Coding System Research Articles

Web-Based Nursing Care Documentation for Students to Support Online Learning

Nursing care is the most critical element in nursing services, which aims to improve the patient’s health status. Ineffective and inefficient care documentation can impact the quality of nursing services. However, the increasingly advanced development of technology provides freedom for the health world to improve the quality of patient-centered services. Educational institutions have also used information technology in learning process activities. As prospective health professionals, students must be equipped with competencies to support their performance. In this study, a web-based nursing care information system was developed to assist students in documenting care activities. The website application was designed to increase student competency in nursing documentation activities to provide high-quality nursing services even though learning is online. The waterfall model approach was used in application design. The design stage started with analyzing application requirements, followed by system design and coding. Next, using a Likert-scale questionnaire, a usability test was performed on 15 beta users to assess the functionality of the application. The results showed that the nursing care website application was easy to use and that students felt satisfied. It is hoped that the website application can be made more attractive by including a decisional support system to make it easier for students to enforce nursing diagnoses on patients.

Machine Learning Approach to Injury Monitoring in Children and Adolescents

Abstract Pediatric injuries are common and associated with individual suffering and costs for the healthcare systems and society. The incidence of injuries among children in Switzerland has so far been insufficiently monitored. Emergency department patient records can be a valuable database to assess medically relevant injuries and gain information for monitoring and evidence-based prevention. The talk will outline our stepwise development of a machine-learning program for a pediatric injury monitoring system and lessons learnt. In this feasibility study we draw on electronic case histories of children treated for injuries between 2018 and 2022 at the University Children’s Hospital Zürich emergency department (N= to 30’884). The ML- approach follows different steps: step 1. utility evaluation of electronic pediatric patient records regarding ML and injury monitoring and prevention requirements in a test-sample of 100 electronic pediatric case histories; step 2. choice/adaptation of coding tree; step 3. coder training and inter-coder reliability testing; step 4. manual annotation of a sub-sample (n 1000) of the electronic pediatric patient records, step 5. design and testing of the ML program in an iterative process, and step 6. performance testing in a random sample of pediatric patient records. First lessons learnt indicate that electronic case history data contain relevant data and fulfill requirements for monitoring and prevention purposes. The IDB coding system proved too complex and detailed for the pediatric data base, providing too few examples for the ML-learning process, and needed to be adapted. Initial data analyses indicate the majority of cases are classified as less severe (73%) and only few (<1%) as very severe but the later provide the most detailed data. ML is a promising approach and makes use of hospital data for a national monitoring of pediatric injuries in Switzerland.

Language Development Opportunities in Curricular and Trade Books: A Content Analysis

ABSTRACTThis content analysis explored children's fictional storybooks and informational science books to determine their differences in high‐quality text features that support oral language (OL) development. We also explored the opportunities for OL development in curricular books compared to trade books. Using systematic coding procedures, we evaluated the genre, vocabulary, syntactic variety, supportive visuals and cultural sensitivity in books from three kindergarten and Grade 1 reading curricula (n = 521) as well as a sample of commercially‐available trade books (n = 48). Descriptive information was averaged to determine the percentage of high‐quality text features included in each book type and genre. Results indicated that informational science trade books offered more opportunities for rich language experiences than their curricular counterparts or any of the fictional books. Thus, there is reason to believe that informational science trade books with high‐quality text features may promote OL development and enhance the learning of young students.

Assessment of the Choked Flow Model of RELAP5 for Application of Inverse Quantification Methods

In the framework of deterministic safety analysis, best estimate plus uncertainty methodologies can provide a more informative detailed analysis of transients than conservative approaches. However, one important step in the application of such methodologies is derivation of uncertainty of the physical models. Probability density functions of the physical model parameters are obtained by applying inverse uncertainty quantification (IUQ) methods to experimental data. The present work deals with evaluation of the experimental database and assessment of the simulation model, which are required steps prior to the application of IUQ methods. The U.S. Nuclear Regulatory Commission system code RELAP5 has been applied for simulation of three experimental databases on choked/critical flow: Sozzi-Sutherland, Super MobyDick, and Marviken Critical Flow Tests. First, the adequacy of the experimental data to the specific target domain is carried out, to then proceed to calibration of the input model parameters by combining the use of Gaussian Process metamodels and optimization schemes. The assessment of the simulation models is performed by evaluating independently accuracy, precision, and consistency through four statistical indicators, including a novel indicator to evaluate the consistency of the results. The final results indicate that the model is capable of reproducing the selected experimental database and overall average accuracy, precision, and consistency below the 10% threshold and can be used for application of IUQ methods.

A Smart QR: Based Library Management System

This research is important since libraries are becoming more technologically advanced. For thousands of people, libraries continue to be the main source of information, even in the age of digital information. This research goal is aimed to provide a smart, easy and user- friendly method for managing library materials using QR code system. Currently, the majority of library management systems need a lot of finance, resources, supervision, and human work. With the implementation of this Quick response (QR) code management system, lots of challenges and efforts is reduced drastically ranging from; money, time, energy and other resources. Users of libraries can access a single application that powers this management system. Users can use the system to issue, return, and manage library materials with little to no help from library staff, thanks to the QR code capability. The developed management system streamlines operations, maintains an audit trail, and increases user accessibility to the library.

Construction of new polygon mesh-type phantoms based on adult Japanese voxel phantoms.

To construct adult Japanese phantoms applicable to individual exposure dose assessments, we created adult Japanese polygon mesh-type male (JPM) and female (JPF) phantoms through modification of the adult Japanese voxel phantoms, JM-103 (male) and JF-103 (female). The body sizes and masses of organs, tissues, and organ contents in JPM and JPF were adjusted to the Japanese averages, except for those unimportant for radiation protection or risks. The JPM and JPF data were converted to tetrahedral mesh-type data and incorporated into the Particle and Heavy Ion Transport code System (PHITS) for dose calculations. The dosimetric characteristics of the JPM and JPF phantoms were validated by calculating their effective doses in the anterior-posterior geometry for the external irradiation of photons with energies of 0.01-20 MeV and compared with those of JM-103 and JF-103 or the reference values given in ICRP Publication 116. The results confirmed no problems applying JPM and JPF to dose assessments in adult Japanese subjects. Furthermore, it was found that JPM and JPF can also accurately calculate the absorbed doses for entire organs and high radiosensitive cell regions with thin, small, and complicated structures.

The Chilean COVID-19 Genomics Network Biorepository: A Resource for Multi-Omics Studies of COVID-19 and Long COVID in a Latin American Population

Although a lack of diversity in genetic studies is an acknowledged obstacle for personalized medicine and precision public health, Latin American populations remain particularly understudied despite their heterogeneity and mixed ancestry. This gap extends to COVID-19 despite its variability in susceptibility and clinical course, where ethnic background appears to influence disease severity, with non-Europeans facing higher hospitalization rates. In addition, access to high-quality samples and data is a critical issue for personalized and precision medicine, and it has become clear that the solution lies in biobanks. The creation of the Chilean COVID-19 Biorepository reported here addresses these gaps, representing the first nationwide multicentric Chilean initiative. It operates under rigorous biobanking standards and serves as one of South America’s largest COVID cohorts. A centralized harmonization strategy was chosen and included unified standard operating procedures, a sampling coding system, and biobanking staff training. Adults with confirmed SARS-CoV-2 infection provided broad informed consent. Samples were collected to preserve blood, plasma, buffy coat, and DNA. Quality controls included adherence to the standard preanalytical code, incident reporting, and DNA concentration and absorbance ratio 260/280 assessments. Detailed sociodemographic, health, medication, and preexisting condition data were gathered. In five months, 2262 participants were enrolled, pseudonymized, and sorted by disease severity. The average Amerindian ancestry considering all participant was 44.0% [SD 15.5%], and this value increased to 61.2% [SD 19.5%] among those who self-identified as Native South Americans. Notably, 279 participants self-identified with one of 12 ethnic groups. High compliance (>90%) in all assessed quality controls was achieved. Looking ahead, our team founded the COVID-19 Genomics Network (C19-GenoNet) focused on identifying genetic factors influencing SARS-CoV-2 outcomes. In conclusion, this bottom-up collaborative effort aims to promote the integration of Latin American populations into global genetic research and welcomes collaborations supporting this endeavor. Interested parties are invited to explore collaboration opportunities through our catalog, accessible online.

Multi-feature enhancement based on sparse networks for single-stage 3D object detection

In the field of autonomous driving, the accuracy and real-time requirements for 3D object detection technology continue to improve, which is directly related to the commercialization process and market popularity of autonomous vehicles. Despite the efficiency of pillar-based coding for onboard systems, it falls short in terms of accuracy and the reduction of incorrect positives. In this paper, we will examine how to solve the problem of high incorrect rate and low accuracy of existing methods. Firstly, a MAP coding module is introduced to optimize previous point cloud feature coding modules, allowing for the efficient extraction of fine-grained features from point cloud data. Then, we introduce an innovative sparse dual attention (SDA) to efficiently filter out irrelevant details in feature extraction, thereby improving the pertinence and efficiency of information extraction. Finally, to address the potential loss of information from single local feature extraction, a local and global fusion module (CTGC) is introduced. Our method has proactively demonstrated its efficiency and accuracy through rigorous experimentation across diverse datasets. Analysis of the results leads to the conclusion that our solutions provide accurate and robust detection results. Code will be available at https://github.com/lcy199905/MyOpenPCDet.git.

Fast Color Image Encryption Algorithm Based on DNA Coding and Multi-Chaotic Systems

At present, there is a growing emphasis on safeguarding image data, yet conventional encryption methods are full of numerous limitations. In order to tackle the limitations of conventional color image encryption methodologies, such as inefficiency and insufficient security, this paper designs an expedited encryption method for color images that uses DNA coding in conjunction with multiple chaotic systems. The encryption algorithm proposed in this paper is based on three-dimensional permutation, global scrambling, one-dimensional diffusion and DNA coding. First of all, the encryption algorithm uses three-dimensional permutation algorithms to scramble the image, which disrupts the high correlation among the image pixels. Second, the RSA algorithm and the SHA-256 hashing algorithm are utilized to derive the starting value necessary for the chaotic system to produce the key. Third, the image is encrypted by using global scrambling and one-dimensional diffusion. Finally, DNA coding rules are used to perform DNA computing. The experimental results indicate that the encryption scheme exhibits a relatively weak inter-pixel correlation, uniform histogram distribution, and an information entropy value approaching eight. This shows that the proposed algorithm is able to protect the image safely and efficiently.

SAM: A Modern System Code for Advanced Non-LWR Safety Analysis

The System Analysis Module (SAM), developed at Argonne National Laboratory and by collaborators at other organizations, is for advanced non–light water reactor safety analysis. SAM aims to provide fast-running, modest-fidelity, whole-plant transient analysis capabilities that are essential for fast-turnaround design scoping and engineering analyses of advanced reactor concepts. To facilitate code development, SAM utilizes the MOOSE object-oriented application framework, its underlying finite element library, and linear and nonlinear solvers to leverage modern advanced software environments and numerical methods. SAM aims to solve tightly coupled physical phenomena, including fission reaction, heat transfer, fluid dynamics, and thermal-mechanical responses in advanced reactor structures, systems, and components with high accuracy and efficiency. This paper gives an overview of the SAM code development, including goals and functional requirements, physical models, current capabilities, verification and validation, software quality assurance, and examples of simulations for advanced nuclear reactor applications.

Accounting Method and Indicators of Multilevel CO2 Emissions Based on Cost During Construction of Shield Tunnels

With the rapid expansion of shield tunnel projects in China, precise accounting of CO2 emissions throughout the entire process is essential for advancing green and low-carbon construction practices. This paper introduces an innovative CO2 accounting methodology utilizing a cost-based carbon emission coding system. It adopts a multilevel approach to carbon emission accounting, aligned with this coding system, which facilitates a detailed examination of carbon emission ratios and characteristics across various construction techniques. The analysis includes six typical shield tunnel projects varying in diameter, focusing on sub-projects to scrutinize CO2 emissions and establish specific indicators. The findings indicate that CO2 emissions per 10,000 yuan of investment, approximately 30.25% (or 3025 kgCO2e/10,000 yuan), are more consistent than those per unit of length. Moreover, the study highlights differing CO2 emission trends among sub-projects compared to whole tunnel projects, assessing emission indicators and distribution patterns in four sub-projects: shield excavation, segmental lining, internal structure, and tunnel reinforcement. From these findings, the paper suggests more precise and tailored strategies for CO2 reduction. This research provides a theoretical basis for future construction planning and carbon management strategies.

Communication Apprehension and Coping Strategies of the English Language Learners: Basis for Interactive Activities Enrichment

Communication apprehension is an uneasiness experienced by all individuals, especially when speaking. Meanwhile, coping strategies are approaches people use in dealing with anxiety. This study determined the level of communication apprehension and coping strategies of the BSED-English major students of the College of Education (CED), Jose Rizal Memorial State University-Tampilisan Campus. The researchers used the descriptive survey method utilizing the survey-questionnaire checklists. The first instrument was adopted from McCroskey's (1981) cited in Diane (2015) Personal Report of Communication Apprehension 24 (PRCA-24). The other one is the validated researchers-made survey statements comprising 30-item statements categorized as strategies for overcoming communication apprehension in group discussions, meetings, interpersonal conversation, and public speaking, with a Cronbach's alpha value of >0.70. The researchers also adopted McCroskey and Richmond's coding and scoring system, weighted mean, and frequency count. Results show that most of the English major students in JRMSU-TC have a moderate level of communication apprehension. Further, there is a difference in the level of communication apprehension when grouped according to group discussion, meetings, interpersonal communication, and public speaking, as well as in the overall weighted mean. Therefore, there is a difference in the level of communication apprehension of the BSED English major students when grouped according to year level. In terms of coping strategies, students used the following strategies: accepting cultural differences, paying attention when talking to others and considering their cultural views, thoroughly preparing before the speech, and thinking of intrinsic and extrinsic motivation as a reward. In this connection, there are proposed interactive activities that may help overcome the students' communication apprehension.

Verification of direct coupling code system using FRENDY version 2 and GENESIS for light water reactor lattices

ABSTRACT This study newly established a direct coupling code system consisting of the nuclear data processing code FRENDY version 2, and the three-dimensional heterogeneous transport code GENESIS (FRENDY-V2/GENESIS) for easy implementation of the random-sampling-based uncertainty quantification considering the implicit effect due to nuclear cross-section (XS) perturbations. The multi-group macroscopic XSs prepared for GENESIS were generated by FRENDY version 2, where the Dancoff factor was calculated by the neutron current method. Then the background XSs were evaluated based on the Carlvik two-term rational approximation. The infinite multiplication factor (k-infinity) and the fission reaction rate distribution in UO2 and MOX lattice geometries were compared with MVP3 to verify the calculation accuracy of FRENDY-V2/GENESIS. The sensitivity analyses on the discretization conditions such as the ray tracing of the method of characteristics were also carried out. Through several comparisons between FRENDY-V2/GENESIS and MVP3, FRENDY-V2/GENESIS with the SHEM 361-group structure calculates the k-infinity within approximately 50 pcm and the fission reaction rate distribution within approximately 0.1% by the root mean square, respectively. Consequently, the applicability of FRENDY-V2/GENESIS was verified, and FRENDY-V2/GENESIS can be used to discuss the implicit effect due to multi-group XS perturbations.

A new AI-assisted data standard accelerates interoperability in biomedical research.

In this paper, we leveraged Large Language Models(LLMs) to accelerate data wrangling and automate labor-intensive aspects of data discovery and harmonization. This work promotes interoperability standards and enhances data discovery, facilitating AI-readiness in biomedical science with the generation of Common Data Elements (CDEs) as key to harmonizing multiple datasets. Thirty-one studies, various ontologies, and medical coding systems served as source material to create CDEs from which available metadata and context was sent as an API request to 4th-generation OpenAI GPT models to populate each metadata field. A human-in-the-loop (HITL) approach was used to assess quality and accuracy of the generated CDEs. To regulate CDE generation, we employed ElasticSearch and HITL to avoid duplicate CDEs and instead, added them as potential aliases for existing CDEs. The generated CDEs are foundational to assess the interoperability potential of datasets by determining how many data set column headers can be correctly mapped to CDEs as well as quantifying compliance with permissible values and data types. Subject matter experts reviewed generated CDEs and determined that 94.0% of generated metadata fields did not require manual revisions. Data tables from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Global Parkinson's Genetic Program (GP2) were used as test cases for interoperability assessments. Column headers from all test cases were successfully mapped to generated CDEs at a rate of 32.4% via elastic search.The interoperability score, a metric for dataset compatibility to CDEs and other connected datasets, based on relevant criteria such as data field completeness and compliance with common harmonization standards averaged 53.8 out of 100 for test cases. With this project, we aim to automate the most tedious aspects of data harmonization, enhancing efficiency and scalability in biomedical research while decreasing activation energy for federated research.

African American Patients' Perspectives on Determinants of Hemodialysis Adherence and Use of Motivational Interviewing to Improve Hemodialysis Adherence.

Compared to White patients, African American (AA) patients have a four-fold higher prevalence of kidney failure and higher hemodialysis non-adherence. Adherence behaviors are influenced by psychosocial factors, including personal meaning of a behavior and self-confidence to enact it. We assessed perspectives of AA hemodialysis patients on unique factors impacting dialysis adherence, and use of motivational interviewing, an evidence-based intervention, to improve these factors, dialysis adherence, and outcomes in AAs. Self-identified AA hemodialysis patients (N=22) watched a brief video describing motivational interviewing and then completed a semi-structured interview or focus group session. Interview questions explored unique barriers and facilitators of hemodialysis adherence in AAs, and perceived utility of motivational interviewing to address these obstacles. Verbatim transcripts and an iterative inductive/deductive approach were used to develop a hierarchical coding system. Three experienced coders independently coded the same two transcripts. Coding was compared and discrepancies were reconciled by a fourth coder or consensus. Transcripts, quotations, and codes were managed using Microsoft Excel 2016 and SPSS version 28.0. Themes and sub-themes emerged and culminated in a novel conceptual model informed by three theoretical models of behavior change: Theory of Self-Care Management for Vulnerable Populations; Social Cognitive Theory; and Self Determination Theory. This conceptual model will inform the design of a culturally tailored, motivational interviewing-based intervention to improve dialysis adherence in AAs. Integrating AA hemodialysis patient perspectives is critical for enhancing dialysis care delivery and the design of effective interventions such as motivational interviewing to improve dialysis adherence in AA and promote kidney health equity. AA hemodialysis patients view motivational interviewing as a tool to clarify patient priorities, build trust, and promote patient-provider therapeutic alliance. Cultural tailoring of motivational interviewing to address unique barriers of AAs with kidney failure will improve adherence and health outcomes in these vulnerable patients.

Three Types of Uncertainty: A Qualitative Study of Family Medicine Residents.

Many past studies have focused on uncertainty in medical practice, yet it is still not well understood in the field of family medicine, especially among residents. The aim of this study was to examine situations in which residents experience uncertainty and the coping strategies they use to deal with it. The results may have implications for advanced training programs and the specialist training in family medicine. We conducted semistandardized interviews with 15 residents and young family doctors from Hesse, Germany and asked them to describe cases in which they experienced uncertainty. In total, 40 cases were reported. Using established methods, we derived a coding system with different categories. Uncertainty occurred in a broad and heterogenous range of cases, and it often involved complex interaction of biomedical, interpersonal, and psychosocial factors. The participants described various strategies that were helpful in dealing with the three different types of uncertainty. To deal with biomedical uncertainty, the residents primarily found information-seeking and consulting more experienced colleagues to be useful. In dealing with interpersonal and psychosocial uncertainty, they applied reflective strategies. Participants suggested open communication and honest dialogue about uncertainty and the thematization of the topic at much earlier stages (eg, during medical studies). Family medicine residents experience uncertainty as an important part of their daily work. They do not necessarily interpret it as a negative phenomenon. Instead, uncertain situations often accompany learning effectiveness and an increase of self-confidence.

The maternity dashboard is an effective tool for the improvement of maternal and neonatal care outcomes: a prospective observational study from Iraq

Background: The implementation of maternal dashboards in hospitals is both feasible and effective in improving healthcare quality. The dashboards provide rapid feedback on local outcomes in an accessible format and enable early detection of potential problems. They serve as a simple computerized tool that graphically presents the health facility’s clinical data, utilizing a traffic-light coding system so as to alert front-line staff about changes in the frequency of clinical outcomes. Aim: The aim of this study was to assess the effectiveness of the maternity dashboard in improving maternal health, reducing Neonatal Intensive Care Unit (NICU) admissions, as well as enhancing maternal and neonatal outcomes in an Iraqi hospital. Methodology: Data were collected over one year, from January 1st to December 30th, 2023. A prospective observational study (hospital-based record review) was conducted at the Obstetrics and Gynaecology Teaching Hospital in Kerbala, Iraq. Data were obtained from the medical records of all patients admitted to the obstetrical ward for labor, delivery, and the postpartum period during 2023. The maternal dashboard, a traffic light program (green, amber, red), was used in order to monitor outcomes. Data were collected manually from the outpatient department, the labor ward, the NICU registers, and the electronic records by using Excel. Results: A total of 136,519 women (who were either pregnant, in labor, or postpartum for up to 42 days) were admitted to the Obstetrics and Gynaecology Teaching Hospital in Kerbala during the study period, with 18,791 of them delivering. The implementation of the maternity dashboard resulted in significant changes: the normal vaginal delivery rate increased from 66% to 68% (p=0.0024), the caesarean section rate decreased from 33% to 31% (p=0.0001), the stillbirth monthly cases declined from 16±5 to 12±3 (p<0.05), the NICU admission monthly instances decreased from 96±14 to 71±17 (p<0.05), and early neonatal death monthly cases were reduced from 12±5 to 9±3 (p<0.05). Conclusion: The study results show that maternity dashboards are vital instruments for monitoring and improving the quality of maternity care. They effectively enhance maternal and neonatal care quality indicators when comparing results before and after their implementation.

Using Deep Learning Neural Networks to Improve Dementia Detection: Automating Coding of the Clock-Drawing Test.

Alzheimer's disease and related dementias (ADRD) is a growing public health concern. The clock-drawing test (CDT), where subjects draw a clock, typically with hands showing 11:10, has been widely used for ADRD-screening. A limitation of including CDT in large-scale studies is that the CDT requires manual coding, which could result in biases if coders interpret and implement coding rules differently. This study created and evaluated an intelligent CDT Clock Scoring system built with Deep Learning Neural Networks (DLNN) to automatically code CDT images. We used a large, publicly available repository of CDT images from the 2011-2019 National Health and Aging Trends Study (NHATS) and compared three advanced DLNN methods - ResNet101, EfficientNet and Vision Transformers (ViT) in coding CDT into binary and ordinal (0 to 5) scores. We extended beyond the traditional nominal classification approach (which does not recognize order) by introducing structured ordering into the coding system and compared DLNN-coded CDT images with manual coding. Results suggest that ViT outperforms ResNet101 and EfficientNet, as well as manual coding. The ordinal coding system has the ability to allow researchers to minimize either under- or over-estimation errors. Starting in 2022, our developed ViT-coding system has been used in NHATS' annual CDT-coding.

Interconnecting peer feedback literacy: Exploring the relationship between providing and acting on peer feedback

Recent research highlights the concept of feedback literacy, focusing on students’ active engagement in feedback processes, including giving and receiving peer feedback. However, most studies rely on self-assessments through surveys and interviews, with few examining actual feedback behaviors. This study explores the behavioral aspects of peer feedback literacy among 844 high school students using an online system for peer feedback and revisions. Multiple measures related to provided ratings and comments as well as use in revisions were extracted using systematic coding. Factor analysis and structural equation modeling identified two distinct elements of high-quality feedback provision—providing features and rating validity—and confirmed a moderate correlation between the ability to provide quality feedback and the use of feedback. These skills were weakly correlated with writing ability. This research contributes to the emerging literature of peer feedback literacy and supports the development of more effective peer feedback training approaches.

Overview of Finnish national patient data repository for research on medical risk assessment

The Kanta Patient Data Repository (PDR) contains healthcare data from the population of Finland for more than a decade. The repository is a continuously expanding real world dataset produced by many information systems and healthcare service providers. Kanta data has been available for secondary uses such as scientific research since 2019. The data can be requested from the Finnish authority Findata. However, before a request has been accepted, it is difficult to assess if the accumulated data allows answering a specific research question. Publicly available descriptions of data structures in the Kanta PDR do not tell how much they are used in practice. This publication enables future data use cases by providing a view on the overall availability of types of structured health data in the Kanta PDR based on a sample of 96 200 medical histories of over 18-year-old patients. We conclude that the Kanta PDR is a promising source of real world data for development and evaluation of medical risk calculators within the Finnish population. The wide coverage of the Finnish population and timeliness of the data are its strengths as a source of research data also outside of Finnish context. However, the limitations on data availability in variable level need to be considered on a case-by-case basis. Main challenges in the use of data in the Kanta PDR are multiple code systems for laboratory results, short durations of recorded data for specific data types, and missing or very rarely used structured format e.g., in cases of tobacco and alcohol use.