Pub Date : 2024-08-05DOI: 10.1016/j.ijmedinf.2024.105587
Sreevatsa Bellary, Pradip Kumar Bala, Shibashish Chakraborty
Introduction
Digital healthcare consultation services, also known as telemedicine, have seen a surge in their usage, especially after the COVID-19 pandemic. The purpose of this study is to investigate the satisfaction determinants of healthcare customers (patients) and healthcare professionals (doctors), providing digital healthcare consultation services.
Methods
The analysis involved scraping online reviews of 11 telemedicine apps meant for patients and 7 telemedicine apps meant for doctors, yielding a total of 44,440 patient reviews and 4748 doctor reviews. A structural topic modeling analysis followed by regression, dominance, correspondence, and emotion analysis was conducted to derive insights.
Results
The study identified ten determinants of satisfaction from patients’ and eight from doctors’ perspectives. For patients, ’service variety and quality’ (β = 0.5527) was the top positive determinant, while ’payment disputes’ (β = -0.1173) and ’in-app membership’ (β = -0.031) negatively impacted satisfaction. For doctors, ’patient consultation management’ (β = 0.2009) was the leading positive determinant, with ’profile management’ (β = -0.1843), ’subscription’ (β = -0.183), and ’customer care support’ (β = -0.0908) being the negative ones. The most influential negative emotion for patients, anger, was closely associated with ’customer care service’ and ’in-app memberships,’ while joy was tied to ’service variety and quality’ and ’offers and discounts.’ For doctors, anger was associated with ’cost-effectiveness,’ and joy with ’app responsiveness.’
Conclusion
This study offers new insights by examining patient and doctor determinants at a granular level which can be used by telemedicine app developers and managers to build customer-centric services.
{"title":"Utilizing online reviews for analyzing digital healthcare consultation services: Examining perspectives of both healthcare customers and healthcare professionals","authors":"Sreevatsa Bellary, Pradip Kumar Bala, Shibashish Chakraborty","doi":"10.1016/j.ijmedinf.2024.105587","DOIUrl":"10.1016/j.ijmedinf.2024.105587","url":null,"abstract":"<div><h3>Introduction</h3><p>Digital healthcare consultation services, also known as telemedicine, have seen a surge in their usage, especially after the COVID-19 pandemic. The purpose of this study is to investigate the satisfaction determinants of healthcare customers (patients) and healthcare professionals (doctors), providing digital healthcare consultation services.</p></div><div><h3>Methods</h3><p>The analysis involved scraping online reviews of 11 telemedicine apps meant for patients and 7 telemedicine apps meant for doctors, yielding a total of 44,440 patient reviews and 4748 doctor reviews. A structural topic modeling analysis followed by regression, dominance, correspondence, and emotion analysis was conducted to derive insights.</p></div><div><h3>Results</h3><p>The study identified ten determinants of satisfaction from patients’ and eight from doctors’ perspectives. For patients, ’service variety and quality’ (β = 0.5527) was the top positive determinant, while ’payment disputes’ (β = -0.1173) and ’in-app membership’ (β = -0.031) negatively impacted satisfaction. For doctors, ’patient consultation management’ (β = 0.2009) was the leading positive determinant, with ’profile management’ (β = -0.1843), ’subscription’ (β = -0.183), and ’customer care support’ (β = -0.0908) being the negative ones. The most influential negative emotion for patients, anger, was closely associated with ’customer care service’ and ’in-app memberships,’ while joy was tied to ’service variety and quality’ and ’offers and discounts.’ For doctors, anger was associated with ’cost-effectiveness,’ and joy with ’app responsiveness.’</p></div><div><h3>Conclusion</h3><p>This study offers new insights by examining patient and doctor determinants at a granular level which can be used by telemedicine app developers and managers to build customer-centric services.</p></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1016/j.ijmedinf.2024.105588
Mingyang Zhang , Xiangzhou Zhang , Mingyang Dai , Lijuan Wu , Kang Liu , Hongnian Wang , Weiqi Chen , Mei Liu , Yong Hu
Objective
Accurate diagnoses and personalized treatments in medicine rely on identifying causality. However, existing causal discovery algorithms often yield inconsistent results due to distinct learning mechanisms. To address this challenge, we introduce MINDMerge, a multi-causal investigation and discovery framework designed to synthesize causal graphs from various algorithms.
Methods
MINDMerge integrates five causal models to reconcile inconsistencies arising from different algorithms. Employing credibility weighting and a novel cycle-breaking mechanism in causal networks, we initially developed and tested MINDMerge using three synthetic networks. Subsequently, we validated its effectiveness in discovering risk factors and predicting acute kidney injury (AKI) using two electronic medical records (EMR) datasets, eICU Collaborative Research Database and MIMIC-III Database. Causal reasoning was employed to analyze the relationships between risk factors and AKI. The identified causal risk factors of AKI were used in building a prediction model, and the prediction model was evaluated using the area under the receiver operating characteristics curve (AUC) and recall.
Results
Synthetic data experiments demonstrated that our model outperformed significantly in capturing ground-truth network structure compared to other causal models. Application of MINDMerge on real-world data revealed direct connections of pulmonary disease, hypertension, diabetes, x-ray assessment, and BUN with AKI. With the identified variables, AKI risk can be inferred at the individual level based on established BNs and prior information. Compared against existing benchmark models, MINDMerge maintained a higher AUC for AKI prediction in both internal (AUC: 0.832) and external network validations (AUC: 0.861).
Conclusion
MINDMerge can identify causal risk factors of AKI, serving as a valuable diagnostic tool for clinical decision-making and facilitating effective intervention.
目的：医学中的精确诊断和个性化治疗依赖于因果关系的识别。然而，由于不同的学习机制，现有的因果发现算法往往产生不一致的结果。为了应对这一挑战，我们引入了 MINDMerge，这是一个多因果调查和发现框架，旨在综合各种算法的因果图：MINDMerge整合了五个因果模型，以调和不同算法产生的不一致性。通过在因果网络中采用可信度加权和新颖的循环打破机制，我们利用三个合成网络初步开发并测试了 MINDMerge。随后，我们利用两个电子病历（EMR）数据集，即 eICU 合作研究数据库和 MIMIC-III 数据库，验证了 MINDMerge 在发现风险因素和预测急性肾损伤（AKI）方面的有效性。利用因果推理分析了风险因素与 AKI 之间的关系。确定的 AKI 因果风险因素被用于建立预测模型，并使用接收者操作特征曲线下面积（AUC）和召回率对预测模型进行评估：合成数据实验表明，与其他因果模型相比，我们的模型在捕捉地面实况网络结构方面表现出色。在真实世界数据中应用 MINDMerge 发现了肺部疾病、高血压、糖尿病、X 光评估和血尿素氮与 AKI 的直接联系。有了确定的变量，就可以根据已建立的 BN 和先验信息推断出个体水平的 AKI 风险。与现有的基准模型相比，MINDMerge 在内部（AUC：0.832）和外部网络验证（AUC：0.861）中都保持了较高的 AKI 预测 AUC：结论：MINDMerge 可识别 AKI 的成因风险因素，是临床决策的重要诊断工具，有助于采取有效的干预措施。
{"title":"Development and validation of a Multi-Causal investigation and discovery framework for knowledge harmonization (MINDMerge): A case study with acute kidney injury risk factor discovery using electronic medical records","authors":"Mingyang Zhang , Xiangzhou Zhang , Mingyang Dai , Lijuan Wu , Kang Liu , Hongnian Wang , Weiqi Chen , Mei Liu , Yong Hu","doi":"10.1016/j.ijmedinf.2024.105588","DOIUrl":"10.1016/j.ijmedinf.2024.105588","url":null,"abstract":"<div><h3>Objective</h3><p>Accurate diagnoses and personalized treatments in medicine rely on identifying causality. However, existing causal discovery algorithms often yield inconsistent results due to distinct learning mechanisms. To address this challenge, we introduce MINDMerge, a multi-causal investigation and discovery framework designed to synthesize causal graphs from various algorithms.</p></div><div><h3>Methods</h3><p>MINDMerge integrates five causal models to reconcile inconsistencies arising from different algorithms. Employing credibility weighting and a novel cycle-breaking mechanism in causal networks, we initially developed and tested MINDMerge using three synthetic networks. Subsequently, we validated its effectiveness in discovering risk factors and predicting acute kidney injury (AKI) using two electronic medical records (EMR) datasets, eICU Collaborative Research Database and MIMIC-III Database. Causal reasoning was employed to analyze the relationships between risk factors and AKI. The identified causal risk factors of AKI were used in building a prediction model, and the prediction model was evaluated using the area under the receiver operating characteristics curve (AUC) and recall.</p></div><div><h3>Results</h3><p>Synthetic data experiments demonstrated that our model outperformed significantly in capturing ground-truth network structure compared to other causal models. Application of MINDMerge on real-world data revealed direct connections of pulmonary disease, hypertension, diabetes, x-ray assessment, and BUN with AKI. With the identified variables, AKI risk can be inferred at the individual level based on established BNs and prior information. Compared against existing benchmark models, MINDMerge maintained a higher AUC for AKI prediction in both internal (AUC: 0.832) and external network validations (AUC: 0.861).</p></div><div><h3>Conclusion</h3><p>MINDMerge can identify causal risk factors of AKI, serving as a valuable diagnostic tool for clinical decision-making and facilitating effective intervention.</p></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-03DOI: 10.1016/j.ijmedinf.2024.105564
Ting-Yun Huang , Chee-Fah Chong , Heng-Yu Lin , Tzu-Ying Chen , Yung-Chun Chang , Ming-Chin Lin
Introduction
The urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic errors, but the literature highlights a need for innovative approaches to optimize diagnostic accuracy and patient outcomes. In response, our study endeavors to create predictive models for timely examinations and interventions by leveraging the patient’s symptoms and vital signs recorded during triage, and in so doing, augment traditional diagnostic methodologies.
Methods
Focusing on four key areas—medication dispensing, vital interventions, laboratory testing, and emergency radiology exams, the study employed Natural Language Processing (NLP) and seven advanced machine learning techniques. The research was centered around the innovative use of BioClinicalBERT, a state-of-the-art NLP framework.
Results
BioClinicalBERT emerged as the superior model, outperforming others in predictive accuracy. The integration of physiological data with patient narrative symptoms demonstrated greater effectiveness compared to models based solely on textual data. The robustness of our approach was confirmed by an Area Under the Receiver Operating Characteristic curve (AUROC) score of 0.9.
Conclusion
The findings of our study underscore the feasibility of establishing a decision support system for emergency patients, targeting timely interventions and examinations based on a nuanced analysis of symptoms. By using an advanced natural language processing technique, our approach shows promise for enhancing diagnostic accuracy. However, the current model is not yet fully mature for direct implementation into daily clinical practice. Recognizing the imperative nature of precision in the ER environment, future research endeavors must focus on refining and expanding predictive models to include detailed timely examinations and interventions. Although the progress achieved in this study represents an encouraging step towards a more innovative and technology-driven paradigm in emergency care, full clinical integration warrants further exploration and validation.
{"title":"A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives","authors":"Ting-Yun Huang , Chee-Fah Chong , Heng-Yu Lin , Tzu-Ying Chen , Yung-Chun Chang , Ming-Chin Lin","doi":"10.1016/j.ijmedinf.2024.105564","DOIUrl":"10.1016/j.ijmedinf.2024.105564","url":null,"abstract":"<div><h3>Introduction</h3><p>The urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic errors, but the literature highlights a need for innovative approaches to optimize diagnostic accuracy and patient outcomes. In response, our study endeavors to create predictive models for timely examinations and interventions by leveraging the patient’s symptoms and vital signs recorded during triage, and in so doing, augment traditional diagnostic methodologies.</p></div><div><h3>Methods</h3><p>Focusing on four key areas—medication dispensing, vital interventions, laboratory testing, and emergency radiology exams, the study employed Natural Language Processing (NLP) and seven advanced machine learning techniques. The research was centered around the innovative use of BioClinicalBERT, a state-of-the-art NLP framework.</p></div><div><h3>Results</h3><p>BioClinicalBERT emerged as the superior model, outperforming others in predictive accuracy. The integration of physiological data with patient narrative symptoms demonstrated greater effectiveness compared to models based solely on textual data. The robustness of our approach was confirmed by an Area Under the Receiver Operating Characteristic curve (AUROC) score of 0.9.</p></div><div><h3>Conclusion</h3><p>The findings of our study underscore the feasibility of establishing a decision support system for emergency patients, targeting timely interventions and examinations based on a nuanced analysis of symptoms. By using an advanced natural language processing technique, our approach shows promise for enhancing diagnostic accuracy. However, the current model is not yet fully mature for direct implementation into daily clinical practice. Recognizing the imperative nature of precision in the ER environment, future research endeavors must focus on refining and expanding predictive models to include detailed timely examinations and interventions. Although the progress achieved in this study represents an encouraging step towards a more innovative and technology-driven paradigm in emergency care, full clinical integration warrants further exploration and validation.</p></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141914627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-02DOI: 10.1016/j.ijmedinf.2024.105583
Mustafa Noaman Kadhim , Dhiah Al-Shammary , Fahim Sufi
Background
Traditional classifier for the classification of diseases, such as K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM), often struggle with high-dimensional medical datasets.
Objective
This study presents a novel classifier to overcome the limitations of traditional classifiers in Parkinson’s disease (PD) detection based on Gower distance.
Methods
We present the Gower distance metric to handle diverse feature sets in voice recordings, which acts as a dissimilarity measure for all feature types, making the model adept at identifying subtle patterns indicative of PD. Additionally, the Cuckoo Search algorithm is employed for feature selection, reducing dimensionality by focusing on key features, thereby lessening the computational load associated with high-dimensional datasets.
Results
The proposed classifier based on Gower distance resulted in an accuracy rate of 98.3% with feature selection and achieved an accuracy of 94.92% without the feature selection method. It outperforms traditional classifiers and recent studies in PD detection from voice recordings.
Conclusions
This accuracy shows the capability of the approach in the correct classification of instances and points out the potential of the approach as a reliable diagnostic tool for the medical practitioner. The findings state that the proposed approach holds promise for improving the diagnosis and monitoring of PD, both within medical institutions and at homes for the elderly.
{"title":"A novel voice classification based on Gower distance for Parkinson disease detection","authors":"Mustafa Noaman Kadhim , Dhiah Al-Shammary , Fahim Sufi","doi":"10.1016/j.ijmedinf.2024.105583","DOIUrl":"10.1016/j.ijmedinf.2024.105583","url":null,"abstract":"<div><h3>Background</h3><p>Traditional classifier for the classification of diseases, such as K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM), often struggle with high-dimensional medical datasets.</p></div><div><h3>Objective</h3><p>This study presents a novel classifier to overcome the limitations of traditional classifiers in Parkinson’s disease (PD) detection based on Gower distance.</p></div><div><h3>Methods</h3><p>We present the Gower distance metric to handle diverse feature sets in voice recordings, which acts as a dissimilarity measure for all feature types, making the model adept at identifying subtle patterns indicative of PD. Additionally, the Cuckoo Search algorithm is employed for feature selection, reducing dimensionality by focusing on key features, thereby lessening the computational load associated with high-dimensional datasets.</p></div><div><h3>Results</h3><p>The proposed classifier based on Gower distance resulted in an accuracy rate of 98.3% with feature selection and achieved an accuracy of 94.92% without the feature selection method. It outperforms traditional classifiers and recent studies in PD detection from voice recordings.</p></div><div><h3>Conclusions</h3><p>This accuracy shows the capability of the approach in the correct classification of instances and points out the potential of the approach as a reliable diagnostic tool for the medical practitioner. The findings state that the proposed approach holds promise for improving the diagnosis and monitoring of PD, both within medical institutions and at homes for the elderly.</p></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1386505624002466/pdfft?md5=8e63bcbdafb3f27ecfbeea695e2e1a42&pid=1-s2.0-S1386505624002466-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-02DOI: 10.1016/j.ijmedinf.2024.105569
Ran Mo, Shihong Hu
{"title":"Retraction notice to “Reliability and validity of the Chinese version of the mobile Agnew Relationship Measure (mARM-C)” [Int. J. Med. Inf. 189 (2024) 105482]","authors":"Ran Mo, Shihong Hu","doi":"10.1016/j.ijmedinf.2024.105569","DOIUrl":"10.1016/j.ijmedinf.2024.105569","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1386505624002326/pdfft?md5=45bf1f147b38a204edb33479980a55f9&pid=1-s2.0-S1386505624002326-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.1016/j.ijmedinf.2024.105582
Sean Randall , Adrian Brown , Anna Ferrante , James Boyd , Suzanne Robinson
Objective
To describe the use of privacy preserving linkage methods operationally in Australia, and to present insights and key learnings from their implementation.
Methods
Privacy preserving record linkage (PPRL) utilising Bloom filters provides a unique practical mechanism that allows linkage to occur without the release of personally identifiable information (PII), while still ensuring high accuracy.
Results
The methodology has received wide uptake within Australia, with four state linkage units with privacy preserving capability. It has enabled access to general practice and private pathology data amongst other, both much sought after datasets previous inaccessible for linkage.
Conclusion
The Australian experience suggests privacy preserving linkage is a practical solution for improving data access for policy, planning and population health research. It is hoped interest in this methodology internationally continues to grow.
{"title":"Implementing privacy preserving record linkage: Insights from Australian use cases","authors":"Sean Randall , Adrian Brown , Anna Ferrante , James Boyd , Suzanne Robinson","doi":"10.1016/j.ijmedinf.2024.105582","DOIUrl":"10.1016/j.ijmedinf.2024.105582","url":null,"abstract":"<div><h3>Objective</h3><p>To describe the use of privacy preserving linkage methods operationally in Australia, and to present insights and key learnings from their implementation.</p></div><div><h3>Methods</h3><p>Privacy preserving record linkage (PPRL) utilising Bloom filters provides a unique practical mechanism that allows linkage to occur without the release of personally identifiable information (PII), while still ensuring high accuracy.</p></div><div><h3>Results</h3><p>The methodology has received wide uptake within Australia, with four state linkage units with privacy preserving capability. It has enabled access to general practice and private pathology data amongst other, both much sought after datasets previous inaccessible for linkage.</p></div><div><h3>Conclusion</h3><p>The Australian experience suggests privacy preserving linkage is a practical solution for improving data access for policy, planning and population health research. It is hoped interest in this methodology internationally continues to grow.</p></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1386505624002454/pdfft?md5=026b0b8604d90c986fb1838e1fa8008d&pid=1-s2.0-S1386505624002454-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.1016/j.ijmedinf.2024.105580
Amir Gandomi , Eusha Hasan , Jesse Chusid , Subroto Paul , Matthew Inra , Alex Makhnevich , Suhail Raoof , Gerard Silvestri , Brett C. Bade , Stuart L. Cohen
Introduction
Radiology scoring systems are critical to the success of lung cancer screening (LCS) programs, impacting patient care, adherence to follow-up, data management and reporting, and program evaluation. Lung CT Screening Reporting and Data System (Lung-RADS) is a structured radiology scoring system that provides recommendations for LCS follow-up that are utilized (a) in clinical care and (b) by LCS programs monitoring rates of adherence to follow-up. Thus, accurate reporting and reliable collection of Lung-RADS scores are fundamental components of LCS program evaluation and improvement. Unfortunately, due to variability in radiology reports, extraction of Lung-RADS scores is non-trivial, and best practices do not exist. The purpose of this project is to compare mechanisms to extract Lung-RADS scores from free-text radiology reports.
Methods
We retrospectively analyzed reports of LCS low-dose computed tomography (LDCT) examinations performed at a multihospital integrated healthcare network in New York State between January 2016 and July 2023. We compared three methods of Lung-RADS score extraction: manual physician entry at time of report creation, manual LCS specialist entry after report creation, and an internally developed, rule-based natural language processing (NLP) algorithm. Accuracy, recall, precision, and completeness (i.e., the proportion of LCS exams to which a Lung-RADS score has been assigned) were compared between the three methods.
Results
The dataset includes 24,060 LCS examinations on 14,243 unique patients. The mean patient age was 65 years, and most patients were male (54 %) and white (75 %). Completeness rate was 65 %, 68 %, and 99 % for radiologists’ manual entry, LCS specialists’ entry, and NLP algorithm, respectively. Accuracy, recall, and precision were high across all extraction methods (>94 %), though the NLP-based approach was consistently higher than both manual entries in all metrics.
Discussion
An NLP-based method of LCS score determination is an efficient and more accurate means of extracting Lung-RADS scores than manual review and data entry. NLP-based methods should be considered best practice for extracting structured Lung-RADS scores from free-text radiology reports.
{"title":"Evaluating the accuracy of lung-RADS score extraction from radiology reports: Manual entry versus natural language processing","authors":"Amir Gandomi , Eusha Hasan , Jesse Chusid , Subroto Paul , Matthew Inra , Alex Makhnevich , Suhail Raoof , Gerard Silvestri , Brett C. Bade , Stuart L. Cohen","doi":"10.1016/j.ijmedinf.2024.105580","DOIUrl":"10.1016/j.ijmedinf.2024.105580","url":null,"abstract":"<div><h3>Introduction</h3><p>Radiology scoring systems are critical to the success of lung cancer screening (LCS) programs, impacting patient care, adherence to follow-up, data management and reporting, and program evaluation. Lung<!--> <!-->CT Screening<!--> <!-->Reporting and Data System (Lung-RADS) is a structured radiology scoring system that provides recommendations for LCS follow-up that are utilized (a) in clinical care and (b) by LCS programs monitoring rates of adherence to follow-up. Thus, accurate reporting and reliable collection of Lung-RADS scores are fundamental components of LCS program evaluation and improvement. Unfortunately, due to variability in radiology reports, extraction of Lung-RADS scores is non-trivial, and best practices do not exist. The purpose of this project is to compare mechanisms to extract Lung-RADS scores from free-text radiology reports.</p></div><div><h3>Methods</h3><p>We retrospectively analyzed reports of LCS low-dose computed tomography (LDCT) examinations performed at a multihospital integrated healthcare network in New York State between January 2016 and July 2023. We compared three methods of Lung-RADS score extraction: manual physician entry at time of report creation, manual LCS specialist entry after report creation, and an internally developed, rule-based natural language processing (NLP) algorithm. Accuracy, recall, precision, and completeness (i.e., the proportion of LCS exams to which a Lung-RADS score has been assigned) were compared between the three methods.</p></div><div><h3>Results</h3><p>The dataset includes 24,060 LCS examinations on 14,243 unique patients. The mean patient age was 65 years, and most patients were male (54 %) and white (75 %). Completeness rate was 65 %, 68 %, and 99 % for radiologists’ manual entry, LCS specialists’ entry, and NLP algorithm, respectively. Accuracy, recall, and precision were high across all extraction methods (>94 %), though the NLP-based approach was consistently higher than both manual entries in all metrics.</p></div><div><h3>Discussion</h3><p>An NLP-based method of LCS score determination is an efficient and more accurate means of extracting Lung-RADS scores than manual review and data entry. NLP-based methods should be considered best practice for extracting structured Lung-RADS scores from free-text radiology reports.</p></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.1016/j.ijmedinf.2024.105585
Yanting Luo , Ruimin Dong , Jinlai Liu, Bingyuan Wu
Background
Atrial fibrillation (AF) is common among intensive care unit (ICU) patients and significantly raises the in-hospital mortality rate. Existing scoring systems or models have limited predictive capabilities for AF patients in ICU. Our study developed and validated machine learning models to predict the risk of in-hospital mortality in ICU patients with AF.
Methods and Results
Medical Information Mart for Intensive Care (MIMIC)-IV dataset and eICU Collaborative Research Database (eICU-CRD) were analyzed. Among ten classifiers compared, adaptive boosting (AdaBoost) showed better performance in predicting all-cause mortality in AF patients. A compact model with 15 features was developed and validated. Both the all variable and compact models exhibited excellent performance with area under the receiver operating characteristic curves (AUCs) of 1(95%confidence interval [CI]: 1.0–1.0) in the training set. In the MIMIC-IV testing set, the AUCs of the all variable and compact models were 0.978 (95% CI: 0.973–0.982) and 0.977 (95% CI: 0.972–0.982), respectively. In the external validation set, the AUCs of all variable and compact models were 0.825 (95% CI: 0.815–0.834) and 0.807 (95% CI: 0.796–0.817), respectively.
Conclusion
An AdaBoost-based predictive model was subjected to internal and external validation, highlighting its strong predictive capacity for assessing the risk of in-hospital mortality in ICU patients with AF.
{"title":"A machine learning-based predictive model for the in-hospital mortality of critically ill patients with atrial fibrillation","authors":"Yanting Luo , Ruimin Dong , Jinlai Liu, Bingyuan Wu","doi":"10.1016/j.ijmedinf.2024.105585","DOIUrl":"10.1016/j.ijmedinf.2024.105585","url":null,"abstract":"<div><h3>Background</h3><p>Atrial fibrillation (AF) is common among intensive care unit (ICU) patients and significantly raises the in-hospital mortality rate. Existing scoring systems or models have limited predictive capabilities for AF patients in ICU. Our study developed and validated machine learning models to predict the risk of in-hospital mortality in ICU patients with AF.</p></div><div><h3>Methods and Results</h3><p>Medical Information Mart for Intensive Care (MIMIC)-IV dataset and eICU Collaborative Research Database (eICU-CRD) were analyzed. Among ten classifiers compared, adaptive boosting (AdaBoost) showed better performance in predicting all-cause mortality in AF patients. A compact model with 15 features was developed and validated. Both the all variable and compact models exhibited excellent performance with area under the receiver operating characteristic curves (AUCs) of 1(95%confidence interval [CI]: 1.0–1.0) in the training set. In the MIMIC-IV testing set, the AUCs of the all variable and compact models were 0.978 (95% CI: 0.973–0.982) and 0.977 (95% CI: 0.972–0.982), respectively. In the external validation set, the AUCs of all variable and compact models were 0.825 (95% CI: 0.815–0.834) and 0.807 (95% CI: 0.796–0.817), respectively.</p></div><div><h3>Conclusion</h3><p>An AdaBoost-based predictive model was subjected to internal and external validation, highlighting its strong predictive capacity for assessing the risk of in-hospital mortality in ICU patients with AF.</p></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S138650562400248X/pdfft?md5=407810158f4d85e210a66889b61699a7&pid=1-s2.0-S138650562400248X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.1016/j.ijmedinf.2024.105584
Nahyun Keum , Junsang Yoo , Sujeong Hur , Soo-Yong Shin , Patricia C. Dykes , Min-Jeoung Kang , Yong Seok Lee , Won Chul Cha
Objective
Drug incompatibility, a significant subset of medication errors, threaten patient safety during the medication administration phase. Despite the undeniably high prevalence of drug incompatibility, it is currently poorly understood because previous studies are focused predominantly on intensive care unit (ICU) settings. To enhance patient safety, it is crucial to expand our understanding of this issue from a comprehensive viewpoint. This study aims to investigate the prevalence and mechanism of drug incompatibility by analysing hospital-wide prescription and administration data.
Methods
This retrospective cross-sectional study, conducted at a tertiary academic hospital, included data extracted from the clinical data warehouse of the study institution on patients admitted between January 1, 2021, and May 31, 2021. Potential contacts in drug pairs (PCs) were identified using the study site clinical workflow. Drug incompatibility for each PC was determined by using a commercial drug incompatibility database, the Trissel’s™ 2 Clinical Pharmaceutics Database (Trissel’s 2 database). Drivers of drug incompatibility were identified, based on a descriptive analysis, after which, multivariate logistic regression was conducted to assess the risk factors for experiencing one or more drug incompatibilities during admission.
Results
Among 30,359 patients (representing 40,061 hospitalisations), 24,270 patients (32,912 hospitalisations) with 764,501 drug prescriptions (1,001,685 IV administrations) were analysed, after checking for eligibility. Based on the rule for determining PCs, 5,813,794 cases of PCs were identified. Among these, 25,108 (0.4 %) cases were incompatible PCs: 391 (1.6 %) PCs occurred during the prescription process and 24,717 (98.4 %) PCs during the administration process. By classifying these results, we identified the following drivers contributing to drug incompatibility: incorrect order factor; incorrect administration factor; and lack of related research. In multivariate analysis, the risk of encountering incompatible PCs was higher for patients who were male, older, with longer lengths of stay, with higher comorbidity, and admitted to medical ICUs.
Conclusions
We comprehensively described the current state of drug incompatibility by analysing hospital-wide drug prescription and administration data. The results showed that drug incompatibility frequently occurs in clinical settings.
{"title":"The potential for drug incompatibility and its drivers − A hospital wide retrospective descriptive study","authors":"Nahyun Keum , Junsang Yoo , Sujeong Hur , Soo-Yong Shin , Patricia C. Dykes , Min-Jeoung Kang , Yong Seok Lee , Won Chul Cha","doi":"10.1016/j.ijmedinf.2024.105584","DOIUrl":"10.1016/j.ijmedinf.2024.105584","url":null,"abstract":"<div><h3>Objective</h3><p>Drug incompatibility, a significant subset of medication errors, threaten patient safety during the medication administration phase. Despite the undeniably high prevalence of drug incompatibility, it is currently poorly understood because previous studies are focused predominantly on intensive care unit (ICU) settings. To enhance patient safety, it is crucial to expand our understanding of this issue from a comprehensive viewpoint. This study aims to investigate the prevalence and mechanism of drug incompatibility by analysing hospital-wide prescription and administration data.</p></div><div><h3>Methods</h3><p>This retrospective cross-sectional study, conducted at a tertiary academic hospital, included data extracted from the clinical data warehouse of the study institution on patients admitted between January 1, 2021, and May 31, 2021. Potential contacts in drug pairs (PCs) were identified using the study site clinical workflow. Drug incompatibility for each PC was determined by using a commercial drug incompatibility database, the Trissel’s™ 2 Clinical Pharmaceutics Database (Trissel’s 2 database). Drivers of drug incompatibility were identified, based on a descriptive analysis, after which, multivariate logistic regression was conducted to assess the risk factors for experiencing one or more drug incompatibilities during admission.</p></div><div><h3>Results</h3><p>Among 30,359 patients (representing 40,061 hospitalisations), 24,270 patients (32,912 hospitalisations) with 764,501 drug prescriptions (1,001,685 IV administrations) were analysed, after checking for eligibility. Based on the rule for determining PCs, 5,813,794 cases of PCs were identified. Among these, 25,108 (0.4 %) cases were incompatible PCs: 391 (1.6 %) PCs occurred during the prescription process and 24,717 (98.4 %) PCs during the administration process. By classifying these results, we identified the following drivers contributing to drug incompatibility: incorrect order factor; incorrect administration factor; and lack of related research. In multivariate analysis, the risk of encountering incompatible PCs was higher for patients who were male, older, with longer lengths of stay, with higher comorbidity, and admitted to medical ICUs.</p></div><div><h3>Conclusions</h3><p>We comprehensively described the current state of drug incompatibility by analysing hospital-wide drug prescription and administration data. The results showed that drug incompatibility frequently occurs in clinical settings.</p></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1386505624002478/pdfft?md5=e43237cd179d38e8fc4bc065b920d798&pid=1-s2.0-S1386505624002478-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1016/j.ijmedinf.2024.105581
Nour Elhouda Tlili , Laurine Robert , Erwin Gerard , Madleen Lemaitre , Anne Vambergue , Jean-Baptiste Beuscart , Paul Quindroit
Introduction
The management of chronic diabetes mellitus and its complications demands customized glycaemia control strategies. Polypharmacy is prevalent among people with diabetes and comorbidities, which increases the risk of adverse drug reactions. Clinical decision support systems (CDSSs) may constitute an innovative solution to these problems. The aim of our study was to conduct a systematic review assessing the value of CDSSs for the management of antidiabetic drugs (AD).
Materials and Methods
We systematically searched the scientific literature published between January 2010 and October 2023. The retrieved studies were categorized as non-specific or AD-specific. The studies’ quality was assessed using the Mixed Methods Appraisal Tool. The review’s results were reported in accordance with the PRISMA guidelines.
Results
Twenty studies met our inclusion criteria. The majority of AD-specific studies were conducted more recently (2020–2023) compared to non-specific studies (2010–2015). This trend hints at growing interest in more specialized CDSSs tailored for prescriptions of ADs. The nine AD-specific studies focused on metformin and insulin and demonstrated positive impacts of the CDSSs on different outcomes, including the reduction in the proportion of inappropriate prescriptions of ADs and in hypoglycaemia events. The 11 nonspecific studies showed similar trends for metformin and insulin prescriptions, although the CDSSs’ impacts were not significant. There was a predominance of metformin and insulin in the studied CDSSs and a lack of studies on ADs such as sodium-glucose cotransporter-2 (SGLT-2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists.
Conclusion
The limited number of studies, especially randomized clinical trials, interested in evaluating the application of CDSS in the management of ADs underscores the need for further investigations. Our findings suggest the potential benefit of applying CDSSs to the prescription of ADs particularly in primary care settings and when targeting clinical pharmacists. Finally, establishing core outcome sets is crucial for ensuring consistent and standardized evaluation of these CDSSs.
{"title":"A systematic review of the value of clinical decision support systems in the prescription of antidiabetic drugs","authors":"Nour Elhouda Tlili , Laurine Robert , Erwin Gerard , Madleen Lemaitre , Anne Vambergue , Jean-Baptiste Beuscart , Paul Quindroit","doi":"10.1016/j.ijmedinf.2024.105581","DOIUrl":"10.1016/j.ijmedinf.2024.105581","url":null,"abstract":"<div><h3>Introduction</h3><p>The management of chronic diabetes mellitus and its complications demands customized glycaemia control strategies. Polypharmacy is prevalent among people with diabetes and comorbidities, which increases the risk of adverse drug reactions. Clinical decision support systems (CDSSs) may constitute an innovative solution to these problems. The aim of our study was to conduct a systematic review assessing the value of CDSSs for the management of antidiabetic drugs (AD).</p></div><div><h3>Materials and Methods</h3><p>We systematically searched the scientific literature published between January 2010 and October 2023. The retrieved studies were categorized as non-specific or AD-specific. The studies’ quality was assessed using the Mixed Methods Appraisal Tool. The review’s results were reported in accordance with the PRISMA guidelines.</p></div><div><h3>Results</h3><p>Twenty studies met our inclusion criteria. The majority of AD-specific studies were conducted more recently (2020–2023) compared to non-specific studies (2010–2015). This trend hints at growing interest in more specialized CDSSs tailored for prescriptions of ADs. The nine AD-specific studies focused on metformin and insulin and demonstrated positive impacts of the CDSSs on different outcomes, including the reduction in the proportion of inappropriate prescriptions of ADs and in hypoglycaemia events. The 11 nonspecific studies showed similar trends for metformin and insulin prescriptions, although the CDSSs’ impacts were not significant. There was a predominance of metformin and insulin in the studied CDSSs and a lack of studies on ADs such as sodium-glucose cotransporter-2 (SGLT-2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists.</p></div><div><h3>Conclusion</h3><p>The limited number of studies, especially randomized clinical trials, interested in evaluating the application of CDSS in the management of ADs underscores the need for further investigations. Our findings suggest the potential benefit of applying CDSSs to the prescription of ADs particularly in primary care settings and when targeting clinical pharmacists. Finally, establishing core outcome sets is crucial for ensuring consistent and standardized evaluation of these CDSSs.</p></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141898990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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