Pathology Informatics Research Articles

Is use of ChatGPT cheating? Students of health professions perceptions

Purpose The purpose of this study is to explore student perceptions of generative AI use and cheating in health professions education. The authors sought to understand how students believe generative AI is acceptable to use in coursework. Materials and methods Five faculty members surveyed students across health professions graduate programs using an updated, validated survey instrument. Students anonymously completed the survey online, which took 10–20 min. Data were then tabulated and reported in aggregate form. Results Nearly 400 students from twelve academic programs including health and rehabilitation science, occupational therapy, physical therapy, physician assistant studies, speech-language pathology, health administration and health informatics, undergraduate healthcare studies, nurse anesthesiology, and cardiovascular perfusion. The majority of students identify the threat of generative AI to graded assignments such as tests and papers, but many believe it is acceptable to use these tools to learn and study outside of graded assignments. Conclusions Generative AI tools provide new options for students to study and learn. Graduate students in the health professions are currently using generative AI applications but are not universally aware or in agreement of how its use threatens academic integrity. Faculty should provide specific guidance on how generative AI applications may be used.

Advances in the Clinical Application of High-throughput Proteomics.

High-throughput proteomics has become an exciting field and a potential frontier of modern medicine since the early 2000s. While significant progress has been made in the technical aspects of the field, translating proteomics to clinical applications has been challenging. This review summarizes recent advances in clinical applications of high-throughput proteomics and discusses the associated challenges, advantages, and future directions. We focus on research progress and clinical applications of high-throughput proteomics in breast cancer, bladder cancer, laryngeal squamous cell carcinoma, gastric cancer, colorectal cancer, and coronavirus disease 2019. The future application of high-throughput proteomics will face challenges such as varying protein properties, limitations of statistical modeling, technical and logistical difficulties in data deposition, integration, and harmonization, as well as regulatory requirements for clinical validation and considerations. However, there are several noteworthy advantages of high-throughput proteomics, including the identification of novel global protein networks, the discovery of new proteins, and the synergistic incorporation with other omic data. We look forward to participating in and embracing future advances in high-throughput proteomics, such as proteomics-based single-cell biology and its clinical applications, individualized proteomics, pathology informatics, digital pathology, and deep learning models for high-throughput proteomics. Several new proteomic technologies are noteworthy, including data-independent acquisition mass spectrometry, nanopore-based proteomics, 4-D proteomics, and secondary ion mass spectrometry. In summary, we believe high-throughput proteomics will drastically shift the paradigm of translational research, clinical practice, and public health in the near future.

Complete Digital Pathology Transition: A Large Multi-Center Experience

IntroductionTransitioning from glass slide pathology to digital pathology for primary diagnostics requires an appropriate laboratory information system, an image management system, and slide scanners; it also reinforces the need for sophisticated pathology informatics including synoptic reporting. Previous reports have discussed the transition itself and relevant considerations for it, but not the selection criteria and considerations for the infrastructure. ObjectiveTo describe the process used to evaluate slide scanners, image management systems, and synoptic reporting systems for a large multisite institution. MethodsSix network hospitals evaluated six slide scanners, three image management systems, and three synoptic reporting systems. Scanners were evaluated based on the quality of image, speed, ease of operation, and special capabilities (including z-stacking, fluorescence and others). Image management and synoptic reporting systems were evaluated for their ease of use and capacity. ResultsAmong the scanners evaluated, the Leica GT450 produced the highest quality images, while the 3DHistech Pannoramic provided fluorescence and superior z-stacking. The newest generation of scanners, released relatively recently, performed better than slightly older scanners from major manufacturers Although the Olympus VS200 was not fully vetted due to not meeting all inclusion criteria, it is discussed herein due to its exceptional versatility. For Image Management Software, the authors believe that Sectra is, at the time of writing the best developed option, but this could change in the very near future as other systems improve their capabilities. All synoptic reporting systems performed impressively. ConclusionsSpecifics regarding quality and abilities of different components will change rapidly with time, but large pathology practices considering such a transition should be aware of the issues discussed and evaluate the most current generation to arrive at appropriate conclusions.

Introducing JMIR Bioinformatics and Biotechnology: A Platform for Interdisciplinary Collaboration and Cutting-Edge Research

JMIR Bioinformatics and Biotechnology supports interdisciplinary research and welcomes contributions that push the boundaries of bioinformatics, genomics, artificial intelligence, and pathology informatics.

Pathology Informatics Summit 2023 David L. Lawrence Convention Center May 22 – May 25 Pittsburgh, PA

Pathology Informatics Summit 2023 David L. Lawrence Convention Center May 22 – May 25 Pittsburgh, PA

Pathology Informatics Summit 2022 David L. Lawrence Convention Center May 9-12 Pittsburgh, PA

Pathology Informatics Summit 2022 David L. Lawrence Convention Center May 9-12 Pittsburgh, PA

Application of informatics in the work of a pathologist: guidelines for learning how to create and use a digital archive of gross images

Pathology informatics has been developing as a response to a large amount of diagnostically relevant morphological information and attempt to the best store and use it. The main components of pathology informatics include digital images of histological slides and gross photographs, telepathology, and electronic data collection. Photo documentation of gross specimens is an integral part for efficient work of a pathologist. Currently, many grossing stations are equipped with continuously recording video cameras. A logical and detailed description of the macroscopic specimens, supported by properly obtained digital photographs, should be the standard of a modern pathology report. However, with the increasing workload of the pathologists, they have less and less time to take gross photographs. That is why, in our opinion, it is important to ask questions such as: what is necessary to photograph, how to take a photo of the gross specimen, and in which order? There are only a few publications on this topic in both domestic and foreign literature. This paper attempts to summarize the literary data on this topic, based on which a list of medical use cases that require a mandatory photo documentation has been created. Practical recommendations have been developed and are outlined for gross photographs. In the context of widespread use of digital photography as a resource for deep learning of neural networks and digital analysis, this article will be useful not only for postgraduate education of pathologists, but also for physicians of other specialties.

Pathology Informatics Education during the COVID-19 Pandemic at Memorial Sloan Kettering Cancer Center (MSKCC).

This review details the development and structure of a four-week rotation in pathology informatics for a resident trainee at Memorial Sloan Kettering Cancer Center (MSKCC) in New York City so that other programs interested in such a rotation can refer to. The role of pathology informatics is exponentially increasing in research and clinical practice. With an ever-expanding role, training in pathology informatics is paramount as pathology training programs and training accreditation bodies recognize the need for pathology informatics in training future pathologists. However, due to its novelty, many training programs are unfamiliar with implementing pathology informatics training. The rotation incorporates educational resources for pathology informatics, guidance in the development, and general topics relevant to pathology informatics training. Informatics topics include anatomic pathology related aspects such as whole slide imaging, laboratory information systems, image analysis, and molecular pathology associated issues such as the bioinformatics pipeline and data processing. Additionally, we highlight how the rotation pivoted to meet the department's informatics needs while still providing an educational experience during the onset of the COVID-19 pandemic. CONCLUSION: As pathology informatics continues to grow and integrate itself into practice, informatics education must also grow to meet the future needs of pathology. As informatics programs develop across institutions, such as the one detailed in this paper, these programs will better equip future pathologists with informatics to approach disease and pathology.

Digital media archive for gross pathology images based on open-source tools and Fast Healthcare Interoperability Resources (FHIR).

Macroscopic examination of surgical pathology and autopsy cases is a fundamental component of anatomic pathology. The photographic documentation of such clinical specimens is essential, and it may be required in certain instances. Our department began using consumer-grade digital cameras in 2005 to improve the practice of gross photography. However, the lack of an application to correctly catalog the photographs resulted in thousands of digital image files scattered across shared network drives, with limited case and patient metadata, making image retrieval a cumbersome and sometimes impossible task. Thirteen years later, we examined the legacy method of acquiring and accessing gross photographs in our department and determined the need for a web-based digital media archive to capture images with structured metadata. Using several open-source tools, including MediaWiki, we developed a flexible platform for building our digital media archive with a data schema based on the Fast Healthcare Interoperability Resources standard. Following a short pilot, we replaced the legacy method of handling gross pathology images with a new acquisition workflow and digital media archive. Through March 2021, 233 distinct users have accessed the system, 58 of which have uploaded 21,024 images. Of those images, 13,684 (65.1%) correspond to surgical pathology images, 4045 (19.2%) belong to neuropathology cases, and 3295 (15.7%) originate from autopsies. We demonstrate the design and implementation of a customizable anatomic pathology digital media archive solution in an academic pathology department setting using a modern standard for exchanging healthcare information electronically. The system’s efficiency and scalability for our current operation will enable us to integrate with other applications and pathology informatics initiatives in the future.

Pathology Informatics and Robotics Strategies for Improving Efficiency of COVID-19 Pooled Testing

The global rise of the coronavirus disease 2019 pandemic resulted in an exponentially increasing demand for severe acute respiratory syndrome coronavirus 2 testing, which resulted in shortage of reagents worldwide. This shortage has been further worsened by screening of asymptomatic populations such as returning employees, students, and so on, as part of plans to reopen the economy. To optimize the utilization of testing reagents and human resources, pool testing of populations with low prevalence has emerged as a promising strategy. Although pooling is an effective solution to reduce the number of reagents used for testing, the process of pooling samples together and tracking them throughout the entire workflow is challenging. To be effective, samples must be tracked into each pool, pool-tested and reported individually. In this article, we address these challenges using robotics and informatics.

What’s in a Name? Comparative Analysis of Laboratory Test Naming Guidelines as Applied to Common Confusing Test Names

Abstract Introduction/Objective Laboratory test names frequently do not enable easy understandability or promote correct test utilization, which leads to difficulty for providers in finding the correct test and results in unnecessary cost and medical errors. Laboratory test names are also largely unstandardized and are not named by a consistent set of conventions. To address these issues, the TRUU-Lab (Test Renaming for Understanding & Utilization) initiative aims to generate a consensus test naming guideline for better human understandability of laboratory test names. These studies address the first aim of the TRUU-Lab initiative: to identify root causes and challenges in understanding and using laboratory test names. Methods We conducted survey studies to capture the most problematic laboratory test names, then performed analysis of these names to identify aspects of these names that led to confusion among providers. A subset of these test names were used to evaluate five existing laboratory test naming guidelines (LOINC, ONC TigerTeam, Pan- Canadian iEHR Viewer Name, Standards for Pathology Informatics (Australia), and ARUP Laboratories internal style guides) for their ability to produce understandable test names. Results 274 survey responses yielded ~100 unique laboratory tests cited as confusing, and highlighted substantial diversity both in the names of these tests between institutions and in respondent opinion on the best alternative names. The top 10 most commonly-cited tests yielded ≥ 3 unique names, and the top 2 tests (Vitamin D and anti- factor Xa) yielded ≥ 10 unique names. Post-survey analysis identified eight characteristics associated with poor understandability of a test name, including ambiguity, abbreviations, homophones, multiple indications for a single test, proprietary names, synonyms, truncation, and “panels” where components are obfuscated. Existing guidelines produced highly variable names given the same prompt, and varied in their ability to avoid pitfalls associated with poor understandability. Conclusion These studies highlight aspects of existing laboratory test names that lead to confusion among ordering providers, and identify the inability of existing laboratory test naming practices to adequately address these issues. Efforts are ongoing within TRUU-Lab to use these results to inform novel laboratory test naming guidelines to promote universal human understandability.

What’s in a Name? Comparative Analysis of Laboratory Test Naming Guidelines as Applied to Common Confusing Test Names

Abstract Laboratory test names frequently do not enable easy understandability or promote correct test utilization, which leads to difficulty for providers in finding the correct test and results in unnecessary cost and medical errors. As a further complication, laboratory test names are largely unstandardized and are not named based on a consistent set of conventions. To address these issues, the TRUU-Lab (Test Renaming for Understanding & Utilization) initiative aims to generate a consensus laboratory test naming guideline for better human understandability of laboratory test names. These studies address the first and second aims of the TRUU-Lab initiative: 1) to identify root causes and challenges in understanding and using laboratory test names, and 2) to share resources related to potential solutions. We initially conducted survey studies to capture the most commonly problematic laboratory test names, then performed analysis of these names to identify aspects of these names that led to confusion among providers. 274 survey responses yielded ~100 unique laboratory tests that respondents felt were confusing, and highlighted substantial diversity both in the names of these tests between institutions and in respondent opinion on the best alternative names, with the top 10 most commonly-cited tests having at least 3 unique names, and the top 2 tests (Vitamin D and anti-factor Xa) having at least 10 unique names. Post-survey analysis identified eight common characteristics associated with poor understandability of a test name, including ambiguity, abbreviations, homophones, multiple indications for a single test, non-descriptive proprietary names, synonyms, truncation due to software limitations, and €œpanels where test components are obfuscated. A subset of the survey-identified confusing test names were used to evaluate existing laboratory test naming guidelines for their ability to produce understandable test names. Five guidelines, including LOINC, ONC TigerTeam, Pan-Canadian iEHR Viewer Name, Standards for Pathology Informatics (Australia), and ARUP Laboratories internal style guides, were evaluated, and produced highly variable names given the same test name prompt. Further, existing guidelines also varied in their ability to avoid pitfalls previously identified as associated with poor understandability. Together, these studies highlight the aspects of existing laboratory test names that lead to confusion among ordering providers, and identify the inability of existing laboratory test naming practices to adequately address these issues. Efforts are ongoing within TRUU-Lab to use these results to inform novel laboratory test naming guidelines that promote universal human understandability. Work is also ongoing to apply these novel guidelines to generate new candidate test names, and conduct survey analysis to evaluate the effects of new test naming guidelines on understandability and correct test utilization.

Leveraging Pathology Informatics Concepts to Achieve Discrete Lab Data for Clinical Use and Translational Research.

Clinical practice is most efficient when physicians have the right information, including pathology and laboratory results, at the point of contact with the patient. In downstream workflows, subsequent groups using lab data want to have it available in a format that is easy to manipulate. With the complexity of electronic medical records, hospital information systems, and the need to accommodate data from outside systems, this is not easy to accomplish. By utilizing a group of concepts from clinical and pathology informatics, system implementations may be improved to achieve relevant laboratory data in a format that is usable by healthcare entities to improve patient care and forward endeavors in precision medicine.

Artificial intelligence driven next-generation renal histomorphometry.

Successful integration of artificial intelligence into extant clinical workflows is contingent upon a number of factors including clinician comprehension and interpretation of computer vision. This article discusses how image analysis and machine learning have enabled comprehensive characterization of kidney morphology for development of automated diagnostic and prognostic renal pathology applications. The primordial digital pathology informatics work employed classical image analysis and machine learning to prognosticate renal disease. Although this classical approach demonstrated tremendous potential, subsequent advancements in hardware technology rendered artificial neural networks '(ANNs) the method of choice for machine vision in computational pathology'. Offering rapid and reproducible detection, characterization and classification of kidney morphology, ANNs have facilitated the development of diagnostic and prognostic applications. In addition, modern machine learning with ANNs has revealed novel biomarkers in kidney disease, demonstrating the potential for machine vision to elucidate novel pathologic mechanisms beyond extant clinical knowledge. Despite the revolutionary developments potentiated by modern machine learning, several challenges remain, including data quality control and curation, image annotation and ontology, integration of multimodal data and interpretation of machine vision or 'opening the black box'. Resolution of these challenges will not only revolutionize diagnostic pathology but also pave the way for precision medicine and integration of artificial intelligence in the process of care.

Informatics Training for Pathology Practice and Research in the Digital Era

Pathology informatics is a relatively new field with limited structured training programs for pathologists, especially for computer programming. Here, we describe our efforts to develop and implement a training program in the department of pathology at the University of Florida to meet these additional needs of current students as well as faculty and staff. Three one-credit courses were created using a flipped classroom design. Each course was assessed with a novel survey instrument, and the impact of the program was further measured 6 months after program completion with interviews of 6 participants and thematic analysis. Course objectives were met but with room for improvement. Major factors that had a positive impact included collaborative learning and real-world practice problems. Also, it improved communication with informatics colleagues as well as job task efficiency and effectiveness. Overall, the program raised awareness of informatics professional development and career path opportunities within pathology.

Innovations: Brave new worlds.

The field of cytopathology, from fine-needle aspiration to computer-assisted screening, has always attracted innovators and creative thinkers. Technology touches every aspect of our world and our cytopathology practice. Opportunities and challenges both are in abundant supply. In this new “Innovations” section, we will highlight the latest developments in technology, social media, and informatics to keep readers up to date. Social media has added new layers of interaction and networking capabilities to our field. The pathology and cytopathology communities have harnessed the power of these technologies to build stronger professional networks, share and discuss the latest research findings, and even conduct multi-institutional research studies. Social media platforms make the world smaller, enabling education and collaborations across continents. Journal clubs now can involve participants from across the country. Twitter chats regarding topics such as the Milan System, ancillary testing in cytology, and noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) flatten hierarchies and allow unprecedented real-time access to experts in the field (Fig. 1). Our recent #CytoChat on the topic of NIFTP featured a real-time discussion of practice patterns, the sharing of useful articles, and expert input from Drs. Faquin and Rossi. This chat drew greater than 60 participants from around the world, garnering greater than 1 million potential impressions. Not sure how to get started with social media? A previously published article provides an overview of what social media can do for you, and how to get started.1 To make sure you do not miss out, follow Cancer Cytopathology on Facebook (https://www.facebook.com/CancerCytopath) and Twitter (https://twitter.com/CancerCytopath). The complexity of workflows and the need to meet quality assurance mandates have precipitated the computerization of the cytology laboratory to improve effectiveness and efficiency. As an early adopter of pathology informatics and its data management principle, the cytology laboratory has in some ways defined how data are collected, accessed, and analyzed. Another area of informatics to which cytology has made early contributions is digital pathology. Ironically, although one of the first successful implementations of digital pathology was the automation of the Papanicolaou test, progress in this realm of informatics has been delayed due to the unique challenges present in cytologic specimens and the use of whole-slide imaging (WSI). Nevertheless, computational progress and improved imaging technology have allowed pathology informatics to enter a period of rapid growth and innovation. Using improved WSI technology and z-stacking modalities to overcome the challenges of digitizing 3-dimensional cytologic material, leading investigators in the field of cytopathology have begun to exploit the advantages offered by digital pathology over traditional microscopy. To that end, this new and exciting section of Cancer Cytopathology will focus on highlighting innovative applications of pathology informatics in the cytology laboratory, including the use of traditional statistical tools and advanced deep-learning approaches for data mining and visualization. In an effort to increase visibility, we also will showcase how digital pathology continues to revolutionize the clinical practice of cytology through the application of WSI in telecytology for rapid on-site evaluation, remote consultation, and digital image analysis for diagnostic testing. To kick off this issue of Cancer Cytopathology, we would like to congratulate Dr. Louis J. Vaickus from Dartmouth-Hitchcock Medical Center in Lebanon, New Hampshire, for winning this year's Young Investigator Challenge (Fig. 2). His work explored the development of a hybrid deep-learning and morphometric algorithm for the analysis of urine cytology specimens.2 This breakthrough article presents the framework for a system that can automate the Paris System for Urine Cytopathology reliably, with the potential to increase the efficiency of digital screening for urine WSI. Xiaoyin “Sara” Jiang, MD, is an associate professor of pathology at Duke University in Durham, North Carolina, and the associate director of the Duke BioRepository and Precision Pathology Center. Her areas of research and expertise are cytopathology and surgical pathology of the head and neck and endocrine systems, with particular interest in thyroid nodules, ultrasound-guided fine-needle aspiration, and novel applications of social media for medical professionals. Emilio Madrigal, DO, is a Cuban-born anatomic and clinical pathologist specializing in cytopathology and clinical informatics at the Massachusetts General Hospital in Boston, Massachusetts. He has special interests in salivary gland cytology and machine intelligence–powered digital image analysis. Dr. Madrigal received his Doctor of Osteopathic Medicine from Rowan University in Glassboro, New Jersey and has been certified by the American Board of Pathology in anatomic and clinical pathology and cytopathology.

Application of Bayesian network modeling to pathology informatics.

In the era of extensive data collection, there is a growing need for a large scale data analysis with tools that can handle many variables in one modeling framework. In this article, we present our recent applications of Bayesian network modeling to pathology informatics. Bayesian networks (BNs) are probabilistic graphical models that represent domain knowledge and allow investigators to process this knowledge following sound rules of probability theory. BNs can be built based on expert opinion as well as learned from accumulating data sets. BN modeling is now recognized as a suitable approach for knowledge representation and reasoning under uncertainty. Over the last two decades BN have been successfully applied to many studies on medical prognosis and diagnosis. Based on data and expert knowledge, we have constructed several BN models to assess patient risk for subsequent specific histopathologic diagnoses and their related prognosis in gynecological cytopathology and breast pathology. These models include the Pittsburgh Cervical Cancer Screening Model assessing risk for histopathologic diagnoses of cervical precancer and cervical cancer, modeling of the significance of benign-appearing endometrial cells in Pap tests, diagnostic modeling to determine whether adenocarcinoma in tissue specimens is of endometrial or endocervical origin, and models to assess risk for recurrence of invasive breast carcinoma and ductal carcinoma in situ. Bayesian network models can be used as powerful and flexible risk assessment tools on large clinical datasets and can quantitatively identify variables that are of greatest significance in predicting specific histopathologic diagnoses and their related prognosis. Resulting BN models are able to provide individualized quantitative risk assessments and prognostication for specific abnormal findings commonly reported in gynecological cytopathology and breast pathology.

Leveraging digital pathology for molecular testing.

Leveraging digital pathology for molecular testing.

Informatics External Quality Assurance (IEQA) Down Under: evaluation of a pilot implementation

AbstractExternal quality assurance (EQA) provides ongoing evaluation to verify that laboratory medicine results conform to quality standards expected for patient care. While attention has focused predominantly on test accuracy, the diagnostic phases, consisting of pre- and post-laboratory phases of testing, have thus far lagged in the development of an appropriate diagnostic-phase EQA program. One of the challenges faced by Australian EQA has been a lack of standardisation or “harmonisation” resulting from variations in reporting between different laboratory medicine providers. This may introduce interpretation errors and misunderstanding of results by clinicians, resulting in a threat to patient safety. While initiatives such as the Australian Pathology Information, Terminology and Units Standardisation (PITUS) program have produced Standards for Pathology Informatics in Australia (SPIA), conformity to these requires regular monitoring to maintain integrity of data between sending (laboratory medicine providers) and receiving (physicians, MyHealth Record, registries) organisations’ systems. The PITUS 16 Informatics EQA (IEQA) Project together with the Royal College of Pathologists of Australasia Quality Assurance Programs (RCPAQAP) has created a system to perform quality assurance on the electronic laboratory message when the laboratory sends a result back to the EQA provider. The purpose of this study was to perform a small scale pilot implementation of an IEQA protocol, which was performed to test the suitability of the system to check compliance of existing Health Level-7 (HL7 v2.4) reporting standards localised and constrained by the RCPA SPIA. Here, we present key milestones from the implementation, including: (1) software development, (2) installation, and verification of the system and communication services, (3) implementation of the IEQA program and compliance testing of the received HL7 v2.4 report messages, (4) compilation of a draft Informatics Program Survey Report for each laboratory and (5) review consisting of presentation of a report showing the compliance checking tool to each participating laboratory.

Informatics External Quality Assurance (IEQA) Down Under: evaluation of a pilot implementation

External quality assurance (EQA) provides ongoing evaluation to verify that laboratory medicine results conform to quality standards expected for patient care. While attention has focused predominantly on test accuracy, the diagnostic phases, consisting of pre- and post-laboratory phases of testing, have thus far lagged in the development of an appropriate diagnostic-phase EQA program. One of the challenges faced by Australian EQA has been a lack of standardisation or “harmonisation” resulting from variations in reporting between different laboratory medicine providers. This may introduce interpretation errors and misunderstanding of results by clinicians, resulting in a threat to patient safety. While initiatives such as the Australian Pathology Information, Terminology and Units Standardisation (PITUS) program have produced Standards for Pathology Informatics in Australia (SPIA), conformity to these requires regular monitoring to maintain integrity of data between sending (laboratory medicine providers) and receiving (physicians, MyHealth Record, registries) organisations’ systems. The PITUS 16 Informatics EQA (IEQA) Project together with the Royal College of Pathologists of Australasia Quality Assurance Programs (RCPAQAP) has created a system to perform quality assurance on the electronic laboratory message when the laboratory sends a result back to the EQA provider. The purpose of this study was to perform a small scale pilot implementation of an IEQA protocol, which was performed to test the suitability of the system to check compliance of existing Health Level-7 (HL7 v2.4) reporting standards localised and constrained by the RCPA SPIA. Here, we present key milestones from the implementation, including: (1) software development, (2) installation, and verification of the system and communication services, (3) implementation of the IEQA program and compliance testing of the received HL7 v2.4 report messages, (4) compilation of a draft Informatics Program Survey Report for each laboratory and (5) review consisting of presentation of a report showing the compliance checking tool to each participating laboratory.