Clinical decision support systems (CDSS) have been around for decades, but it wasn’t until the widespread adoption of electronic health records (EHRs) that they began to gain traction. The Institute of Medicine’s 1999 report, “To Err is Human,” brought attention to the problem of medical errors and the need for systems that could help healthcare providers make more informed decisions. CDSS emerged as a potential solution to this problem.

What are CDSS?

 

CDSS are computer-based programs that provide healthcare professionals with information, alerts, and recommendations to support clinical decision making. They can help providers with tasks such as diagnosing a patient, selecting a treatment plan, and monitoring patient progress. With the rapid evolution of AI in healthcare, many technology software solutions can be considered a form of clinical decision support if they provide information that impacts the decisions that clinicians make.  

AI’s role in CDSS

 

AI in healthcare can be integrated with CDSS to improve the accuracy and speed of diagnosis and treatment, reduce medical errors, and ultimately improve patient outcomes. AI algorithms can analyze large amounts of data from electronic medical records and medical imaging, providing real-time recommendations to healthcare professionals. AI can also personalize the recommendations based on individual patient characteristics to improve the effectiveness of treatment and care management. However, as with any use of AI in healthcare, ethical and privacy concerns must be carefully considered and addressed to ensure that patient rights and safety are protected. Despite potential risks, the future of CDSS will likely be fueled by innovations in AI.

FDA’s role in CDSS

 

The FDA recognizes the potential benefits of CDSS in healthcare, but they also recognize the need to ensure their safety and effectiveness. The FDA has issued guidance to regulate certain types of CDSS as medical devices, and has classified them based on their intended use and potential risk to patients. This regulation will arguably add more work for certain CDSS companies, and the FDA aims to strike a balance between promoting innovation in healthcare technology while ensuring that patient safety and well-being are protected. The FDA continues to monitor and update its guidance on CDSS as the technology and its applications evolve, as many CDSS companies are challenging the current guidance. 

Challenges in Adopting CDSS

 

CDSS adoption is not always seamless. A few of the major challenges to their integration include: 

1. 1. Fear: Some fear CDSS will replace or undermine the expertise of healthcare professionals. Healthcare providers may feel that the system is taking away their autonomy or making decisions for them. It is important to emphasize that CDSS is not meant to replace human judgment but rather to enhance it. The goal is to provide providers with additional information and insights to make more informed decisions.
   2. Integration with existing workflows: CDSS needs to be integrated seamlessly into existing clinical workflows and processes to be effective. Failure to do so can lead to resistance and non-adoption of the technology by healthcare providers.
   3. Usability and user experience: CDSS needs to be easy to use and intuitive for healthcare providers with varying levels of technical expertise. A poor user experience can lead to frustration, resistance, and non-adoption.
   4. Data quality and accuracy: CDSS relies heavily on accurate and reliable data to provide effective decision support. Poor data quality, inaccuracies, or missing data can lead to incorrect or ineffective recommendations, which can undermine the credibility and trust in the technology.
   5. Cost and return on investment (ROI): The cost of implementing and maintaining CDSS can be a significant barrier to adoption. Healthcare organizations need to weigh the potential benefits and ROI against the costs and resources required to implement and maintain the technology.
   6. Legal and regulatory compliance: CDSS needs to comply with various legal and regulatory requirements, such as patient privacy and security laws, and FDA regulations, depending on the nature of the system. Failure to comply with these requirements can lead to legal and financial consequences for healthcare organizations.

Increasing Usefulness of CDSS

 

In addition to addressing common challenges with adoption of CDSS, improving the usefulness of these systems will increase adoption. One critical factor in the usefulness of CDSS is the quality of information provided by the system. CDSS must have access to accurate and up-to-date data to provide recommendations that are relevant and useful to healthcare providers. This typically requires the integration of CDSS with EHRs and other clinical systems.

Another factor that affects the usefulness of CDSS is the design of the system. The design of CDSS systems should be user-friendly, intuitive, and easy to use. They should also be seamlessly integrated into healthcare providers’ workflows to minimize disruption. 

The perceived usefulness of the system is another critical factor influencing the adoption of CDSS. If clinicians aren’t confident in the expected performance, ease of use, relevance of information, trust in the knowledge base, practical training, and computer skills, the system won’t be perceived as useful and adoption of it will be challenging. Improving perceived usefulness begins with communication and collaboration on the platform and how it can best benefit the clinicians who will use it. 

Conclusion

 

CDSS have the potential to improve patient outcomes and reduce medical errors. However, their integration and usefulness depends on several factors, including the quality of the information provided, the design of the system, and the perceived benefits to healthcare providers and organizations. To maximize the potential of CDSS, healthcare providers, technology developers, and researchers must work together to design and implement systems that are safe, accurate, user-friendly, and seamlessly integrated into clinical workflows.

CalmWave has studied clinical decision support systems, and believes they will play a critical role in the future of healthcare delivery.  Schedule a demo to learn more about CalmWave’s take on clinical decision support systems.

Further reading:

 

1. Institute of Medicine (US) Committee on Quality of Health Care in America, “To Err Is Human”
2. Blumenthal, D. “Launching HITECH”
3. Bates, D. W., & Gawande, A. A. “Improving safety with information technology”
4. Bright, T. J., Wong, A., Dhurjati, R., Bristow, E., Bastian, L., Coeytaux, R. R., … & Williams Jr, J. W. “Effect of clinical decision-support systems: a systematic review”
5. Kuperman, G. J., Bobb, A., Payne, T. H., Avery, A. J., Gandhi, T. K., Burns, G., … & Bates, D. W. “Medication-related clinical decision support in computerized provider order entry systems: a review”
6. Kim, H., Jung, K., & Lee, J. Physicians’ Perceptions on the usefulness of contextual information for prioritizing and presenting alerts in computerized physician order entry systems.
7. Sittig, D. F., & Singh, H. Defining health information technology–related errors: new developments since to err is human. 
8. Lee, J., Lee, H., Lee, H. Y., Kim, Y. J., & Kang, B. H. Physicians’ perceptions and use of a health information exchange: a pilot program in South Korea.