Standardized Coding Research Articles

Vehicle log automation and accessibility in cloud computings

Traditionally, the so-called Automotive Embedded Computing has been applied for safety purposes. Subsequently, with the advent of entertainment technologies and the Internet, Automotive Embedded Computing started to be applied in a wide range of functionalities. Among the advances in this area is the On-Board Diagnostic Interface – OBD, whose main functionality is to take the information from the Electronic Control Units – ECU to the external environment. Initially, this interface allowed the identification of problems, based on standardized error codes, only for external devices. Later, with the advancement of other technologies, such as Smartphones and Cloud Computing, new applications for data collected through the OBD interface emerged. In this context, the present research was developed considering the current trends of Automotive Embedded Computing and the importance and advances in the use of On-Board Diagnosis. The objectives of this research are to obtain codes of anomalies, collected in the Electronic Control Units – ECU of the vehicle, through the OBD interface, its storage in a cloud database, and eventual consultations and subsequent analyses. Thus, a solution consisting of four applications and a database was developed to test the feasibility of exploring a large set of operational vehicle data, storing them in the cloud, and which could be used for different purposes, but above all, would allow a fast and consistent evolution of new vehicle designs. After the development of the software suite, several tests were carried out, and the solution proved viable for the fulfillment of the proposed objective.

The Journey to a FAIR CORE DATA SET for Diabetes Research in Germany

The German Center for Diabetes Research (DZD) established a core data set (CDS) of clinical parameters relevant for diabetes research in 2021. The CDS is central to the design of current and future DZD studies. Here, we describe the process and outcomes of FAIRifying the initial version of the CDS. We first did a baseline evaluation of the FAIRness using the FAIR Data Maturity Model. The FAIRification process and the results of this assessment led us to convert the CDS into the recommended format for spreadsheets, annotating the parameters with standardized medical codes, licensing the data set, enriching the data set with metadata, and indexing the metadata. The FAIRified version of the CDS is more suitable for data sharing in diabetes research across DZD sites and beyond. It contributes to the reusability of health research studies.

Implementation of a Standardized Tool for Root Cause Analysis Selection.

This study sought to determine if a standardized root cause analysis (RCA2) selection algorithm, developed by the Veterans Affairs, would select high-risk events for RCA. Physician-entered incident reports for all surgical service admissions and perioperative visits were queried over 12 months in the DATIX Clinical Incident Management System. Independent reviewers assigned potential harm and event frequency scores using an institutional scoring system and then calculated and rounded average scores. These were classified using RCA2 terminology (catastrophic, major, moderate, minor for harm; frequent, occasional, uncommon, remote for frequency). The scores were then evaluated with the standardized Safety Assessment Code Matrix (SAC) algorithm from the National Patient Safety Foundation's RCA2 guidelines to determine Potential Harm Scores. The SAC combines severity and probability to determine the necessity of conducting an RCA. Catastrophic and major high-frequency events (matrix score = 3) were classified as "RCA recommended." The study then compared cases selected for RCAs using the updated RCA2 algorithm against cases selected using a current, institutional-specific RCA selection process. One hundred four cases were reviewed, comprising 20 catastrophic, 48 major harm, 26 moderate harm, and 10 minor harm events. After removing 9 high-variance cases, our institution's current selection process selected 18 cases for RCAs, including 6/20 catastrophic, 8/39 major harm, and 4/36 moderate/minor harm events. Only 17.3% of cases had an RCA completed, while the standardized RCA2 algorithm recommended investigation for 56.7% of patient safety events, based on SAC Matrix scoring. Current RCA selection processes rendered 4 RCAs on low potential harm or low-frequency events, while 45 potential high-frequency, high potential harm events did not complete RCAs. Standardizing the selection of patient safety incidents for RCA using the RCA2 algorithm improves case identification based on the event frequency and potential harm score. Thus, this algorithm has the potential to advance patient safety.

On the development and validation of large language model-based classifiers for identifying social determinants of health

The assessment of social determinants of health (SDoH) within healthcare systems is crucial for comprehensive patient care and addressing health disparities. Current challenges arise from the limited inclusion of structured SDoH information within electronic health record (EHR) systems, often due to the lack of standardized diagnosis codes. This study delves into the transformative potential of large language models (LLM) to overcome these challenges. LLM-based classifiers-using Bidirectional Encoder Representations from Transformers (BERT) and A Robustly Optimized BERT Pretraining Approach (RoBERTa)-were developed for SDoH concepts, including homelessness, food insecurity, and domestic violence, using synthetic training datasets generated by generative pre-trained transformers combined with authentic clinical notes. Models were then validated on separate datasets: Medical Information Mart for Intensive Care-III and our institutional EHR data. When training the model with a combination of synthetic and authentic notes, validation on our institutional dataset yielded an area under the receiver operating characteristics curve of 0.78 for detecting homelessness, 0.72 for detecting food insecurity, and 0.83 for detecting domestic violence. This study underscores the potential of LLMs in extracting SDoH information from clinical text. Automated detection of SDoH may be instrumental for healthcare providers in identifying at-risk patients, guiding targeted interventions, and contributing to population health initiatives aimed at mitigating disparities.

Ensemble neural models for ICD code prediction using unstructured and structured healthcare data

Disease coding is the process of assigning one or more standardized diagnostic codes to clinical notes that are maintained by health practitioners (e.g. clinicians) to track patient condition. Such a coding process is often expensive and error-prone, as human medical coders primarily perform it. Automating diagnostic coding using Artificial Intelligence is seen as an essential solution in Hospital Information Management Systems and approaches built on Convolutional Neural Networks currently perform best. In this work, a neural model built on unstructured clinical text for enabling automatic diagnostic coding for given patient discharge summaries is proposed. We incorporate a structured self-attention mechanism designed to boost learning of label-specific vectors and the significant clinical text snippets associated with a certain label for this purpose. These vectors are then combined with a novel code description pipeline leveraging the descriptions provided for each standardized diagnostic code. The proposed model achieved best performance in terms of standard metrics over the MIMIC-III dataset, outperforming models based on Longformers and Knowledge graphs. Furthermore, to leverage structured clinical data to enhance the proposed model, and to enable improved diagnostic code prediction, model ensembling is considered. A neural model built on structured data by leveraging supervised machine learning algorithms such as random forest and boosting, is designed for multi-class code classification. Experimental results revealed that the proposed ensemble models show promising performance compared to traditional models that rely solely on unstructured or structured clinical data, emphasizing their suitability for real-world deployment.

ICDXML: enhancing ICD coding with probabilistic label trees and dynamic semantic representations

Accurately assigning standardized diagnosis and procedure codes from clinical text is crucial for healthcare applications. However, this remains challenging due to the complexity of medical language. This paper proposes a novel model that incorporates extreme multi-label classification tasks to enhance International Classification of Diseases (ICD) coding. The model utilizes deformable convolutional neural networks to fuse representations from hidden layer outputs of pre-trained language models and external medical knowledge embeddings fused using a multimodal approach to provide rich semantic encodings for each code. A probabilistic label tree is constructed based on the hierarchical structure existing in ICD labels to incorporate ontological relationships between ICD codes and enable structured output prediction. Experiments on medical code prediction on the MIMIC-III database demonstrate competitive performance, highlighting the benefits of this technique for robust clinical code assignment.

Machine Learning-Based Methods for Code Smell Detection: A Survey

Code smells are early warning signs of potential issues in software quality. Various techniques are used in code smell detection, including the Bayesian approach, rule-based automatic antipattern detection, antipattern identification utilizing B-splines, Support Vector Machine direct, SMURF (Support Vector Machines for design smell detection using relevant feedback), and immune-based detection strategy. Machine learning (ML) has taken a great stride in this area. This study includes relevant studies applying ML algorithms from 2005 to 2024 in a comprehensive manner for the survey to provide insight regarding code smell, ML algorithms frequently applied, and software metrics. Forty-two pertinent studies allow us to assess the efficacy of ML algorithms on selected datasets. After evaluating various studies based on open-source and project datasets, this study evaluated additional threats and obstacles to code smell detection, such as the lack of standardized code smell definitions, the difficulty of feature selection, and the challenges of handling large-scale datasets. The current studies only considered a few factors in identifying code smells, while in this study, several potential contributing factors to code smells are included. Several ML algorithms are examined, and various approaches, datasets, dataset languages, and software metrics are presented. This study provides the potential of ML algorithms to produce better results and fills a gap in the body of knowledge by providing class-wise distributions of the ML algorithms. Support Vector Machine, J48, Naive Bayes, and Random Forest models are the most common for detecting code smells. Researchers can find this study helpful in better anticipating and taking care of software development design and implementation issues. The findings from this study, which highlight the practical implications of ML algorithms in software quality improvement, will help software engineers fix problems during software design and development to ensure software quality.

Geopolymer Cement in Pavement Applications: Bridging Sustainability and Performance

Sustainability and the quest for a more robust construction material cannot be divorced from each other. While Portland cement has revolutionized the construction sector, its environmental toll, particularly in greenhouse gas emissions and global warming, cannot be ignored. Addressing this dilemma requires embracing alternatives like geopolymer cement/geopolymer binder (GPC/GPB). Over the last few decades, considerable strides have been achieved in advancing GPC as a sustainable construction material, including its utilization in pavement construction. Despite these advances, gaps still exist in GPC optimal potential in pavement construction, as most studies have concentrated on specific attributes rather than on a comprehensive evaluation. To bridge this gap, this review adopts a novel, holistic approach by integrating environmental impacts with performance metrics. To set the stage, this review first delves into the geopolymer concept from a chemistry perspective, providing an essential broad overview for exploring GPC’s innovations and implications in pavement applications. The findings reveal that GPC not only significantly reduces greenhouse gas emissions and energy consumption compared to Portland cement but also enhances pavement performance. Further, GPC concrete pavement exhibits superior mechanical, durability, and thermal properties to ensure its long-term performance in pavement applications. However, challenges to GPC utilization as a pavement material include the variability of raw materials, the need for suitable hardeners, the lack of standardized codes and procedures, cost competitiveness, and limited field data. Despite these challenges, the process of geopolymerization presents GPC as a sustainable material for pavement construction, aligning with Sustainable Development Goals (SDGs) 3, 9, 11, and 12.

Denial of reproductive potential: a predictor of unperceived pregnancy in an Austrian neonaticide sample.

This study aims to describe the phenomenon of unperceived pregnancy followed by neonaticide with a focus on the lack of awareness of reproductive potential in an Austrian sample. An explorative comparative study of neonaticide cases with single and repeat perpetrators was conducted using nationwide register-based data from 1995 to 2017. A total number of 55 cases out of 66 were included in the analysis. A standardized coding sheet was used and calculations were performed. 48 women gave birth to 101 children, of which 55 were killed, 23 children lived out of home care and 23 lived with the perpetrator We found a higher fertility rate in both neonaticide perpetrators in the single (1,9) and the repeat group (4,25) in comparison to the general population (1,4). The use of contraception was only 31% among neonaticide perpetrators, deviating substantially from the general Austrian population age group (16-29yrs) which used contraception in 91%. The neonaticide perpetrators used an effective contraception method (pearl-index < 4) in only 2%, whereas 20% of the general population did so. The number of unperceived pregnancies was high in both groups (50/55) 91%. Future case reports and forensic evaluations should take reproductive behavior into account, as it may offer valuable insights into the events leading up to neonaticide. Our findings suggest that denial of reproductive potential often precedes unperceived pregnancies. In the Austrian cohort, women who experienced unperceived pregnancies resulting in unassisted births and subsequent neonaticide showed a low prevalence of contraceptive use. This is particularly noteworthy given that the primary motive for neonaticide is unwanted pregnancy.

Real-world insights on anticoagulation therapies in non-valvular atrial fibrillation: a comprehensive electronic health record-based study using natural language processing and machine learning

Abstract Background Limited evidence exists regarding real-world practices and outcomes of oral anticoagulation in patients with Non-Valvular Atrial Fibrillation (NVAF). Purpose To conduct a retrospective real-world data (RWD) study using Natural Language Processing (NLP) and Machine Learning (ML) on Electronic Health Records (EHRs) to evaluate the efficacy and safety of Direct Oral Anticoagulants (DOACs) and vitamin K antagonists (VKAs) in NVAF patients. Methods Retrospective study in 15 Spanish hospitals between 2014-2020 including adult NVAF patients treated with oral anticoagulants without anticoagulation history, recent thrombotic events, rheumatic mitral valvular heart disease, mitral valve stenosis, venous thromboembolism, nor pregnancy. We employed EHRead®, a free text analysis engine utilizing NLP and ML to extract RWD from EHRs based in SNOMED CT (standardized coding system). Free-text information was extracted and organized in an anonymized database for population-based descriptive and comparative analysis. After matching patients using propensity-score matching (PSM), we used Kaplan-Meier curves to compare VKA vs DOAC efficacy on thrombotic prevention, safety, and mortality. Results After evaluating 138,773,332 EHRs from 4.6 million patients, 44,292 NVAF patients were included; 35,267 (79.6%) treated with VKA and 9,025 (20.4%) with DOAC. Half of them were men (53.0%) with a mean (SD) age of 76.3 (12.7) years. VKA-treated patients tended to be older, with more cardiovascular comorbidities and higher CHA2DS2-VASc scores than DOACs-treated patients (Table 1). Using PSM, 8,930 patients were matched per subgroup with standardized mean differences below 10% for all confounders. The comparative analysis showed that DOAC-treated patients had a significant lower risk of thrombotic, minor bleeding events, and mortality than VKA-treated ones, with no significant differences in major bleeding (Figure 1). Conclusion The application of NLP and ML allowed us to create an extensive, plausible, and detailed database of Spanish NVAF patients from RWD in EHRs. In our cohort, we found a higher efficacy and lower mortality and minor bleeding events with DOACs over VKAs, without differences in major bleeding events. This study was sponsored by Pfizer and Bristol Myers Squibb. JCS, MA &amp; CS were paid consultants to Pfizer and Bristol Myers Squibb in connection with the development of this abstract. CAC &amp; LMS are employes of the SAVANA who were paid consultants to Pfizer and Bristol Myers Squibb in connection with the development of this abstract. DA &amp; SFC are employees of Pfizer.Table 1Figure 1

A COMPREHENSIVE REVIEW OF LIFE CYCLE ASSESSMENT AND ENERGY EFFICIENCY IN 3D PRINTING FOR CONSTRUCTION: CURRENT STATE, BENEFITS, LIMITATIONS, AND FUTURE OUTLOOK

ABSTRACT The agenda of Industry 4.0 strongly affects design and construction at all its phases, and three-Dimensional Printing (3DP) is an essential part of it. The emerging technology has the potential to become a more valid and accepted form of construction. This research is based on a literature review regarding the relationships between the concepts of Life Cycle Assessment (LCA) and energy efficiency for 3DP in construction research to understand the developments. Systematic bibliometric and scientometric analyses are used as data analysis techniques to make a detailed comparison. The comparison allowed for assessing and determining the current state, benefits, limitations, and future outlook. Current research is based on insights examining materials first (80%), followed by walls (15%), buildings (11%), and 3DP technology (6%). The findings highlight that 3DP technology offers significant advantages in terms of time efficiency, reliability, ecological impact, and sustainability within the construction industry. However, several challenges, such as the complexity of material mixture content design, the lack of standardized 3DP materials and codes, and the limited availability of experts in the field, prevent its widespread adoption. Further advancement of 3DP requires the development of standards, policies, training and materials for its full implementation in the construction industry.

INTERPRETABLE AND GENERALIZABLE HTS CODE CLASSIFICATION FRAMEWORK

The automation of Harmonized Tariff Schedule (HTS) code classification - a crucial task in international trade that entails assigning standardized codes to goods for tariff and regulatory purposes - is examined in this paper using GPT-3, a cutting-edge Large Language Model (LLM). The focus on creative prompt-engineering techniques and the usage a frozen model approach eliminates the need for further model fine-tuning. The suggested approach makes use of GPT-3’s built-in capabilities to decipher intricate product descriptions and reliably assign them to the appropriate HTS categories. We show through an array of experiments that strategic prompt adjustments including few-shot prompting allowed the model's initial top-1 accuracy of 23 percent to be significantly increased to an average of 73 percent. The main strengths of the approach are the explainability for the chosen category as well as the fully generalizable nature of the methodology.

The Evidence Project: Protocol for Systematic Reviews of Behavioral Interventions and Behavioral Aspects of Biomedical Interventions for HIV Prevention, Treatment, and Health Service Delivery in Low- and Middle-Income Countries.

The Evidence Project conducts systematic reviews and meta-analyses of HIV behavioral interventions, behavioral aspects of biomedical interventions, combination prevention strategies, modes of service delivery, and integrated programs in low- and middle-income countries. Here, we present the overall protocol for our reviews. For each topic, we conduct a comprehensive search of five online databases, complemented by secondary reference searching. Articles are included if they are published in peer-reviewed journals and present pre/post or multi-arm data on outcomes of interest. Data are extracted from each included article by two trained coders working independently using standardized coding forms, with differences resolved by consensus. Risk of bias is assessed with the Evidence Project tool. Data are synthesized descriptively, and meta-analysis is conducted when there are similarly measured outcomes across studies. For over 20 years, this approach has allowed us to synthesize literature on the effectiveness of interventions and contribute to the global HIV response.

Playing with the child within: Early parenthood experiences of parents who were out-of-home placed during childhood

Experience of out-of-home care (OHC) is associated with life-long vulnerability, and research indicates that parents with experiences of OHC are at higher risk of having their own children placed in out-of-home care. However, research on these parents is limited and lacks a resilience perspective. Here, we focused on parents with OHC experience as children but with custody of their own children (N = 12) and asked where these parents can draw strength in parenting, using a standardized interview with focus on thoughts, feelings, and parenting experiences. Importantly, all content related to OHC experience was disclosed in the context of discussing parental caregiving, without any specific questions asked. We investigated whether caregiving representations associated with the ability to promote security in the child were linked to parents’ attachment scripts, comprising implicit expectations of sensitivity and helpfulness in attachment-related interactions, or reflective functioning, denoting the capacity to take the mental perspective of the child. We coded interviews following standardized coding instructions, for reflective thinking and other important variables that capture strategies for processing relational and emotional themes. Narratives in which the parents had spontaneously made references to their childhood experiences were coded through qualitative content analysis. Results showed that autonomous caregiving representations were associated with the content in parents’ attachment scripts but not their reflective functioning. Themes reveal distinct tendencies in these parents’ caregiving representations: Own adverse childhood experiences were often expressed as separation anxiety or feelings of guilt in relation to parenting. An unmet need for support was common , as were concerns about stigma and vulnerability associated with disseminating information about their background. Nevertheless, parental caregiving involves for those parents motivation to break patterns of adversity, provide sensitive parenting, but also understand, accept oneself and develop.

Leveraging machine learning and NLP for enhanced cohorting and RxNorm mapping in Electronic Health Records (EHRs)

This work addresses the combination of machine learning (ML) and natural language processing (NLP) approaches to optimize the process of courting and RxNorm mapping inside Electronic Health Records (EHRs). Cohorting patients based on comparable traits or diseases is vital for clinical research, but it generally depends on time-consuming manual techniques and is prone to mistakes. Similarly, mapping pharmaceutical names to standardized codes such as RxNorm promotes interoperability and data analysis but may be challenging owing to variances in how drugs are reported. Leveraging ML and NLP may automate and optimize these procedures, leading to more efficient cohort identification and precise medication mapping. We offer a thorough technique for integrating ML and NLP algorithms in EHR systems, including data preparation, feature engineering, model training, and assessment. Through testing and analysis, we show the usefulness of our technique in enhancing cohorting accuracy and RxNorm mapping precision. The findings underline the promise of ML and NLP in revolutionizing EHR data management, leading to improved patient care and simplified research procedures.

Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial.

Computer-assisted clinical coding (CAC) tools are designed to help clinical coders assign standardized codes, such as the ICD-10 (International Statistical Classification of Diseases, Tenth Revision), to clinical texts, such as discharge summaries. Maintaining the integrity of these standardized codes is important both for the functioning of health systems and for ensuring data used for secondary purposes are of high quality. Clinical coding is an error-prone cumbersome task, and the complexity of modern classification systems such as the ICD-11 (International Classification of Diseases, Eleventh Revision) presents significant barriers to implementation. To date, there have only been a few user studies; therefore, our understanding is still limited regarding the role CAC systems can play in reducing the burden of coding and improving the overall quality of coding. The objective of the user study is to generate both qualitative and quantitative data for measuring the usefulness of a CAC system, Easy-ICD, that was developed for recommending ICD-10 codes. Specifically, our goal is to assess whether our tool can reduce the burden on clinical coders and also improve coding quality. The user study is based on a crossover randomized controlled trial study design, where we measure the performance of clinical coders when they use our CAC tool versus when they do not. Performance is measured by the time it takes them to assign codes to both simple and complex clinical texts as well as the coding quality, that is, the accuracy of code assignment. We expect the study to provide us with a measurement of the effectiveness of the CAC system compared to manual coding processes, both in terms of time use and coding quality. Positive outcomes from this study will imply that CAC tools hold the potential to reduce the burden on health care staff and will have major implications for the adoption of artificial intelligence-based CAC innovations to improve coding practice. Expected results to be published summer 2024. The planned user study promises a greater understanding of the impact CAC systems might have on clinical coding in real-life settings, especially with regard to coding time and quality. Further, the study may add new insights on how to meaningfully exploit current clinical text mining capabilities, with a view to reducing the burden on clinical coders, thus lowering the barriers and paving a more sustainable path to the adoption of modern coding systems, such as the new ICD-11. clinicaltrials.gov NCT06286865; https://clinicaltrials.gov/study/NCT06286865. DERR1-10.2196/54593.

A nationwide epidemiological study of testicular torsion: Analysis of the Japanese National Database.

Testicular torsion is a severe urological emergency caused by the twisting of the spermatic cord. The nationwide incidence of testicular torsion in Japan has not been previously reported. Accordingly, we aimed to estimate the nationwide incidence of testicular torsion using the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) and examine the orchiectomy rate. This cross-sectional study was based on data from the NDB. We extracted data of patients aged < 21 years with documented testicular torsion and relevant treatment from January 2018 to December 2020. Testicular torsion was identified based on the Japanese standardized disease codes. The national incidence rate between 2018 and 2020 was calculated and assessed according to age and region of origin. Orchiectomy rates were evaluated according to age. The nationwide incidence rates of testicular torsion were 14.46, 15.09, and 15.88 per 100,000 males aged < 21 years in 2018, 2019, and 2020, respectively. The orchiectomy rate was 7.1%. Testicular torsion was most frequently observed during winter. A similar trend was observed nationwide. To the best of our knowledge, this study is the first to report the nationwide incidence of testicular torsion in Japan.

Technical language processing for Prognostics and Health Management: applying text similarity and topic modeling to maintenance work orders

AbstractModern manufacturing paradigms have incorporated Prognostics and Health Management (PHM) to implement data-driven methods for fault detection, failure prediction, and assessment of system health. The maintenance operation has similarly benefitted from these advancements, and predictive maintenance is now being used across the industry. Despite these developments, most of the approaches in maintenance rely on numerical data from sensors and field devices for any sort of analysis. Text data from Maintenance Work Orders (MWOs) contain some of the most crucial information pertaining to the functioning of systems and components, but are still regarded as ‘black holes’, i.e., they store valuable data without being used in decision-making. The analysis of this data can help save time and costs in maintenance. While Natural Language Processing (NLP) methods have been very successful in understanding and examining text data from non-technical sources, progress in the analysis of technical text data has been limited. Non-technical text data are usually structured and consist of standardized vocabularies allowing the use of out-of-the-box language processing methods in their analysis. On the other hand, records from MWOs are often semi-structured or unstructured; and consist of complicated terminologies, technical jargon, and industry-specific abbreviations. Deploying traditional NLP to such data can result in an imprecise and flawed analysis which can be very costly. Owing to these challenges, we propose a Technical Language Processing (TLP) framework for PHM. To illustrate its capabilities, we use text data from MWOs of aircraft to address two scenarios. First, we predict corrective actions for new maintenance problems by comparing them with existing problems using syntactic and semantic textual similarity matching and evaluate the results with cosine similarity scores. In the second scenario, we identify and extract the most dominant topics and salient terms from the data using Latent Dirichlet Allocation (LDA). Using the results, we are able to successfully link maintenance problems to standardized maintenance codes used in the aviation industry.

Patient-Centric Approach to Personalized Electronic Medical Records via QR Code in Japan.

Government policies in the United States and the European Union promote standardization and value creation in the use of FAIR (findability, accessibility, interoperability, and reusability) data, which can enhance trust in digital health systems and is crucial for their success. Trust is built through elements such as FAIR data access, interoperability, and improved communication, which are essential for fostering innovation in digital health technologies. This Viewpoint aims to report on exploratory research demonstrating the feasibility of testing a patient-centric data flow model facilitating semantic interoperability on precision medical information. In this global trend, the interoperable interface called Sync for Science-J (S4S-J) for linking electronic medical records (EMRs) and personal health records was launched as part of the Basic Policy for Economic and Fiscal Management and Reform in Japan. S4S-J controls data distribution consisting of EMR and patient-generated health data and converts this information into QR codes that can be scanned by mobile apps. This system facilitates data sharing based on personal information beliefs and unlocks siloed Internet of Things systems with a privacy preference manager. In line with Japanese information handling practices, the development of a mobile cloud network will lower barriers to entry and enable accelerated data sharing. To ensure cross-compatibility and compliance with future international data standardization, S4S-J conforms to the Health Level 7 Fast Health Care Interoperability Resources standard and uses the international standardized logical observation identifiers names and codes (LOINC) to redefine medical terms used in different terminology standards in different medical fields. It is developed as an applied standard in medical information intended for industry, health care services, and research through secondary use of data. A multicenter collaborative study was initiated to investigate the effectiveness of this system; this was a registered, multicenter, randomized controlled clinical trial, the EMBRACE study of the mobile health app M♡Link for hyperglycemic disorders in pregnancy, which implements an EMR-personal health record interoperable interface via S4S-J. Nevertheless, the aforementioned new challenges, the pivotal Health Level 7 Fast Health Care Interoperability Resources system, and LOINC data mapping were successfully implemented. Moreover, the preliminary input of EMR-integrated patient-generated health data was successfully shared between authorized medical facilities and health care providers in accordance with the patients' preferences. The patient-centric data flow of the S4S-J in Japan is expected to guarantee the right to data portability, which promotes the maximum benefit of use by patients themselves, which in turn contributes to the promotion of open science.

The role of multidisciplinary team meeting histopathology review and its impact on revised reports: Analysis of a national quality improvement program.

We conducted the first Irish national study assessing the value of multidisciplinary team meeting review in pathology practice and its impact on error detection before treatment. Public and private pathology laboratories across Ireland capture their quality activities using standardized codes and submit their data to a centralized database (National Quality Assurance Intelligence System) overseen by the National Histopathology Quality Improvement (NHQI) program. A total of 1,437,746 histopathology and cytopathology cases submitted to the NHQI program over a 60-month period (January 2017 to December 2021) were included in this study. Cases were analyzed with respect to multidisciplinary team meeting peer review and the presence of a revised report (amended or corrected report), a surrogate marker for error detection before treatment. Across all cases assessed, 13.74% (197,587) underwent multidisciplinary team meeting discussion. Cases discussed at review had a statistically significantly higher rate of revised reports (1.25% [2470]) than cases not discussed at review (0.16% [1959]) (Pearson χ2, 6619.26; P < .0001; odds ratio, 8.00 [95% CI, 7.54-8.49]). Overall, multidisciplinary team meeting review made it 8 times more likely to detect an error before treatment. Cancer resections had the highest rate of review at 55.29% (46,806), reflecting the prioritization of oncology case discussion at review meetings. The multidisciplinary team meeting review process plays a valuable role in pathology error detection. A pathologist's participation in the review process comes with a clinically significant workload that needs to be recognized for future workforce planning. This study highlighted the positive role pathologists play in enhancing patient safety.