Introduction
Clinical diagnostics are the mainstay for physicians to make decisions regarding disease treatment, management, and prevention. In-vitro diagnostics (IVDs) play a key role in the healthcare industry and have strongly contributed to controlling the spread of the SARS-CoV-2 virus. The clinical diagnostic industry has been rapidly trying to keep pace with the coronavirus and its emerging new mutants. Clinical diagnostic labs have been facing challenges such as ensuring the accuracy of test results, reducing the turnaround time, and securely delivering test reports to patients and physicians. Labs are adopting innovative techniques and new tests, upgrading their equipment, and implementing the latest software applications to manage a high volume of test requests, securely manage data, and meet reporting requirements.
In the wake of new emerging variants of the SARS-CoV-2 virus, the way forward for diagnostics is point-of-care testing, predictive healthcare, real-time testing, and leveraging Artificial Intelligence (AI) to predict disease outcomes and treatment regimes.
Challenges in Clinical Diagnostics
- 1. Variability in lab test results
- 2. Data silos that restrict the easy access and use of data across departments
- 3. The large number & variety of available tests complicates the selection of the correct test
How Can Digital Technologies Help?
The union of digital technologies and clinical diagnostics will help advance and improve patient experience and outcomes. At the same time, it will help improve the speed and efficiency of the testing process. Advances in omics and personalized medicine powered by automation technologies are shaping the future of clinical diagnostics. It will help improve performance, productivity, efficiency, and security without sacrificing reliability or accessibility.
Latest Trends in Clinical Diagnostics
1. Point-of-Care Testing
In point-of-care testing, the rapid testing of patients is performed at the ease of their homes. This facilitates quick diagnosis and enables physicians to commence the treatment of patients on time, preventing disease aggravation. It can also reduce the turnover time of results. Point-of-care testing simplifies the testing procedure and analysis. The results are stored in a secure digital environment, which can be accessed by healthcare providers and patients anytime and from anywhere.
2. Predictive Genetic Testing
Predictive genetic tests use tissue samples such as blood, hair, and skin to predict diseases and their future risks in humans. This provides substantial benefits as the treatment measures could be started well in advance. The information aggregated through big data and other sources can help healthcare companies develop healthy lifestyle recommendations for patients. Digitization and meaningful data support predictive analysis and better clinical decisions. Electronic medical records (EMRs) are the starting point for predictive analysis.
3. Diagnostics in Real-time
Wearables such as smartwatches and fitness bands collect a plethora of physical health information such as heart rate, blood pressure, body temperature, respiratory rate, blood oxygen level, and body motion. Compared to traditional diagnostic tests, real-time diagnostics help in improved and reliable decision-making since it collects more data points for effective decision-making. Their continuous monitoring ability provides instant feedback on a person’s well-being or health routine.
4. CDS Tools
Clinical decision support (CDS) tools can help transform clinical diagnostics. They help healthcare providers decide the steps in diagnosing or treating patients. CDS tools help analyze enormous amounts of digital diagnostic data and suggest treatment steps.
5. Lab Optimization Solutions
Clinical diagnostic labs continuously focus on reducing the number of unnecessary repetitive tests and adding more value to the necessary ones. Implementing robust business intelligence tools and analytics IT systems can quickly analyze a vast amount of test result data and find sources of unnecessary testing. This technology helps overcome test data overload by eliminating unneeded tests and improving value for necessary tests, saving resources and time in clinical diagnostic labs.
6. Artificial Intelligence (AI)
Artificial intelligence systems can help healthcare providers diagnose diseases based on medical images and data. AI is much more than what a digital transformation looks like in healthcare. Chatbots and virtual health assistants are a few known AI-based technologies. AI is well observed in precision medicine, medical imaging, drug discovery, and genomics. AI is gaining popularity throughout the healthcare system, from administrative and diagnostics to treatment.
Does Your Lab Support Digitization?
Many clinical labs, hospitals, and other healthcare organizations are migrating to cloud solutions to secure their storage and adopt new cloud-based applications. Secure cloud-based solutions, such as a Laboratory Information Management System (LIMS), also known as Clinical LIMS or diagnostics management system, make it easier to store records, manage data, transfer files between the staff, and enhance collaboration among staff. Furthermore, a diagnostics management system can be integrated with laboratory equipment and software, such as business intelligence tools, ERP, and CDS tools to support data interoperability and support decision-making.
Conclusion
Although the latest digital trends bring new opportunities for clinical diagnostics, they also present challenges, such as concerns regarding safety and data integrity of patients. However, the benefits outweigh the concerns. Digital trends in diagnostics can transform clinical diagnostics and make healthcare more accessible and affordable with improved outcomes. A diagnostics management system can digitally transform lab operations, support data interoperability, and enable labs to adapt to the latest trends to future-proof their operations.