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Automation streamlines a previously labor-intensive process and virtually eliminates sampling or labeling errors. The techniques of molecular biology and genetics continue to assist the physician in the diagnosis, prognosis, and treatment of patients with neoplastic diseases.
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To address the problem of semantic inconsistencies between medical databases, semantic network representations can be utilized to automate the matching of medical concepts between the databases. The performance of automated concept matching was tested by creating semantic network representations for two laboratory databases, one from a pediatric hospital and the other from an oncology institute. The matching algorithms identified all equivalent concepts that were present in both databases, and did not leave any equivalent concepts unmatched. By automatically identifying semantically equivalent concepts, the Medical Information Acquisition and Transmission Enabler (MEDIATE) facilitates data exchange between heterogeneous systems because no pre-negotiation is required. Consequently, system scalability and stability is improved.
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BACKGROUND: Recently Mohs micrographic surgery, which is widely used for the removal of nonmelanoma skin cancers, has been used to remove lentigo maligna with both rush permanent sections and frozen sections. Several investigators have incorporated the use of immunohistochemical techniques to aid in the interpretation of the specimens. OBJECTIVE: To determine the current practices of Mohs surgery laboratories, including the use of immunostains and automation of laboratory processes. METHODS: A total of 108 laboratories responded to a written questionnaire with 13 items about the types of tumors resected, routine stains performed, average number of slides processed per day, and use of automation and immunostains. RESULTS: Forty-nine percent of the laboratories are completely manual and 51% are automated. The Linistainer automated system, which is the predominant one used, decreased processing time by about 30% and provided an estimated 21-30% improved quality. Automation was associated with the number of slides processed. Immunostaining is performed by a limited number of laboratories which use the technique for basal and squamous cell carcinoma, lentigo maligna, and dermatofibrosarcoma protuberans. CONCLUSION: Automation of routine slide preparation with a Linistainer decreased staining variability by providing a consistent environment, and decreased processing time. Most laboratories do not perform immunostaining. The relatively high cost of reagents, lack of a reliable automated process, the additional time to process specimens, and the additional technician and physician time makes the procedure impractical for many laboratories.
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Robotic technology is going to have a profound impact on the clinical laboratory of the future. Faced with increased pressure to reduce health care spending yet increase services to patients, many laboratories are looking for alternatives to the inflexible or "fixed" automation found in many clinical analyzers. Robots are being examined by many clinical pathologists as an attractive technology which can adapt to the constant changes in laboratory testing. Already, laboratory designs are being altered to accommodate robotics and automated specimen processors. However, the use of robotics and computer intelligence in the clinical laboratory is still in its infancy. Successful examples of robotic automation exist in several laboratories. Investigators have used robots to automate endocrine testing, high performance liquid chromatography, and specimen transportation. Large commercial laboratories are investigating the use of specimen processors which combine the use of fixed automation and robotics. Robotics have also reduced the exposure of medical technologists to specimens infected with viral pathogens. The successful examples of clinical robotics applications were a result of the cooperation of clinical chemists, engineers, and medical technologists. At the University of Virginia we have designed and implemented a robotic critical care laboratory. Initial clinical experience suggests that robotic performance is reliable, however, staff acceptance and utilization requires continuing education. We are also developing a robotic cyclosporine which promises to greatly reduce the labor costs of this analysis. The future will bring lab wide automation that will fully integrate computer artificial intelligence and robotics. Specimens will be transported by mobile robots. Specimen processing, aliquotting, and scheduling will be automated.(ABSTRACT TRUNCATED AT 250 WORDS)
Examination of the urine, possibly the earliest diagnostic test in medicine, has only recently benefited from automation; one previous twentieth-century advance was substitution of specific reagents for earlier nonspecific chemical tests that were subject to interferences. In the mid-1970s, semiautomated urinalysis instruments were developed to read and record chemical test reactions used in manual urinalysis. Further, in 1984 the first instrument coupling automated intelligent microscopy (AIM) with a dipstick reader became available, fully automating both the biochemical screening and microscopic examination of urine specimens. This assessment describes AIM, as embodied in the Yellow IRIS instrument, and also discusses alternative strategies that laboratorians are developing to improve the cost effectiveness of conventional urinalysis.
We evaluated the Abbott Bichromatic Analyzer-100 (ABA-100) for use in the routine clinical chemistry laboratory by examining 13 different determinations that can be performed on the instrument. Results with the Du Pont "aca" and Technicon continuous-flow systems were compared to the ABA-100 in terms of upper limits of linearity, inter-run coefficient of variation, and results for samples from patients. The upper limits of linearity for the methods on the ABA-100 exceeded all of those for the continuous-flow systems, except for urea nitrogen. Precision of the ABA-100 was as good as or better than that of the aca for all determinations, except for glucose in a normal control serum and creatine kinase and creatinine in an above-normal control serum.
The purpose of infectious disease surveillance is to inform the public health policy makers on the incidence and trends of infectious diseases and to trigger appropriate actions to control infectious disease outbreaks. The enormous amount of data collected with automated laboratory-based surveillance systems require automated algorithms for detecting unexpected aberrations that may signal infectious disease outbreaks. In this paper, we explore the potential of hierarchical time series models to detect deviations from expected incidence. As these count data are extremely noisy it can be expected that these models are suitable to detect signal from noise and accommodate for possible autocorrelation. The proposed procedure consists of three steps; (1) the model parameters are estimated by empirical Bayes on a training period of, e.g. a year; (2) the expected values are updated for small time steps (e.g. daily) as new data arrive; (3) threshold levels are updated conditionally on the past expected values and an alarm is triggered when the threshold level is exceeded. To test the potential of the models we estimated sensitivity, specificity and timeliness on simulated time series and compared the results with an alternative approach of a linear regression model adjusted for trends and season. We also used two observed series for Rubella notifications and Salmonella infections and compared our findings with the expert opinions on these series. The hierarchical time series models approach shows high sensitivity and specificity and correctly identifies outbreaks at an early stage. This, in our opinion, makes the proposed model a reliable tool for adequate automated detection of infectious disease outbreaks.
In the course of respiratory infections, the efficacy of microbiologic diagnosis has increased years after years, in term of specificity, sensitivity and rapidity. New pathogenic agents have been described such as: Legionella pneumophila, Chlamydia pneumoniae, Hantavirus. Some viruses have been well characterized as responsible for seasonal outbreaks using rapid tools for identification. Needs for efficient diagnostic tools became more obvious when specific antiviral drugs appeared on the market. So technologic developments improved the efficacy of microbiologic diagnosis and anticipate a better specificity as well as sensitivity with the help of molecular biology. Respiratory syncytial virus is one of the major infectious agents found in respiratory infections in young children and newborns. On the whole it was detected in more than one third of pediatric nasopharyngeal aspirations received in our laboratory and more than 50% during the peak of the winter epidemics. The method of direct antigen detection by immunofluorescence with the help of monoclonal antibodies allowed us to establish an incidence curve of these recurrent outbreaks, beginning in December to stop usually by the end of April. During this same period, influenza A virus, seldom influenza B virus, were detected in many nasopharyngeal specimens. Other viruses, parainfluenza 1 to 3 and Adenovirus, were irregularly detected all along the year. In the great majority of nasopharyngeal aspirations with a positive virus detection, one virus only was observed. Antigen detection methods were also developed for some bacteria such as Chlamydia pneumoniae, Legionella pneumophila. Although serology is not frequently used by pediatricians, it is still necessary for the diagnosis of Mycoplasma pneumoniae infections. A direct antigen detection test is now available, but its sensitivity needs to be evaluated. On the other hand serologic diagnosis may be extremely useful when long lasting or treatment resistant respiratory infections occur. Seroconversion or four-fold increasing titers to one pathogen may be observed when a second serum sample is tested together with the first serum of this patient. The diagnostic yield will be all the more efficient that time between both samples is long. Molecular biology techniques will significantly change the way to investigate an infection. Presently these methods are used in research laboratories, but automated technologies will facilitate routine laboratory workload. Screening methods using multiplex PCR are also promising.
This paper traces the historical development of the practice of clinical biochemistry in Singapore and presents the current status of the special branch of laboratory medicine in the country. The rapid expansion in the field which has taken place over the last three decades, has no parallel in any other medical specialty. This has presented unique problems. The approaches taken to solve these problems are described and the following areas are highlighted: scope of biochemical investigations, research and development, methodology and instrumentation, laboratory workload, automation and computerisation, costing of laboratory service, reference values, quality control, training, education, international relations and professional activities.
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The quality of bioanalytical methods is often determined by the quality of sample preparation. Using a robot for sample treatment may give better results than manual sample preparation, since the robot lacks human behaviour and incidental errors that are part of it. The use of a laboratory robot has the additional advantage of giving each sample the same analytical history, resulting in better reproducibility. An automated method has been developed for the analysis of drugs in plasma using a laboratory robot. Theophylline was used as a probe drug. Sample preparation was automated with a Zymate II robot, followed by separation and quantitation on an HPLC-system. The robotic method showed a good correlation with the manual method, while sample throughput was doubled.
Growing complexity of diagnostic tests, combined with increased workload, stringent laboratory accreditation demands, continuous shortening of turn-around-time and budget restrictions have forced laboratories to automate most of their iterative tasks. Introduction of artificial intelligence by means of expert systems has gained an important place in this automation process. Different parts of clinical laboratory activity can benefit from their implementation and the present project deals with one aspect, namely the clinical interpretation of diagnostic tests. This paper describes how j.MD, a new Java based expert system shell, was used to reprogram the expert system for interpretation of amylase isoenzyme patterns that has been in use for many years in our laboratory, and that was originally programmed in Pro.MD, a Prolog based expert system shell. One of the most important advantages of the j.MD system is its bidirectional link with the laboratory information system. This project shows how expert systems for the interpretation of complex diagnostic tests that demand specific expertise can become an integrated part of the automated clinical chemistry lab.