Remember the era of “one-size-fits-all” in medicine and healthcare? Well, that era is dying out – and fast. In precision medicine, every patient’s genetic blueprint determines the course of action. And the immediate implication is that laboratories must manage a flood of complex, individualized data without missing a beat.
Traditional Laboratory Information Systems (LIS) platforms that were designed and built for routine tasks simply can’t keep up. In order to turn genomic insights into personalized care, labs require platforms that were designed for the nuances of molecular workflows, for big‑data handling, and for seamless integration with clinical decision tools.
Confusing? Not really! Let’s break it down, shall we?
Data Complexity in Precision Medicine
Precision labs rarely process single results; it’s more like they juggle entire profiles. A single genome sequence will potentially produce hundreds of gigabytes of raw data. Further down the pipeline, bioinformatics will generate variant calls and risk scores that, eventually, must link back to the original patient sample.
W an LIS that can track multi‑step protocols and parent‑child sample relationships, labs risk losing context or misattributing variants. Furthermore, combining genomic data with clinical records (such as phenotypes, lab values, and family history) will probably create a dataset that demands flexible data models and scalable storage.
So, given all that… Where does the next generation LIS come into play?
Managing Genetic and Molecular Workflows
Considering legacy systems and younger platforms, not all LIS platforms are built the same. In molecular diagnostics, key capabilities usually include:
- Multi‑Stage Protocol Tracking: From extraction through preparation and sequencing, every step requires time‑stamped audit trails and meticulous QC checkpoints.
- Bio-informatics Integration: High-throughput sequencers should automatically send FASTQ or BAM files to alignment and variant-calling tools, with the results flowing directly back into the patient reports. This is usually achieved using bioinformatics pipelines – automated workflows set up to handle the data transfer.
- Configurable Result Fields: Unlike simple numeric test values, genomic results require structured fields for gene names, variant classifications, pathogenicity scores, and clinical interpretations.
According to the Healthcare Innovation Group, the features mentioned depend on an architecture that treats genomic datasets as a high priority, and not as random tasks that are thrown into an outdated legacy system.
Especially those systems with the DOS-oriented OS. And you’ll be surprised how many labs still use them.
Precision medicine demands solutions that can go beyond traditional EMRs, capable of storing, managing, and serving insights that originate from genetic testing across diverse care settings. So, when your LIS delivers clean, integrated genomic and clinical data, personalized care becomes possible, with:
- Targeted Treatment Selections: Oncologists rely on accurate variant interpretations to match patients with targeted treatments. Labs using integrated genomic LIS can achieve up to 30% faster turnaround for molecular profiles, ensuring patients receive the right medicine without delay.
- Pharmacogenomic Dosing: An LIS that flags clinically actionable results directly in the report empowers clinicians to adjust prescriptions at the point of care.
- Continuous Learning: An LIS that can enable real-time analyses will close the loop between lab findings and patient outcomes.
The Bottom Line
Integrating genomic data within advanced and customized clinical workflows doesn’t stop at uncovering actionable variants in 17% of participants. No, the right integrations also inform preventive strategies and surveillance plans. Thus, the conclusion is clear: personalized medicine isn’t something we can all imagine as a must for the future – it’s happening now.
However, without the right infrastructure, personalized medicine’s full potential is out of reach. As the volume and complexity of genetic data continue to grow, labs must evolve beyond their legacy LIS systems and embrace platforms that were purpose-built for precision diagnostics – such as LabOS.
It’s not just about handling more data; it’s about making that data meaningful, accessible, and actionable at every point of care.
➡️ POWER UP YOUR GENOMIC LAB
