Errors occur every day in healthcare institutions and research facilities. Medical lab errors can be very costly, setting hospitals back hundreds (sometimes thousands) of dollars for every mislabeled sample, causing irreparable harm to the physical and mental health of the patient. Errors in research also have a broad impact, skewing results and wasting precious materials—which are often irreplaceable—and years of effort.
Gene expression microarrays generate extremely high amounts of transcriptomic data. These datasets may account for thousands of genes from hundreds of different individuals and can be used to identify genes associated with a particular disease or to assess gene expression profiles in response to a given therapy. Transcriptomic datasets are usually uploaded to a larger database, such as Gene Expression Omnibus (GEO), where others can review the data and draw their own conclusions. In effect, these datasets don’t just shape the hypothesis of the paper from which it was published; they influence all other scientists using those datasets to guide their own research.
In both clinical and research labs, it is often necessary to label material or equipment with sensitive information. This can include patient information or confidential experimental data. Once the patient is discharged, or the samples are either processed or no longer needed, these labels must be discarded; however, the information on the labels must still remain private. In certain cases, this may prove to be more difficult than anticipated. Here are 3 unique label options to help ensure that discarded material can’t be used to procure sensitive information.
Artificial intelligence (AI) has been a popular topic ever since it was introduced in 1956 by John McCarthy. It quickly captured the imagination of Hollywood, leading to many blockbuster movies being made using AI as a plot device, including the Terminator franchise. However, until now AI has remained as only science fiction, as it’s only recently that computers have become powerful enough to integrate AI into something appreciably functional, allowing some of the top companies in the world, such as Google, IBM, and Apple, to design systems that learn on their own. Gartner, a research and advisory company who publishes a yearly list of the most hyped technologies (termed the Gartner Hype Cycle), has placed AI-associated technologies at the top of their list.1 With companies like PathAI, Freenome, and Benevolent AI all entering the market, it hasn’t taken long for scientists to adapt AI to solving complex biological and medical problems as well.
Healthcare institutions tend to use diverse systems for labeling specimens, each incorporating fail-safes at different levels of collection and processing. Many hospitals practice the Swiss cheese model of error prevention, using multiple layers (or fail-safes) to cover up any possible holes, preventing errors from slipping through.1 When it comes to reducing labeling errors, researchers have identified several types of interventions that act as additional fail-safes, many of them incorporating modern technology, such as barcodes, radio frequency identification (RFID), and automated systems.
For Part 1 of the series, detailing the costs of labeling errors in the clinic, click here.
Studying the real-time effects of labeling errors in the lab is extremely difficult. Billions of patient specimens, including blood and urine samples, as well as biopsies taken from multiple tissues and organs, are continually processed on a daily basis in clinical labs. Fortunately, several large-scale studies throughout the last 20 years have attempted to shed light on the clinical consequences of labeling errors in an effort to improve patient care and reduce healthcare costs worldwide.