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Process Considerations for Clinical Laboratory Automation

Posted by Craig Rubenstein on Mar 12, 2015 5:16:45 AM

OLYMPUS DIGITAL CAMERAAs diagnostic lab managers consider implementation of process automation to achieve improved results, there are a number of common factors that can complicate or prevent a successful transition from manual handling/processing. Lab staff often performs “corrective” tasks without formal recognition, resulting in many processing steps being left out of the documented workflow. Engineering an automated process to satisfy the documented workflow may fail to account for these “hidden” process steps. Among the first tasks to be performed in a process improvement/automation program is a detailed workflow study with a specific focus on “casual” process steps.

Human dexterity and sight are extremely sophisticated, and current clinical laboratory automation technology is not yet able to tackle many tasks that are easily performed by the average lab tech. As a result, variability and errors in the materials that flow through the lab are easily and smoothly reconciled in a manual workflow, but may be detrimental to a reliable, efficient automated workflow.

Some examples of material variability commonly found in the clinical lab are:

Labels

  • Mispositioned labels
  • “Flagged” labels
  • Inconsistent print quality
  • “Foreign” barcode schema

Tubes

  • Multiple tubes banded together
  • Parafilm/foil on tubes
  • Loose closures

Specimen Quality

  • Unspun specimen
  • QNS specimen
  • Other specimen quality issues (H/I/L)

Design/Dimensional Concerns

  • Sample tubes are incompatible with analyzer, process instrument and/or automation
  • Tubes and/or racks that do not provide “lead-in” to assure reliable loading/unloading
  • Racks with inadequate space between tubes and/or adjacent features
  • Racks that do not support tubes in correct, vertical orientation
  • Rack features that interfere with smooth tube loading/unloading
  • Damaged racks

Many of the above issues are easily recognized and corrected (or filtered out) within the manual workflow. Experienced lab staff can quickly recognize specimen quality issues, compensate for design/dimensional issues, and remove extraneous materials on specimens. Automation must be designed to work reliably at high throughput rates with minimal downtime, so it is important to examine the workflow to uncover the presence of any variables, and to pursue the source of any inconsistencies. Well-designed automation systems will rarely damage non-compliant specimens, nor will the equipment suffer damage when handling materials with one or more of these issues, but process flow may be interrupted and require attention from lab staff to resume operation. Optimal results are obtained when the workflow is refined to assure consistent materials entering the system, and, of course, properly trained and motivated staff to assure effective management and maintenance of the equipment.

Lean workflow will be vastly simpler to achieve when a greater degree of standardization is established in the collection/testing market. Due, in part, to the regulatory oversight and adherence to established methods, adoption of updated technology and processes is extremely slow. Requirements for human-readable patient information on a label impose a great deal of complexity and cost, where a unique barcode (or RFID tag) would reliably identify each tube and eliminate labeling and barcode–related costs and errors. Narrowing the range of tube configurations and sizes will minimize the need for pouring off/aliquoting specimens and simplify automation. IVD manufacturers impose significant cost and complexity on automation equipment (and labs) by requiring use of a proprietary rack in their analyzers.

Recognizing and controlling the sources of variability and non-compliance within lab workflow is key to preparing for successful automation of specimen processing. Well-designed and implemented automation systems will relieve lab staff of routine processing tasks, allowing them to focus on reconciling problems and assuring on-time reporting of results.

By Craig Rubenstein, Life Science Technology Leader

Craig Rubenstein is Life Science Technology Leader – Yaskawa America, Inc., Motoman Robotics Division

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Topics: Laboratory Automation

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