As a group we devised our first test instrument, incorporating budgetary measures by area of investigation, personnel statistics, training and tenure data, and casework data that include information on case type, number of test samples, and more. Our goal was to evaluate what constitutes best practices. This exhaustive process helped illuminate the key areas that affect performance and can be efficiently adjusted to provide an almost immediate, positive impact. This knowledge helped us create the much more concise collection tool we use to this day, LabRAT.

As today’s participants enter data into the LabRAT spreadsheet, it calculates pertinent metrics the labs can use and track on a regular basis. This in itself is illuminating for some labs that may have access to the data through their LIMS systems but have rarely had the time to collect the information in such a coordinated and interlocking way. But what we find most interesting—and what draws many labs to the project—is the way our participant labs are using the data, how they are changing practices.

Real-world Results
Backlogs are a great example. Given the data we’ve collected over a wide sample population, we can quite confidently talk about where the greatest efficiencies lie and how to achieve these in the most expeditious way. We know where labs are performing particularly well. Take DNA for example. We found that once a lab corrects inefficiencies in workload, individuals in the lab from non-DNA areas of investigation often have additional downtime. Labs may use that downtime to train these folks to triage DNA casework, which leads to much better throughput in the growing demand for DNA casework. The cost is minimal—you’re not adding any staff—and suddenly you’re working through backlogs so quickly that you have additional time to focus on other cases. Given the large group of labs we’ve worked with, we have a good idea what size is optimal and can share this with each other to improve our work.

In addition to exposing areas for improvement, Foresight participating labs also enjoy the benefits of seeing where they fit within the larger group. This in and of itself can be enlightening. In some cases, a lab will find they may not be able to improve their efficiency beyond their current level. The data may show the lab is more costly than average in a particular area, but external factors like low crime rates (e.g., less need for a particular type of test) or higher compensation requirements due to geography have a significant impact. Uncovering this type of result is not necessarily a bad thing; however, lab personnel may want to think about outsourcing certain types of cases to adjust for this effect.