JMIR Medical Informatics最新文献

{"title":"Effectiveness of Outpatient Chronic Pain Management for Middle-Aged Patients by Internet Hospitals: Retrospective Cohort Study.","authors":"Ling Sang, Bixin Zheng, Xianzheng Zeng, Huizhen Liu, Qing Jiang, Maotong Liu, Chenyu Zhu, Maoying Wang, Zengwei Yi, Keyu Song, Li Song","doi":"10.2196/54975","DOIUrl":"https://doi.org/10.2196/54975","url":null,"abstract":"<p><strong>Background: </strong>Chronic pain is widespread and carries a heavy disease burden, and there is a lack of effective outpatient pain management. As an emerging internet medical platform in China, internet hospitals have been successfully applied for the management of chronic diseases. There are also a certain number of patients with chronic pain that use internet hospitals for pain management. However, no studies have investigated the effectiveness of pain management via internet hospitals.</p><p><strong>Objective: </strong>The aim of this retrospective cohort study was to explore the effectiveness of chronic pain management by internet hospitals and their advantages and disadvantages compared to traditional physical hospital visits.</p><p><strong>Methods: </strong>This was a retrospective cohort study. Demographic information such as the patient's sex, age, and number of visits was obtained from the IT center. During the first and last patient visits, information on outcome variables such as the Brief Pain Inventory (BPI), medical satisfaction, medical costs, and adverse drug events was obtained through a telephone follow-up. All patients with chronic pain who had 3 or more visits (internet or offline) between September 2021, and February 2023, were included. The patients were divided into an internet hospital group and a physical hospital group, according to whether they had web-based or in-person consultations, respectively. To control for confounding variables, propensity score matching was used to match the two groups. Matching variables included age, sex, diagnosis, and number of clinic visits.</p><p><strong>Results: </strong>A total of 122 people in the internet hospital group and 739 people in the physical hospital group met the inclusion criteria. After propensity score matching, 77 patients in each of the two groups were included in the analysis. There was not a significant difference in the quality of life (QOL; QOL assessment was part of the BPI scale) between the internet hospital group and the physical hospital group (P=.80), but the QOL of both groups of patients improved after pain management (internet hospital group: P<.001; physical hospital group: P=.001). There were no significant differences in the pain relief rate (P=.25) or the incidence of adverse events (P=.60) between the two groups. The total cost (P<.001) and treatment-related cost (P<.001) of the physical hospital group were higher than those of the internet hospital group. In addition, the degree of satisfaction in the internet hospital group was greater than that in the physical hospital group (P=.01).</p><p><strong>Conclusions: </strong>Internet hospitals are an effective way of managing chronic pain. They can improve patients' QOL and satisfaction, reduce treatment costs, and can be used as part of a multimodal strategy for chronic pain self-management.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e54975"},"PeriodicalIF":3.1,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a Telemonitoring System Using Electronic National Early Warning Scores for Patients Receiving Medical Home Care: Pilot Implementation Study.","authors":"Cheng-Fu Lin, Pei-Jung Chang, Hui-Min Chang, Ching-Tsung Chen, Pi-Shan Hsu, Chieh-Liang Wu, Shih-Yi Lin","doi":"10.2196/63425","DOIUrl":"10.2196/63425","url":null,"abstract":"<p><strong>Background: </strong>Telehealth programs and wearable sensors that enable patients to monitor their vital signs have expanded due to the COVID-19 pandemic. The electronic National Early Warning Score (e-NEWS) system helps identify and respond to acute illness.</p><p><strong>Objective: </strong>This study aimed to implement and evaluate a comprehensive telehealth system to monitor vital signs using e-NEWS for patients receiving integrated home-based medical care (iHBMC). The goal was to improve the early detection of patient deterioration and enhance care delivery in home settings. The system was deployed to optimize remote monitoring in iHBMC and reduce emergency visits and hospitalizations.</p><p><strong>Methods: </strong>The study was conducted at a medical center and its affiliated home health agency in central Taiwan from November 1, 2022, to October 31, 2023. Patients eligible for iHBMC were enrolled, and sensor data from devices such as blood pressure monitors, thermometers, and pulse oximeters were transmitted to a cloud-based server for e-NEWS calculations at least twice per day over a 2-week period. Patients with e-NEWSs up to 4 received nursing or physician recommendations and interventions based on abnormal physiological data, with reassessment occurring after 2 hours.</p><p><strong>Unlabelled: </strong>A total of 28 participants were enrolled, with a median age of 84.5 (IQR 79.3-90.8) years, and 32% (n=9) were male. All participants had caregivers, with only 5 out of 28 (18%) able to make decisions independently. The system was implemented across one medical center and its affiliated home health agency. Of the 28 participants, 27 completed the study, while 1 exited early due to low blood pressure and shortness of breath. The median e-NEWS value was 4 (IQR 3-6), with 397 abnormal readings recorded. Of the remaining 27 participants, 8 participants had earlier home visits due to abnormal readings, 6 required hypertension medication adjustments, and 9 received advice on oxygen supplementation. Overall, 24 out of 28 (86%) participants reported being satisfied with the system.</p><p><strong>Conclusions: </strong>This study demonstrated the feasibility of implementing a telehealth system integrated with e-NEWS in iHBMC settings, potentially aiding in the early detection of clinical deterioration. Although caregivers receive training and resources for their tasks, the system may increase their workload, which could lead to higher stress levels. The small sample size, short monitoring duration, and regional focus in central Taiwan may further limit the applicability of the findings to areas with differing countries, regions, and health care infrastructures. Further research is required to confirm its impact.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e63425"},"PeriodicalIF":3.1,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11694153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900938","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":"Daily automated prediction of delirium risk in hospitalized patients: Model development and validation.","authors":"Kendrick Matthew Shaw, Yu-Ping Shao, Manohar Ghanta, Valdery Moura Junior, Eyal Y Kimchi, Timothy T Houle, Oluwaseun Akeju, Michael Brandon Westover","doi":"10.2196/60442","DOIUrl":"10.2196/60442","url":null,"abstract":"<p><strong>Background: </strong>Delirium is common in hospitalized patients and correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screening and prevention.</p><p><strong>Objective: </strong>To develop a machine learning algorithm to identify patients at highest risk of delirium in the hospital each day in an automated fashion based on data available in the electronic medical record, reducing the barrier to large-scale delirium screening.</p><p><strong>Methods: </strong>We developed and compared multiple machine learning models on a retrospective dataset of all hospitalized adult patients with recorded Confusion Assessment Method (CAM) screens at a major academic medical center from April 2nd, 2016 to January 16th 2019, comprising 23006 patients. The patient's age, gender, and all available laboratory values, vital signs, prior CAM screens, and medication administrations were used as potential predictors. Four machine learning approaches were investigated: logistic regression with L1-regularization, multilayer perceptrons, random forests, and boosted trees. Model development used 80% of the patients; the remaining 20% were reserved for testing the final models. Lab values, vital signs, medications, gender, and age were used to predict a positive CAM screen in the next 24 hours.</p><p><strong>Results: </strong>The boosted tree model achieved the greatest predictive power, with a 0.92 area under the receiver operator characteristic curve (AUROC) (95% Confidence Interval (CI) 0.913-9.22), followed by the random forest at 0.91 (95% CI 0.909-0.918), multilayer perceptron at 0.86 (95% CI 0.850-0.861), and logistic regression at 0.85 (95% CI 0.841-0.852). These AUROCs decreased to 0.78-0.82 and 0.74-0.80 when limited to patients not currently or never delirious, respectively.</p><p><strong>Conclusions: </strong>A boosted tree machine learning model was able to identify hospitalized patients at elevated risk for delirium in the next 24 hours. This may allow for automated delirium risk screening and more precise targeting of proven and investigational interventions to prevent delirium.</p><p><strong>Clinicaltrial: </strong></p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualizing Patient Pathways and Identifying Data Repositories in a UK Neurosciences Center: Exploratory Study.","authors":"Jo Knight, Vishnu Vardhan Chandrabalan, Hedley C A Emsley","doi":"10.2196/60017","DOIUrl":"10.2196/60017","url":null,"abstract":"<p><strong>Background: </strong>Health and clinical activity data are a vital resource for research, improving patient care and service efficiency. Health care data are inherently complex, and their acquisition, storage, retrieval, and subsequent analysis require a thorough understanding of the clinical pathways underpinning such data. Better use of health care data could lead to improvements in patient care and service delivery. However, this depends on the identification of relevant datasets.</p><p><strong>Objective: </strong>We aimed to demonstrate the application of business process modeling notation (BPMN) to represent clinical pathways at a UK neurosciences center and map the clinical activity to corresponding data flows into electronic health records and other nonstandard data repositories.</p><p><strong>Methods: </strong>We used BPMN to map and visualize a patient journey and the subsequent movement and storage of patient data. After identifying several datasets that were being held outside of the standard applications, we collected information about these datasets using a questionnaire.</p><p><strong>Results: </strong>We identified 13 standard applications where neurology clinical activity was captured as part of the patient's electronic health record including applications and databases for managing referrals, outpatient activity, laboratory data, imaging data, and clinic letters. We also identified 22 distinct datasets not within standard applications that were created and managed within the neurosciences department, either by individuals or teams. These were being used to deliver direct patient care and included datasets for tracking patient blood results, recording home visits, and tracking triage status.</p><p><strong>Conclusions: </strong>Mapping patient data flows and repositories allowed us to identify areas wherein the current electronic health record does not fulfill the needs of day-to-day patient care. Data that are being stored outside of standard applications represent a potential duplication in the effort and risks being overlooked. Future work should identify unmet data needs to inform correct data capture and centralization within appropriate data architectures.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e60017"},"PeriodicalIF":3.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11707554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883741","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":"Building a Foundation for High-Quality Health Data: Multihospital Case Study in Belgium.","authors":"Jens Declerck, Bert Vandenberk, Mieke Deschepper, Kirsten Colpaert, Lieselot Cool, Jens Goemaere, Mona Bové, Frank Staelens, Koen De Meester, Eva Verbeke, Elke Smits, Cami De Decker, Nicky Van Der Vekens, Elin Pauwels, Robert Vander Stichele, Dipak Kalra, Pascal Coorevits","doi":"10.2196/60244","DOIUrl":"10.2196/60244","url":null,"abstract":"<p><strong>Background: </strong>Data quality is fundamental to maintaining the trust and reliability of health data for both primary and secondary purposes. However, before the secondary use of health data, it is essential to assess the quality at the source and to develop systematic methods for the assessment of important data quality dimensions.</p><p><strong>Objective: </strong>This case study aims to offer a dual aim-to assess the data quality of height and weight measurements across 7 Belgian hospitals, focusing on the dimensions of completeness and consistency, and to outline the obstacles these hospitals face in sharing and improving data quality standards.</p><p><strong>Methods: </strong>Focusing on data quality dimensions completeness and consistency, this study examined height and weight data collected from 2021 to 2022 within 3 distinct departments-surgical, geriatrics, and pediatrics-in each of the 7 hospitals.</p><p><strong>Results: </strong>Variability was observed in the completeness scores for height across hospitals and departments, especially within surgical and geriatric wards. In contrast, weight data uniformly achieved high completeness scores. Notably, the consistency of height and weight data recording was uniformly high across all departments.</p><p><strong>Conclusions: </strong>A collective collaboration among Belgian hospitals, transcending network affiliations, was formed to conduct this data quality assessment. This study demonstrates the potential for improving data quality across health care organizations by sharing knowledge and good practices, establishing a foundation for future, similar research.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e60244"},"PeriodicalIF":3.1,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11683741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900937","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":"Automated Pathologic TN Classification Prediction and Rationale Generation From Lung Cancer Surgical Pathology Reports Using a Large Language Model Fine-Tuned With Chain-of-Thought: Algorithm Development and Validation Study.","authors":"Sanghwan Kim, Sowon Jang, Borham Kim, Leonard Sunwoo, Seok Kim, Jin-Haeng Chung, Sejin Nam, Hyeongmin Cho, Donghyoung Lee, Keehyuck Lee, Sooyoung Yoo","doi":"10.2196/67056","DOIUrl":"10.2196/67056","url":null,"abstract":"<p><strong>Background: </strong>Traditional rule-based natural language processing approaches in electronic health record systems are effective but are often time-consuming and prone to errors when handling unstructured data. This is primarily due to the substantial manual effort required to parse and extract information from diverse types of documentation. Recent advancements in large language model (LLM) technology have made it possible to automatically interpret medical context and support pathologic staging. However, existing LLMs encounter challenges in rapidly adapting to specialized guideline updates. In this study, we fine-tuned an LLM specifically for lung cancer pathologic staging, enabling it to incorporate the latest guidelines for pathologic TN classification.</p><p><strong>Objective: </strong>This study aims to evaluate the performance of fine-tuned generative language models in automatically inferring pathologic TN classifications and extracting their rationale from lung cancer surgical pathology reports. By addressing the inefficiencies and extensive parsing efforts associated with rule-based methods, this approach seeks to enable rapid and accurate reclassification aligned with the latest cancer staging guidelines.</p><p><strong>Methods: </strong>We conducted a comparative performance evaluation of 6 open-source LLMs for automated TN classification and rationale generation, using 3216 deidentified lung cancer surgical pathology reports based on the American Joint Committee on Cancer (AJCC) Cancer Staging Manual8th edition, collected from a tertiary hospital. The dataset was preprocessed by segmenting each report according to lesion location and morphological diagnosis. Performance was assessed using exact match ratio (EMR) and semantic match ratio (SMR) as evaluation metrics, which measure classification accuracy and the contextual alignment of the generated rationales, respectively.</p><p><strong>Results: </strong>Among the 6 models, the Orca2_13b model achieved the highest performance with an EMR of 0.934 and an SMR of 0.864. The Orca2_7b model also demonstrated strong performance, recording an EMR of 0.914 and an SMR of 0.854. In contrast, the Llama2_7b model achieved an EMR of 0.864 and an SMR of 0.771, while the Llama2_13b model showed an EMR of 0.762 and an SMR of 0.690. The Mistral_7b and Llama3_8b models, on the other hand, showed lower performance, with EMRs of 0.572 and 0.489, and SMRs of 0.377 and 0.456, respectively. Overall, the Orca2 models consistently outperformed the others in both TN stage classification and rationale generation.</p><p><strong>Conclusions: </strong>The generative language model approach presented in this study has the potential to enhance and automate TN classification in complex cancer staging, supporting both clinical practice and oncology data curation. With additional fine-tuning based on cancer-specific guidelines, this approach can be effectively adapted to other cancer types.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e67056"},"PeriodicalIF":3.1,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11699504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142869650","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":"An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study.","authors":"Lang Cao, Jimeng Sun, Adam Cross","doi":"10.2196/60665","DOIUrl":"10.2196/60665","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Rare diseases affect millions worldwide but sometimes face limited research focus individually due to low prevalence. Many rare diseases do not have specific International Classification of Diseases, Ninth Edition (ICD-9) and Tenth Edition (ICD-10), codes and therefore cannot be reliably extracted from granular fields like \"Diagnosis\" and \"Problem List\" entries, which complicates tasks that require identification of patients with these conditions, including clinical trial recruitment and research efforts. Recent advancements in large language models (LLMs) have shown promise in automating the extraction of medical information, offering the potential to improve medical research, diagnosis, and management. However, most LLMs lack professional medical knowledge, especially concerning specific rare diseases, and cannot effectively manage rare disease data in its various ontological forms, making it unsuitable for these tasks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Our aim is to create an end-to-end system called automated rare disease mining (AutoRD), which automates the extraction of rare disease-related information from medical text, focusing on entities and their relations to other medical concepts, such as signs and symptoms. AutoRD integrates up-to-date ontologies with other structured knowledge and demonstrates superior performance in rare disease extraction tasks. We conducted various experiments to evaluate AutoRD's performance, aiming to surpass common LLMs and traditional methods.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;AutoRD is a pipeline system that involves data preprocessing, entity extraction, relation extraction, entity calibration, and knowledge graph construction. We implemented this system using GPT-4 and medical knowledge graphs developed from the open-source Human Phenotype and Orphanet ontologies, using techniques such as chain-of-thought reasoning and prompt engineering. We quantitatively evaluated our system's performance in entity extraction, relation extraction, and knowledge graph construction. The experiment used the well-curated dataset RareDis2023, which contains medical literature focused on rare disease entities and their relations, making it an ideal dataset for training and testing our methodology.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;On the RareDis2023 dataset, AutoRD achieved an overall entity extraction F1-score of 56.1% and a relation extraction F1-score of 38.6%, marking a 14.4% improvement over the baseline LLM. Notably, the F1-score for rare disease entity extraction reached 83.5%, indicating high precision and recall in identifying rare disease mentions. These results demonstrate the effectiveness of integrating LLMs with medical ontologies in extracting complex rare disease information.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;AutoRD is an automated end-to-end system for extracting rare disease information from text to build knowledge graphs, addressing critical limitations of existing LLMs by impr","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e60665"},"PeriodicalIF":3.1,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11683654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142856735","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":"Information Mode-Dependent Success Rates of Obtaining German Medical Informatics Initiative-Compliant Broad Consent in the Emergency Department: Single-Center Prospective Observational Study.","authors":"Felix Patricius Hans, Jan Kleinekort, Melanie Boerries, Alexandra Nieters, Gerhard Kindle, Micha Rautenberg, Laura Bühler, Gerda Weiser, Michael Clemens Röttger, Carolin Neufischer, Matthias Kühn, Julius Wehrle, Anna Slagman, Antje Fischer-Rosinsky, Larissa Eienbröker, Frank Hanses, Gisbert Wilhelm Teepe, Hans-Jörg Busch, Leo Benning","doi":"10.2196/65646","DOIUrl":"10.2196/65646","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The broad consent (BC) developed by the German Medical Informatics Initiative is a pivotal national strategy for obtaining patient consent to use routinely collected data from electronic health records, insurance companies, contact information, and biomaterials for research. Emergency departments (EDs) are ideal for enrolling diverse patient populations in research activities. Despite regulatory and ethical challenges, obtaining BC from patients in ED with varying demographic, socioeconomic, and disease characteristics presents a promising opportunity to expand the availability of ED data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the success rate of obtaining BC through different consenting approaches in a tertiary ED and to explore factors influencing consent and dropout rates.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A single-center prospective observational study was conducted in a German tertiary ED from September to December 2022. Every 30th patient was screened for eligibility. Eligible patients were informed via one of three modalities: (1) directly in the ED, (2) during their inpatient stay on the ward, or (3) via telephone after discharge. The primary outcome was the success rate of obtaining BC within 30 days of ED presentation. Secondary outcomes included analyzing potential influences on the success and dropout rates based on patient characteristics, information mode, and the interaction time required for patients to make an informed decision.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 11,842 ED visits, 419 patients were screened for BC eligibility, with 151 meeting the inclusion criteria. Of these, 68 (45%) consented to at least 1 BC module, while 24 (15.9%) refused participation. The dropout rate was 39.1% (n=59) and was highest in the telephone-based group (57/109, 52.3%) and lowest in the ED group (1/14, 7.1%). Patients informed face-to-face during their inpatient stay following the ED treatment had the highest consent rate (23/27, 85.2%), while those approached in the ED or by telephone had consent rates of 69.2% (9/13 and 36/52). Logistic regression analysis indicated that longer interaction time significantly improved consent rates (P=.03), while female sex was associated with higher dropout rates (P=.02). Age, triage category, billing details (inpatient treatment), or diagnosis did not significantly influence the primary outcome (all P&gt;.05).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Obtaining BC in an ED environment is feasible, enabling representative inclusion of ED populations. However, discharge from the ED and female sex negatively affected consent rates to the BC. Face-to-face interaction proved most effective, particularly for inpatients, while telephone-based approaches resulted in higher dropout rates despite comparable consent rates to direct consenting in the ED. The findings underscore the importance of tailored consent strategies and maintaining consenting staff in EDs and on the war","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":" ","pages":"e65646"},"PeriodicalIF":3.1,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688594/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774995","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":"Enhancing Standardized and Structured Recording by Elderly Care Physicians for Reusing Electronic Health Record Data: Interview Study.","authors":"Charlotte A W Albers, Yvonne Wieland-Jorna, Martine C de Bruijne, Martin Smalbrugge, Karlijn J Joling, Marike E de Boer","doi":"10.2196/63710","DOIUrl":"10.2196/63710","url":null,"abstract":"<p><strong>Background: </strong>Elderly care physicians (ECPs) in nursing homes document patients' health, medical conditions, and the care provided in electronic health records (EHRs). However, much of these health data currently lack structure and standardization, limiting their potential for health information exchange across care providers and reuse for quality improvement, policy development, and scientific research. Enhancing this potential requires insight into the attitudes and behaviors of ECPs toward standardized and structured recording in EHRs.</p><p><strong>Objective: </strong>This study aims to answer why and how ECPs record their findings in EHRs and what factors influence them to record in a standardized and structured manner. The findings will be used to formulate recommendations aimed at enhancing standardized and structured data recording for the reuse of EHR data.</p><p><strong>Methods: </strong>Semistructured interviews were conducted with 13 ECPs working in Dutch nursing homes. We recruited participants through purposive sampling, aiming for diversity in age, gender, health care organization, and use of EHR systems. Interviews continued until we reached data saturation. Analysis was performed using inductive thematic analysis.</p><p><strong>Results: </strong>ECPs primarily use EHRs to document daily patient care, ensure continuity of care, and fulfill their obligation to record specific information for accountability purposes. The EHR serves as a record to justify their actions in the event of a complaint. In addition, some respondents also mentioned recording information for secondary purposes, such as research and quality improvement. Several factors were found to influence standardized and structured recording. At a personal level, it is crucial to experience the added value of standardized and structured recording. At the organizational level, clear internal guidelines and a focus on their implementation can have a substantial impact. At the level of the EHR system, user-friendliness, interoperability, and guidance were most frequently mentioned as being important. At a national level, the alignment of internal guidelines with overarching standards plays a pivotal role in encouraging standardized and structured recording.</p><p><strong>Conclusions: </strong>The results of our study are similar to the findings of previous research in hospital care and general practice. Therefore, long-term care can learn from solutions regarding standardized and structured recording in other health care sectors. The main motives for ECPs to record in EHRs are the daily patient care and ensuring continuity of care. Standardized and structured recording can be improved by aligning the recording method in EHRs with the primary care process. In addition, there are incentives for motivating ECPs to record in a standardized and structured way, mainly at the personal, organizational, EHR system, and national levels.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e63710"},"PeriodicalIF":3.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11681280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823015","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":"Survival After Radical Cystectomy for Bladder Cancer: Development of a Fair Machine Learning Model.","authors":"Samuel Carbunaru, Yassamin Neshatvar, Hyungrok Do, Katie Murray, Rajesh Ranganath, Madhur Nayan","doi":"10.2196/63289","DOIUrl":"10.2196/63289","url":null,"abstract":"<p><strong>Background: </strong>Prediction models based on machine learning (ML) methods are being increasingly developed and adopted in health care. However, these models may be prone to bias and considered unfair if they demonstrate variable performance in population subgroups. An unfair model is of particular concern in bladder cancer, where disparities have been identified in sex and racial subgroups.</p><p><strong>Objective: </strong>This study aims (1) to develop a ML model to predict survival after radical cystectomy for bladder cancer and evaluate for potential model bias in sex and racial subgroups; and (2) to compare algorithm unfairness mitigation techniques to improve model fairness.</p><p><strong>Methods: </strong>We trained and compared various ML classification algorithms to predict 5-year survival after radical cystectomy using the National Cancer Database. The primary model performance metric was the F₁-score. The primary metric for model fairness was the equalized odds ratio (eOR). We compared 3 algorithm unfairness mitigation techniques to improve eOR.</p><p><strong>Results: </strong>We identified 16,481 patients; 23.1% (n=3800) were female, and 91.5% (n=15,080) were \"White,\" 5% (n=832) were \"Black,\" 2.3% (n=373) were \"Hispanic,\" and 1.2% (n=196) were \"Asian.\" The 5-year mortality rate was 75% (n=12,290). The best naive model was extreme gradient boosting (XGBoost), which had an F₁-score of 0.860 and eOR of 0.619. All unfairness mitigation techniques increased the eOR, with correlation remover showing the highest increase and resulting in a final eOR of 0.750. This mitigated model had F₁-scores of 0.86, 0.904, and 0.824 in the full, Black male, and Asian female test sets, respectively.</p><p><strong>Conclusions: </strong>The ML model predicting survival after radical cystectomy exhibited bias across sex and racial subgroups. By using algorithm unfairness mitigation techniques, we improved algorithmic fairness as measured by the eOR. Our study highlights the role of not only evaluating for model bias but also actively mitigating such disparities to ensure equitable health care delivery. We also deployed the first web-based fair ML model for predicting survival after radical cystectomy.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e63289"},"PeriodicalIF":3.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11694706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823017","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}