Healthcare Informatics Research最新文献

Data Mining to Identify the Right Interventions for the Right Patient for Heart Failure: A Real-World Study.

IF 2.3

Healthcare Informatics Research Pub Date : 2025-01-01 Epub Date: 2025-01-31 DOI: 10.4258/hir.2025.31.1.66

Keni Lee, Ramzi Argoubi, Halley Costantino

{"title":"Data Mining to Identify the Right Interventions for the Right Patient for Heart Failure: A Real-World Study.","authors":"Keni Lee, Ramzi Argoubi, Halley Costantino","doi":"10.4258/hir.2025.31.1.66","DOIUrl":"10.4258/hir.2025.31.1.66","url":null,"abstract":"Objectives: To identify the right interventions for the right heart failure (HF) patients in the real-world setting using machine learning (ML) trained on individual-level clinical data linked with social determinants of health (SDOH) data.Methods: In this retrospective cohort study, point-of-care claims data from Komodo Health and SDOH data from the National Health and Wellness Survey (NHWS), from January 2014-December 2020, were linked. Data mining was conducted using K-means clustering, an ML tool. Komodo Health data were used to access longitudinal data for the selected patient cohorts and crosssectional data from NHWS for additional patient information. The primary outcome was HF-related hospitalizations; secondary outcomes, all-cause hospitalization and all-cause mortality. Use of digital healthcare (DHC)/non-DHC interventions and related outcomes were also assessed.Results: The study population included 353 HF patients (mean age, 63.5 years; 57.2% women). The use of non-DHC (75.9%-81.9%) and DHC (4.0%-9.1%) interventions increased from baseline to followup. Overall, 17.0% of patients had HF-related hospitalizations (DHC, 6.9%; non-DHC, 16.5%) and 45.0% had all-cause hospitalization (DHC, 75.0%; non-DHC, 50.9%). Two archetypes with distinct patient profiles were identified. Archetype 1 (vs. 2) characterised by older age, greater disease severity, more comorbidities, more medication use, took steps to prevent heart attack/problems, had better lifestyle, higher HF-related hospitalizations (18.3% vs. 16.3%) and lower all-cause hospitalizations (42.9% vs. 46.3%). The trends remained the same regardless of the intervention type.Conclusions: Identification of patient archetypes with distinct profiles can be useful to understand underlying disease subtypes, identify specific interventions, predict clinical outcomes, and define the right intervention for the right patient.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 1","pages":"66-87"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457610","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}

引用次数: 0

Utility of Treatment Pattern Analysis Using a Common Data Model: A Scoping Review.

IF 2.3

Healthcare Informatics Research Pub Date : 2025-01-01 Epub Date: 2025-01-31 DOI: 10.4258/hir.2025.31.1.4

Eun-Gee Park, Min Jung Kim, Jinseo Kim, Kichul Shin, Borim Ryu

{"title":"Utility of Treatment Pattern Analysis Using a Common Data Model: A Scoping Review.","authors":"Eun-Gee Park, Min Jung Kim, Jinseo Kim, Kichul Shin, Borim Ryu","doi":"10.4258/hir.2025.31.1.4","DOIUrl":"10.4258/hir.2025.31.1.4","url":null,"abstract":"Objectives: We aimed to derive observational research evidence on treatment patterns through a scoping review of common data model (CDM)-based publications.Methods: We searched the medical literature databases PubMed and EMBASE, as well as the Observational Health Data Sciences and Informatics (OHDSI) website, for papers published between January 1, 2010 and August 21, 2023 to identify research papers relevant to our topic.Results: Eighteen articles satisfied the inclusion criteria for this scoping review. We summarized study characteristics such as phenotypes, patient numbers, data periods, countries, Observational Medical Outcomes Partnership (OMOP) CDM databases, and definitions of index date and target cohort. Type 2 diabetes mellitus emerged as the most frequently studied disease, covered in five articles, followed by hypertension and depression, each addressed in four articles. Biguanides, with metformin as the primary drug, were the most commonly prescribed first-line treatments for type 2 diabetes mellitus. Most studies utilized sunburst plots to visualize treatment patterns, whereas two studies used Sankey plots. Various software tools were employed for treatment pattern analysis, including JavaScript, the open-source ATLAS by OHDSI, R code, and the R package \"TreatmentPatterns.\"Conclusions: This study provides a comprehensive overview of research on treatment patterns using the CDM, highlighting the growing importance of OMOP CDM in enabling multinational observational network studies and advancing collaborative research in this field.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 1","pages":"4-15"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457776","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}

引用次数: 0

Review of the 2024 Fall Conference of the Korean Society of Medical Informatics-AI's Role in Shaping Modern Healthcare.

IF 2.3

Healthcare Informatics Research Pub Date : 2025-01-01 Epub Date: 2025-01-31 DOI: 10.4258/hir.2025.31.1.1

Jisan Lee, Taehoon Ko, Kwangmo Yang, Younghee Lee

引用次数: 0

Feature Selection for Hypertension Risk Prediction Using XGBoost on Single Nucleotide Polymorphism Data.

IF 2.3

Healthcare Informatics Research Pub Date : 2025-01-01 Epub Date: 2025-01-31 DOI: 10.4258/hir.2025.31.1.16

Lailil Muflikhah, Tirana Noor Fatyanosa, Nashi Widodo, Rizal Setya Perdana, Solimun, Hana Ratnawati

{"title":"Feature Selection for Hypertension Risk Prediction Using XGBoost on Single Nucleotide Polymorphism Data.","authors":"Lailil Muflikhah, Tirana Noor Fatyanosa, Nashi Widodo, Rizal Setya Perdana, Solimun, Hana Ratnawati","doi":"10.4258/hir.2025.31.1.16","DOIUrl":"10.4258/hir.2025.31.1.16","url":null,"abstract":"Objectives: Hypertension, commonly known as high blood pressure, is a prevalent and serious condition affecting a significant portion of the adult population globally. It is a chronic medical issue that, if left unaddressed, can lead to severe health complications, including kidney problems, heart disease, and stroke. This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk.Methods: We propose using the high dimensionality of genetic variations (i.e., SNPs) to build a classifier model for prediction. In this study, SNPs were used as markers for hypertension in patients. We utilized the OpenSNP dataset, which includes 19,697 SNPs from 2,052 samples. Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps.Results: The experimental results identified 292 SNPs that exhibited high performance, with an F1-score of 98.55%, precision of 98.73%, recall of 98.38%, and overall accuracy of 98%. This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness.Conclusions: We developed a model for predicting hypertension using the SNPs dataset. The high dimensionality of SNP data was effectively managed to identify significant features as biomarkers using the XGBoost feature selection method. The results indicate high performance in predicting the risk of hypertension.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 1","pages":"16-22"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457532","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}

引用次数: 0

Interactive Engagement with Self-Paced Learning Content in a Didactic Course.

IF 2.3

Healthcare Informatics Research Pub Date : 2025-01-01 Epub Date: 2025-01-31 DOI: 10.4258/hir.2025.31.1.96

Nazlee Sharmin, Shahram Houshyar, Thomas R Stevenson, Ava K Chow

{"title":"Interactive Engagement with Self-Paced Learning Content in a Didactic Course.","authors":"Nazlee Sharmin, Shahram Houshyar, Thomas R Stevenson, Ava K Chow","doi":"10.4258/hir.2025.31.1.96","DOIUrl":"10.4258/hir.2025.31.1.96","url":null,"abstract":"Objectives: A growing number of health professional institutions around the world are embracing innovative technologies to increase student engagement, primarily to improve clinical and simulated learning experiences. Didactic learning is an essential component of dental and medical curricula. However, limited research is available regarding the implementation of technology-infused teaching in classroom settings. We developed self-paced interactive learning content using the HTML5 Package (H5P) to promote student engagement in a didactic course within a dental hygiene program.Methods: A total of 52 interactive artifacts were created and administered to students as supplementary learning material. A descriptive study was conducted to explore student perceptions and engagement with the H5P content, as well as to evaluate the impact of these artifacts on academic performance.Results: Students performed significantly better on exam questions associated with interactive H5P content posted in the learning management system compared to other questions. Most students were highly engaged with the H5P content during the week leading up to each summative assessment. However, two of the three students with the highest course grades demonstrated consistent engagement with this content throughout the course.Conclusions: Our results highlight the effectiveness of interactive content created using the H5P platform in fostering student engagement. The development of self-paced interactive materials may benefit various aspects of didactic teaching, including both synchronous and asynchronous online learning.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 1","pages":"96-106"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457641","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}

引用次数: 0

FIT4PEDON: Mobile Nutrition Counseling Application Effectiveness and Usability for Childhood Cancer Survivors.

IF 2.3

Healthcare Informatics Research Pub Date : 2025-01-01 Epub Date: 2025-01-31 DOI: 10.4258/hir.2025.31.1.37

R Bavatharani, V Supriya, Julius Xavier Scott, Suresh Sankaranarayanan

{"title":"FIT4PEDON: Mobile Nutrition Counseling Application Effectiveness and Usability for Childhood Cancer Survivors.","authors":"R Bavatharani, V Supriya, Julius Xavier Scott, Suresh Sankaranarayanan","doi":"10.4258/hir.2025.31.1.37","DOIUrl":"10.4258/hir.2025.31.1.37","url":null,"abstract":"Objectives: Conventional face-to-face nutrition counseling has played a crucial role in promoting healthy habits. However, the emergence of digital health technologies has introduced mobile app-based nutrition counseling as an effective alternative. This research aims to develop and evaluate the usability and effectiveness of the FIT4PEDON mobile nutrition counseling application in promoting healthy dietary behaviors and lifestyle modifications among childhood cancer survivors (CCS).Methods: This study employed a mixed-methods approach, incorporating both survey and qualitative and quantitative analyses. A total of 33 health care professional experts participated. The reliability of the questionnaire was assessed using the Kuder-Richardson method, and its content validity was confirmed through expert evaluation. Usability testing was conducted with a validated questionnaire.Results: The development process resulted in two applications: an Android mobile application and an admin web application. The findings indicated that a significant proportion of experts endorsed the app for dietary management. Statistical analysis showed significant differences between \"yes\" and \"no\" responses. However, no significant differences were found when comparing responses across different sex or age groups.Conclusions: The FIT4PEDON application shows promise in supporting CCS to adopt healthier lifestyles. Nevertheless, the study underscores the necessity for further research, particularly focusing on specific age groups of experts with relevant experience, to achieve more conclusive results. Leveraging technology through mobile apps has the potential to improve the quality of survivorship care and foster sustained engagement in long-term care for pediatric cancer survivors.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 1","pages":"37-47"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457703","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}

引用次数: 0

Triage Data-Driven Prediction Models for Hospital Admission of Emergency Department Patients: A Systematic Review.

IF 2.3

Healthcare Informatics Research Pub Date : 2025-01-01 Epub Date: 2025-01-31 DOI: 10.4258/hir.2025.31.1.23

Hyun A Shin, Hyeonji Kang, Mona Choi

{"title":"Triage Data-Driven Prediction Models for Hospital Admission of Emergency Department Patients: A Systematic Review.","authors":"Hyun A Shin, Hyeonji Kang, Mona Choi","doi":"10.4258/hir.2025.31.1.23","DOIUrl":"10.4258/hir.2025.31.1.23","url":null,"abstract":"Objectives: Emergency department (ED) overcrowding significantly impacts healthcare efficiency, safety, and resource management. Predictive models that utilize triage information can streamline the admission process. This review evaluates existing hospital admission prediction models that have been developed or validated using triage data for adult ED patients.Methods: A systematic search of PubMed, Embase, CINAHL, Web of Science, and the Cochrane Library was conducted. Studies were selected if they developed or validated predictive models for hospital admission using triage data from adult ED patients. Data extraction adhered to the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies), and the risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool).Results: Twenty studies met the inclusion criteria, employing logistic regression and machine learning techniques. Logistic regression was noted for its traditional use and clinical interpretability, whereas machine learning provided enhanced flexibility and potential for better predictive accuracy. Common predictors included patient demographics, triage category, vital signs, and mode of arrival. The area under the curve values for model performance ranged from 0.80 to 0.89, demonstrating strong discriminatory ability. However, external validation was limited, and there was variability in outcome definitions and model generalizability.Conclusions: Predictive models based on triage data show promise in supporting ED operations by facilitating early predictions of hospital admissions, which could help decrease boarding times and enhance patient flow. Further research is necessary to validate these models in various settings to confirm their applicability and reliability.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 1","pages":"23-36"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457773","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}

引用次数: 0

Deep Learning Model-Based Detection of Anemia from Conjunctiva Images.

IF 2.3

Healthcare Informatics Research Pub Date : 2025-01-01 Epub Date: 2025-01-31 DOI: 10.4258/hir.2025.31.1.57

Najmus Sehar, Nirmala Krishnamoorthi, C Vinoth Kumar

{"title":"Deep Learning Model-Based Detection of Anemia from Conjunctiva Images.","authors":"Najmus Sehar, Nirmala Krishnamoorthi, C Vinoth Kumar","doi":"10.4258/hir.2025.31.1.57","DOIUrl":"10.4258/hir.2025.31.1.57","url":null,"abstract":"Objectives: Anemia is characterized by a reduction in red blood cells, leading to insufficient levels of hemoglobin, the molecule responsible for carrying oxygen. The current standard method for diagnosing anemia involves analyzing blood samples, a process that is time-consuming and can cause discomfort to participants. This study offers a comprehensive analysis of non-invasive anemia detection using conjunctiva images processed through various machine learning and deep learning models. The focus is on the palpebral conjunctiva, which is highly vascular and unaffected by melanin content.Methods: Conjunctiva images from both anemic and non-anemic participants were captured using a smartphone. A total of 764 conjunctiva images were augmented to 4,315 images using the deep convolutional generative adversarial network model to prevent overfitting and enhance model robustness. These processed and augmented images were then utilized to train and test multiple models, including statistical regression, machine learning algorithms, and deep learning frameworks.Results: The stacking ensemble framework, which includes the models VGG16, ResNet-50, and InceptionV3, achieved a high area under the curve score of 0.97. This score demonstrates the framework's exceptional capability in detecting anemia through a noninvasive approach.Conclusions: This study introduces a noninvasive method for detecting anemia using conjunctiva images obtained with a smartphone and processed using advanced deep learning techniques.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 1","pages":"57-65"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457614","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}

引用次数: 0

Extreme Prototyping for a Community Health Worker Medical Application.

IF 2.3

Healthcare Informatics Research Pub Date : 2025-01-01 Epub Date: 2025-01-31 DOI: 10.4258/hir.2025.31.1.88

Jan Noel Molon

{"title":"Extreme Prototyping for a Community Health Worker Medical Application.","authors":"Jan Noel Molon","doi":"10.4258/hir.2025.31.1.88","DOIUrl":"10.4258/hir.2025.31.1.88","url":null,"abstract":"Objectives: Noncommunicable diseases (NCDs) pose a significant burden, especially in low- and middle-income countries such as the Philippines. To tackle this issue, the Department of Health launched the Philippine Package of Essential Non-Communicable Disease Interventions (PhilPEN), which includes the use of the Noncommunicable Disease Risk Assessment Form. However, healthcare workers have encountered difficulties due to the form's complexity and the lengthy process required. This study aimed to create a mobile medical app for community health workers by adapting the PhilPEN Noncommunicable Disease Risk Assessment Form using the extreme prototyping framework. The focus was on simplifying data collection and improving the usability of health technology solutions.Methods: The study employed a qualitative research methodology, which included key informant interviews, linguistic validation, and cognitive debriefing. The extreme prototyping framework was utilized for app development, comprising static prototype, dynamic prototype, and service implementation phases. The app was developed with HTML5, CSS3, JavaScript, and Apache Cordova, adhering to World Health Organization (WHO) guidelines and PhilHealth Circular.Results: The development process involved three prototype cycles, each consisting of multiple mini-cycles of feedback, system design, coding, and testing. Version 1.xx was aligned with WHO guidelines, Version 2.xx integrated the Department of Health NCD Risk Assessment Form, and Version 3.xx adapted to the updated form with expanded requirements.Conclusions: The extreme prototyping framework was effectively applied in the development of a medical mobile app, facilitating the integration of health science and information technology. Future research should continue to validate the effectiveness of this approach and identify specific nuances related to health science applications.","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 1","pages":"88-95"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457529","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}

引用次数: 0

Era of Digital Healthcare: Emergence of the Smart Patient.

IF 2.3

Healthcare Informatics Research Pub Date : 2025-01-01 Epub Date: 2025-01-31 DOI: 10.4258/hir.2025.31.1.107

Dooyoung Huhh, Kwangsoo Shin, Miyeong Kim, Jisan Lee, Hana Kim, Jinho Choi, Suyeon Ban

引用次数: 0