Informatics in Medicine Unlocked最新文献

{"title":"LMVT: A hybrid vision transformer with attention mechanisms for efficient and explainable lung cancer diagnosis","authors":"Jesika Debnath ,&nbsp;Al Shahriar Uddin Khondakar Pranta ,&nbsp;Amira Hossain ,&nbsp;Anamul Sakib ,&nbsp;Hamdadur Rahman ,&nbsp;Rezaul Haque ,&nbsp;Md.Redwan Ahmed ,&nbsp;Ahmed Wasif Reza ,&nbsp;S M Masfequier.Rahman Swapno ,&nbsp;Abhishek Appaji","doi":"10.1016/j.imu.2025.101669","DOIUrl":"10.1016/j.imu.2025.101669","url":null,"abstract":"<div><div>Lung cancer continues to be a leading cause of cancer-related deaths worldwide due to its high mortality rate and the complexities involved in diagnosis. Traditional diagnostic approaches often face issues such as subjectivity, class imbalance, and limited applicability across different imaging modalities. To tackle these problems, we introduce Lung MobileVIT (LMVT), a lightweight hybrid model that combines a Convolutional Neural Network (CNN) and a Transformer for multiclass lung cancer classification. LMVT utilizes depthwise separable convolutions for local texture extraction while employing multi-head self-attention (MHSA) to capture long-range global dependencies. Furthermore, we integrate attention mechanisms based on the Convolutional Block Attention Module (CBAM) and feature selection techniques derived from the Simple Gray Level Difference Method (SGLDM) to improve discriminative focus and minimize redundancy. LMVT utilizes attention recalibration to enhance the saliency of the minority class, while also incorporating curriculum augmentation strategies that balance representation across underrepresented classes. The model has been trained and validated using two public datasets (IQ-OTH/NCCD and LC25000) and evaluated for both 3-class and 5-class classification tasks. LMVT achieved an impressive 99.61 % accuracy and 99.22 % F1-score for the 3-class classification, along with 99.75 % accuracy and 99.44 % specificity for the 5-class classification. This performance surpasses that of several recent Vision Transformer (ViT) architectures. Statistical significance tests and confidence intervals confirm the reliability of these performance metrics, while an analysis of model complexity supports its capability for potential deployment. To enhance clinical interpretability, the model is integrated with explainable AI (XAI) and is implemented within a web-based diagnostic application for analyzing CT and histopathology images. This study highlights the potential of hybrid ViT architectures in creating scalable and interpretable data-driven tools for practical use in lung cancer diagnostics.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101669"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PhenoQC: An integrated toolkit for quality control of phenotypic data in genomic research","authors":"Jorge Miguel Silva,&nbsp;José Luis Oliveira","doi":"10.1016/j.imu.2025.101693","DOIUrl":"10.1016/j.imu.2025.101693","url":null,"abstract":"<div><h3>Background:</h3><div>Large-scale genomic research requires robust, consistent phenotypic datasets for meaningful genotype–phenotype correlations. However, diverse collection protocols, incomplete entries, and heterogeneous terminologies frequently compromise data quality and slows downstream analysis.</div></div><div><h3>Methodology:</h3><div>To address these issues, we present PhenoQC, a high-throughput, configuration-driven toolkit that unifies schema validation, ontology-based semantic alignment, and missing-data imputation in a single workflow. Its modular architecture leverages chunk-based parallelism to handle large datasets, while customizable schemas enforce structural and type constraints. PhenoQC applies user-defined and state-of-the-art machine learning-based imputation and performs multi-ontology mapping with fuzzy matching to harmonize phenotype text. It also quantifies potential imputation-induced distributional shifts by reporting standardized mean difference, variance ratio, and Kolmogorov–Smirnov statistics for numeric variables, and population stability index and Cramér’s <span><math><mi>V</mi></math></span> for categorical variables, with user-configurable thresholds. The toolkit provides command-line and graphical interfaces for seamless integration into automated pipelines and interactive curation environments.</div></div><div><h3>Results:</h3><div>We benchmarked PhenoQC on synthetic datasets with up to 100,000 records and it demonstrated near-linear scalability and full recovery of artificially missing numeric values.Moreover, PhenoQC’s ontology alignment achieved over 97% accuracy under textual corruption. Finally, using two real clinical datasets, PhenoQC successfully imputed missing values, enforced schema compliance, and flagged data anomalies without significant overhead.</div></div><div><h3>Conclusions:</h3><div>PhenoQC saves manual curation time and ensures consistent, analysis-ready phenotypic data through its streamlined system. Its adaptable design adjusts to evolving ontologies and domain-specific rules, empowering researchers to conduct more reliable studies.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"58 ","pages":"Article 101693"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145117699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel comparison of CellaVision DC-1 and microscopic assessment of blood film morphology in paediatrics","authors":"Heba Sharif ,&nbsp;Denise E. Jackson ,&nbsp;Genia Burchall","doi":"10.1016/j.imu.2025.101690","DOIUrl":"10.1016/j.imu.2025.101690","url":null,"abstract":"<div><h3>Background</h3><div>The aim of this study was to evaluate the blood film assessment of CellaVision DC-1 compared to conventional microscopy in stained peripheral blood (PB) films from paediatric samples.</div></div><div><h3>Methods</h3><div>Blood films (n = 50) including clinically normal samples as well as common pathological conditions, were collected and examined by conventional microscopy and CellaVision DC-1. Manual microscopy counts vs. automated WBC differentiation and RBC grading via Cellavision, including manual re-classification, were compared to expert morphologist reporting. Using statistical analysis, the following metrics were measured including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).</div></div><div><h3>Results</h3><div>The reliability of RBC grading ranged between 60 and 100 % sensitivity and 55–74 % specificity for CellaVision method compared to 78–93 % sensitivity with manual microscopy, demonstrating the latter as the superior method. Additionally, DC-1 misclassified the presence of blasts for lymphocytes, with 67 % compared to 100 % specificity with the gold standard microscopy. Both pre- and post-classification, re-classifications, and manual microscopy showed strong correlations of WBC differential counts with expert/known readings, mainly for neutrophils and lymphocytes (<span><math><mrow><msup><mi>R</mi><mrow><mn>2</mn><mo>:</mo></mrow></msup></mrow></math></span> 0.60–0.85). In terms of time, CellaVision took 1 min longer to scan and assess each slide than did light microscopy, which could affect timely diagnosis and treatment decisions.</div></div><div><h3>Conclusion</h3><div>The use of CellaVision DC-1 may be beneficial to diagnostic laboratories in the adult setting; however, further research should focus on enhancing automated analysis when assessing paediatric samples that demand human intellect and critical thinking. Medical Scientist training and software development are recommended. Manual microscopy is faster and more accurate. Slide signing and DC-1 classifications of unclassified WBCs need scientist intervention.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"58 ","pages":"Article 101690"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards trustworthy AI-driven leukemia diagnosis: A hybrid Hierarchical Federated Learning and explainable AI framework","authors":"Khadija Pervez ,&nbsp;Syed Irfan Sohail ,&nbsp;Faiza Parwez ,&nbsp;Muhammad Abdullah Zia","doi":"10.1016/j.imu.2025.101618","DOIUrl":"10.1016/j.imu.2025.101618","url":null,"abstract":"<div><div>Accurate detection and classification of microscopic cells from acute lymphoblastic leukemia remain challenging due to the difficulty of differentiating between cancerous and healthy cells. This paper proposes a novel approach to identify and categorize acute lymphoblastic leukemia that uses explainable artificial intelligence and federated learning to train models across multiple institutions while keeping patient information decentralized and encrypted. The framework trains EfficientNetB3 for the classification of leukemia cells and incorporates explainability techniques to make decisions of the underlying model transparent and interpretable. The framework employs a hierarchical federated learning approach that allows distributed learning across clinical centers, ensuring that sensitive patient data remain localized. Explainability techniques such as saliency maps, occlusion sensitivity, and randomized input sampling for explanation with relevant evaluation scores are integrated in the framework to provide visual and textual explanations of model’s predictions to enhance interpretability. The experiments were carried out on a publicly available dataset consisting of 15,135 microscopic images. The performance of the proposed model was benchmarked against traditional centralized models and classical federated learning techniques. The proposed model demonstrated a 2.5% improvement in accuracy (96.5%) and a 5.4% increase in F1-score (94.4%) compared to baseline models. Hierarchical federated learning reduced communication costs by 15% while maintaining data privacy. The integration of explainable artificial intelligence improved the transparency of model decisions, with a high area under the ROC curve (AUC) of 0.98 for the classification of leukemia cells. These results suggest that the proposed framework offers a robust solution for intelligent systems for medical diagnostics and can also be extended to other medical imaging tasks.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101618"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing the Sensitivity Frontier in digital contact tracing: Comparative analysis of proposed methods toward maximized utility","authors":"Junko Ami ,&nbsp;Yanbo Pang ,&nbsp;Hiroshi Masui ,&nbsp;Takashi Okumura ,&nbsp;Yoshihide Sekimoto","doi":"10.1016/j.imu.2025.101622","DOIUrl":"10.1016/j.imu.2025.101622","url":null,"abstract":"<div><div>During the COVID-19 pandemic, many countries adopted Digital Contact Tracing (DCT) technology to control infections. However, the widely-used Bluetooth Low Energy (BLE)-based DCT requires both the infected individual and the contact to have the application activated to detect exposure. Forcing citizens to install the DCT application could compromise their privacy. Therefore, to make DCT a truly usable tool, it is crucial to develop a DCT system that possesses high sensitivity, without depending on the application usage rate.</div><div>The Computation of Infection Risk via Confidential Locational Entries (CIRCLE) is a DCT method that utilizes connection logs from mobile phone base stations, theoretically offering much higher sensitivity than BLE-based DCT. However, its real performance has not been proven, and thus, this paper estimates the sensitivity and specificity of both BLE-based DCT and CIRCLE in a comparative setting. The estimation combines simulated movement patterns of residents with real-world data from app usage in Japan, utilizing both simulation and numerical modeling, with missing data supplemented through sensitivity analysis.</div><div>The sensitivity of BLE-based DCT is severely limited by the application’s usage rate, with an estimated baseline of just 10.9%, and even under highly optimistic assumptions, it only reaches 27.0%. In contrast, CIRCLE demonstrated a significantly higher sensitivity of 85.6%, greatly surpassing BLE-based DCT. The specificity of CIRCLE, though, decreased as the number of infected individuals increased, dropping to less than half of BLE-based DCT’s specificity during widespread infection. The BLE-based DCT used during the pandemic suffers from low sensitivity. While CIRCLE has specificity challenges, it provides exceptionally high sensitivity. Integrating these methods could redefine the design of digital contact tracing, leading to better utility for future infection control.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101622"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Customer relationship management systems in clinical laboratories: A systematic review","authors":"Jonel Bation ,&nbsp;Mary Ann Jaro ,&nbsp;Lheyniel Jane Nery ,&nbsp;Mudjahidin Mudjahidin ,&nbsp;Andre Parvian Aristio ,&nbsp;Eddie Bouy Palad ,&nbsp;Jason Chavez ,&nbsp;Lemuel Clark Velasco","doi":"10.1016/j.imu.2025.101628","DOIUrl":"10.1016/j.imu.2025.101628","url":null,"abstract":"<div><div>The implementation of Customer Relationship Management (CRM) Systems in clinical laboratories is crucial in improving customer relationships, service quality, and operational efficiency that aligns with a patient-centric care model. This study utilizes the PRISMA guidelines in reviewing and synthesizing 26 journal articles using the People, Process, Technology (PPT) framework to analyze the roles of people involved in clinical settings, the processes by which laboratory services were delivered, and the technological considerations enhancing patient care. Results revealed that the successful implementation of CRM systems in clinical laboratories depends on the aligned efforts of both developers and end-users. Subsequently, marketing processes and customer service were then found out to be crucial for the successful utilization of CRM systems in clinical laboratories. The features and the system integration techniques of CRM systems were found out to be vital in developing efficient operations, enhancing data analysis, and extending accessibility. The research gap analysis, on the one hand, shows that the effectiveness of CRM systems on the patients, the lack of qualitative methods, and the development of corrective actions to increase patient satisfaction are relevant areas of research concerns to optimize the effectiveness of implementing different CRM systems.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101628"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Usability and accessibility in mHealth stroke apps: An empirical assessment","authors":"Pontus Svensson ,&nbsp;Shuanglan Lin ,&nbsp;Leonardo Horn Iwaya","doi":"10.1016/j.imu.2025.101616","DOIUrl":"10.1016/j.imu.2025.101616","url":null,"abstract":"<div><h3>Background</h3><div>Cerebrovascular accidents or strokes continue to be among the leading causes of death and disability worldwide. This has stressed the need to design digital health solutions that can be effectively used by patients, caregivers, and medical professionals, helping to alleviate the global disease burden. In this context, mobile health (mHealth) apps are shown to be valuable solutions for bridging healthcare gaps.</div></div><div><h3>Objective</h3><div>In this study, we aim to evaluate the quality aspects of usability and accessibility of stroke-related mHealth apps for Android. We seek to identify prevalent issues and discuss recommendations to enhance user experience and app quality.</div></div><div><h3>Methods</h3><div>We selected 16 mHealth stroke apps, accounting for more than 219k downloads. The apps were assessed through different methods, including accessibility testing with the Google Accessibility Scanner, overall quality assessment with the Mobile Application Rating Scale (MARS), and usability testing using heuristic evaluations.</div></div><div><h3>Results</h3><div>Our findings show significant issues with the apps’ touch target sizes and text contrast, which are particularly important for stroke app users with impaired vision and motor skills. MARS evaluations revealed that some apps, such as the Constant Therapy app, excelled in engagement and functionality. In contrast, many apps scored lower due to limited functionality and unclear/confusing interfaces, such as Stroke Recovery Predictor and Conversation Therapy Lite. Heuristic evaluations also highlighted several usability violations, such as a lack of “Visibility of System Status” and “Insufficient Error Messaging.”</div></div><div><h3>Conclusion</h3><div>Overall, most apps presented deficiencies in several aspects of usability and accessibility. As recommendations, developers can increase touch target sizes, improve text contrast, increase functional variety, optimise navigation, and enhance user engagement strategies. Addressing such issues can help improve the stroke apps’ usability and accessibility, aiming for better health outcomes for stroke patients.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101616"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient2Trial: From patient to participant in clinical trials using large language models","authors":"Surabhi Datta ,&nbsp;Kyeryoung Lee ,&nbsp;Liang-Chin Huang ,&nbsp;Hunki Paek ,&nbsp;Roger Gildersleeve ,&nbsp;Jonathan Gold ,&nbsp;Deepak Pillai ,&nbsp;Jingqi Wang ,&nbsp;Mitchell K. Higashi ,&nbsp;Lizheng Shi ,&nbsp;Percio S. Gulko ,&nbsp;Hua Xu ,&nbsp;Chunhua Weng ,&nbsp;Xiaoyan Wang","doi":"10.1016/j.imu.2025.101615","DOIUrl":"10.1016/j.imu.2025.101615","url":null,"abstract":"<div><h3>Purpose</h3><div>Large language models (LLMs) exhibit promising language understanding and generation capabilities and have been adopted for various clinical use cases. Investigating the feasibility of leveraging LLMs in building a clinical trial retrieval system for patients is crucial as it can greatly enhance the patient enrollment process by prioritizing the most suitable trials pertaining to a patient. In this work, we develop an LLM-assisted system focused on a patient-initiated approach, allowing patients with specific conditions to directly find eligible trials by completing disorder-specific questionnaires.</div></div><div><h3>Methods</h3><div>We obtained clinical trial eligibility criteria (from ClinicalTrials.gov) and simulated patient questionnaires (or topics) from the Text REtrieval Conference (TREC) 2023 Clinical Trials Track conducted by the National Institute of Standards and Technology (NIST), in which we also participated. These topics cover eight disorders across diverse domains, namely glaucoma, anxiety, chronic obstructive pulmonary disease, breast cancer, Covid-19, rheumatoid arthritis, sickle cell anemia, and type 2 diabetes. A Generative Pre-trained Transformer model (GPT-4) was employed for system development. We conducted both quantitative and qualitative evaluation using 37 patient topics.</div></div><div><h3>Results</h3><div>The system achieved an overall Precision@10 (proportion of relevant trials) of 0.7351 and NDCG@10 (considers ranking order of relevant trials) of 0.8109, indicating its effectiveness in retrieving ranked lists of suitable trials for patients. Notably, for eight out of 37 patient topics, all the top 10 retrieved trials were relevant. The system scored the highest on breast cancer (NDCG@10 = 0.9347, Precision@10 = 0.84) and the lowest on type 2 diabetes (NDCG@10 = 0.61, Precision@10 = 0.475). One probable reason could be that the information in breast cancer topics is relatively straightforward to match. Qualitative error analysis classified errors into four categories (e.g., difficulty in correctly matching inclusion criteria) and further highlighted strengths (e.g., ability to make clinical inference).</div></div><div><h3>Conclusion</h3><div>We demonstrated the feasibility of integrating LLMs in identifying and ranking suitable trials for patients across multiple disorders. Further work is required to assess the system's generalizability on other disorders and patient information sources. This system has the potential to expedite the patient-trial matching process by suggesting a ranked list of applicable trials to patients and clinicians.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101615"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning unbiased risk prediction based algorithms in healthcare: A case study with primary care patients","authors":"Vibhuti Gupta ,&nbsp;Julian Broughton ,&nbsp;Ange Rukundo ,&nbsp;Lubna J. Pinky","doi":"10.1016/j.imu.2025.101627","DOIUrl":"10.1016/j.imu.2025.101627","url":null,"abstract":"<div><div>The proliferation of Artificial Intelligence (AI) has revolutionized the healthcare domain with technological advancements in conventional diagnosis and treatment methods. These advancements lead to faster disease detection, and management and provide personalized healthcare solutions. However, most of the clinical AI methods developed and deployed in hospitals have algorithmic and data-driven biases due to insufficient representation of specific race, gender, and age group which leads to misdiagnosis, disparities, and unfair outcomes. Thus, it is crucial to thoroughly examine these biases and develop computational methods that can mitigate biases effectively. This paper critically analyzes this problem by exploring different types of data and algorithmic biases during both pre-processing and post-processing phases to uncover additional, previously unexplored biases in a widely used real-world healthcare dataset of primary care patients. Additionally, effective strategies are proposed to address gender, race, and age biases, ensuring that risk prediction outcomes are equitable and impartial. Through experiments with various machine learning algorithms leveraging the Fairlearn tool, we have identified biases in the dataset, compared the impact of these biases on the prediction performance, and proposed effective strategies to mitigate these biases. Our results demonstrate clear evidence of racial, gender-based, and age-related biases in the healthcare dataset used to guide resource allocation for patients and have profound impact on the prediction performance which leads to unfair outcomes. Thus, it is crucial to implement mechanisms to detect and address unintended biases to ensure a safe, reliable, and trustworthy AI system in healthcare.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101627"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using implementation science to develop and deploy an oncology electronic health record","authors":"Carla Taramasco ,&nbsp;Rene Noel ,&nbsp;Gastón Márquez ,&nbsp;Diego Robles","doi":"10.1016/j.imu.2025.101625","DOIUrl":"10.1016/j.imu.2025.101625","url":null,"abstract":"<div><div>The management of oncology clinical processes involves the efficient management of data using electronic clinical records to effectively monitor and treat oncology patients. As the process of treating and monitoring cancer patients involves multiple stakeholders with differing perspectives, the implementation and deployment of oncology clinical registries represent a significant challenge. In this study, we address this complexity by employing a technique that helps translate implementation strategies into requirement identification methods, which are subsequently disseminated throughout the implementation and deployment phases of health information systems. We applied this technique to develop an electronic health record for the national cancer plan in Chile. The findings indicate that six implementation strategies are essential to addressing stakeholder needs, as well as three requirement identification techniques to describe the underlying problem. Furthermore, a study conducted with 27 stakeholders revealed that the perception of the oncology electronic clinical record has considerable acceptance in three critical functionalities related to the clinical process of oncology patient management. The use of implementation science strategies provides an alternative approach to understanding the underlying problem that stakeholders face when they require healthcare technologies.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101625"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}