Automation and Process Re-engineering Work Together to Achieve Six Sigma Quality: A 27-Year History of Continuous Improvement.

BACKGROUND: In most clinical laboratories, examination quality is considered excellent, whereas pre-/postexamination quality is an area for focused improvement. In our organization, 1 pre-/postexamination quality metric, namely, lost specimens, as tracked continuously for 27 years, has demonstrated steady improvement. During this period, many of our processes transitioned to highly automated effectors. Concurrently, we implemented behavioral controls and reengineered error-prone processes. We believe that this bilateral approach has conclusively lowered our lost specimen rates. METHODS: Using data spanning 27 years, we plotted the correlation between lost specimens and the implementation dates for 8 major phases of automation, as well as 19 process improvements and engineering controls. RESULTS: The lost specimen rate decreased nearly 100-fold. In Six Sigma terms, the 12 month moving average for lost specimens currently hovers at approximately 5.94 sigma, with 11 months at or better than 6 sigma. Although the combination of implementation of process improvements, engineering controls, and automation contributed to the reduction, automation was the most significant contributor. CONCLUSIONS: The custom automation in use by our laboratory has led to improved pre-/postexamination quality. Although this automation may not be possible for all laboratories, our description of 19 behavior and engineering controls may be useful to others seeking to design high quality pre-/postexamination processes.

Nonanalytic Laboratory Automation: A Quarter Century of Progress.

Clinical laboratory automation has blossomed since the 1989 AACC meeting, at which Dr. Masahide Sasaki first showed a western audience what his laboratory had implemented. Many diagnostics and other vendors are now offering a variety of automated options for laboratories of all sizes. Replacing manual processing and handling procedures with automation was embraced by the laboratory community because of the obvious benefits of labor savings and improvement in turnaround time and quality. Automation was also embraced by the diagnostics vendors who saw automation as a means of incorporating the analyzers purchased by their customers into larger systems in which the benefits of automation were integrated to the analyzers.This report reviews the options that are available to laboratory customers. These options include so called task-targeted automation-modules that range from single function devices that automate single tasks (e.g., decapping or aliquoting) to multifunction workstations that incorporate several of the functions of a laboratory sample processing department. The options also include total laboratory automation systems that use conveyors to link sample processing functions to analyzers and often include postanalytical features such as refrigerated storage and sample retrieval.Most importantly, this report reviews a recommended process for evaluating the need for new automation and for identifying the specific requirements of a laboratory and developing solutions that can meet those requirements. The report also discusses some of the practical considerations facing a laboratory in a new implementation and reviews the concept of machine vision to replace human inspections.

Use of Automation and Process Improvement to Achieve a Six Sigma Level of Nonanalytic Quality.

BACKGROUND: Clinical laboratories have focused on quality for more than 60 years. While analytic quality is considered excellent in most laboratories, nonanalytic quality is an area for focused improvement. One of our quality metrics, lost samples, has been tracked continuously for 25 years and has demonstrated steady improvement. Nonanalytic processes have become highly automated within our organization, which, we believe, was a major factor in reducing lost samples. We have also implemented numerous behavioral controls and completed many process reengineering projects that have had a demonstrable effect on lost sample rates. Our objective in this study was to determine the overall contributions of our error-proofing methods to reducing lost samples. METHODS: Using data spanning 25 years, we plotted the correlation between lost samples and the implementation dates for 8 major phases of automation along with 16 process improvements and engineering controls. RESULTS: The lost sample rate decreased nearly 100-fold. In Six Sigma terms, the 12-month moving average for lost samples currently hovers around 5.85-sigma, with several months at or better than 6-sigma. While implementation of process improvements, engineering controls, and automation all contributed to the reduction, automation was the most significant contributor. CONCLUSIONS: The custom automation in use by our laboratory has led to improved nonanalytic quality. Although this level of automation might not be possible for all laboratories, our description of 16 behavior and engineering controls may be useful to other laboratories seeking to design high-quality nonanalytic processes.

Invention and validation of an automated camera system that uses optical character recognition to identify patient name mislabeled samples.

BACKGROUND: Mislabeled samples are a serious problem in most clinical laboratories. Published error rates range from 0.39/1000 to as high as 1.12%. Standardization of bar codes and label formats has not yet achieved the needed improvement. The mislabel rate in our laboratory, although low compared with published rates, prompted us to seek a solution to achieve zero errors. METHODS: To reduce or eliminate our mislabeled samples, we invented an automated device using 4 cameras to photograph the outside of a sample tube. The system uses optical character recognition (OCR) to look for discrepancies between the patient name in our laboratory information system (LIS) vs the patient name on the customer label. All discrepancies detected by the system's software then require human inspection. The system was installed on our automated track and validated with production samples. RESULTS: We obtained 1 009 830 images during the validation period, and every image was reviewed. OCR passed approximately 75% of the samples, and no mislabeled samples were passed. The 25% failed by the system included 121 samples actually mislabeled by patient name and 148 samples with spelling discrepancies between the patient name on the customer label and the patient name in our LIS. Only 71 of the 121 mislabeled samples detected by OCR were found through our normal quality assurance process. CONCLUSIONS: We have invented an automated camera system that uses OCR technology to identify potential mislabeled samples. We have validated this system using samples transported on our automated track. Full implementation of this technology offers the possibility of zero mislabeled samples in the preanalytic stage.

Laboratory automation: total and subtotal.

Worldwide, perhaps 2000 or more clinical laboratories have implemented some form of laboratory automation, either a modular automation system, such as for front-end processing, or a total laboratory automation system. This article provides descriptions and examples of these various types of automation. It also presents an outline of how a clinical laboratory that is contemplating automation should approach its decision and the steps it should follow to ensure a successful implementation. Finally, the role of standards in automation is reviewed.

Development and validation of an automated thawing and mixing workcell.

BACKGROUND: Working toward a goal of total laboratory automation, we are automating manual activities in our highest volume laboratory section. Because half of all specimens arriving in this laboratory section are frozen, we began by developing an automated workcell for thawing frozen specimens and mixing the thawed specimens to remove concentration gradients resulting from freezing and thawing. METHODS: We developed an initial robotic workcell that removed specimens from the transport system's conveyor, blew high-velocity room temperature air at the tubes, mixed them, and replaced them on the conveyor. Aliquots of citrated plasma were frozen with thermocouples immersed in the tubes, and thawing times and temperatures were monitored. Completeness of mixing of thawed specimens was studied by careful removal of small aliquots from the uppermost layer of the upright tubes without disturbing tube contents and analysis of total protein and electrolytes. RESULTS: High velocity ambient air aimed directly at tubes ranging from 12 x 75 to 16 x 100 mm brought specimens to room temperature in a maximum of 23 min. Adequate mixing of the specimens by the workcell's robot required only 2 approximate 126 degrees movements from an upright starting point, a surprising observation, because laboratorians are usually trained to mix 10 or 20 times. We also observed that, in a frozen overfilled tube, resulting analyte concentrations will be lower because more concentrated solutes leak from the tube. CONCLUSIONS: A high-throughput, automated thawing and mixing workcell was successfully built, validated, and installed on our automated transport and sorting system.

Automated transport and sorting system in a large reference laboratory: part 1. Evaluation of needs and alternatives and development of a plan.

BACKGROUND: Our laboratory, a large, commercial, esoteric reference laboratory, sought some form of total laboratory automation to keep pace with rapid growth of specimen volumes as well as to meet competitive demands for cost reduction and improved turnaround time. METHODS: We conducted a systematic evaluation of our needs, which led to the development of a plan to implement an automated transport and sorting system. We systematically analyzed and studied our specimen containers, test submission requirements and temperatures, and the workflow and movement of people, specimens, and information throughout the laboratory. We performed an intricate timing study that identified bottlenecks in our manual handling processes. We also evaluated various automation options. RESULTS: The automation alternative viewed to best meet our needs was a transport and sorting system from MDS AutoLab. Our comprehensive plan also included a new standardized transport tube; a centralized automated core laboratory for higher volume tests; a new "automation-friendly" software system for order entry, tracking, and process control; a complete reengineering of our order-entry, handling, and tracking processes; and remodeling of our laboratory facility and specimen processing area. CONCLUSIONS: The scope of this project and its potential impact on overall laboratory operations and performance justified the extensive time we invested (nearly 4 years) in a systematic approach to the evaluation, design, and planning of this project.

Automated transport and sorting system in a large reference laboratory: part 2. Implementation of the system and performance measures over three years.

BACKGROUND: Our laboratory implemented a major automation system in November 1998. A related report describes a 4-year process of evaluation and planning leading to system installation. This report describes the implementation and performance results over 3 years since the system was placed into use. METHODS: Project management software was used to track the project. Turnaround times of our top 500 tests before and after automation were measured. We compared the rate of hiring of employees and the billed unit per employee ratio before and after automation by use of linear regression analysis. Finally, we analyzed the financial contribution of the project through an analysis of return on investment. RESULTS: Since implementation, the volume of work transported and sorted has grown to >15,000 new tubes and >25,000 total tubes per day. Median turnaround time has decreased by an estimated 7 h, and turnaround time at the 95th percentile has decreased by 12 h. Lost specimens have decreased by 58%. A comparison of pre- and post-implementation hiring rates of employees estimated a savings of 33.6 employees, whereas a similar comparison of ratios of billed units per employee estimated a savings of 49.1 employees. Using the higher figure, we estimated that the $4.02 million cost of the project would be paid off approximately 4.9 years subsequent to placing the system into daily use. CONCLUSIONS: The overall automation project implemented in our laboratory has contributed considerably to improvement of key performance measures and has met our original project objectives.