Optimizing Patient Care Through Smart Clinical Decision Support
In recent years, the healthcare field has undergone a significant transformation, driven by a dynamic shift toward digital healthcare. The widespread adoption of preventive healthcare among the population has led laboratories to adopt modern health technology practices. These advancements aim to address challenges such as high-test volumes, strained capacities, overburdened laboratory staff, and, most importantly, the need for rapid turnaround times of test results.
These growing demands have accelerated the adoption of a powerful technology — Clinical Decision Support Systems (CDSS) — has emerged to address these challenges. By acting as an intelligent assistant to healthcare professionals, Artificial Intelligence-led CDSS can optimize test selection, streamline workflows, and ensure efficient processing of the growing sample volume. This translates to faster turnaround times, reduced costs for both patients and healthcare institutions, and ultimately in improving patient care and experience.
CDSS has proven to be extremely useful as a decision support tool, highlighting potential abnormalities and prompting further review of laboratory test results. Although the final verification and interpretation of test results remain the responsibility of a qualified lab professional, the time taken to arrive at a decision, or to progress to the next step of diagnosis and to take further action, is significantly reduced.
This blog, therefore, will discuss in detail about the various advantages clinical decision support software can bring to table to augment laboratory services and in turn complement business growth.
Test Result Automation
One of the most significant advantages of CDSS software lies in its ability to automate test result interpretation using built-in algorithms. Considering provisional and differential diagnosis, these algorithms can group test results based on a combination of normal and abnormal values, effectively prioritizing reports that require further attention from a laboratory professional. This targeted approach is especially beneficial in large volume centers where labs process a high number of samples daily (like in corporate wellness programs or in comprehensive health packages). By filtering out routine results and highlighting potential concerns, CDSS frees up valuable time for lab professionals to focus on complex cases and ensure timely diagnosis for patients.
Ninety percent of corporate wellness programs that include routine medical examinations for new hires involve batch testing. Since many of these individuals are young and healthy, a large portion of the test results are expected to be normal, indicating no immediate health concerns. This scenario is where Clinical Decision Support Systems can excel. While CDSS is not designed to fully automate report verification due to the need for human expertise in complex cases, it can automate repetitive tasks for expected results.
By utilizing pre-defined algorithms, CDSS can analyze these batches efficiently and generate quicker reports in a standardized format. This streamlines the workflow for laboratory professionals, freeing them to focus on tests requiring their critical review and ensuring timely diagnosis for individuals with abnormal results.
Smart Report generation
Smart – be it work or appearance – who doesn’t like it? Well, the AI-based architecture of a CDSS is perfectly designed to yield test results that are presented in the smartest manner. Unlike traditional laboratory reports that present a mere list of raw numbers without much context, a CDSS leverages built-in algorithms to analyze vast amounts of data and generate intelligent reports. Additionally, CDSS can flag potential abnormalities and suggest possible diagnoses at the very beginning of the final laboratory test report (as a summary), providing valuable insights for laboratory professionals and to clinicians.
This smart report generation capability of CDSS significantly improves communication and collaboration between the laboratory and healthcare providers, ultimately leading to faster and more accurate diagnoses for patients.
Ease in Reflex Testing
Reflex testing is a strategic approach in clinical diagnostics where an initial test result automatically triggers additional testing cascade if certain criteria are met. To be more precise, Reflex testing protocols allow clinical laboratories to perform second line diagnostic tests on existing specimens based on the results of initially ordered tests.
This approach can be highly efficient and cost-effective, particularly when guided by an AI-led CDSS. It is proved to address the biggest pain point in reflex testing for laboratories, which is the potential for over testing and inefficiency.
Reflex testing can support optimal clinical laboratory test ordering and diagnosis. In current clinical practice, reflex testing typically relies on simple “if-then” rules; however, this limits their scope since most test ordering decisions involve more complexity than what a simple rule will allow.
The AI-ML algorithms built in the CDSS helps provide clear guidance on the appropriate reflex test sequence. Hence, this eliminates the need for manual research and decision-making for lab professionals and thereafter clinicians, leading to a more efficient workflow.
Auto verification of routine test results
In the last decade, we have been witnessing a rising interest in preventive healthcare among people of all demographics and age group. Health-conscious individuals go in for periodic health checkups to keep at bay most non-communicable diseases. Apart from this, labs have been witnessing a surge in periodic diabetic testing packages and other blood related parameter testing (example thyroid panel tests) to maintain a healthy life. Such interests have posed a great demand for laboratory services, straining lab professionals and other resources to go beyond their operational capacities.
In such scenarios, it is wise for laboratories to automate routine test result verification, particularly normal wellness results. This would free up valuable time for laboratory professionals, allowing them to focus on complex cases and investigations requiring their specific expertise. This could lead to faster turnaround times for all patients.
The AI and ML algorithms, when properly trained on vast datasets of labelled test results, can potentially achieve a higher degree of consistency and accuracy compared to manual verification, minimizing human error.
The other benefit of a CDSS generated auto verification report is that labs can ensure consistent formatting and interpretation of test results, improving clarity and communication between the laboratory and clinicians.
Sukraa’s SAILRT Clinico-Pathological Decision Support
Sukraa Software Solutions’ SAILRT Clinico-Pathological Decision Support (CDS) is a best-in-class AI-ML software architecture developed with all the requirements in mind.
The Automated Laboratory Process Orchestration is designed to maximize your laboratory’s efficiency and productivity, while also ensuring higher cost-effectiveness.
SAILRT offers a round-the-clock, 24/7 modular solution that is equipment-agnostic, compatible with multi-technology, multi-site, multi-discipline, and multi-supplier solutions. The middleware has been designed to support all laboratory testing equipment brands and devices, making it a versatile software application that overlays seamlessly, yielding accurate results.
The In-built AI-powered Multi-Rule Engine in SAILRT can systematically and automatically apply cross-validated rules to perform QC bracketing, auto-verification, auto-reflex testing, auto-calculation of results & auto result processing. Our technology is a blend of Auto Verification & Artificial Intelligence (AV-AI concept), built on cross-disciplinary and clinical disease-specific algorithms.
To know more about Sukraa’s Clinical Decision Support Software, get in touch at sales@sukraa.in or digital@sukraa.in.
