The advancement in technology has empowered every industry worldwide. The Healthcare industry is also gradually gaining momentum in the adoption of digital software solutions. Technological progress has gifted the healthcare industry a powerful assistant – Clinical Decision Support Software (CDSS).
CDSS links health observations with knowledge and influences health choices by clinicians for improved healthcare. The current healthcare market trend depicts that the global CDSS market value will reach $1.8 billion by 2025 from $1.2 billion in 2020 with a CAGR of 9.1%.
CDSS facilitates various healthcare tasks encompassing diagnostics, drug control, disease management and more. The article aims to deliver an in-depth explanation of the concept and helps to develop a better understanding. The topics covered under this article are listed as:
A clinical decision support software is a health IT system developed to assist with clinical decision-making tasks (Clinical Decision Support). It is a set of programmatic tools that extract data from a patient’s EHRs and transfers it to medical professionals for quick clinical decision-making. The knowledge-based CDSS uses if-then statements to extract information and delivers actionable recommendations to clinicians. Such databases can also provide complimentary materials like data reports, guidelines, templates of clinical documents and more. It also allows clinicians to set reminders or alerts to keep track of their patients.
CDSS is built using technologies like Machine Learning, Natural Language Processing, and Big Data analytics to help clinicians analyze massive health data. AI technologies enable streamlining the clinical processes through workflow automation.
What are the categories of CDSS?
Knowledge-based CDSS is built on the top of the knowledge base that consists of every piece of data structured in if-then rules. The rules (if-then statements) are created to evaluate the data and produce an action or output. The rules are made using practice-based, literature-based or patient-directed evidence.
Non-knowledge based CDSS
Non-knowledge-based CDSS also requires data sources, but the decision is dependent on AI, ML or statistical pattern recognition instead of being programmed to follow expert medical knowledge.
CDSS is composed of:
Base: In knowledge-based, the base is the rules that are programmed into the system. While in non-knowledge-based, it is the algorithm used to model the decision.
Inference engine: It uses AI-determined rules and data structures and implements them to patient’s clinical data to produce an output or action presented to the end-user (physician) through a communication mechanism.
Communication mechanism: The communication mechanism may comprise an application, a website or an EHR frontend interface through which the end-user can interact with the system.
What are the functions of Clinical Decision Support Software?
Administrative functions CDSS supports ordering of procedures and tests, patients’ emergency and clinical and diagnostic coding. The designed algorithms suggest a list of diagnostics codes to help physicians in choosing the most suitable one.
Diagnostics support A CDSS for clinical diagnosis is termed a Diagnostic Decision Support System. It provides computerized consultation by analyzing the user’s data fed to it and outputs a list of probable diagnoses. Unfortunately, poor accuracy generally due to gaps in data availability and flawed system requiring manual data entry has made the system inefficient for diagnosis.
Clinical management CDSS performs various clinical management functions: adherence to clinical guidelines, standardizing order sets for targeted cases, reminders for tests, alerts to a specific protocol for patients it pertains to, etc. It also assists with patient management on research/ treatment protocol, follow-up for referrals, tracking and placing orders and ensuring preventive care. CDSS also alerts clinicians to reach out to patients who have not followed management plans.
How to implement CDSS effectively?
1. Implementation strategy
For providers A preliminary analysis of clinical needs is required before deploying clinical decision support software. It is an effort-intensive task, but a pre-analysis could reveal weak links in the care provision. CDSS can be thus used extensively to work on its improvement.
For health IT vendors IT experts should involve experienced health professionals early in the development cycle to increase user-centricity rather than working independently. Developers should discuss essential aspects with end-users and set corresponding priorities. Another critical issue that needs to be resolved is over-reliance on the system. Developers must understand that CDSS is an assistant, not the decision-maker. Therefore, it is recommended to avoid an authoritarian tone while designing the system and allow doctors to make decisions independently, even if they don’t agree with the CDSS recommendation completely.
For IT and clinicians’ cooperation Only a coordinated system makes its way to success. Medical professionals need to know which features of the software bring ease to their daily operations. It is only accomplished by allowing clinicians to perform user testing. Training, technical support, and involvement in all development stages are of high value to the users. Therefore, it is conclusive that seamless cooperation leads to better clinical decision support software adoption in a clinical setting. A successful deployment is not a one-time effort; instead, it is a never-ending process.
2. Implementation steps
Step 1: Define the project’s scope
The first and most important aspect is to assess the organization’s readiness to accept the latest software tools. The concept of “adaptive reserve” must be considered while evaluating the adoption of change. Adaptive reserve states resources available to plan for, adapt to and reflect on a significant organizational change.
Ensure to discuss the project with developers and all technology users, including physicians, nurses, administrators, etc. A collaborative approach must be adopted, which involves every user’s participation and helps develop a user-centric product.
Step 2: Single vision
Organizations must ensure that they are working on the same vision with the same ideas. It is necessary to develop a shared definition of vital elements like intervals between cancer screenings, etc. By applying implementation strategies, make outcomes user-centric and ultimately improve goals. When all the stakeholders are on the same page, it effectively reduces any conflict in the system.
Step 3: Fine-tune implementation tools
Organizations must ensure that implementation tools work as intended and mainly work well for the use cases at hand. Various functionalities are developed and deployed by EHR vendors as part of their product suites. Providers must have a robust working relationship with their vendors.
Step 4: Continuous improvement
Once the project is completed and is delivered, start collecting feedback from stakeholders. Feedbacks are just as crucial as getting stakeholders’ input before going live. Based on feedback, improvements must be made in the system for better user experiences. Proper maintenance and upgradation of the system should be done at regular intervals.
Benefits of Clinical Decision Support Software
CDSS assists clinicians in multiple ways:
Reduced medication errors One of the top benefits is that it reduces manual operations related to drug selection. A study revealed that the cost of medication errors amounts to 40 billion USD per year in the US. Moreover, 75% of these errors are preventable. The main reason found behind such errors was the clinician’s lack of concentration due to fatigue. CDSS effectively eliminates this issue by automating the entire process, thus preventing medication errors that may lead to drug confrontation, incorrect dosages, and other inefficiencies.
Preventive care CDSS ensures patients’ adherence to their treatment plans and notifies the concerned clinician if they fail to follow the recommendations.
Reduce misdiagnoses The most common causes for misdiagnoses include atypical presentations, cognitive errors, provider bias and uncommon disease processes. NCBI states that around 10-30% of medical errors are diagnosis errors. Decision support can bring a drastic improvement in these stats as it helps clinicians identify the possibilities quickly. A medical image analysis solution is a perfect example of diagnosis support.
Improve efficiency and patient throughput According to NCBI research, $17-29 billion is spent per year on inaccurate patient treatment due to misdiagnosis. CDSS is highly efficient in dealing with such inaccuracies. Through CDSS, clinicians could quickly determine correct dosages based on accurate diagnoses, save time and eliminate unnecessary costs.
Increased accessibility A single place to access all information makes accessibility quick and easy for clinicians. Regular updates and validated information ensures that all clinicians are well-informed. Accessing the most recent medical resources at a single place could eliminate the extra investments for additional resources at multiple logins.
CDSS has augmented healthcare providers in various decisions and care-taking tasks, and it continues actively to support the delivery of quality care. Many health-tech experts have already adopted CDSS to bring transformation to their organizations.
If you’re intrigued with technological implementations in your healthcare business and looking for help developing and deploying clinical decision support software, connect with our experts and get your idea converted into reality.
Akash Takyar is the founder and CEO at LeewayHertz. The experience of building over 100+ platforms for startups and enterprises allows Akash to rapidly architect and design solutions that are scalable and beautiful. Akash's ability to build enterprise-grade technology solutions has attracted over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.
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