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Back to the Basics of Clinical Decision Support Systems (CDSS)

Clinical Decision Support Systems

CDSS gather, store, and analyze data from various EHR systems. They provide alerts, reminders, and guidelines to doctors so they can make informed decisions backed by evidence [1]. With CDSS, clinicians can provide personalized and preventive patient care that leads to better health outcomes. 

CDSS are made up of a data management system, a processing system, and a user interface [2]. The data management system stores sensitive patient information on symptoms, clinical data, and lab reports [2]. They also stores information on diseases so they can make connections using machine learning between past data and current data [2]. This allows the processing system to apply algorithms and create datasets for each patient [2]. Finally, these results are visible through the user interface. 

Types of CDSS


Knowledge-based CDSS use ‘if-then’ relationships between different types of patient data in order to send alerts to clinicians [2]. The actual decision is made by the doctor, but these types of CDSS are usually used to support decisions made by doctors. 


Nonknowledge-based CDSS are different from knowledge-based because they integrate machine learning in order to find connections in past data [2]. They do this with genetic algorithms that create and analyze various solutions until it finds the best models [2]. This type of CDSS also implements artificial neural networks that make connections similar to the way humans do so that relationships can be made between various data [2]. This allows the machine to be trained so that it can learn and adjust its predictions based on different databases. However, nonknowledge-based CDSS aren’t usually used currently since it requires a lot of time to train the machine and it requires a large dataset in order to ensure valid models [2].

Different Applications of CDSS

CDSS have various uses in the healthcare industry. First, they can be used to prevent adverse drug effects due to medication errors [2]. CDSS alert doctors if a patient receives medications that may cause allergies or if it can react negatively with another medication they are taking. They can go even further by giving guidelines for the correct dosage, which accounts for 60% of prescribing errors [2]. Furthermore, CDSS can also be used as a diagnostic support tool that compares patient data to a knowledge base in order to determine potential treatments [2]. For instance, imaging technology can be used with AI in order to alert doctors of any concerning observations. Additionally, CDSS can lower treatment costs by suggesting cheaper alternatives for medications and by preventing duplicate testing [2]. Lastly, CDSS provide doctors with a management tool that keeps track of various patient data over time.

Benefits of CDSS

As stated previously, CDSS promote better health outcomes for patients. Since CDSS are often integrated with EHR systems, they can use stored data sets to make connections between different data in order to provide alerts and recommendations [3]. They also make sure that the right information is sent at the right time through the correct channels in order to streamline support and prevent overburdening doctors [2]. Moreover, CDSS promote preventive care by screening for potential diseases and providing effective treatment plans based on current patient data. This helps to reduce hospital readmissions and allows patients to save on healthcare-related costs. Finally, CDSS can make recommendations for testing and decrease duplicate testing and screenings. Overall, CDSS use large amounts of data to make useful connections that improve patient safety and quality care.

Current Challenges & Burdens

Although CDSS come with many benefits, they can also be burdening if they are not implemented effectively. For instance, CDSS may send too many alerts to doctors. If CDSS cannot prioritize certain alerts over others, they may lead to doctors missing alerts [2]. Moreover, CDSS should be paired with an EHR system so they can be used effectively [2]. This allows the CDSS to send their predictions and alerts directly into the EHR system so they can be implemented by doctors and tracked. Finally, implementing CDSS can be costly and will require more investments based on the level of customization applied to the infrastructure [2]. 

Telehealth integrated with CDSS

Telehealth has been used more throughout the years due to COVID-19. Providers are able to manage patients with chronic diseases and provide care through various platforms. More importantly, patients feel included in their treatment plans when they are able to be included in the decision-making process [4]. Providers are also able to provide treatment resources to patients and caretakers through telehealth. Furthermore, telehealth allows doctors to stay updated with the patient’s progress long-term [4]. Through the use of CDSS, information gathered from telehealth can be used to further provide personalized and preventive care. For instance, CDSS can track a patient’s overall condition and keep track of suggested diagnostic options [4]. CDSS can also provide doctors and patients with alerts and reminders during the treatment process. This allows doctors to be more engaged in the diagnostic process and offers personalized and evidence-based treatment options to patients. 


HITS provides healthcare management services and works with providers in the development of health informatics tools that promote safe, timely, and secure patient care. We take pride in our services and settle for nothing other than 100% quality solutions for our clients. Furthermore, HITS focuses on transforming health care by analyzing medical solutions and designing technological innovations. Having the right team assist with data sharing is crucial to encouraging collaborative and secure care for patients. If you’re looking for the right team, HITS is it! You can reach out to us directly at info@healthitsol.com. Check out this link if you’re interested in having a 15-minute consultation with us: https://bit.ly/3RLsRXR.


  1. https://www.cdc.gov/dhdsp/pubs/guides/best-practices/clinical-decision-support.htm
  2. https://www.altexsoft.com/blog/clinical-decision-support-systems/
  3. https://healthitanalytics.com/features/understanding-the-basics-of-clinical-decision-support-systems
  4. https://www.healthcareitnews.com/news/telehealth-and-clinical-decision-support-ideal-synergy-patient-clinician-engagement
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