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Objectives: Public health laboratories (PHLs) are essential components of US Public Health Service operations. The health information technology that supports PHLs is central to effective and efficient laboratory operations and overall public health response to infectious disease management. This analysis presents key information on how the Nebraska Public Health Laboratory (NPHL) information technology system evolved to meet the demands of the COVID-19 pandemic. Materials and Methods: COVID-19 presented numerous, unforeseen information technology system challenges. The most notable challenges requiring changes to NPHL software systems and capability were improving efficiency of the laboratory operation due to high-volume testing, responding daily to demands for timely data for analysis by partner systems, interfacing with multiple testing (equipment) platforms, and supporting community-based specimen collection programs. Results: Improvements to the NPHL information technology system enabled NPHL to perform >121 000 SARS-CoV-2 polymerase chain reaction tests from March 2020 through January 2022 at a sustainable rate of 2000 SARS-CoV-2 tests per day, with no increase in laboratory staffing. Electronic reporting of 62 000 rapid antigen tests eliminated paper reporting and extended testing services throughout the state. Collection of COVID-19 symptom data before specimen collection enabled NPHL to make data-driven decisions to perform pool testing and conserve testing kits when supplies were low. Practice Implications: NPHL information technology applications proved essential for managing health care provider workload, prioritizing the use of scarce testing supplies, and managing Nebraska's overall pandemic response. The NPHL experience provides useful examples of a highly capable information technology system and suggests areas for additional attention in the PHL environment, including a focus on end users, collaboration with various partners, and investment in information technology. [ABSTRACT FROM AUTHOR]

While VCF formatted files are the lingua franca of next-generation sequencing, most EHRs do not provide native VCF support. As a result, labs often must send non-structured PDF reports to the EHR. On the other hand, while FHIR adoption is growing, most EHRs support HL7 interoperability standards, particularly those based on the HL7 Version 2 (HL7v2) standard. The HL7 Version 2 genomics component of the HL7 Laboratory Results Interface (HL7v2 LRI) standard specifies a formalism for the structured communication of genomic data from lab to EHR. We previously described an open-source tool (vcf2fhir) that converts VCF files into HL7 FHIR format. In this report, we describe how the utility has been extended to output HL7v2 LRI data that contains both variants and variant annotations (e.g., predicted phenotypes and therapeutic implications). Using this HL7v2 converter, we implemented an automated pipeline for moving structured genomic data from the clinical laboratory to EHR. We developed an open source hl7v2GenomicsExtractor that converts genomic interpretation report files into a series of HL7v2 observations conformant to HL7v2 LRI. We further enhanced the converter to produce output conformant to Epic's genomic import specification and to support alternative input formats. An automated pipeline for pushing standards-based structured genomic data directly into the EHR was successfully implemented, where genetic variant data and the clinical annotations are now both available to be viewed in the EHR through Epic's genomics module. Issues encountered in the development and deployment of the HL7v2 converter primarily revolved around data variability issues, primarily lack of a standardized representation of data elements within various genomic interpretation report files. The technical implementation of a HL7v2 message transformation to feed genomic variant and clinical annotation data into an EHR has been successful. In addition to genetic variant data, the implementation described here releases the valuable asset of clinically relevant genomic annotations provided by labs from static PDFs to calculable, structured data in EHR systems. [ABSTRACT FROM AUTHOR]

While VCF formatted files are the lingua franca of next-generation sequencing, most EHRs do not provide native VCF support. As a result, labs often must send non-structured PDF reports to the EHR. On the other hand, while FHIR adoption is growing, most EHRs support HL7 interoperability standards, particularly those based on the HL7 Version 2 (HL7v2) standard. The HL7 Version 2 genomics component of the HL7 Laboratory Results Interface (HL7v2 LRI) standard specifies a formalism for the structured communication of genomic data from lab to EHR. We previously described an open-source tool (vcf2fhir) that converts VCF files into HL7 FHIR format. In this report, we describe how the utility has been extended to output HL7v2 LRI data that contains both variants and variant annotations (e.g., predicted phenotypes and therapeutic implications). Using this HL7v2 converter, we implemented an automated pipeline for moving structured genomic data from the clinical laboratory to EHR. We developed an open source hl7v2GenomicsExtractor that converts genomic interpretation report files into a series of HL7v2 observations conformant to HL7v2 LRI. We further enhanced the converter to produce output conformant to Epic's genomic import specification and to support alternative input formats. An automated pipeline for pushing standards-based structured genomic data directly into the EHR was successfully implemented, where genetic variant data and the clinical annotations are now both available to be viewed in the EHR through Epic's genomics module. Issues encountered in the development and deployment of the HL7v2 converter primarily revolved around data variability issues, primarily lack of a standardized representation of data elements within various genomic interpretation report files. The technical implementation of a HL7v2 message transformation to feed genomic variant and clinical annotation data into an EHR has been successful. In addition to genetic variant data, the implementation described here releases the valuable asset of clinically relevant genomic annotations provided by labs from static PDFs to calculable, structured data in EHR systems.

• Westgard quality control rules have poor error detection capability unless combined. • Moving average algorithms applied on quality control data outperform Westgard rules. • Simple moving average algorithms perform comparably to weighted average forms. • Application of moving average algorithms can simplify laboratory quality control. Internal quality control (IQC) is traditionally interpreted against predefined control limits using multi-rules or 'Westgard rules'. These include the commonly used 1:3s and 2:2s rules. Either individually or in combination, these rules have limited sensitivity for detection of systematic errors. In this proof-of-concept study, we directly compare the performance of three moving average algorithms with Westgard rules for detection of systematic error. In this simulation study, 'error-free' IQC data (control case) was generated. Westgard rules (1:3s and 2:2s) and three moving average algorithms (simple moving average (SMA), weighted moving average (WMA), exponentially weighted moving average (EWMA); all using ±3SD as control limits) were applied to examine the false positive rates. Following this, systematic errors were introduced to the baseline IQC data to evaluate the probability of error detection and average number of episodes for error detection (ANEed). From the power function graphs, in comparison to Westgard rules, all three moving average algorithms showed better probability of error detection. Additionally, they also had lower ANEed compared to Westgard rules. False positive rates were comparable between the moving average algorithms and Westgard rules (all <0.5%). The performance of the SMA algorithm was comparable to the weighted algorithms forms (i.e. WMA and EWMA). Application of an SMA algorithm on IQC data improves systematic error detection compared to Westgard rules. Application of SMA algorithms can simplify laboratories IQC strategy. [ABSTRACT FROM AUTHOR]

• Solitary use of tumor markers is of limited value in the surveillance of HCC caused by HCV. • We explored the utility of combining lab tests being monitored in the surveillance. • A logistic regression model (LRM4) composed of AST, ALT, and AFP greatly enhanced the accuracy of detecting HCC. • For model validation, we devised a new scheme of applying it retroactively to a series of previous month datasets. • LRM4 found reliable even in earlier months can prompt clinicians to perform timely image study. We developed a laboratory test-based regression model for early detection of hepatocellular carcinoma (HCC) associated with HCV in its surveillance. This matched case–control study was conducted by enrolling 452 patients with chronic hepatitis and/or cirrhosis, including 129 patients complicated with HCC. One-to-one propensity score matching was performed by referring to sex, age, and fibrosis-4 index, which resulted in 102 patients each in HCC and non-HCC groups. Logistic regression models (LRM) for distinguishing the two groups were explored from variable combinations of laboratory tests. The model was validated by our new scheme of applying it retroactively to trimonthly previous datasets. Models with a practical level of diagnostic accuracy (C-statistic) were α-fetoprotein (AFP) alone (0.810), LRM3 comprising AFP, AST, and ALT (0.850), and LRM4 comprising AFP, AFP/(AST × ALT), and AST (0.862). After retroactive application of each model, LRM4 showed the highest distinction of the two groups at −12M, −6M, −3M with C-statistics of 0.654, 0.786, 0.834, respectively. LRM4 was accurate even after limiting cases to early-stage HCC. LRM4 was proved useful in prompting clinicians to perform timely image study in the surveillance. The retroactive validation scheme is applicable to assess diagnostic models of other neoplastic diseases. [ABSTRACT FROM AUTHOR]

Data and information technology is a key component in responding to covid19 disease and is an effective way for informing personnel, resource management, and inter-intra organizational communication. During the pandemic, technology-based tools enable organizations to facilitate the widespread information dissemination, real-time tracking, meetings and daily activities virtualization and telemedicine visits for patients, physicians, and other health care providers as a major risk group of COVID-19. [ABSTRACT FROM AUTHOR]

Harmonization initiatives in laboratory medicine seek to eliminate or reduce illogical variations in service to patients, clinicians and other healthcare professionals. Significant effort will be required to achieve consistent application of terminology, units and reporting across laboratory testing providers. Current variations in practice for nomenclature, reference intervals, flagging, units, standardization and traceability between analytical methods, and presentation of cumulative result data are inefficient and inconvenient, or worse yet, patient safety risks. All aspects of laboratory service across the "total testing process" ultimately depend on concise, reliable communication. Clinical terminologies (e.g. SNOMED-CT, LOINC, IFCC/IUPAC NPU) provide a mechanism to correctly identify an analyte or panel of tests within a request for testing and communicate the results back to the clinician or electronic health record (EHR). Electronic systems for requesting and reporting laboratory testing are said to be interoperable when reliable connection and communication of content occur. Modern electronic reports and EHRs will provide greater flexibility and functionality, but also require effective guidelines or standards to ensure consistent representation of laboratory data. Programs to harmonize service in these areas require ongoing local, national and international efforts and should incorporate stakeholders from laboratories, medical staff, information technology and informatics specialists, patient representatives and government. The process of identifying harmonized best practice, then ensuring uptake across many laboratory testing providers, is generally iterative rather than "one off". New opportunities for additional harmonization will be generated as analytical performance, standardization and traceability, and diagnosis and treatment continue to evolve. [ABSTRACT FROM AUTHOR]

Drug combination testing in the pharmaceutical industry has typically been driven by late-stage opportunistic strategies rather than by early testing to identify drug combinations for clinical investigation that may deliver improved efficacy. A rationale for combinations exists across a number of diseases in which pathway redundancy or resistance to therapeutics are evident. However, early assays are complicated by the absence of both assay formats representative of disease biology and robust infrastructure to screen drug combinations in a medium-throughput capacity. When applying drug combination testing studies, it may be difficult to translate a study design into the required well contents for assay plates because of the number of compounds and concentrations involved. Dispensing these plates increases in difficulty as the number of compounds and concentration points increase and compounds are subsequently rolled onto additional labware. We describe the development of a software tool, in conjunction with the use of acoustic droplet technology, as part of a compound management platform, which allows the design of an assay incorporating combinations of compounds. These enhancements to infrastructure facilitate the design and ordering of assay-ready compound combination plates and the processing of combinations data from high-content organotypic assays. [ABSTRACT FROM AUTHOR]

Laboratory informatics is defined as the specialized application of information technology to optimize and extend laboratory operations. Rising with the tide of informatics in general, laboratory informatics is one of the fastest growing areas of laboratory-related technology. However, this technological growth has outstripped the expertise of the ones who stand to gain most from it: scientists and other end users in the laboratory. This gap could be bridged by specialists in laboratory informatics who are well grounded in the scientific basis of lab operations, yet also trained in informatics and its particular applications in the lab. However, formal educational programs in laboratory informatics are lacking. To set an educational agenda, laboratory informatics must be delineated as a field, and based on that delineation, a curriculum must be developed that meets the standards of higher education. To address these issues, this paper gives an overview of informatics, describes the context of laboratory informatics in this setting, explains the emergence of laboratory informatics as a distinct field, sets the place for laboratory informatics in higher education by suggesting the nature and scope of the curriculum, and briefly describes the laboratory informatics initiative at Indiana University. [Copyright &y& Elsevier]

Background: Delay or failure to view test results in a hospital setting can lead to delayed diagnosis, risk of patient harm, and represents inefficiency. Factors influencing this were investigated to identify how timeliness and completeness of test review could be improved through an evidence-based redesign of the use of clinical test review software., Methods: A cross-section of all abnormal hematology and biochemistry results which were published on a digital test review platform over a 3-year period were investigated. The time it took for clinicians to view these results, and the results that were not viewed within 30 days, were analyzed relative to time of the week, the detailed type of test, and an indicator of patient record data quality., Results: The majority of results were viewed within 90 min, and 93.9% of these results viewed on the digital platform within 30 days. There was significant variation in results review throughout the week, shown to be due to an interplay between technical and clinical workflow factors. Routine results were less likely to be reviewed, as were those with patient record data quality issues., Conclusion: The evidence suggests that test result review would be improved by stream-lining access to the result platform, differentiating between urgent and routine results, improving handover of responsibility for result review, and improving search for temporary patient records. Altering the timing of phlebotomy rounds and a review of the appropriateness of routine test requests at the weekend may also improve result review rates., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Harmonization initiatives in laboratory medicine seek to eliminate or reduce illogical variations in service to patients, clinicians and other healthcare professionals. Significant effort will be required to achieve consistent application of terminology, units and reporting across laboratory testing providers. Current variations in practice for nomenclature, reference intervals, flagging, units, standardization and traceability between analytical methods, and presentation of cumulative result data are inefficient and inconvenient, or worse yet, patient safety risks. All aspects of laboratory service across the "total testing process" ultimately depend on concise, reliable communication. Clinical terminologies (e.g. SNOMED-CT, LOINC, IFCC/IUPAC NPU) provide a mechanism to correctly identify an analyte or panel of tests within a request for testing and communicate the results back to the clinician or electronic health record (EHR). Electronic systems for requesting and reporting laboratory testing are said to be interoperable when reliable connection and communication of content occur. Modern electronic reports and EHRs will provide greater flexibility and functionality, but also require effective guidelines or standards to ensure consistent representation of laboratory data. Programs to harmonize service in these areas require ongoing local, national and international efforts and should incorporate stakeholders from laboratories, medical staff, information technology and informatics specialists, patient representatives and government. The process of identifying harmonized best practice, then ensuring uptake across many laboratory testing providers, is generally iterative rather than "one off". New opportunities for additional harmonization will be generated as analytical performance, standardization and traceability, and diagnosis and treatment continue to evolve.

DNA-encoded libraries (DELs) have emerged as an efficient and cost-effective drug discovery tool for the exploration and screening of very large chemical space using small-molecule collections of unprecedented size. Herein, we report an integrated automation and informatics system designed to enhance the quality, efficiency, and throughput of the production and affinity selection of these libraries. The platform is governed by software developed according to a database-centric architecture to ensure data consistency, integrity, and availability. Through its versatile protocol management functionalities, this application captures the wide diversity of experimental processes involved with DEL technology, keeps track of working protocols in the database, and uses them to command robotic liquid handlers for the synthesis of libraries. This approach provides full traceability of building-blocks and DNA tags in each split-and-pool cycle. Affinity selection experiments and high-throughput sequencing reads are also captured in the database, and the results are automatically deconvoluted and visualized in customizable representations. Researchers can compare results of different experiments and use machine learning methods to discover patterns in data. As of this writing, the platform has been validated through the generation and affinity selection of various libraries, and it has become the cornerstone of the DEL production effort at Lilly.

With the recent results of promising cancer vaccines and immunotherapy1-5, immune monitoring has become increasingly relevant for measuring treatment-induced effects on T cells, and an essential tool for shedding light on the mechanisms responsible for a successful treatment. Flow cytometry is the canonical multi-parameter assay for the fine characterization of single cells in solution, and is ubiquitously used in pre-clinical tumor immunology and in cancer immunotherapy trials. Current state-of-the-art polychromatic flow cytometry involves multi-step, multi-reagent assays followed by sample acquisition on sophisticated instruments capable of capturing up to 20 parameters per cell at a rate of tens of thousands of cells per second. Given the complexity of flow cytometry assays, reproducibility is a major concern, especially for multi-center studies. A promising approach for improving reproducibility is the use of automated analysis borrowing from statistics, machine learning and information visualization21-23, as these methods directly address the subjectivity, operator-dependence, labor-intensive and low fidelity of manual analysis. However, it is quite time-consuming to investigate and test new automated analysis techniques on large data sets without some centralized information management system. For large-scale automated analysis to be practical, the presence of consistent and high-quality data linked to the raw FCS files is indispensable. In particular, the use of machine-readable standard vocabularies to characterize channel metadata is essential when constructing analytic pipelines to avoid errors in processing, analysis and interpretation of results. For automation, this high-quality metadata needs to be programmatically accessible, implying the need for a consistent Application Programming Interface (API). In this manuscript, we propose that upfront time spent normalizing flow cytometry data to conform to carefully designed data models enables automated analysis, potentially saving time in the long run. The ReFlow informatics framework was developed to address these data management challenges.