Automation, Connectivity and “Big Data”
Today, laboratory automation and information technologies are driving a new era of “fast microbiology” with inter-connectivity and generation of billions of pieces of information.
Continuous efforts to improve microbiological diagnostic devices have enabled the automation of many common laboratory procedures, leading to spectacular improvements in the speed of sample testing and results delivery. Tools readily available today can detect pathogens far faster than traditional culture-based methods, generating improved lab efficiency and considerably reducing time to results. A key goal of health technology assessments (HTA) in the field of diagnostics is to demonstrate the positive impact of these technological advances on patients and the healthcare system.
Another key development is in the area of informatics – both in terms of connectivity and “big data” management. Connectivity has been dramatically improved between individual diagnostic systems and between those systems and the Laboratory Information System (LIS). Connectivity between labs and clinicians has also been revolutionized by linking the LIS with portable “smart” devices. All of these connections can accelerate and facilitate clinicans’ access to the most relevant information, in order to optimize patient care.
The production of billions of pieces of diagnostic-related data, from these machines, labs and connections, offers the potential to perform far more in-depth analyses than has previously been possible. Such analyses can lead to better disease risk prediction, improved patient selection for personalized medicine and improved evidence-based guidelines for optimal diagnostic testing.
In the field of antibiotic resistance, diagnostic inter-connectivity is crucial for providing rapid results to clinicians in order to optimize the choice of antibiotics and therapies in the most timely manner. “Big data” analyses have allowed us to determine which diagnostics are most useful for the detection of certain pathogens, and which patients are most likely to benefit from preventive measures, as just a few examples.