A Comprehensive Guide to Clinical Decision Support Software
Clinical decision support software (CDSS) includes decision support tools in healthcare that provide real-time assistance for medical decision-making. Despite their growing popularity and proven benefits, there are some challenges to CDSS implementation. So let us explore what the software implies, the advantages and applications of CDSS, how to choose a CDSS vendor and some of the potential issues associated with using the systems in healthcare.
What Is Clinical Decision Support Software?
First occurred in the 1970s, clinical decision support software was designed to help healthcare professionals make informed decisions on patient care. By providing access to current, relevant, evidence-based medical information, physicians get more insights into their patient’s health backgrounds and cases and can provide better care.
CDSS architecture is built on advanced AI and ML algorithms that allow processing and analyzing stored and real-time data to determine the most suitable decisions that physicians can refer to. CDSSs also facilitate the automation of various processes in a healthcare facility, enhancing workflow efficiency.
Clinical Decision Support Software — Market Share
Despite the challenges we will discuss further, CDSS is a rapidly growing sector in healthcare. The decision support technology makes up a good share of the US healthcare market. Rounded to a total of $5.5B in 2021, the CDSS market share is expected to reach $12.4B in 2030.
And it is no wonder that clinical decision support solutions continue to grow in use — their benefits are significant to all parties involved. Patients receive more quality care while clinicians now have the resources to do their job better. Healthcare facilities, in turn, benefit from efficiency and better resource allocation. Let’s discuss the benefits in detail next.
Benefits of Clinical Decision Support System
Clinical decision support software has the potential to bring numerous benefits to healthcare. Here is a detailed overview of each.
Enhanced Decision-Making Process
CDSS architecture can assist providers in making informed decisions about patient care by giving access to current and relevant data. It means clinicians receive more scenarios for diagnoses and treatment, including rare cases, within a shorter time frame. Patients with complex and less frequent conditions can have more chances of successful treatment and diagnoses than without a CDSS implemented.
Improved Patient Outcomes
One of the primary benefits of a clinical decision support system is the potential to improve patient outcomes, as reported in a study. Since clinicians get deeper insights into the patient’s cases with the help of the software, they make more accurate diagnoses and determine the best treatment plans. Besides, CDSSs help manage the treatment progress through alerts and notifications, increasing the chances of treatment success.
Increased Efficiency in the Healthcare System
CDSSs can also help increase efficiency in the healthcare system. These solutions give healthcare providers the latest medical information, including studies and research findings. Therefore, CDSSs can reduce the time and resources spent on research, freeing providers to focus on patient care.
Reduced Medical Errors
Another benefit of CDSS is the potential to reduce medical errors, such as misdiagnosis or incorrect treatment plans. Medical errors are a frequent issue in the US healthcare sector, with 1 in 5 patients experiencing them annually, 50% of which are caused by wrong prescriptions.
With this, the role of CDSS in healthcare services is crucial. Reducing medical errors means safer patient outcomes and less burnout among physicians. Besides, such reduction also leads to fewer lawsuits and hence, fewer financial and reputational losses for clinics.
Better Use of Resources
CDSSs also enable healthcare providers to allocate their resources better. For example, it has been found that after CDSS implementation, physicians run up to 31.5% fewer unnecessary tests than before. That translates into significant expense savings, allowing to redirect the funds into other, more valuable areas while providing better and more cost-efficient patient care.
Examples of Clinical Decision Support Systems Application
CDSS has many applications in the healthcare field, and here are some examples of its use.
Diagnosis Support
CDSS provides healthcare facilities with access to medical information to diagnose patients. The medical data includes patient symptoms, medical history, relevant test results and research findings on similar cases. And when powered by AI and ML algorithms, healthcare vendors reach accurate diagnoses more quickly and efficiently, making the final decision on which diagnosis to proceed with.
For example, the Mayo Clinic is a leading medical center that uses CDSS to improve patient outcomes and enhance the quality of care. Mayo Clinic’s CDSS architecture is designed to improve and speed up intensive care unit (ICU) tasks, including admission and rounding, helping medical staff to make fewer errors as they move through the ICU workflows.
Treatment Planning and Drug Management
CDSS assists healthcare providers in determining the best treatment plans for patients. That includes drug interactions, dosage recommendations and contraindications. The tool also helps physicians determine the most appropriate medications for patients based on their medical history and current medications.
For instance, Oracle Cerner offers health information technology solutions, including EHRs and CDSS. Meanwhile, Cerner’s tool is designed to assist with many clinical decision-making tasks, such as medication management, diagnosis, treatment, and care planning.
Patient Monitoring and Management
Another CDSS application is to monitor patients and manage their care, especially those with chronic diseases. That can involve tracking vital signs, monitoring patient progress and providing reminders for follow-up appointments and treatments. CDSSs can also send alerts for health-threatening events or patient condition changes requiring immediate attention.
For example, Medtronic, a leading medical device company, offers a range of CDSSs to assist with managing chronic conditions, such as diabetes and heart failure. Medtronic’s software provides real-time patient data and analytics to healthcare vendors, helping to ensure that patients receive appropriate and timely care.
Clinical Documentation
CDSSs are also used to improve the clinical documentation process, which covers electronic health records integration for healthcare providers to access comprehensive patient information in real time. CDSSs can also bring tools for documenting patient information during visits, including progress notes, treatment plans and diagnostic results.
For example, Epic Systems’ CDSS application ensures evidence-based recommendations and alerts to healthcare organizations at the point of care, improving the accuracy and speed of decision-making to deliver more efficient services to patients.
Clinical Guideline Implementation
Finally, CDSSs can be used to implement clinical guidelines in a healthcare setting. That implies providing physicians with access to the latest guidelines and best practices in healthcare and tools for tracking and monitoring adherence to these guidelines. Clinical guideline compliance can help improve the quality of care and patient outcomes while reducing medical errors and unnecessary tests and prescriptions.
Choosing Clinical Decision Support System Vendors
When selecting a clinical decision support system vendor, it is vital to consider some factors to ensure that the tool meets your needs. Here are five factors to analyze before making the final decision on your CDSS partner.
1. Your Organization’s Needs and Goals
The first step in choosing a CDSS vendor is identifying your business requirements. That includes determining the type of information you have to access, the kind of decision-making tools you need and the level of integration you want with existing clinical decision support software. Of course, this factor also covers evaluating your current infrastructure to see what your facility’s system is capable of and what is missing.
2. Evaluating Vendor’s Reputation and Experience
It is essential to choose a vendor with a good reputation and experience in building a CDSS. This means you have to look at the vendor’s track record in the industry, customer feedback on their examples of clinical decision support systems and any awards or recognition they have received. Also, an expert team will be ready to answer your questions during a consultation to get a clear view of your future partnership in creating a CDSS.
3. Assessing System Features and Capabilities
Besides, it is crucial to assess the vendor’s capability to deliver the exact system you need. That involves evaluating how the CDSS architecture they offer will meet your needs and goals, the ease of the system for the users and the level of customization available as your organization grows or changes.
4. Cost and Budget Considerations
Cost is a significant factor in selecting a CDSS vendor. It is important to set your budget for CDSS development and implementation and compare the costs of different providers to determine the project with the best quality-to-value ratio for your organization. While it may seem a drastic expense initially, remember that CDSS benefits are numerous, like cost reduction via administrative task automation and minimizing medical errors. So, in the long run, integrating a CDSS into your organization’s infrastructure is a cost-efficient solution.
5. User Feedback and Testimonials
Finally, you should consider user feedback and testimonials. That includes feedback from healthcare providers who have utilized the system and can share insight into the software’s effectiveness and ease of use. Since a CDSS will significantly change your company’s workflows, it is crucial to ensure that your future decision support software is not confusing and challenging. Otherwise, it would deliver the opposite effect than it is supposed to, posing a risk of making more medical errors and decreasing the quality of care physicians provide.
Challenges of Clinical Decision Support System
While clinical decisions support solutions have the potential to bring many benefits to the healthcare system, some challenges come with implementing them. Some of the most common are interoperability, lack of standardization, data quality and accuracy and resistance to change.
Interoperability
One of the significant CDSS challenges is interoperability with existing healthcare systems. Interoperability refers to the ability of different systems to communicate and exchange data. That includes accessing and incorporating loads of patient information from EHRs, laboratory tools, and other sources with no data loss.
The challenge is also associated with properly integrating CDSS with current systems. Overall, interoperability covers integrating with patient-doctor platforms and other clinical systems. That implies that the new CDSS should integrate with the existing infrastructure of a healthcare organization while neither of the systems malfunctions.
The lack of interoperability can limit the effectiveness of CDSS and make it difficult to provide comprehensive and accurate information to healthcare vendors. This challenge highlights the importance of choosing a CDSS vendor that is compatible with existing healthcare systems and has a strong track record of interoperability.
Lack of Standardization
Another challenge of implementing CDSS is the need for more standardization in the field. Systems may have different user interfaces, leading to confusion and difficulties in using them effectively. That can also make it challenging for healthcare providers to compare and evaluate different options. Thus, they may need more clarity and consistency in using CDSSs, while the quality and reliability of the information the system provides to clinicians can also be harmful.
Lastly, the lack of standardization can affect trust in the information provided by CDSS, decreasing the likelihood that healthcare facilities will use the systems adequately. Eventually, this translates into the inability of healthcare organizations and their patients to make the most use of the CDSS benefits.
Data Quality and Accuracy
Another challenge of implementing CDSS is ensuring the quality and accuracy of the big data that the system uses. Incorrect or outdated data can lead to wrong decisions, misdiagnoses and other drastic scenarios that can negatively impact patients’ treatment outcomes. Besides, the associated challenge is ensuring the information is relevant to the patient’s current condition. For example, a patient with a history of heart disease may not need to be screened for breast cancer, so CDSS must provide only relevant information for that patient.
Thus, it is crucial to guarantee that the data is complete, up-to-date, relevant, and accurately reflects the patient’s medical history, test results, vitals and current condition. After all, data accuracy and quality also involve properly incorporating patients’ imaging studies and other test results into their records.
Resistance to Change
Resistance to change is a common challenge in adopting new technology in healthcare. Healthcare providers may worry about the potential impact on their workflows and the potential for decreased autonomy in decision-making. This resistance can result in reduced adoption rates and may negatively impact the overall effectiveness of the CDSS implementation.
To address these concerns, healthcare organizations need to involve their staff in the implementation process and provide training and support to help them understand the benefits of the clinical decision support system and use it effectively. These measures include providing hands-on training sessions, offering support resources such as user manuals and tutorials and assigning dedicated support staff to assist with any questions or concerns.
Additionally, it may be beneficial to involve healthcare providers in the design and development process of the CDSS system to ensure that it meets their specific needs and requirements. That can help build confidence regarding the upcoming change and encourage adoption, as providers are more likely to use a system they have helped create and understand.
Ultimately, addressing the mentioned challenges requires careful planning, the collaboration between healthcare providers and technology experts and ongoing training, monitoring and evaluation to ensure that the system provides the desired outcomes.
Conclusion
Clinical decision support systems have the potential to bring many benefits to healthcare services, including improved patient outcomes, reduced medical errors and increased efficiency. However, there are also challenges associated with implementing these systems, such as interoperability, data quality and accuracy, lack of standardization and resistance to change.
But now that you know what to expect from the process, you can start exploring your vendor options. Contact us to discuss your organization’s needs and develop a project plan for your CDSS implementation.
