BMC Medical Informatics and Decision Making最新文献

{"title":"Correction: Machine learning predicts pulmonary long Covid sequelae using clinical data.","authors":"Ermanno Cordelli, Paolo Soda, Sara Citter, Elia Schiavon, Christian Salvatore, Deborah Fazzini, Greta Clementi, Michaela Cellina, Andrea Cozzi, Chandra Bortolotto, Lorenzo Preda, Luisa Francini, Matteo Tortora, Isabella Castiglioni, Sergio Papa, Diego Sona, Marco Alì","doi":"10.1186/s12911-025-02918-8","DOIUrl":"10.1186/s12911-025-02918-8","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"68"},"PeriodicalIF":3.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11812242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143390157","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":"Synthesis of the clinical utilities and issues of intraoperative imaging devices in clinical reports: a systematic review and thematic synthesis.","authors":"Hiroyuki Suzuki, Yusuke Tsuboko, Manabu Tamura, Ken Masamune, Kiotaka Iwasaki","doi":"10.1186/s12911-025-02915-x","DOIUrl":"10.1186/s12911-025-02915-x","url":null,"abstract":"<p><strong>Background: </strong>Intraoperative imaging devices (i-ID), such as intraoperative optical coherence tomography (iOCT), offer surgeons critical insights previously unobservable, enhancing surgical precision and safety. Despite their benefits, i-IDs present challenges that necessitate early identification and synthesis of clinical issues to promote safer surgical implementation. This study aims to explore the potential of Qualitative Evidence Synthesis (QES) for synthesising qualitative evidence from clinical reports regarding the clinical utility and issues associated with iOCT devices.</p><p><strong>Methods: </strong>In June 2022, we conducted a systematic literature search using PubMed, Web of Science, Embase, and the Cochrane Library for articles on iOCT for retinal surgery. Criteria included articles in English, with at least ten cases, and providing qualitative insights into iOCT's utilities and issues. We performed thematic synthesis from the identified articles using qualitative data analysis software, beginning with initial coding of the 'Results' and 'Discussion' sections to create themes reflecting iOCT's utilities and issues. The created themes were further refined through axial coding and were used to construct a model illustrating iOCT's potential influence on patient outcomes. The reliability and validity of the themes were ensured through independent coding, expert consultations, and iterative revisions to achieve consensus among reviewers.</p><p><strong>Results: </strong>The QES approach enabled systematic data extraction and synthesis, providing a comprehensive view of both the utilities and issues associated with iOCT. Our findings emphasise the significant role of iOCT in enhancing decision-making, specifically in membrane peeling tasks and in detecting preoperatively undetected conditions such as full-thickness macular holes. This study also revealed critical insights into the technical challenges associated with iOCT, including device malfunctions and procedural interruptions, which are vital for improving device safety and integration into surgical practice.</p><p><strong>Conclusion: </strong>The application of QES facilitated a thorough investigation into the clinical utilities and issues of iOCT, encouraging the application of this method in the ongoing evaluation of i-ID technologies. This initial experience with QES confirms its potential in synthesising qualitative clinical data and suggests its applicability to other i-ID modalities. This approach enhances the reliability of findings and provides a solid foundation for assessing clinical utilities and issues for policymakers and medical specialists.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"70"},"PeriodicalIF":3.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11812201/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143390160","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":"Bayesian learning-based agent negotiation model to support doctor-patient shared decision making.","authors":"Xin Chen, Yong Liu, Fei-Ping Hong, Ping Lu, Jiang-Tao Lu, Kai-Biao Lin","doi":"10.1186/s12911-024-02839-y","DOIUrl":"10.1186/s12911-024-02839-y","url":null,"abstract":"<p><strong>Background: </strong>Agent negotiation is widely used in e-commerce negotiation, cloud service service-level agreements, and power transactions. However, few studies have adapted alternative negotiation models to negotiation processes between healthcare professionals and patients due to the fuzziness, ethics, and importance of medical decision making.</p><p><strong>Method: </strong>We propose a Bayesian learning based bilateral fuzzy constraint agent negotiation model (BLFCAN). It support mutually beneficial agreement on treatment between doctors and patients. The proposed model expresses the imprecise preferences and behaviors of doctors and patients through fuzzy constrained agents. To improve negotiation efficiency and social welfare, the Bayesian learning method is adopted in the proposed model to predict the opponent's preference.</p><p><strong>Results: </strong>The proposed model achieves 55.4% to 64.2% satisfaction for doctors and 69-74.5% satisfaction for patients in terms of individual satisfaction. In addition, the proposed BLFCAN can increase overall satisfaction by 26.5-29% in fewer rounds, and it can alter the negotiation strategy in a flexible manner for various negotiation scenarios.</p><p><strong>Conclusions: </strong>BLFCAN reduces communication time and cost, helps avoid potential conflicts, and reduces the impact of emotions and biases on decision-making. In addition, the BLFCAN model improves the agreement satisfaction of both parties and the total social welfare.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"67"},"PeriodicalIF":3.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11812200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143390156","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":"Exploration of the optimal deep learning model for english-Japanese machine translation of medical device adverse event terminology.","authors":"Ayako Yagahara, Masahito Uesugi, Hideto Yokoi","doi":"10.1186/s12911-025-02912-0","DOIUrl":"10.1186/s12911-025-02912-0","url":null,"abstract":"<p><strong>Background: </strong>In Japan, reporting of medical device malfunctions and related health problems is mandatory, and efforts are being made to standardize terminology through the Adverse Event Terminology Collection of the Japan Federation of Medical Device Associations (JFMDA). Internationally, the Adverse Event Terminology of the International Medical Device Regulators Forum (IMDRF-AET) provides a standardized terminology collection in English. Mapping between the JFMDA terminology collection and the IMDRF-AET is critical to international harmonization. However, the process of translating the terminology collections from English to Japanese and reconciling them is done manually, resulting in high human workloads and potential inaccuracies.</p><p><strong>Objective: </strong>The purpose of this study is to investigate the optimal machine translation model for the IMDRF-AET into Japanese for the part of a function for the automatic terminology mapping system.</p><p><strong>Methods: </strong>English-Japanese parallel data for IMDRF-AET published by the Ministry of Health, Labor and Welfare in Japan was obtained from 50 sentences randomly extracted from the terms and their definitions. These English sentences were fed into the following machine translation models to produce Japanese translations: mBART50, m2m-100, Google Translation, Multilingual T5, GPT-3, ChatGPT, and GPT-4. The evaluations included the quantitative metrics of BiLingual Evaluation Understudy (BLEU), Character Error Rate (CER), Word Error Rate (WER), Metric for Evaluation of Translation with Explicit ORdering (METEOR), and Bidirectional Encoder Representations from Transformers (BERT) score, as well as qualitative evaluations by four experts.</p><p><strong>Results: </strong>GPT-4 outperformed other models in both the quantitative and qualitative evaluations, with ChatGPT showing the same capability, but with lower quantitative scores, in the qualitative evaluation. Scores of other models, including mBART50 and m2m-100, lagged behind, particularly in the CER and BERT scores.</p><p><strong>Conclusion: </strong>GPT-4's superior performance in translating medical terminology, indicates its potential utility in improving the efficiency of the terminology mapping system.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"66"},"PeriodicalIF":3.3,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11807297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373811","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":"Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.","authors":"Lesia Mochurad, Viktoriia Babii, Yuliia Boliubash, Yulianna Mochurad","doi":"10.1186/s12911-025-02894-z","DOIUrl":"10.1186/s12911-025-02894-z","url":null,"abstract":"<p><p>The relevance of the study is due to the growing number of diseases of the cerebrovascular system, in particular stroke, which is one of the leading causes of disability and mortality in the world. To improve stroke risk prediction models in terms of efficiency and interpretability, we propose to integrate modern machine learning algorithms and data dimensionality reduction methods, in particular XGBoost and optimized principal component analysis (PCA), which provide data structuring and increase processing speed, especially for large datasets. For the first time, explainable artificial intelligence (XAI) is integrated into the PCA process, which increases transparency and interpretation, providing a better understanding of risk factors for medical professionals. The proposed approach was tested on two datasets, with accuracy of 95% and 98%. Cross-validation yielded an average value of 0.99, and high values of Matthew's correlation coefficient (MCC) metrics of 0.96 and Cohen's Kappa (CK) of 0.96 confirmed the generalizability and reliability of the model. The processing speed is increased threefold due to OpenMP parallelization, which makes it possible to apply it in practice. Thus, the proposed method is innovative and can potentially improve forecasting systems in the healthcare industry.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"63"},"PeriodicalIF":3.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11806876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370559","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":"Tough choices: the experience of family members of critically ill patients participating in ECMO treatment decision-making: a descriptive qualitative study.","authors":"Xiangying Yang, Yao Lin, Amao Tang, Xiaokang Zeng, Weiying Dai, Qian Zhang, Li Ning","doi":"10.1186/s12911-025-02876-1","DOIUrl":"10.1186/s12911-025-02876-1","url":null,"abstract":"<p><strong>Background: </strong>ECMO treatment for critically ill patients mostly requires family members to make surrogate decisions. However, the process and experience of family members' participation in decision making have not been well described.</p><p><strong>Purpose: </strong>To explore the experience of family members of critically ill patients who were asked to consent to ECMO treatment and to gain insight into the factors that promote and hinder their decision-making.</p><p><strong>Methods: </strong>A descriptive qualitative study. Data were collected using a semi-structured interview method and analysed using traditional content analysis approaches. The cohort included nineteen family members of critically ill ICU patients from a general hospital in China.</p><p><strong>Results: </strong>Eleven family members consented to ECMO treatment, and 8 refused. 4 themes and 10 subthemes emerged: (1) tough choices: the dilemma in the emergency situation, the guilt and remorse after giving up; (2) rationalisation of decision-making: ethics and morality guide decision-making, expected efficacy influences decision making, and past experience promotes decision making; (3) decision-making methods: independent decision-making, group decision-making, decision making based on patient preferences; (4) influencing factors of decision making: information and communication, social support.</p><p><strong>Conclusion: </strong>The findings provide insights and a basis for promoting efficient ECMO decision-making in clinical practice. It may be difficult to improve the time it takes to make the decision without sacrificing the quality of the decision. Healthcare professionals should provide timely emotional support, informational support, and comprehensive social support to assist them in making efficient decisions while respecting the treatment preferences of the decision-makers.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"65"},"PeriodicalIF":3.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11806576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370565","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":"Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia.","authors":"Wudneh Ketema Moges, Awoke Seyoum Tegegne, Aweke A Mitku, Esubalew Tesfahun, Solomon Hailemeskel","doi":"10.1186/s12911-025-02917-9","DOIUrl":"10.1186/s12911-025-02917-9","url":null,"abstract":"<p><strong>Background: </strong>Low birth weight (LBW) is a critical global health issue that affects infants disproportionately, particularly in developing countries. This study adopted causal machine learning (CML) algorithms for predicting LBW in newborns, drawing from midwife-led continuity care (MLCC).</p><p><strong>Methods: </strong>A quasi-experimental study was carried out in the North Shoa Zone of Ethiopia from August 2019 to September 2020. A total of 1166 women were allocated into two groups. The first group, the MLCC group, received all their antenatal, labor, birth, and immediate post-natal care from a single midwife. The second group received care from various staff members at different times throughout their pregnancy and childbirth. In this study, CML was implemented to predict LBW. Data preprocessing, including data cleaning, was conducted. CML was then employed to identify the most suitable classifier for predicting LBW. Gradient boosting algorithms were used to estimate the causal effect of MLCC on LBW. Moreover, meta-learner algorithms were utilized to estimate the individual treatment effect (ITE), the average treatment effect (ATE), and performance. Moreover, meta-learner algorithms were utilized to estimate the individual treatment effect (ITE), the average treatment effect (ATE), and performance.</p><p><strong>Results: </strong>The study results revealed that Causal K-Nearest Neighbors (CKNN) was the most effective classifier based on accuracy and estimated LBW using a 94.52% accuracy, 90.25% precision, 92.57% recall, and an F1 score of 88.2%. Meconium aspiration, perinatal mortality, pregnancy-induced hypertension, vacuum babies in need of resuscitation, and previous surgeries on their reproductive organs were identified as the top five features affecting LBW. The estimated impact of MLCC versus other professional groups on LBW was analyzed using gradient boosting algorithms and was found to be 0.237. The estimated ATE for the S-learner was 0.284, which is lower than the true ATE of 0.216. Additionally, the estimated ITE for both the T-learner and X-learner was less than -0.5, indicating that mothers would not choose to participate in the MLCC program.</p><p><strong>Conclusions: </strong>Based on these findings, the CKNN classifier demonstrated a higher accuracy and effectiveness. The S-learner and R-learner models, utilizing the XGBoost Regressor and BaseSRegressor, provided accurate estimations of ITE for assessing the impact of the MLCC program. Promoting the MLCC program could help stabilize LBW outcomes.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"64"},"PeriodicalIF":3.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11806756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370543","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":"Patients' and plastic surgeons' experiences with an online patient decision aid for breast reconstruction: considerations for nationwide implementation.","authors":"Jacqueline A Ter Stege, Ellen G Engelhardt, Leonie A E Woerdeman, Hester S A Oldenburg, Jacobien M Kieffer, Daniela E E Hahn, Frederieke H van Duijnhoven, Martine A van Huizum, Regina The, Klemens Karssen, Marianne Kuenen, Miranda A Gerritsma, Quinten Pq Ruhe, Irene S Krabbe-Timmerman, Martijne Van't Riet, Nikola An Kimmings, Eveline M L Corten, Kerry A Sherman, Arjen J Witkamp, Eveline M A Bleiker","doi":"10.1186/s12911-024-02832-5","DOIUrl":"10.1186/s12911-024-02832-5","url":null,"abstract":"<p><strong>Background: </strong>Women diagnosed with breast cancer undergoing a mastectomy often have the option to undergo breast reconstruction (BR). BR decisions are complex and have considerable impact. We developed a patient decision aid (pDA) to support patients' BR decision-making. Here, we assess patients' and physicians' use of the BR pDA and their views on the barriers and facilitators for widespread implementation.</p><p><strong>Methods: </strong>Participants completed a questionnaire, and back-end data of the pDA was analyzed.</p><p><strong>Results: </strong>Of 116 eligible patients, 113 patients accessed the BR pDA (median age: 50 years and 50% were highly educated. Most patients (72%) were satisfied with the pDA and 74% would recommend the BR pDA to other women facing the same choice. Patients' preferences regarding how much, what kind and how to present information varied. Plastic surgeons (N = 22; 71% response) were satisfied with the pDA. Their key factors for implementation included the perceived match between information and clinical practice, costs, impact on patients, and support from peers and management for the tool.</p><p><strong>Conclusions: </strong>As the BR pDA was highly valued by its end users, the identified factors for implementation should be taken into account.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"62"},"PeriodicalIF":3.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363714","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":"Ontology-based expansion of virtual gene panels to improve diagnostic efficiency for rare genetic diseases.","authors":"Jaemoon Shin, Toyofumi Fujiwara, Hirotomo Saitsu, Atsuko Yamaguchi","doi":"10.1186/s12911-025-02910-2","DOIUrl":"10.1186/s12911-025-02910-2","url":null,"abstract":"<p><strong>Background: </strong>Virtual Gene Panels (VGP) comprising disease-associated causal genes are utilized in the diagnosis of rare genetic diseases to evaluate candidate genes identified by whole-genome and whole-exome sequencing. VGPs generated by the PanelApp software were utilized in a UK 100,000 Genome Project pilot study to filter candidate genes, thus enhancing diagnostic efficiency for rare diseases. However, PanelApp also filtered out disease-causing genes in nearly 50% of the cases.</p><p><strong>Methods: </strong>Here, we propose various methods for optimized approach to design VGPs that significantly improve the diagnostic efficiency by leveraging the hierarchical structure of the Mondo disease ontology, without excluding disease-causing genes. We also performed computational experiments on an evaluation dataset comprising 74 patients to determine the optimal VGP design method.</p><p><strong>Results: </strong>Our results demonstrate that the proposed method can significantly enhance rare disease diagnosis efficiency by automatically identifying candidate genes. The proposed method successfully designed VGPs that improve diagnosis efficiency without excluding disease-causing genes.</p><p><strong>Conclusion: </strong>We have developed novel methods for VGP design that leverage the hierarchical structure of the Mondo disease ontology to improve rare genetic disease diagnosis efficiency. This approach identifies candidate genes without excluding disease-causing genes, and thereby improves diagnostic efficiency.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 Suppl 1","pages":"59"},"PeriodicalIF":3.3,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254614","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":"Healthcare trajectories of aging individuals during their last year of life: application of process mining methods to administrative health databases.","authors":"Delphine Bosson-Rieutort, Alexandra Langford-Avelar, Juliette Duc, Benjamin Dalmas","doi":"10.1186/s12911-025-02898-9","DOIUrl":"10.1186/s12911-025-02898-9","url":null,"abstract":"<p><strong>Context: </strong>World is aging and the prevalence of chronic diseases is raising with age, increasing financial strain on organizations but also affecting patients' quality of life until death. Research on healthcare trajectories has gained importance, as it can help anticipate patients' needs and optimize service organization. In an overburdened system, it is essential to develop automated methods based on comprehensive and reliable and already available data to model and predict healthcare trajectories and future utilization. Process mining, a family of process management and data science techniques used to derive insights from the data generated by a process, can be a solid candidate to provide a useful tool to support decision-making.</p><p><strong>Objective: </strong>We aimed to (1) identify the healthcare baseline trajectories during the last year of life, (2) identify the differences in trajectories according to medical condition, and (3) identify adequate settings to provide a useful output.</p><p><strong>Methods: </strong>We applied process mining techniques on a retrospective longitudinal cohort of 21,255 individuals who died between April 1, 2014, and March 31, 2018, and were at least 66 years or older at death. We used 6 different administrative health databases (emergency visit, hospitalisation, homecare, medical consultation, death register and administrative), to model individuals' healthcare trajectories during their last year of life.</p><p><strong>Results: </strong>Three main trajectories of healthcare utilization were highlighted: (i) mainly accommodating a long-term care center; (ii) services provided by local community centers in combination with a high proportion of medical consultations and acute care (emergency and hospital); and (iii) combination of consultations, emergency visits and hospitalization with no other management by local community centers or LTCs. Stratifying according to the cause of death highlighted that LTC accommodation was preponderant for individuals who died of physical and cognitive frailty. Conversely, services offered by local community centers were more prevalent among individuals who died of a terminal illness. This difference is potentially related to the access to and use of palliative care at the end-of-life, especially home palliative care implementation.</p><p><strong>Conclusion: </strong>Despite some limitations related to data and visual limitations, process mining seems to be a method that is both relevant and simple to implement. It provides a visual representation of the processes recorded in various health system databases and allows for the visualization of the different trajectories of healthcare utilization.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"58"},"PeriodicalIF":3.3,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11796206/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254420","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}