Artificial Intelligence in Hand Surgery - How Generative AI is Transforming the Hand Surgery Landscape.

Artificial intelligence (AI) has witnessed significant advancements, reshaping various industries, including healthcare. The introduction of ChatGPT by OpenAI in November 2022 marked a pivotal moment, showcasing the potential of generative AI in revolutionising patient care, diagnosis and treatment. Generative AI, unlike traditional AI systems, possesses the ability to generate new content by understanding patterns within datasets. This article explores the evolution of AI in healthcare, tracing its roots to the term coined by John McCarthy in 1955 and the contributions of pioneers like John Von Neumann and Alan Turing. Currently, generative AI, particularly Large Language Models, holds promise across three broad categories in healthcare: patient care, education and research. In patient care, it offers solutions in clinical document management, diagnostic support and operative planning. Notable advancements include Microsoft's collaboration with Epic for integrating AI into electronic medical records (EMRs), enhancing clinical data management and patient care. Furthermore, generative AI aids in surgical decision-making, as demonstrated in plastic, orthopaedic and hepatobiliary surgeries. However, challenges such as bias, hallucination and integration with EMR systems necessitate caution and ongoing evaluation. The article also presents insights from the implementation of NUHS Russell-GPT, a generative AI chatbot, in a hand surgery department, showcasing its utility in administrative tasks but highlighting challenges in surgical planning and EMR integration. The survey showed unanimous support for incorporating AI into clinical settings, with all respondents being open to its use. In conclusion, generative AI is poised to enhance patient care and ease physician workloads, starting with automating administrative tasks and evolving to inform diagnoses, tailored treatment plans, as well as aid in surgical planning. As healthcare systems navigate the complexities of integrating AI, the potential benefits for both physicians and patients remain significant, offering a glimpse into a future where AI transforms healthcare delivery. Level of Evidence: Level V (Diagnostic).

AagingBase: a comprehensive database of anti-aging peptides.

The process of aging is an intrinsic and inevitable aspect of life that impacts every living organism. As biotechnological advancements continue to shape our understanding of medicine, peptide therapeutics have emerged as a promising strategy for anti-aging interventions. This is primarily due to their favorable attributes, such as low immunogenicity and cost-effective production. Peptide-based treatments have garnered widespread acceptance and interest in aging research, particularly in the context of age-related therapies. To effectively develop anti-aging treatments, a comprehensive understanding of the physicochemical characteristics of anti-aging peptides is essential. Factors such as amino acid composition, instability index, hydrophobic areas and other relevant properties significantly determine their efficacy as potential therapeutic agents. Consequently, the creation of 'AagingBase', a comprehensive database for anti-aging peptides, aims to facilitate research on aging by leveraging the potential of peptide therapies. AagingBase houses experimentally validated 282 anti-aging peptides collected from 54 research articles and 236 patents. Employing state-of-the-art computational techniques, the acquired sequences have undergone rigorous physicochemical calculations. Furthermore, AagingBase presents users with various informative analyses highlighting atomic compositions, secondary structure fractions, tertiary structure, amino acid compositions and frequencies. The database also offers advanced search and filtering options and similarity search, thereby aiding researchers in understanding their biological functions. Hence, the database enables efficient identification and prioritization of potential peptide candidates in geriatric medicine and holds immense potential for advancing geriatric medicine research and innovations. AagingBase can be accessed without any restriction. Database URL: https://project.iith.ac.in/cgntlab/aagingbase/.

Quality control data management with unity real-time in molecular virology.

INTRODUCTION: Quality control (QC) is one component of an overarching quality management system (QMS) that aims at assuring laboratory quality and patient safety. QC data must be acceptable prior to reporting patients' results. Traditionally, QC statistics, records, and corrective actions were tracked at the Johns Hopkins Molecular Virology Laboratory using Microsoft Excel. Unity Real-Time (UnityRT), a QMS software (Bio-Rad Laboratories), which captures and analyzes QC data by instrument and control lot per assay, was implemented and its impact on the workflow was evaluated. The clinical utility of real-time QC monitoring using UnityRT is highlighted with a case of subtle QC trending of HIV-1 quantitative control results. METHODS: A comprehensive workflow analysis was performed, with a focus on Epstein Barr Virus (EBV) and BKV quantitative viral load testing (Roche cobas 6800). The number of QC steps and time to complete each step were assessed before and after implementing UnityRT. RESULTS: Our assessment of monthly QC data review revealed a total of 10 steps over 57 min when using Microsoft Excel, versus 6 steps over 11 min when using UnityRT. HIV-1 QC monitoring revealed subtle trending of the low positive control above the mean from November to December 2022, correlating with a change in the reagent kit lot. This associated with a shift in patients' results from positives below the lower limit of quantification to positives between 20 and 100 copies/mL. CONCLUSIONS: UnityRT consolidated QC analyses, monitoring, and tracking corrective actions. UnityRT was associated with significant time savings, which along with the interfaced feature of the QC capture and data analysis, have improved the workflow and reduced the risk of laboratory errors. The HIV-1 case revealed the value of the real-time monitoring of QC.

Development and Use of a Tech-Based Data Management System for a Cognitive Rehabilitation Randomized Controlled Trial for People With Type 2 Diabetes.

Successful technology-based interventions to improve patients' self-management are providing an incentive for researchers to develop and implement their own technology-based interventions. However, the literature lacks guidance on how to do this. In this article, we describe the electronic process with which we designed and implemented a technology-based data management system to implement a randomized controlled trial of a comprehensive cognitive rehabilitation intervention to improve cognitive function and diabetes self-management in people with type 2 diabetes. System development included feasibility assessment, interdisciplinary collaboration, design mapping, and use of institutionally and commercially available software. The resulting framework offers a template to support the development of technology-based interventions. Initial development may be time-consuming, but the benefits of the technology-based format surpass any drawbacks.

Architecture of the Mass Spectrometry Data Management Pipeline in the SMART-CARE Project.

In the SMART-CARE project- a systems medicine approach to stratification of cancer recurrence in Heidelberg, Germany - a streamlined mass-spectrometry (MS) workflow for identification of cancer relapse was developed. This project has multiple partners from clinics, laboratories and computational teams. For optimal collaboration, consistent documentation and centralized storage, the linked data repository was designed. Clinical, laboratory and computational group members interact with this platform and store meta- and raw-data. The specific architectural choices, such as pseudonymization service, uploading process and other technical specifications as well as lessons learned are presented in this work. Altogether, relevant information in order to provide other research groups with a head-start for tackling MS data management in the context of systems medicine research projects is described.

The French Experience with a Population-Based Esophageal Atresia Registry (RENATO).

This paper presented a national register for esophageal atresia (EA) started in January 2008. We report our experience about the conception of this database and its coordination. Data management and data quality are also detailed. In 2023, more than 2,500 patients with EA are included. Prevalence of EA in France was calculated at 1.8/10,000 live birth. Main clinical results are listed with scientific publications issued directly from the register.

The PACIFIC ontology for heterogeneous data management in cardiology.

With the emergence of health data warehouses and major initiatives to collect and analyze multi-modal and multisource data, data organization becomes central. In the PACIFIC-PRESERVED (PhenomApping, ClassIFication, and Innovation for Cardiac Dysfunction - Heart Failure with PRESERVED LVEF Study, NCT04189029) study, a data driven research project aiming at redefining and profiling the Heart Failure with preserved Ejection Fraction (HFpEF), an ontology was developed by different data experts in cardiology to enable better data management in a complex study context (multisource, multiformat, multimodality, multipartners). The PACIFIC ontology provides a cardiac data management framework for the phenomapping of patients. It was built upon the BMS-LM (Biomedical Study -Lifecycle Management) core ontology and framework, proposed in a previous work to ensure data organization and provenance throughout the study lifecycle (specification, acquisition, analysis, publication). The BMS-LM design pattern was applied to the PACIFIC multisource variables. In addition, data was structured using a subset of MeSH headings for diseases, technical procedures, or biological processes, and using the Uberon ontology anatomical entities. A total of 1372 variables were organized and enriched with annotations and description from existing ontologies and taxonomies such as LOINC to enable later semantic interoperability. Both, data structuring using the BMS-LM framework, and its mapping with published standards, foster interoperability of multimodal cardiac phenomapping datasets.

A real-world data analysis-based study of Chinese medicine treatment patterns after breast cancer surgery.

Based on the real clinical data of Hospital Information System to explore the common clinical syndromes of traditional Chinese medicine after breast cancer surgery, analysis of traditional Chinese medicine in the treatment of breast cancer after the compatibility law. The real medical records of breast cancer patients after surgery in a tertiary hospital in Sichuan Province were collected and screened to build a medical record database. Python language was used for data preprocessing to remove outliers and fill in missing values. Using International Business Machines Corporation (IBM) Statistical Product and Service Solutions (SPSS) Modeler software, Apriori association rules algorithm for data analysis, mining Chinese medicine treatment of breast cancer after common syndromes and the corresponding medication rules. A total of 472 cases of clinical real medical record data were included. Data analysis showed that there were 42 TCM syndromes after breast cancer surgery, of which the highest frequency was liver depression and spleen deficiency, qi deficiency and blood stasis, qi stagnation and blood stasis, qi and blood deficiency, qi and yin deficiency, phlegm and blood stasis. A total of 416 kinds of traditional Chinese medicine were involved. High-frequency drugs included angelica sinensis, coix seed, bupleurum, ginger magnolia bark, keel, oyster, astragalus, platycodon grandiflorum, antler frost, vinegar tortoise shell, poria cocos, lily, Jianqu, Ophiopogon japonicus (Maidong), Shancigu, etc. A total of 18 pairs of commonly used drug combinations were excavated, such as Fushen-Gancao-Chaihu-Angelica, Huangqi-Baishao-Jianghoupu, Chaihu-Huanhua-Maidong-Lily, Baizhu-Huangqi-Maidong, Fuling-Baishao, etc. The clinical syndrome type of traditional Chinese medicine after breast cancer surgery is mainly liver depression and spleen deficiency syndrome. The clinical treatment is mainly soothing liver and relieving depression, and harmonizing liver and spleen. Analyze the syndrome type and the corresponding drug compatibility law, and provide decision support for the clinical dialectical prescription of traditional Chinese medicine after breast cancer surgery.

Osteosarcoma Explorer: A Data Commons With Clinical, Genomic, Protein, and Tissue Imaging Data for Osteosarcoma Research.

PURPOSE: Osteosarcoma research advancement requires enhanced data integration across different modalities and sources. Current osteosarcoma research, encompassing clinical, genomic, protein, and tissue imaging data, is hindered by the siloed landscape of data generation and storage. MATERIALS AND METHODS: Clinical, molecular profiling, and tissue imaging data for 573 patients with pediatric osteosarcoma were collected from four public and institutional sources. A common data model incorporating standardized terminology was created to facilitate the transformation, integration, and load of source data into a relational database. On the basis of this database, a data commons accompanied by a user-friendly web portal was developed, enabling various data exploration and analytics functions. RESULTS: The Osteosarcoma Explorer (OSE) was released to the public in 2021. Leveraging a comprehensive and harmonized data set on the backend, the OSE offers a wide range of functions, including Cohort Discovery, Patient Dashboard, Image Visualization, and Online Analysis. Since its initial release, the OSE has experienced an increasing utilization by the osteosarcoma research community and provided solid, continuous user support. To our knowledge, the OSE is the largest (N = 573) and most comprehensive research data commons for pediatric osteosarcoma, a rare disease. This project demonstrates an effective framework for data integration and data commons development that can be readily applied to other projects sharing similar goals. CONCLUSION: The OSE offers an online exploration and analysis platform for integrated clinical, molecular profiling, and tissue imaging data of osteosarcoma. Its underlying data model, database, and web framework support continuous expansion onto new data modalities and sources.

The Trajectory of Pediatric Cancer Data and Collection in the United States.

The past several years have been marked by substantial growth in pediatric cancer data and collection across the world. In the United States, multiple projects and standard setters have laid a foundation for the growth of this data, and the need for an overview and explanation of a few of the programs directly relevant to cancer registrars has become apparent. This article will discuss 3 initiatives that highlight many of the efforts and intricacies involved with the collection of pediatric cancer data in the cancer registry world: the National Childhood Cancer Registry, the Toronto Pediatric Cancer Stage Guidelines, and the Pediatric Site-Specific Data Items Work Group.

Building Data Infrastructure for Disease-Focused Health Economics Research.

BACKGROUND: Data infrastructure for cancer research is centered on registries that are often augmented with payer or hospital discharge databases, but these linkages are limited. A recent alternative in some states is to augment registry data with All-Payer Claims Databases (APCDs). These linkages capture patient-centered economic outcomes, including those driven by insurance and influence health equity, and can serve as a prototype for health economics research. OBJECTIVES: To describe and assess the utility of a linkage between the Colorado APCD and Colorado Central Cancer Registry (CCCR) data for 2012-2017. RESEARCH DESIGN, PARTICIPANTS, AND MEASURES: This cohort study of 91,883 insured patients evaluated the Colorado APCD-CCCR linkage on its suitability to assess demographics, area-level data, insurance, and out-of-pocket expenses 3 and 6 months after cancer diagnosis. RESULTS: The linkage had high validity, with over 90% of patients in the CCCR linked to the APCD, but gaps in APCD health plans limited available claims at diagnosis. We highlight the advantages of the CCCR-APCD, such as granular race and ethnicity classification, area-level data, the ability to capture supplemental plans, medical and pharmacy out-of-pocket expenses, and transitions in insurance plans. CONCLUSIONS: Linked data between registries and APCDs can be a cornerstone of a robust data infrastructure and spur innovations in health economics research on cost, quality, and outcomes. A larger infrastructure could comprise a network of state APCDs that maintain linkages for research and surveillance.

RUBic: rapid unsupervised biclustering.

Biclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein-protein interactions and gene expressions. However, for robust performance in recently emerging large health datasets, it is important for new biclustering algorithms to be scalable and fast. We present a rapid unsupervised biclustering (RUBic) algorithm that achieves this objective with a novel encoding and search strategy. RUBic significantly reduces the computational overhead on both synthetic and experimental datasets shows significant computational benefits, with respect to several state-of-the-art biclustering algorithms. In 100 synthetic binary datasets, our method took [Formula: see text] s to extract 494,872 biclusters. In the human PPI database of size [Formula: see text], our method generates 1840 biclusters in [Formula: see text] s. On a central nervous system embryonic tumor gene expression dataset of size 712,940, our algorithm takes   101 min to produce 747,069 biclusters, while the recent competing algorithms take significantly more time to produce the same result. RUBic is also evaluated on five different gene expression datasets and shows significant speed-up in execution time with respect to existing approaches to extract significant KEGG-enriched bi-clustering. RUBic can operate on two modes, base and flex, where base mode generates maximal biclusters and flex mode generates less number of clusters and faster based on their biological significance with respect to KEGG pathways. The code is available at ( https://github.com/CMATERJU-BIOINFO/RUBic ) for academic use only.

Effective data quality management for electronic medical record data using SMART DATA.

OBJECTIVES: In the medical field, we face many challenges, including the high cost of data collection and processing, difficult standards issues, and complex preprocessing techniques. It is necessary to establish an objective and systematic data quality management system that ensures data reliability, mitigates risks caused by incorrect data, reduces data management costs, and increases data utilization. We introduce the concept of SMART data in a data quality management system and conducted a case study using real-world data on colorectal cancer. METHODS: We defined the data quality management system from three aspects (Construction - Operation - Utilization) based on the life cycle of medical data. Based on this, we proposed the "SMART DATA" concept and tested it on colorectal cancer data, which is actual real-world data. RESULTS: We define "SMART DATA" as systematized, high-quality data collected based on the life cycle of data construction, operation, and utilization through quality control activities for medical data. In this study, we selected a scenario using data on colorectal cancer patients from a single medical institution provided by the Clinical Oncology Network (CONNECT). As SMART DATA, we curated 1,724 learning data and 27 Clinically Critical Set (CCS) data for colorectal cancer prediction. These datasets contributed to the development and fine-tuning of the colorectal cancer prediction model, and it was determined that CCS cases had unique characteristics and patterns that warranted additional clinical review and consideration in the context of colorectal cancer prediction. CONCLUSIONS: In this study, we conducted primary research to develop a medical data quality management system. This will standardize medical data extraction and quality control methods and increase the utilization of medical data. Ultimately, we aim to provide an opportunity to develop a medical data quality management methodology and contribute to the establishment of a medical data quality management system.

The Importance of Quality Assurance in Radiation Oncology Clinical Trials.

Clinical trials have been the center of progress in modern medicine. In oncology, we are fortunate to have a structure in place through the National Clinical Trials Network (NCTN). The NCTN provides the infrastructure and a forum for scientific discussion to develop clinical concepts for trial design. The NCTN also provides a network group structure to administer trials for successful trial management and outcome analyses. There are many important aspects to trial design and conduct. Modern trials need to ensure appropriate trial conduct and secure data management processes. Of equal importance is the quality assurance of a clinical trial. If progress is to be made in oncology clinical medicine, investigators and patient care providers of service need to feel secure that trial data is complete, accurate, and well-controlled in order to be confident in trial analysis and move trial outcome results into daily practice. As our technology has matured, so has our need to apply technology in a uniform manner for appropriate interpretation of trial outcomes. In this article, we review the importance of quality assurance in clinical trials involving radiation therapy. We will include important aspects of institution and investigator credentialing for participation as well as ongoing processes to ensure that each trial is being managed in a compliant manner. We will provide examples of the importance of complete datasets to ensure study interpretation. We will describe how successful strategies for quality assurance in the past will support new initiatives moving forward.