JMIR Medical Informatics最新文献

{"title":"Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing-Assisted Intelligent Systematic Literature Review System.","authors":"Frank J Manion, Jingcheng Du, Dong Wang, Long He, Bin Lin, Jingqi Wang, Siwei Wang, David Eckels, Jan Cervenka, Peter C Fiduccia, Nicole Cossrow, Lixia Yao","doi":"10.2196/54653","DOIUrl":"10.2196/54653","url":null,"abstract":"<p><strong>Background: </strong>Systematic literature review (SLR), a robust method to identify and summarize evidence from published sources, is considered to be a complex, time-consuming, labor-intensive, and expensive task.</p><p><strong>Objective: </strong>This study aimed to present a solution based on natural language processing (NLP) that accelerates and streamlines the SLR process for observational studies using real-world data.</p><p><strong>Methods: </strong>We followed an agile software development and iterative software engineering methodology to build a customized intelligent end-to-end living NLP-assisted solution for observational SLR tasks. Multiple machine learning-based NLP algorithms were adopted to automate article screening and data element extraction processes. The NLP prediction results can be further reviewed and verified by domain experts, following the human-in-the-loop design. The system integrates explainable articificial intelligence to provide evidence for NLP algorithms and add transparency to extracted literature data elements. The system was developed based on 3 existing SLR projects of observational studies, including the epidemiology studies of human papillomavirus-associated diseases, the disease burden of pneumococcal diseases, and cost-effectiveness studies on pneumococcal vaccines.</p><p><strong>Results: </strong>Our Intelligent SLR Platform covers major SLR steps, including study protocol setting, literature retrieval, abstract screening, full-text screening, data element extraction from full-text articles, results summary, and data visualization. The NLP algorithms achieved accuracy scores of 0.86-0.90 on article screening tasks (framed as text classification tasks) and macroaverage F1 scores of 0.57-0.89 on data element extraction tasks (framed as named entity recognition tasks).</p><p><strong>Conclusions: </strong>Cutting-edge NLP algorithms expedite SLR for observational studies, thus allowing scientists to have more time to focus on the quality of data and the synthesis of evidence in observational studies. Aligning the living SLR concept, the system has the potential to update literature data and enable scientists to easily stay current with the literature related to observational studies prospectively and continuously.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e54653"},"PeriodicalIF":3.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Health Care Professionals' Perspectives on the Use of a Medication and Care Support System and Recommendations for Designing a Similar Tool for Family Caregivers: Interview Study Among Health Care Professionals.","authors":"Aimerence Ashimwe, Nadia Davoody","doi":"10.2196/63456","DOIUrl":"10.2196/63456","url":null,"abstract":"<p><strong>Background: </strong>With the aging population on the rise, the demand for effective health care solutions to address adverse drug events is becoming increasingly urgent. Telemedicine has emerged as a promising solution for strengthening health care delivery in home care settings and mitigating drug errors. Due to the indispensable role of family caregivers in daily patient care, integrating digital health tools has the potential to streamline medication management processes and enhance the overall quality of patient care.</p><p><strong>Objective: </strong>This study aims to explore health care professionals' perspectives on the use of a medication and care support system (MCSS) and collect recommendations for designing a similar tool for family caregivers.</p><p><strong>Methods: </strong>Fifteen interviews with health care professionals in a home care center were conducted. Thematic analysis was used, and 5 key themes highlighting the importance of using the MCSS tool to improve medication management in home care were identified.</p><p><strong>Results: </strong>All participants emphasized the necessity of direct communication between health care professionals and family caregivers and stated that family caregivers need comprehensive information about medication administration, patient conditions, and symptoms. Furthermore, the health care professionals recommended features and functions customized for family caregivers.</p><p><strong>Conclusions: </strong>This study underscored the importance of clear communication between health care professionals and family caregivers and the provision of comprehensive instructions to promote safe medication practices. By equipping family caregivers with essential information via a tool similar to the MCSS, a proactive approach to preventing errors and improving outcomes is advocated.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e63456"},"PeriodicalIF":3.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11541148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review.","authors":"Miguel Nunes, Joao Bone, Joao C Ferreira, Luis B Elvas","doi":"10.2196/60164","DOIUrl":"10.2196/60164","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;In response to the intricate language, specialized terminology outside everyday life, and the frequent presence of abbreviations and acronyms inherent in health care text data, domain adaptation techniques have emerged as crucial to transformer-based models. This refinement in the knowledge of the language models (LMs) allows for a better understanding of the medical textual data, which results in an improvement in medical downstream tasks, such as information extraction (IE). We have identified a gap in the literature regarding health care LMs. Therefore, this study presents a scoping literature review investigating domain adaptation methods for transformers in health care, differentiating between English and non-English languages, focusing on Portuguese. Most specifically, we investigated the development of health care LMs, with the aim of comparing Portuguese with other more developed languages to guide the path of a non-English-language with fewer resources.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to research health care IE models, regardless of language, to understand the efficacy of transformers and what are the medical entities most commonly extracted.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This scoping review was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) methodology on Scopus and Web of Science Core Collection databases. Only studies that mentioned the creation of health care LMs or health care IE models were included, while large language models (LLMs) were excluded. The latest were not included since we wanted to research LMs and not LLMs, which are architecturally different and have distinct purposes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our search query retrieved 137 studies, 60 of which met the inclusion criteria, and none of them were systematic literature reviews. English and Chinese are the languages with the most health care LMs developed. These languages already have disease-specific LMs, while others only have general-health care LMs. European Portuguese does not have any public health care LM and should take examples from other languages to develop, first, general-health care LMs and then, in an advanced phase, disease-specific LMs. Regarding IE models, transformers were the most commonly used method, and named entity recognition was the most popular topic, with only a few studies mentioning Assertion Status or addressing medical lexical problems. The most extracted entities were diagnosis, posology, and symptoms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The findings indicate that domain adaptation is beneficial, achieving better results in downstream tasks. Our analysis allowed us to understand that the use of transformers is more developed for the English and Chinese languages. European Portuguese lacks relevant studies and should draw examples from other non-English languages to develop these models and drive pr","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e60164"},"PeriodicalIF":3.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11535799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation.","authors":"Jesse Kruse, Joshua Wiedekopf, Ann-Kristin Kock-Schoppenhauer, Andrea Essenwanger, Josef Ingenerf, Hannes Ulrich","doi":"10.2196/57569","DOIUrl":"10.2196/57569","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Reaching meaningful interoperability between proprietary health care systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from the source to target systems. The Mirth Connect Server, an open-source communication server, offers, in addition to the exchange functionality, functions for simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) has recently become increasingly prevalent in national health care systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR mapping language (FML).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;In this study, a generic approach is developed, which allows for the application of declarative mapping rules defined using FML in an exchangeable manner. A transformation engine is required to execute the mapping rules.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;FHIR natively defines resources to support the conversion of instance data, such as an FHIR StructureMap. This resource encodes all information required to transform data from a source system to a target system. In our approach, this information is defined in an implementation-independent manner using FML. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in JavaScript format. These channels can then be deployed to the Mirth Connect Server.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from detailed declarative mapping rules based on the underlying StructureMaps. Implementation of the translate functionality is provided by the integration of a terminology server, and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system was evaluated for its practical use by transforming Labordatenträger version 2 (LDTv.2) laboratory results into Medical Information Object (Medizinisches Informationsobjekt) laboratory reports in accordance with the National Association of Statutory Health Insurance Physicians' specifications and into the HL7 (Health Level Seven) Europe Laboratory Report. The system could generate complex structures, but LDTv.2 lacks some information to fully comply with the specification.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The tool for the auto-generation of Mirth channels was successfully presented. Our tests reveal the feasibility of using the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and the FHIR are well established in medical informatics, the combination offers space for more research, especially with regard to FML. Simultaneously, it can be stated that the mapping language still has implementation-related shortcomings that can be compensated by Mirth Connec","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e57569"},"PeriodicalIF":3.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11508034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability Framework Across Implementation Stages to Identify Key Strategies to Facilitate Clinical Decision Support System Integration Within a Large Metropolitan Health Service: Interview and Focus Group Study.","authors":"Manasha Fernando, Bridget Abell, Steven M McPhail, Zephanie Tyack, Amina Tariq, Sundresan Naicker","doi":"10.2196/60402","DOIUrl":"10.2196/60402","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Computerized clinical decision support systems (CDSSs) enhance patient care through real-time, evidence-based guidance for health care professionals. Despite this, the effective implementation of these systems for health services presents multifaceted challenges, leading to inappropriate use and abandonment over the course of time. Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework, this qualitative study examined CDSS adoption in a metropolitan health service, identifying determinants across implementation stages to optimize CDSS integration into health care practice.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to identify the theory-informed (NASSS) determinants, which included multiple CDSS interventions across a 2-year period, both at the health-service level and at the individual hospital setting, that either facilitate or hinder the application of CDSSs within a metropolitan health service. In addition, this study aimed to map these determinants onto specific stages of the implementation process, thereby developing a system-level understanding of CDSS application across implementation stages.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Participants involved in various stages of the implementation process were recruited (N=30). Participants took part in interviews and focus groups. We used a hybrid inductive-deductive qualitative content analysis and a framework mapping approach to categorize findings into barriers, enablers, or neutral determinants aligned to NASSS framework domains. These determinants were also mapped to implementation stages using the Active Implementation Framework stages approach.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Participants comprised clinical adopters (14/30, 47%), organizational champions (5/30, 16%), and those with roles in organizational clinical informatics (5/30, 16%). Most determinants were mapped to the organization level, technology, and adopter subdomains. However, the study findings also demonstrated a relative lack of long-term implementation planning. Consequently, determinants were not uniformly distributed across the stages of implementation, with 61.1% (77/126) identified in the exploration stage, 30.9% (39/126) in the full implementation stage, and 4.7% (6/126) in the installation stages. Stakeholders engaged in more preimplementation and full-scale implementation activities, with fewer cycles of monitoring and iteration activities identified.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;These findings addressed a substantial knowledge gap in the literature using systems thinking principles to identify the interdependent dynamics of CDSS implementation. A lack of sustained implementation strategies (ie, training and longer-term, adopter-level championing) weakened the sociotechnical network between developers and adopters, leading to communication barriers. More rigorous implementation planning, encompassing all 4 implementation stages, may, in a","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e60402"},"PeriodicalIF":3.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study.","authors":"Shengyu Liu, Anran Wang, Xiaolei Xiu, Ming Zhong, Sizhu Wu","doi":"10.2196/59782","DOIUrl":"10.2196/59782","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Named entity recognition (NER) models are essential for extracting structured information from unstructured medical texts by identifying entities such as diseases, treatments, and conditions, enhancing clinical decision-making and research. Innovations in machine learning, particularly those involving Bidirectional Encoder Representations From Transformers (BERT)-based deep learning and large language models, have significantly advanced NER capabilities. However, their performance varies across medical datasets due to the complexity and diversity of medical terminology. Previous studies have often focused on overall performance, neglecting specific challenges in medical contexts and the impact of macrofactors like lexical composition on prediction accuracy. These gaps hinder the development of optimized NER models for medical applications.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to meticulously evaluate the performance of various NER models in the context of medical text analysis, focusing on how complex medical terminology affects entity recognition accuracy. Additionally, we explored the influence of macrofactors on model performance, seeking to provide insights for refining NER models and enhancing their reliability for medical applications.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study comprehensively evaluated 7 NER models-hidden Markov models, conditional random fields, BERT for Biomedical Text Mining, Big Transformer Models for Efficient Long-Sequence Attention, Decoding-enhanced BERT with Disentangled Attention, Robustly Optimized BERT Pretraining Approach, and Gemma-across 3 medical datasets: Revised Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA), BioCreative V CDR, and Anatomical Entity Mention (AnatEM). The evaluation focused on prediction accuracy, resource use (eg, central processing unit and graphics processing unit use), and the impact of fine-tuning hyperparameters. The macrofactors affecting model performance were also screened using the multilevel factor elimination algorithm.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The fine-tuned BERT for Biomedical Text Mining, with balanced resource use, generally achieved the highest prediction accuracy across the Revised JNLPBA and AnatEM datasets, with microaverage (AVG_MICRO) scores of 0.932 and 0.8494, respectively, highlighting its superior proficiency in identifying medical entities. Gemma, fine-tuned using the low-rank adaptation technique, achieved the highest accuracy on the BioCreative V CDR dataset with an AVG_MICRO score of 0.9962 but exhibited variability across the other datasets (AVG_MICRO scores of 0.9088 on the Revised JNLPBA and 0.8029 on AnatEM), indicating a need for further optimization. In addition, our analysis revealed that 2 macrofactors, entity phrase length and the number of entity words in each entity phrase, significantly influenced model performance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Th","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e59782"},"PeriodicalIF":3.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effects of Electronic Health Records on Medical Error Reduction: Extension of the DeLone and McLean Information System Success Model.","authors":"Bester Chimbo, Lovemore Motsi","doi":"10.2196/54572","DOIUrl":"10.2196/54572","url":null,"abstract":"<p><strong>Background: </strong>Medical errors are becoming a major problem for health care providers and those who design health policies. These errors cause patients' illnesses to worsen over time and can make recovery impossible. For the benefit of patients and the welfare of health care providers, a decrease in these errors is required to maintain safe, high-quality patient care.</p><p><strong>Objective: </strong>This study aimed to improve the ability of health care professionals to diagnose diseases and reduce medical errors.</p><p><strong>Methods: </strong>Data collection was performed at Dr George Mukhari Academic Hospital using convenience sampling. In total, 300 health care professionals were given a self-administered questionnaire, including doctors, dentists, pharmacists, physiologists, and nurses. To test the study hypotheses, multiple linear regression was used to evaluate empirical data.</p><p><strong>Results: </strong>In the sample of 300 health care professionals, no significant correlation was found between medical error reduction (MER) and knowledge quality (KQ) (β=.043, P=.48). A nonsignificant negative relationship existed between MER and information quality (IQ) (β=-.080, P=.19). However, a significant positive relationship was observed between MER and electronic health records (EHR; β=.125, 95% CI 0.005-0.245, P=.042).</p><p><strong>Conclusions: </strong>Increasing patient access to medical records for health care professionals may significantly improve patient health and well-being. The effectiveness of health care organizations' operations can also be increased through better health information systems. To lower medical errors and enhance patient outcomes, policy makers should provide financing and support for EHR adoption as a top priority. Health care administrators should also concentrate on providing staff with the training they need to operate these systems efficiently. Empirical surveys in other public and private hospitals can be used to further test the validated survey instrument.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e54572"},"PeriodicalIF":3.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11525084/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review.","authors":"Abolfazl Mollalo, Bashir Hamidi, Leslie A Lenert, Alexander V Alekseyenko","doi":"10.2196/56343","DOIUrl":"10.2196/56343","url":null,"abstract":"<p><strong>Background: </strong>Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.</p><p><strong>Objective: </strong>This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes.</p><p><strong>Methods: </strong>We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains.</p><p><strong>Results: </strong>A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited.</p><p><strong>Conclusions: </strong>This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e56343"},"PeriodicalIF":3.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11522649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging Data Models in Health Care With a Novel Intermediate Query Format for Feasibility Queries: Mixed Methods Study.","authors":"Lorenz Rosenau, Julian Gruendner, Alexander Kiel, Thomas Köhler, Bastian Schaffer, Raphael W Majeed","doi":"10.2196/58541","DOIUrl":"10.2196/58541","url":null,"abstract":"<p><strong>Background: </strong>To advance research with clinical data, it is essential to make access to the available data as fast and easy as possible for researchers, which is especially challenging for data from different source systems within and across institutions. Over the years, many research repositories and data standards have been created. One of these is the Fast Healthcare Interoperability Resources (FHIR) standard, used by the German Medical Informatics Initiative (MII) to harmonize and standardize data across university hospitals in Germany. One of the first steps to make these data available is to allow researchers to create feasibility queries to determine the data availability for a specific research question. Given the heterogeneity of different query languages to access different data across and even within standards such as FHIR (eg, CQL and FHIR Search), creating an intermediate query syntax for feasibility queries reduces the complexity of query translation and improves interoperability across different research repositories and query languages.</p><p><strong>Objective: </strong>This study describes the creation and implementation of an intermediate query syntax for feasibility queries and how it integrates into the federated German health research portal (Forschungsdatenportal Gesundheit) and the MII.</p><p><strong>Methods: </strong>We analyzed the requirements for feasibility queries and the feasibility tools that are currently available in research repositories. Based on this analysis, we developed an intermediate query syntax that can be easily translated into different research repository-specific query languages.</p><p><strong>Results: </strong>The resulting Clinical Cohort Definition Language (CCDL) for feasibility queries combines inclusion criteria in a conjunctive normal form and exclusion criteria in a disjunctive normal form, allowing for additional filters like time or numerical restrictions. The inclusion and exclusion results are combined via an expression to specify feasibility queries. We defined a JSON schema for the CCDL, generated an ontology, and demonstrated the use and translatability of the CCDL across multiple studies and real-world use cases.</p><p><strong>Conclusions: </strong>We developed and evaluated a structured query syntax for feasibility queries and demonstrated its use in a real-world example as part of a research platform across 39 German university hospitals.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e58541"},"PeriodicalIF":3.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review.","authors":"Allison Grothman, William J Ma, Kendra G Tickner, Elliot A Martin, Danielle A Southern, Hude Quan","doi":"10.2196/49781","DOIUrl":"10.2196/49781","url":null,"abstract":"<p><strong>Background: </strong>Electronic medical records (EMRs) contain large amounts of detailed clinical information. Using medical record review to identify conditions within large quantities of EMRs can be time-consuming and inefficient. EMR-based phenotyping using machine learning and natural language processing algorithms is a continually developing area of study that holds potential for numerous mental health disorders.</p><p><strong>Objective: </strong>This review evaluates the current state of EMR-based case identification for depression and provides guidance on using current algorithms and constructing new ones.</p><p><strong>Methods: </strong>A scoping review of EMR-based algorithms for phenotyping depression was completed. This research encompassed studies published from January 2000 to May 2023. The search involved 3 databases: Embase, MEDLINE, and APA PsycInfo. This was carried out using selected keywords that fell into 3 categories: terms connected with EMRs, terms connected to case identification, and terms pertaining to depression. This study adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.</p><p><strong>Results: </strong>A total of 20 papers were assessed and summarized in the review. Most of these studies were undertaken in the United States, accounting for 75% (15/20). The United Kingdom and Spain followed this, accounting for 15% (3/20) and 10% (2/20) of the studies, respectively. Both data-driven and clinical rule-based methodologies were identified. The development of EMR-based phenotypes and algorithms indicates the data accessibility permitted by each health system, which led to varying performance levels among different algorithms.</p><p><strong>Conclusions: </strong>Better use of structured and unstructured EMR components through techniques such as machine learning and natural language processing has the potential to improve depression phenotyping. However, more validation must be carried out to have confidence in depression case identification algorithms in general.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e49781"},"PeriodicalIF":3.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}