BMC Medical Informatics and Decision Making最新文献

{"title":"Determining the impact of mobile-based self-care applications on reducing anxiety in healthcare providers: a systematic review.","authors":"Mohammad Mahdi Askarizadeh, Leila Gholamhosseini, Reza Khajouei, Saeedeh Homayee, Fatemeh Askarizadeh, Leila Ahmadian","doi":"10.1186/s12911-024-02817-4","DOIUrl":"10.1186/s12911-024-02817-4","url":null,"abstract":"<p><strong>Background: </strong>Healthcare providers (HCP) face various stressful conditions in hospitals that result in the development of anxiety disorders. However, due to heavy workloads, they often miss the opportunity for self-care. Any effort to diminish this problem improves the quality of Healthcare providers and enhances patient safety. various applications have been developed to empower Healthcare providers and reduce their anxiety, but these applications do not meet all their individual and professional needs. The objective of this study was to investigate the impact of mobile-based self-care applications on reducing anxiety in healthcare providers.</p><p><strong>Methods: </strong>In this study, keywords such as anxiety, self-care, healthcare providers, and mobile health were used to search PubMed, Scopus, and Web of Science for papers published in the recent ten years (2014-2024). We used the PRISMA diagram to report the results. Ten out of 2515 retrieved articles that addressed the effect of mobile-based self-care applications on Healthcare providers' anxiety were included for analysis. Data were extracted using a data collection form designed based on the research objective. We used this form to collect data including the author's name, publication year, country, study type, intervention duration, study objectives, platform used, Modules presented in technologies, Methods of reducing anxiety, questionnaire details, and Effectiveness assessment. Data collected from the studies were analyzed by SPSS-21 using frequency and percentage.</p><p><strong>Results: </strong>Based on the results, studies were conducted in nine different countries, and the intervention duration and strategies for reducing anxiety using self-care applications ranged from two weeks to four months. The impact of mobile health applications, their content, and intervention strategies on reducing anxiety were positive. The anxiety-reduction strategies were varied among applications. Anxiety reduction strategies in this study included mindfulness, cognitive-behavioral therapy, physical activities, breathing exercises, dietary regimes, and nature exploration through virtual reality. Cognitive-behavioral therapy and mindfulness constituted the most frequently applied reduction techniques across the studies to reduce anxiety in Healthcare providers.Furthermore, the findings revealed the effectiveness of interventions in reducing other mental disorders such as anxiety, stress, depression, drug abuse, and psychotropic drug use of Healthcare providers.</p><p><strong>Conclusion: </strong>The use of mobile health applications with practical strategies is effective in reducing anxiety and can also reduce other anxiety disorders in Healthcare professional.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"37"},"PeriodicalIF":3.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11760082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027826","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":"Death risk prediction model for patients with non-traumatic intracerebral hemorrhage.","authors":"Yidan Chen, Xuhui Liu, Mingmin Yan, Yue Wan","doi":"10.1186/s12911-025-02865-4","DOIUrl":"10.1186/s12911-025-02865-4","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to assess the risk of death from non-traumatic intracerebral hemorrhage (ICH) using a machine learning model.</p><p><strong>Methods: </strong>1274 ICH patients who met the specified inclusion and exclusion criteria were analyzed retrospectively in the MIMIC IV 3.0 database. Patients were randomly divided into training, validation, and testing datasets in a ratio of 6:2:2 based on the outcome distribution. Data from the Second Hospital of Lanzhou University were used as an external validation set. This study used LASSO regression and multivariable logistic regression analysis to screen for features. We then employed XGBoost to construct a machine-learning model. The model's performance was evaluated using ROC curve analysis, calibration curve analysis, clinical decision curve analysis, sensitivity, specificity, accuracy, and F1 score. Conclusively, the SHapley Additive exPlanations (SHAP) method was employed to interpret the model's predictions.</p><p><strong>Results: </strong>Deaths occurred in 572 out of the 1274 ICH cases included in the study, resulting in an incidence rate of 44.9%. The XGBoost model achieved a high AUC when predicting deaths in ICH patients (train: 0.814, 95%CI: 0.784 - 0.844; validation: 0.715, 95%CI: 0.653 - 0.777; test: 0.797, 95%CI: 0.743 - 0.851). The importance of SHAP variables in the model ranked from high to low was: 'GCS motor', 'Age', 'GCS eyes', 'Low density lipoprotein (LDL)', ' Albumin', ' Atrial fibrillation', and 'Gender'. The XGBoost model demonstrated good predictive performance in both the validation and external validation datasets.</p><p><strong>Conclusions: </strong>The XGBoost machine learning model we built has demonstrated strong performance in predicting the risk of death from ICH. Furthermore, the SHAP provides the possibility of interpreting machine learning results.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"35"},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11755980/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143022268","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":"Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.","authors":"Ling-Chun Sun, Chia-Chiang Lee, Hung-Yen Ke, Chih-Yuan Wei, Ke-Feng Lin, Shih-Sung Lin, Hsin Hsiu, Ping-Nan Chen","doi":"10.1186/s12911-025-02872-5","DOIUrl":"10.1186/s12911-025-02872-5","url":null,"abstract":"<p><strong>Background: </strong>As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of ECG diagnostic research. In recent diagnostic methodologies, Morse Continuous Wavelet Transform (MsCWT) is a feature extraction technique utilized to draw out distinctive attributes of ECG signals. In our study, we explore the employment of MsCWT in the classification of AF with ECG signals in a continuum.</p><p><strong>Results: </strong>We present a MsCWT image-based deep learning machine for AF differentiation. For the training, validation, and test sets, we achieved average accuracies of 97.94%, 97.84%, and 91.32%; and overall F1 scores of 97.13%, 96.86%, and 89.41% respectively. Moreover, AUC ROC curves of over 0.99 were obtained for all classes in the training and validation sets; and were over 0.9679 for the test set.</p><p><strong>Conclusions: </strong>Training deep learning machines for the classification of AF with MsCWT-based images demonstrated to yield favorable outcomes and achieved superior performance amongst studies utilizing the same dataset. Though minimal, the conversion of signals into wavelet form with MsCWT may drastically improve outcomes not only in future ECG signal studies; but all signal-based diagnostics.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"22 Suppl 5","pages":"349"},"PeriodicalIF":3.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11752627/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143000614","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":"Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.","authors":"Masoumeh Vali, Hossein Motahari Nezhad, Levente Kovacs, Amir H Gandomi","doi":"10.1186/s12911-024-02754-2","DOIUrl":"10.1186/s12911-024-02754-2","url":null,"abstract":"<p><p>This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We conducted a systematic review and random-effect meta-analysis summarizing predictive model development and validation studies using machine learning in diverse samples to predict PTSD. Model performances were pooled using the area under the curve (AUC) with a 95% confidence interval (CI). Heterogeneity in each meta-analysis was measured using I². The risk of bias in each study was appraised using the PROBAST tool. 48% of the 23 included studies had a high ROB, and the remaining had unclear. Tree-based models were the primarily used algorithms and showed promising results in predicting PTSD outcomes for various groups, as indicated by their pooled AUCs: military incidents (0.745), sexual or physical trauma (0.861), natural disasters (0.771), medical trauma (0.808), firefighters (0.96), and alcohol-related stress (0.935). However, the applicability of these findings is limited due to several factors, such as significant variability among the studies, high and unclear risks of bias, and a shortage of models that maintain accuracy when tested in new settings. Researchers should follow the reporting standards for AI/ML and adhere to the PROBAST guidelines. It is also essential to conduct external validations of these models to ensure they are practical and relevant in real-world settings.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"34"},"PeriodicalIF":3.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11752770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143000600","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":"Towards a decision support system for post bariatric hypoglycaemia: development of forecasting algorithms in unrestricted daily-life conditions.","authors":"Francesco Prendin, Olivia Streicher, Giacomo Cappon, Eva Rolfes, David Herzig, Lia Bally, Andrea Facchinetti","doi":"10.1186/s12911-025-02856-5","DOIUrl":"10.1186/s12911-025-02856-5","url":null,"abstract":"<p><strong>Background: </strong>Post bariatric hypoglycaemic (PBH) is a late complication of weight loss surgery, characterised by critically low blood glucose levels following meal-induced glycaemic excursions. The disabling consequences of PBH underline the need for the development of a decision support system (DSS) that can warn individuals about upcoming PBH events, thus enabling preventive actions to avoid impending episodes. In view of this, we developed various algorithms based on linear and deep learning models to forecast PBH episodes in the short-term.</p><p><strong>Methods: </strong>We leveraged a dataset obtained from 50 patients with PBH after Roux-en-Y gastric bypass, monitored for up to 50 days under unrestricted real-life conditions. Algorithms' performance was assessed by measuring Precision, Recall, F1-score, False-alarms-per-day and Time Gain (TG).</p><p><strong>Results: </strong>The run-to-run forecasting algorithm based on recursive autoregressive model (rAR) outperformed the other techniques, achieving Precision of 64.38%, Recall of 84.43%, F1-score of 73.06%, a median TG of 10 min and 1 false alarm every 6 days. More complex deep learning models demonstrated similar median TG but inferior forecasting capabilities with F1-score ranging from 54.88% to 64.10%.</p><p><strong>Conclusions: </strong>Real-time forecasting of PBH events using CGM data as a single input imposes high demands on various types of prediction algorithms, with CGM data noise and rapid postprandial glucose dynamics representing the key challenges. In this study, the run-to-run rAR yielded most satisfactory results with accurate PBH event predictive capacity and few false alarms, thereby indicating potential for the development of DSS for people with PBH.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"33"},"PeriodicalIF":3.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11749296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143000553","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":"Mitigating bias in AI mortality predictions for minority populations: a transfer learning approach.","authors":"Tianshu Gu, Wensen Pan, Jing Yu, Guang Ji, Xia Meng, Yongjun Wang, Minghui Li","doi":"10.1186/s12911-025-02862-7","DOIUrl":"10.1186/s12911-025-02862-7","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographic biases, particularly affecting racial and ethnic minorities. The objective of this study is to investigate the demographic biases in AI models predicting COVID-19 mortality and to assess the effectiveness of transfer learning in improving model fairness across diverse demographic groups.</p><p><strong>Methods: </strong>This retrospective cohort study used a population-based dataset of COVID-19 cases from the Centers for Disease Control and Prevention (CDC), spanning the years 2020-2024. The study analyzed AI model performance across different racial and ethnic groups and employed transfer learning techniques to improve model fairness by adapting pre-trained models to the specific demographic and clinical characteristics of the population.</p><p><strong>Results: </strong>Decision Tree (DT) and Random Forest (RF) models consistently showed improvements in accuracy, precision, and ROC-AUC scores for Non-Hispanic Black, Hispanic/Latino, and Asian populations. The most significant precision improvement was observed in the DT model for Hispanic/Latino individuals, which increased from 0.3805 to 0.5265. The precision for Asians or Pacific Islanders in the DT model increased from 0.4727 to 0.6071, and for Non-Hispanic Blacks, it rose from 0.5492 to 0.6657. Gradient Boosting Machines (GBM) produced mixed results, showing accuracy and precision improvements for Non-Hispanic Black and Asian groups, but declines for the Hispanic/Latino and American Indian groups, with the most significant decline in precision, which dropped from 0.4612 to 0.2406 in the American Indian group. Logistic Regression (LR) demonstrated minimal changes across all metrics and groups. For the Non-Hispanic American Indian group, most models showed limited benefits, with several performance metrics either remaining stable or declining.</p><p><strong>Conclusions: </strong>This study demonstrates the potential of AI in predicting COVID-19 mortality while also underscoring the critical need to address demographic biases. The application of transfer learning significantly improved the predictive performance of models across various racial and ethnic groups, suggesting these techniques are effective in mitigating biases and promoting fairness in AI models.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"30"},"PeriodicalIF":3.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11742213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143000548","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 prehospital decision-making: exploring user needs and design considerations for clinical decision support systems.","authors":"Enze Bai, Zhan Zhang, Yincao Xu, Xiao Luo, Kathleen Adelgais","doi":"10.1186/s12911-024-02844-1","DOIUrl":"10.1186/s12911-024-02844-1","url":null,"abstract":"<p><strong>Background: </strong>In prehospital emergency care, providers face significant challenges in making informed decisions due to factors such as limited cognitive support, high-stress environments, and lack of experience with certain patient conditions. Effective Clinical Decision Support Systems (CDSS) have great potential to alleviate these challenges. However, such systems have not yet been widely adopted in real-world practice and have been found to cause workflow disruptions and usability issues. Therefore, it is critical to investigate how to design CDSS that meet the needs of prehospital providers while accounting for the unique characteristics of prehospital workflows.</p><p><strong>Methods: </strong>We conducted semi-structured interviews with 20 prehospital providers recruited from four Emergency Medical Services (EMS) agencies in an urban area in the northeastern U.S. The interviews focused on the decision-making challenges faced by prehospital providers, their technological needs for decision support, and key considerations for the design and implementation of a CDSS that can seamlessly integrate into prehospital care workflows. The data were analyzed using content analysis to identify common themes.</p><p><strong>Results: </strong>Our qualitative study identified several challenges in prehospital decision-making, including limited access to diagnostic tools, insufficient experience with certain critical patient conditions, and a lack of cognitive support. Participants highlighted several desired features to make CDSS more effective in the dynamic, hands-busy, and cognitively demanding prehospital context, such as automatic prompts for possible patient conditions and treatment options, alerts for critical patient safety events, AI-powered medication identification, and easy retrieval of protocols using hands-free methods (e.g., voice commands). Key considerations for successful CDSS adoption included balancing the frequency and urgency of alerts to reduce alarm fatigue and workflow disruptions, facilitating real-time data collection and documentation to enable decision generation, and ensuring trust and accountability while preventing over-reliance when using CDSS.</p><p><strong>Conclusion: </strong>This study provides empirical insights into the challenges and user needs in prehospital decision-making and offers practical and system design implications for addressing these issues.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"31"},"PeriodicalIF":3.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11742207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143000579","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":"Implementation of a Laboratory Information Management System (LIMS) for microbiology in Timor-Leste: challenges, mitigation strategies, and end-user experiences.","authors":"Tessa Oakley, Juliao Vaz, Fausto da Silva, Raikos Allan, Deonisia Almeida, Karen Champlin, Endang Soares da Silva, Ari Jayanti Tilman, Ian Marr, Heidi Smith-Vaughan, Jennifer Yan, Joshua R Francis","doi":"10.1186/s12911-024-02831-6","DOIUrl":"10.1186/s12911-024-02831-6","url":null,"abstract":"<p><strong>Background: </strong>Effective diagnostic capacity is crucial for clinical decision-making, with up to 70% of decisions in high-resource settings based on laboratory test results. However, in low- and middle-income countries (LMIC) access to diagnostic services is often limited due to the absence of Laboratory Information Management Systems (LIMS). LIMS streamline laboratory operations by automating sample handling, analysis, and reporting, leading to improved quality and faster results. Despite these benefits, sustainably implementing LIMS in LMIC is challenging due to high costs, inadequate infrastructure, and limited technical expertise.</p><p><strong>Methods: </strong>This study evaluated the implementation of a customised microbiology LIMS at the National Health Laboratory (NHL) in Timor-Leste. The LIMS was deployed in November 2020, with an accompanying online results portal introduced in early 2021. The implementation was assessed via a checklist based on key challenges and requirements for LIMS in LMIC, alongside a post-implementation survey of scientists and clinicians.</p><p><strong>Results: </strong>The assessment revealed significant improvements in laboratory processes, including enhanced sample throughput, data management, and result reporting. The LIMS reduced transcription errors and standardised reporting of antimicrobial susceptibility testing (AST), improving data quality and accessibility. However, challenges such as unreliable internet connectivity and the need for ongoing funding and technical support persist. The user satisfaction survey, with responses from 19 laboratory scientists and 15 clinicians, revealed positive feedback on workflow improvements and result accessibility, although concerns about internet speed, sustainability, and the need for further training were noted.</p><p><strong>Conclusion: </strong>This study highlights the importance of careful planning, customisation, and stakeholder engagement in LIMS implementation in LMIC. The success in Timor-Leste demonstrates the potential for improved laboratory quality and patient outcomes, but also underscores the need for ongoing investment in infrastructure, technical expertise, and sustainability planning.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"32"},"PeriodicalIF":3.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11748579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143000597","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":"Empowering personalized oncology: evolution of digital support and visualization tools for molecular tumor boards.","authors":"Cosima Strantz, Dominik Böhm, Thomas Ganslandt, Melanie Börries, Patrick Metzger, Thomas Pauli, Andreas Blaumeiser, Alexander Scheiter, Ian-Christopher Jung, Jan Christoph, Iryna Manuilova, Konstantin Strauch, Arsenij Ustjanzew, Niklas Reimer, Hauke Busch, Philipp Unberath","doi":"10.1186/s12911-024-02821-8","DOIUrl":"10.1186/s12911-024-02821-8","url":null,"abstract":"<p><strong>Background: </strong>Molecular tumor boards (MTBs) play a pivotal role in personalized oncology, leveraging complex data sets to tailor therapy for cancer patients. The integration of digital support and visualization tools is essential in this rapidly evolving field facing fast-growing data and changing clinical processes. This study addresses the gap in understanding the evolution of software and visualization needs within MTBs and evaluates the current state of digital support. Alignment between user requirements and software development is crucial to avoid waste of resources and maintain trust.</p><p><strong>Methods: </strong>In two consecutive nationwide medical informatics projects in Germany, surveys and expert interviews were conducted as stage 1 (n = 14), stage 2 (n = 30), and stage 3 (n = 9). Surveys, via the SoSci Survey tool, covered participants' roles, working methods, and support needs. The second survey additionally addressed requirements for visualization solutions in molecular tumor boards. These aimed to understand diverse requirements for preparation, implementation, and documentation. Nine semi-structured expert interviews complemented quantitative findings through open discussion.</p><p><strong>Results: </strong>Using quantitative and qualitative analyses, we show that existing digital tools may improve therapy recommendations and streamline MTB case preparation, while continuous training and system improvements are needed.</p><p><strong>Conclusions: </strong>Our study contributes to the field by highlighting the importance of developing user-centric, customizable software solutions that can adapt to the fast-paced environment of MTBs to advance personalized oncology. In doing so, it lays the foundation for further advances in personalized medicine in oncology and points to a shift towards more efficient, technology-driven clinical decision-making processes. This research not only enriches our understanding of the integration of digital tools into MTBs, but also signals a broader shift towards technological innovation in healthcare.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"29"},"PeriodicalIF":3.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11736948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143000576","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":"FHIR PIT: a geospatial and spatiotemporal data integration pipeline to support subject-level clinical research.","authors":"Karamarie Fecho, Juan J Garcia, Hong Yi, Griffin Roupe, Ashok Krishnamurthy","doi":"10.1186/s12911-024-02815-6","DOIUrl":"10.1186/s12911-024-02815-6","url":null,"abstract":"<p><strong>Background: </strong>Environmental exposures such as airborne pollutant exposures and socio-economic indicators are increasingly recognized as important to consider when conducting clinical research using electronic health record (EHR) data or other sources of clinical data such as survey data. While numerous public sources of geospatial and spatiotemporal data are available to support such research, the data are challenging to work with due to inconsistencies in file formats and spatiotemporal resolutions, computational challenges with large file sizes, and a lack of tools for patient- or subject-level data integration.</p><p><strong>Results: </strong>We developed FHIR PIT (HL7® Fast Healthcare Interoperability Resources Patient data Integration Tool) as an open-source, modular, data-integration software pipeline that consumes EHR data in FHIR® format and integrates the data at the level of the patient or subject with environmental exposures data of varying spatiotemporal resolutions and file formats. We applied FHIR PIT to generate \"integrated feature tables\" containing patient- or subject-level EHR data integrated with environmental exposures data on two cohorts: one on patients with asthma and related common pulmonary disorders; and a second on patients with primary ciliary dyskinesia and related rare pulmonary disorders. The data were then exposed via the open Integrated Clinical and Environmental Exposures Service, which was then queried to explore relationships between exposures to two representative airborne pollutants (particulate matter and ozone) and annual emergency department or inpatient visits for respiratory issues. We found that hospitalizations for respiratory issues were more common among patients exposed to relatively high levels of particulate matter and ozone and were higher overall among patients with primary ciliary dyskinesia than among patients with asthma.</p><p><strong>Conclusions: </strong>Our manuscript describes a major release of FHIR PIT v1.0 and includes a technical demonstration use case and a clinical application on the use of FHIR PIT to support research on environmental exposures and health outcomes related to asthma and primary ciliary dyskinesia. For application of the tool to common data models (CDMs) other than FHIR, we offer open-source conversion tools to map from the PCORnet, i2b2, and OMOP CDMs to FHIR.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"24"},"PeriodicalIF":3.3,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11734467/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142982914","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}