Vol. 18 • Issue 9
• Page 35
When the word "automation" is used in the clinical laboratory, most people think of track systems and robotics to move specimens around. Automation, however, is much broader than automating the physical movement of samples. Automating the information handling or informatics using software such as middleware and quality assurance programs automates tasks throughout the pre-analytical, analytical, post-analytical and quality assurance tasks in a clinical laboratory.
Pre-Analytical
One of the most difficult areas to automate is the ordering of serum indices. Many labs order serum indices on all samples to ensure these tests are run on samples when required. One of the downsides of this approach is that it wastes throughput capacity of the analyzer.
Another approach is to have serum indices ordered in the laboratory information system (LIS), but this method relies on staff properly ordering these additional tests and often results in human error. By applying rules to an order in middleware, however, serum indices can be ordered only when tests are ordered that are affected by the indices and only for samples that are serum. This improves the efficiency of the analyzer by only running indices when required and ensures they are consistently ordered when necessary. Middleware can then apply the results flags to the tests or assays that have interference and not require the LIS to accept serum indices when the LIS did not have to order them.
Analytical
When discussing automating tasks during the analytical phase, the first term that usually comes to mind is "autoverification." This can be described as automating the thought processes of your best medical technologists as they make decisions on the acceptability of a result. While this is an effective solution, other ways to automate tasks in a step-wise approach exist if a lab is not ready to implement a full autoverification chemistry system.
Most labs will have comments that need to be reported out to the physician to provide diagnostic information. Many times when the medical technologist is reviewing the result he forgets to add the comment or there is inconsistency with comments. Middleware can be used to add a comment automatically to a result based on certain conditions. For example, when the Hemolysis (H) index is high, middleware can automatically add comments to affected results. While this is usually part of an autoverification system, rules in middleware can be added incrementally to reduce the number of steps required to review results.
Another example of automating tasks in the analytical phase is to attach instructions on how to handle a specimen when an exception occurs. If, for instance, the SOP for triglycerides states that you must ultra-centrifuge the sample if it is Lipemic (L), middleware could be used to attach this instruction to the triglyceride result if the L index was above a certain level.
Post-analytical
One of the quickest and largest gains in productivity obtained by using middleware is to implement a specimen storage tracking system. With a minimal time and financial investment, a lab can reduce the time it takes to find a specimen for an add-on test. Storage tracking systems can reduce time to find a sample by 60 percent,1reduce hours of FTE time daily2and alert staff if they try to store a sample that has pending tests on it.
Quality Assurance
Quality control (QC) is one of the most important aspects of the lab, but also can require a large number of resources to manage it. By automating routine tasks in this area, considerable gains can be achieved. Some labs use a QC program that is not part of the LIS. To get the QC results from analyzers into the program, the lab either has to manually enter them or use a complicated import procedure to export the results, then import them into the QC program. Middleware could be used to interface the analyzers to the LIS and the QC program. Once this is implemented, automatic notifications of QC violations or exceptions could be sent to the appropriate staff.
Tracking of tasks-including instrument maintenance-is another time-consuming aspect of quality assurance. This often is a manual process tracked in notebooks or binders and reviewed a week later by a supervisor, at which point it's too late to correct any issues. Using middleware, the maintenance could be tracked electronically and provide quick retrieval of records. The supervisor could also be immediately notified if maintenance for an analyzer was not performed at the scheduled time (or performed incorrectly).
One of the most overlooked areas is method evaluation. Orders are usually manually programmed into the analyzer; the calculations around the results are performed using a calculator. Traditionally the outcome is recorded on paper and stored in a binder. The first step to automating this process is to use a spreadsheet to automate the calculations. This also involves a lot of manual steps (i.e., data entry) and some level of expertise to create the formulas. Specialty software to perform the calculations and store the results electronically is the next step in automating this process. Middleware could then be used to order the tests on the analyzer and automatically transfer the results to the method evaluation software. The data mining capability may also be used to find samples for the method evaluation experiments.
Paul Dean is implementation consultant, Data Innovations.
References
1. Zibrat SJ, Berg LA, McLawhon RW. "Evaluation and Comparison of the Roche PSD1 Task-Targeted Automation System to Manual Postanalytical Methods for Specimen Archiving and Retrieval," poster presentation at the AACC Annual Meeting, Chicago, July 29-August 2, 2001.
2. No Monkey in the Middle. Clinical Lab Products, June 2008.
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