As Genetic Testing Booms, Low-Code LIMS Leads The Way
The human genome project took 13 years (starting in 1990), included thousands of researchers, and close to $3 billion. It is now possible for patients to get their entire DNA sequence for $300 to $600. Advances in DNA sequencing and analysis systems have made genetic testing a viable solution for diagnosing diseases and providing preventive care in clinical contexts.
Genetic testing offers healthcare providers an arsenal of tools to make diagnosing diseases and the risk of developing a condition more straightforward. Now healthcare providers can pre-emptively establish treatment plans. As genetic testing becomes more affordable, the impact on healthcare accelerates.
COVID: THE AWAKENING
The COVID-19 crisis exposed the vulnerabilities in commercial labs software.
Healthcare authorities worldwide asked hospitals to postpone most nonemergency procedures and focus on COVID patients. While the disease wasn’t genetic, labs had to find the RNA virus within the patient to diagnose, and the processes and tests were similar.
Genetic testing labs had to somehow manage their usual patient flow and tests along with the vast number of samples from patients suspected of COVID-19 infection. Labs tested anywhere from 4,000 to 20,000 samples daily during the crisis, a significant increase in a single test type.
Labs had to devise new protocols for collecting samples, coming up with solutions to send test kits and collect samples from patient homes. They also had to develop solutions to streamline their workflows to manage the labs effectively with minimal on-site resources. They discovered that traditional LIMS did not have good API connectivity to accept new order sources and that integrations were painfully slow and expensive.
TRADITIONAL LIMS CAN’T SUPPORT THE UPSURGE IN GENETIC TESTING.
The upsurge in genetic testing creates new challenges for lab managers. Designed for small labs, most traditional LIMS support only a limited number of tests. As labs have grown, the software has yet to evolve, leading to delays in processing tests, errors in data entry, and difficulty managing compliance requirements. The pain points are caused by the following:
Large volumes of samples: Genetic labs now process hundreds or even thousands of samples daily. The software must be able to track and manage this large volume of data.
Diverse types of tests: Genetic labs offer a wide range of tests, from simple DNA sequencing to complex microarray analysis. The software needs to be able to track and manage all of these tests.
The need for integration fueled by AI: LIMS must be able to integrate with other lab systems and order sources.
The promise of AI-infused workflows will accelerate the demands for integration.
Changing regulations: The regulations governing genetic testing are constantly evolving. The software needs to be able to adapt to these changes quickly and easily.
Complex and difficult-to-understand reports.
LARGE SAMPLE VOLUMES STRESS MANUAL PROCESSES.
Financially disciplined labs must scale testing volumes without adding excess staff to process the samples. Many LIMS aren’t tuned for volume, unable to capture orders from multiple sources, track samples with bar codes, process exceptions for efficient, professional review, or notify stakeholders when results are available. Complex reports lead to questions, and lack of automation sparks downstream manual fixes.
When volume grows exponentially across a range of equipment and test protocols, efficiency suffers. With end-to- end precision, low-code LIMS can tailor workflows to specific instruments, integrations, and reports – removing unnecessary human handling where possible.
THE STEADY FLOW OF NEW TESTS DEMANDS LIMS ADJUSTMENTS
Over the last five years, researchers have made significant progress in genetic testing technology, leading to the development of new test types that are more accurate, affordable, and accessible. These new tests are becoming increasingly popular as they offer a range of benefits, including the ability to diagnose diseases earlier, identify individuals at risk of developing certain conditions, and personalize treatment.
Whole-genome sequencing (WGS), a type of genetic testing that analyzes the entire DNA sequence of an individual, is used to diagnose a wide range of rare diseases, cancer, and cardiovascular disease.
Exome sequencing analyzes the genome's protein-coding regions and is used to diagnose genetic conditions caused by mutations in these regions.
Genome-wide association studies (GWAS) look for associations between genetic variants and disease and are used to identify genetic risk factors for a wide range of conditions, including Alzheimer's disease, heart disease, and cancer.
Polygenic risk scores use information from multiple genetic variants to predict an individual's risk of developing a disease so that they can be monitored or offered preventive measures.
Direct-to-consumer genetic testing (DTCGT) is available to consumers without needing a prescription from a doctor.
Low-code LIMS are essential for laboratories that need to support the demands of new tests, including workflows, validations, data sources, and reporting.
KEEPING UP WITH A CHANGING REGULATORY ENVIRONMENT IS ESSENTIAL.
Many traditional LIMS, designed to address generic lab workflows, are difficult to customize to meet the needs of genetic labs. When regulations specific to genetic testing aren’t infused into LIMS processes, it is difficult to comply with them. Labs that can not update their LIMS to meet new requirements must add manual oversight processes.
Many reasons make Slingr's low-code LIMS the front-runner.
Low-code LIMS are flexible and can be customized to meet changing needs, including regulatory and compliance. Because low-code LIMS can be enhanced with new features, implementing a cohesive compliance program specific to genetic lab operations is fast and affordable.
Slingr low-code LIMS are cloud-native and offer compliance at every layer, including data back-ups, platform-level audit, and logging, and fine-grained permissions to restrict data sharing to necessary parties.
AI WILL PUSH THE DEMAND FOR INTEGRATION.
Traditional LIMS systems not designed for integration are often tricky and time-consuming to integrate with other systems. Because it usually requires custom development, integrating LIMS can be complex and time-consuming. LIMS and other methods often use different data formats and terminologies, making it challenging to ensure that data is accurate and consistent.
The world increasingly recognizes the opportunities for AI to change the healthcare landscape. We are beginning this new frontier, and there’s no certainty for the best path forward for labs or healthcare systems. However, lab systems must be prepared to infuse AI – where relevant – into workflows as the benefits become apparent.
TAILORING REPORTS FOR PATIENTS, PHYSICIANS, AND GENETIC COUNSELORS WILL DRAMATICALLY IMPROVE THE STAKEHOLDER EXPERIENCE.
Genetic testing is more complicated than a blood test. Most patients cannot interpret the risks associated with specific genes or different genetic conditions by reading a report intended for a physician or genetic counselor. Genetic tests look for changes in DNA, the genetic material that makes up our bodies. These changes can be minimal and difficult to understand without a background in genetics.
Because they often include a lot of information about the genes tested, the changes found, and the possible implications of these changes for the patient's health, genetic testing reports can be very long, making it difficult for patients to find the information they need to understand their results.
The interpretation of genetic test results can be complicated, making it critical that reports are tailored to convey the necessary information to the patient, physician, and genetics professionals.
GENETIC TESTING LABS NEED SLINGR LOW-CODE LIMS
Add new features and scale
Meet compliance requirements
Automate processes
Integrate with other systems
Provide an affordable technology foundation for the future
