Background: Clinical pathology laboratories increasingly use complex instruments that incorporate chromatographic separation, e.g. liquid chromatography, with mass detection for rapid identification and quantification of biochemicals, biomolecules, or pharmaceuticals. Electronic data management for these instruments through interfaces with laboratory information systems (LIS) is not generally available from the instrument manufacturers or LIS vendors. Unavailability of a data management interface is a limiting factor in the use of these instruments in clinical laboratories where there is a demand for high-throughput assays with turn-around times that meet patient care needs. Materials and Methods: Professional society guidelines for design and transfer of data between instruments and LIS were used in the development and implementation of the interface. File transfer protocols and support utilities were written to facilitate transfer of information between the instruments and the LIS. An interface was created for liquid chromatography-tandem mass spectroscopy and inductively coupled plasma-mass spectroscopy instruments to manage data in the Sunquest^® LIS. Results: Interface validation, implementation and data transfer fidelity as well as training of technologists for use of the interface was performed by the LIS group. The technologists were familiarized with the data verification process as a part of the data management protocol. The total time for the technologists for patient/control sample data entry, assay results data transfer, and results verification was reduced from approximately 20 s per sample to <1 s per sample. Sample identification, results data entry errors, and omissions were eliminated. There was electronic record of the technologist performing the assay runs and data management. Conclusions: Development of a data management interface for complex, chromatography instruments in clinical laboratories has resulted in rapid, accurate, verifiable information transfers between instruments and LIS. This has eliminated manual data entry that is prone to errors and enabled technologists to focus on analytical applications on the instruments.

Introduction: Historically, effective clinical utilization of image analysis and pattern recognition algorithms in pathology has been hampered by two critical limitations: 1) the availability of digital whole slide imagery data sets and 2) a relative domain knowledge deficit in terms of application of such algorithms, on the part of practicing pathologists. With the advent of the recent and rapid adoption of whole slide imaging solutions, the former limitation has been largely resolved. However, with the expectation that it is unlikely for the general cohort of contemporary pathologists to gain advanced image analysis skills in the short term, the latter problem remains, thus underscoring the need for a class of algorithm that has the concurrent properties of image domain (or organ system) independence and extreme ease of use, without the need for specialized training or expertise. Results: In this report, we present a novel, general case pattern recognition algorithm, Spatially Invariant Vector Quantization (SIVQ), that overcomes the aforementioned knowledge deficit. Fundamentally based on conventional Vector Quantization (VQ) pattern recognition approaches, SIVQ gains its superior performance and essentially zero-training workflow model from its use of ring vectors, which exhibit continuous symmetry, as opposed to square or rectangular vectors, which do not. By use of the stochastic matching properties inherent in continuous symmetry, a single ring vector can exhibit as much as a millionfold improvement in matching possibilities, as opposed to conventional VQ vectors. SIVQ was utilized to demonstrate rapid and highly precise pattern recognition capability in a broad range of gross and microscopic use-case settings. Conclusion: With the performance of SIVQ observed thus far, we find evidence that indeed there exist classes of image analysis/pattern recognition algorithms suitable for deployment in settings where pathologists alone can effectively incorporate their use into clinical workflow, as a turnkey solution. We anticipate that SIVQ, and other related class-independent pattern recognition algorithms, will become part of the overall armamentarium of digital image analysis approaches that are immediately available to practicing pathologists, without the need for the immediate availability of an image analysis expert.