide guidelines for disease management or preventive care. They are also known to increase adherence to such guidelines. The guidelines are based on the latest research, best practices, and relevant protocols.

Order entry and documentation

Decision support solutions are integrated with electronic health records (EHR) and computerized provider order entry (CPOE) systems. Health professionals can enter medication orders and other patient care orders. The software can check these orders, identify issues, and suggest alternatives if needed.

Patient education

CDSS can assist with patient education by generating patient-specific educational materials based on the patient's condition and medications.

Best clinical decision support systems

In 2023, there are types of clinical decision support systems on the market, and their number will only grow as the market grows. At Binariks, we can assist healthcare organizations with implementation of popular CDSS. Here is an overview of some of the best clinical decision support systems:

1. IBM Watson Health

The products of IBM Watson Health have advanced data integration capacity, which allows the company to make large amounts of unstructured data. Moreover, Watson's core strength is its ability to understand and interpret human language through Natural Language Processing (NLP).

IBM Watson Health is compatible with all of the widely used EHR systems, unlike many of the other market leaders. Moreover, the tools are available for Android and IoS devices. Here is the list of CDSSs provided by the company:

• Micromedex Clinical Knowledge is a flagship clinical decision support system (CDSS) for over 4,500 medical establishments. It consists of a medication management tool with information on drug interaction, a disease and condition management module, and a toxicology management module for identifying poisons and chemical spills. This is a comprehensive system that has few analogs.
• Watson for Oncology: Uses AI to review large volumes of medical data to recommend cancer treatments.
• Watson for Genomics: Analyzes genetic data to provide actionable insights and personalized treatment options. The focus is on cancer genomics.
• Watson Care Manager: Assists in care management and creates individualized care plans.
• UpToDate by Wolters Kluwer Health covers nearly all medical conditions. It provides comprehensive information and diagnosis, management, and treatment options, regularly updated to reflect the latest research. This is decision support software integrated with most EHR systems. It is used by over 2 million users worldwide.

Other clinical decision support solutions by the company include Medi-Span, which offers drug information and interaction checking, and Sentri7, a real-time clinical surveillance and analytics tool.

2. Siemens Healthineers

The solutions by Siemens have great reporting capacity and user-friendly AI. However, solutions are compatible only with Protis Data Management System and Siemens hardware.

• Siemens is mostly known for decision support solutions. Their decision support tool, Healthinerers, is often tied to diagnostics. While it can apply to many conditions, it often emphasizes those where imaging or lab diagnostics play a pivotal role. Types of testing supported by this CDSS include cerebrospinal fluid (CSF) testing, anemia assessment, and a 10-year cardiovascular risk estimation.
• Another offering by Siemens is AI-Rad Companion, a cloud-based AI decision support module for radiology with automated post-processing of imaging datasets.

3. Hearst (Zynx Health)

Zynx Health is a subsidiary of Hearst with offerings that provide evidence-based guidance for many conditions. The primary focus of these cloud solutions is ensuring best-practice care for the condition being treated. Zynx integrates with other electronic health record (EHR) systems. In particular, it is compatible with widely used Epic Systems.

Here is the list of decision support solutions by Zynx Health:

• ZynxOrder provides evidence-based order sets for deployment and customization. The goal is to decrease variability and ensure the best outcomes for the patient.
• ZynxCare offers care plans based on best practices for nursing and interdisciplinary teams.
• ZynxAmbulatoryCare is an evidence-based decision support software for the outpatient setting specifically.

4. Cerner Clinical Decision Support

EHR system and integrated CDSS by Cerner Corporation provides decision support across virtually all conditions.

Cerner also has separate tools for critical conditions that might result in fatal outcomes if not treated in time, making it a leader in critical condition management. This includes sepsis CDSS that detects early signs of sepsis and acute kidney injury solution for early detection of such injury based on urine tests. These modules have an integrated rapid response component that helps ensure intervention when vital signs drop rapidly.

Cerner products integrate with the company's EHR systems, software, and hardware. However, this is barely a minus, as Cerner EHR is among the most widely used worldwide.

5. Philips

Philips CDSS tools are for conditions that require constant monitoring or have a significant diagnostic imaging component. Their clinical decision support solutions are designed to go hand-in-hand with the company's monitoring tools. The solution is only compatible with Siemens IntelliVue monitors.

Some of the Philips Healthcare types of clinical decision support systems include:

• IntelliSpace Cognition platform that uses AI to assist clinicians in assessing cognitive impairments.
• Horizon Trends is for vital sign monitoring.
• ST Map is a specific solution for the ST segment of ECG useful for acute coronary syndrome.
• ProtocolWatch is a vital sign tool for sepsis criteria evaluation.
• IntelliSpace Precision Medicine is a tool for personalized care with the elements of genomics and oncology.
• Histogram Trends is a module for accessing the quality of treatment through time.

CDSS vs. RPM: What's the difference

Clinical Decision Support Systems (CDSS) and Remote Patient Monitoring (RPM) are two distinct but complementary components of health information technology. They can be used together to enhance patient care.

CDSS technology assists care practitioners with clinical decision-making by matching every patient's case with the clinical knowledge base and corresponding assessments. Clinical decision support applications provide alerts and reminders to practitioners and offer clinical guidelines and diagnostic support. CDSS in healthcare also helps to contextualize information for personalized patient care.

RPM is a technology for remote monitoring of patients outside of the clinical setting. RPM collects a wide range of health data from the patient and allows for real-time monitoring of the patient's condition.

Here is how the two differ:

To sum it up, decision support systems in healthcare are used in clinical settings and integrated into clinical workflows. RPMs are tied to the patient but not tied to one place. They can be used outside the traditional clinical environment, such as in a patient's home or care facility.

Besides, CDSS and RPM can be used together to improve patient outcomes. For example, a CDSS could use data from a patient's blood glucose monitor (part of RPM) to recommend adjustments to their insulin regimen. CDSSs can improve the RPM experience by analyzing patient condition changes, in particular, using AI and machine learning to cater the data to the specific circumstances.

Challenges and limitations of CDSS

Despite obvious benefits of clinical decision support systems in healthcare, they also face limitations. Most of the limitations stem from the complexity of these systems. Remote IT teams like Binariks can help your organization navigate the challenges. Here are a couple of the most significant ones:

• The algorithm transparency and trust: In clinical decision support solutions, the exact way decisions or predictions are made isn't always clear. While the system might generate a recommendation, sometimes the logic behind the predictions is hard to attain. If clinicians don't understand how the CDSS arrived at a particular decision, they are likely to not trust or act on its recommendations. Therefore, having transparency, proof of validation, and options for feedback is essential.
• Alert fatigue: One key function of CDSS is to provide alerts to healthcare providers. However, too many alerts, sometimes incorrect, can lead to 'alert fatigue.' This is a state when providers become desensitized and may start to ignore or override alerts, even critical ones. The system of priority alerts and alert customization can help address these problems.
• Maintenance of knowledge base: In healthcare, regulations and clinical guidelines are subject to constant change. Many types of clinical decision support systems are not that fast at adjusting to fast-paced changes. Timely maintenance and updates are an answer, but these can be expensive.
• Cost of implementation: About 74% of users report issues with the financial viability of CDSS in healthcare (Source). Initial implementation and customization are expensive, but maintenance and the need to train new staff add to the cost. Choosing modular solutions that accommodate your budget can solve this issue.
• Lack of interoperability: CDSS have significant interoperability challenges when integrating with EHR and other systems used in hospital settings. CDSS may be designed as isolated systems that can't communicate effectively with others. Issues also arise from programming complexities and the variety of clinical data sources. However, interoperability standards, such as HL7 and FHIR , can help solve these issues.

Our experience

With the abundance of CDSSs on the market, there are advantages to each type and brand of clinical decision support solutions. However, it is best to perform a risk-benefit analysis before selecting your CDSS.

Due to the complexity of CDSS development and integration, a good strategy is to involve a remote IT team in the development of decision support software.

At Binariks, our team of experts can help you build custom CDSS from scratch or assist with adopting an existing CDSS in healthcare.

Here is what we can do:

• Develop a custom CDSS from scratch. The system can be built with scalability in mind.
• Ensure that the CDSS seamlessly integrates with existing Electronic Health Records (EHR) systems.
• Routinely update the CDSS with the latest guidelines and evidence-based practices.
• Monitor the system's performance.
• Set up mechanisms to collect user feedback and make iterative improvements to the CDSS based on this feedback.