clinical decision support system tools and examples

Clinical Decision Support System – Benefits, Examples, Tools and Much More

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

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 solution, 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.

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 are the Benefits of Clinical Decision Support?

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. 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%.

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

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.

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.

Checklist Before Clinical Decision Support System Implementation

Irrespective of the technical foundation, the effectiveness of 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.

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 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 benefitting 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 initiating 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 that resistance can 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?

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 along 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.

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

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 customize, scalable and robust infrastructure for CDSS, which meets the needs of your organization.

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
Medispan
Allscripts
Cerner
Elsevier
Truven Health Analytics
Zynx Health