JMIR Medical Informatics最新文献

Background: Social media platforms allow individuals to openly gather, communicate, and share information about their interactions with health care services, becoming an essential supplemental means of understanding patient experience.

Objective: We aimed to identify common discussion topics related to health care experience from the public's perspective and to determine areas of concern from patients' perspectives that health care providers should act on.

Methods: This study conducted a spatiotemporal analysis of the volume, sentiment, and topic of patient experience-related posts on the Weibo platform developed by Sina Corporation. We applied a supervised machine learning approach including human annotation and machine learning-based models for topic modeling and sentiment analysis of the public discourse. A multiclassifier voting method based on logistic regression, multinomial naïve Bayes, and random forest was used.

Results: A total of 4008 posts were manually classified into patient experience topics. A patient experience theme framework was developed. The accuracy, precision, recall, and F-measure of the method integrating logistic regression, multinomial naïve Bayes, and random forest for patient experience themes were 0.93, 0.95, 0.80, 0.77, and 0.84, respectively, indicating a satisfactory prediction. The sentiment analysis revealed that negative sentiment posts constituted the highest proportion (3319/4008, 82.81%). Twenty patient experience themes were discussed on the social media platform. The majority of the posts described the interpersonal aspects of care (2947/4008, 73.53%); the five most frequently discussed topics were "health care professionals' attitude," "access to care," "communication, information, and education," "technical competence," and "efficacy of treatment."

Conclusions: Hospital administrators and clinicians should consider the value of social media and pay attention to what patients and their family members are communicating on social media. To increase the utility of these data, a machine learning algorithm can be used for topic modeling. The results of this study highlighted the interpersonal and functional aspects of care, especially the interpersonal aspects, which are often the "moment of truth" during a service encounter in which patients make a critical evaluation of hospital services.

Background: The enzymatic system of cytochrome P450 (CYP450) is a group of enzymes involved in the metabolism of drugs present in the liver. Literature records instances of underdosing of drugs due to the concurrent administration of another drug that strongly induces the same cytochrome for which the first drug is a substrate and overdosing due to strong inhibition. IT solutions have been proposed to raise awareness among prescribers to mitigate these interactions.

Objective: This study aimed to develop a drug interaction dashboard for Cytochrome-mediated drug interactions (DIDC) using a health care data warehouse to display results that are easily readable and interpretable by clinical experts.

Methods: The initial step involved defining requirements with expert pharmacologists. An existing model of interactions involving the (CYP450) was used. A program for the automatic detection of cytochrome-mediated drug interactions (DI) was developed. Finally, the development and visualization of the DIDC were carried out by an IT engineer. An evaluation of the tool was carried out.

Results: The development of the DIDC was successfully completed. It automatically compiled cytochrome-mediated DIs in a comprehensive table and provided a dedicated dashboard for each potential DI. The most frequent interaction involved paracetamol and carbamazepine with CYP450 3A4 (n=50 patients). The prescription of tacrolimus with CYP3A5 genotyping pertained to 675 patients. Two experts qualitatively evaluated the tool, resulting in overall satisfaction scores of 6 and 5 out of 7, respectively.

Conclusions: At our hospital, measurements of molecules that could have altered concentrations due to cytochrome-mediated DIs are not systematic. These DIs can lead to serious clinical consequences. The purpose of this DIDC is to provide an overall view and raise awareness among prescribers about the importance of measuring concentrations of specific drugs and metabolites. Ultimately, the tool could lead to an individualized approach and become a prescription support tool if integrated into prescription assistance software.

Background: Atrial fibrillation (AF) is a progressive disease, and its clinical type is classified according to the AF duration: paroxysmal AF, persistent AF (PeAF; AF duration of less than 1 year), and long-standing persistent AF (AF duration of more than 1 year). When considering the indication for catheter ablation, having a long AF duration is considered a risk factor for recurrence, and therefore, the duration of AF is an important factor in determining the treatment strategy for PeAF.

Objective: This study aims to improve the accuracy of the cardiologists' diagnosis of the AF duration, and the steps to achieve this goal are to develop a predictive model of the AF duration and validate the support performance of the prediction model.

Methods: The study included 272 patients with PeAF (aged 20-90 years), with data obtained between January 1, 2015, and December 31, 2023. Of those, 189 (69.5%) were included in the study, excluding 83 (30.5%) who met the exclusion criteria. Of the 189 patients included, 145 (76.7%) were used as training data to build the machine learning (ML) model and 44 (23.3%) were used as test data for predictive ability of the ML model. Using a questionnaire, 10 cardiologists (group A) evaluated whether the test data (44 patients) included AF of more than a 1-year duration (phase 1). Next, the same questionnaire was performed again after providing the ML model's answer (phase 2). Subsequently, another 10 cardiologists (group B) were shown the test results of group A, were made aware of the limitations of their own diagnostic abilities, and were then administered the same 2-stage test as group A.

Results: The prediction results with the ML model using the test data provided 81.8% accuracy (72% sensitivity and 89% specificity). The mean percentage of correct answers in group A was 63.9% (SD 9.6%) for phase 1 and improved to 71.6% (SD 9.3%) for phase 2 (P=.01). The mean percentage of correct answers in group B was 59.8% (SD 5.3%) for phase 1 and improved to 68.2% (SD 5.9%) for phase 2 (P=.007). The mean percentage of answers that differed from the ML model's prediction for phase 2 (percentage of answers where cardiologists did not trust the ML model and believed their own determination) was 17.3% (SD 10.3%) in group A and 20.9% (SD 5%) in group B and was not significantly different (P=.85).

Conclusions: ML models predicting AF duration improved the diagnostic ability of cardiologists. However, cardiologists did not entirely rely on the ML model's prediction, even if they were aware of their diagnostic capability limitations.

Background: Clinical named entity recognition (CNER) is a fundamental task in natural language processing used to extract named entities from electronic medical record texts. In recent years, with the continuous development of machine learning, deep learning models have replaced traditional machine learning and template-based methods, becoming widely applied in the CNER field. However, due to the complexity of clinical texts, the diversity and large quantity of named entity types, and the unclear boundaries between different entities, existing advanced methods rely to some extent on annotated databases and the scale of embedded dictionaries.

Objective: This study aims to address the issues of data scarcity and labeling difficulties in CNER tasks by proposing a dataset augmentation algorithm based on proximity word calculation.

Methods: We propose a Segmentation Synonym Sentence Synthesis (SSSS) algorithm based on neighboring vocabulary, which leverages existing public knowledge without the need for manual expansion of specialized domain dictionaries. Through lexical segmentation, the algorithm replaces new synonymous vocabulary by recombining from vast natural language data, achieving nearby expansion expressions of the dataset. We applied the SSSS algorithm to the Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa) + conditional random field (CRF) and RoBERTa + Bidirectional Long Short-Term Memory (BiLSTM) + CRF models and evaluated our models (SSSS + RoBERTa + CRF; SSSS + RoBERTa + BiLSTM + CRF) on the China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2019 datasets.

Results: Our experiments demonstrated that the models SSSS + RoBERTa + CRF and SSSS + RoBERTa + BiLSTM + CRF achieved F1-scores of 91.30% and 91.35% on the CCKS-2017 dataset, respectively. They also achieved F1-scores of 83.21% and 83.01% on the CCKS-2019 dataset, respectively.

Conclusions: The experimental results indicated that our proposed method successfully expanded the dataset and remarkably improved the performance of the model, effectively addressing the challenges of data acquisition, annotation difficulties, and insufficient model generalization performance.

Background: The integration of information systems in health care and social welfare organizations has brought significant changes in patient and client care. This integration is expected to offer numerous benefits, but simultaneously the implementation of health information systems and client information systems can also introduce added stress due to the increased time and effort required by professionals.

Objective: This study aimed to examine whether professional groups and the factors that contribute to successful implementation (participation in information systems development and satisfaction with software providers' development work) are associated with the well-being of health care and social welfare professionals.

Methods: Data were obtained from 3 national cross-sectional surveys (n=9240), which were carried out among Finnish health care and social welfare professionals (registered nurses, physicians, and social welfare professionals) in 2020-2021. Self-rated stress and stress related to information systems were used as indicators of well-being. Analyses were conducted using linear and logistic regression analysis.

Results: Registered nurses were more likely to experience self-rated stress than physicians (odds ratio [OR] -0.47; P>.001) and social welfare professionals (OR -0.68; P<.001). They also had a higher likelihood of stress related to information systems than physicians (b=-.11; P<.001). Stress related to information systems was less prevalent among professionals who did not participate in information systems development work (b=-.14; P<.001). Higher satisfaction with software providers' development work was associated with a lower likelihood of self-rated stress (OR -0.23; P<.001) and stress related to information systems (b=-.36 P<.001). When comparing the professional groups, we found that physicians who were satisfied with software providers' development work had a significantly lower likelihood of stress related to information systems (b=-.12; P<.001) compared with registered nurses and social welfare professionals.

Conclusions: Organizations can enhance the well-being of professionals and improve the successful implementation of information systems by actively soliciting and incorporating professional feedback, dedicating time for information systems development, fostering collaboration with software providers, and addressing the unique needs of different professional groups.

Background: Diagnosis-related group (DRG) payment has become the main way of medical expenses settlement, and its application is more and more extensive.

Objective: This study aimed to explore the correlation between DRG weights and nursing time and to develop a predictive model for nursing time in the cardiology department based on DRG weights and other factors.

Methods: The convenience sampling method was used to select patients who were hospitalised in the cardiology ward of our hospital between April 2023 and April 2024 as the study participants. Nursing time was measured by direct and indirect nursing time. For the distribution of nursing time with different demographic characteristics, Pearson correlation was used to analyse the relationship between DRG weights and nursing time and multiple linear regression was used to analyse the influencing factors of total nursing time.

Results: A total of 103 subjects were included in this study. The DRG weights were positively correlated with ln(direct nursing time), ln(indirect nursing time) and ln(total nursing time) (r = 0.480, r = 0.394, r = 0.448, all P < .001). Moreover, age was positively correlated with the three nursing times (r = 0.235, r = 0.192, r = 0.235, all P < .001); activities of daily living (ADL) on admission was negatively correlated with the three nursing times (r = -0.316, r = -0.252, r = -0.301, all P < .001); and nursing level on the first day of admission was positively correlated with the three nursing times (r = 0.333, r = 0.332, r = 0.352, all P < .001). Furthermore, the multivariate analysis found that nursing levels on the first day of admission, complications or comorbidities, DRG weights and ADL on admission were the influencing factors of the nursing time of patients (R2 = 0.328, F = 69.58, P < .001).

Conclusions: Diagnosis-related group weights showed a strong correlation with nursing time and can be used to predict nursing time, which may assist in nursing resource allocation in cardiology departments.

Clinicaltrial:

Background: Social networking services (SNS) closely reflect the lives of individuals in modern society and generate large amounts of data. Previous studies have extracted drug information using relevant SNS data. In particular, it is important to detect adverse drug reactions (ADRs) early using drug surveillance systems. To this end, various deep learning methods have been used to analyze data in multiple languages in addition to English.

Objective: A cautionary drug that can cause ADRs in older patients was selected, and Korean SNS data containing this drug information were collected. Based on this information, we aimed to develop a deep learning model that classifies drug ADR posts based on a recurrent neural network.

Methods: In previous studies, ketoprofen, which has a high prescription frequency and, thus, was referred to the most in posts secured from SNS data, was selected as the target drug. Blog posts, café posts, and NAVER Q&A posts from 2005 to 2020 were collected from NAVER, a portal site containing drug-related information, and natural language processing techniques were applied to analyze data written in Korean. Posts containing highly relevant drug names and ADR word pairs were filtered through association analysis, and training data were generated through manual labeling tasks. Using the training data, an embedded layer of word2vec was formed, and a Bidirectional Long Short-Term Memory (Bi-LSTM) classification model was generated. Then, we evaluated the area under the curve with other machine learning models. In addition, the entire process was further verified using the nonsteroidal anti-inflammatory drug aceclofenac.

Results: Among the nonsteroidal anti-inflammatory drugs, Korean SNS posts containing information on ketoprofen and aceclofenac were secured, and the generic name lexicon, ADR lexicon, and Korean stop word lexicon were generated. In addition, to improve the accuracy of the classification model, an embedding layer was created considering the association between the drug name and the ADR word. In the ADR post classification test, ketoprofen and aceclofenac achieved 85% and 80% accuracy, respectively.

Conclusions: Here, we propose a process for developing a model for classifying ADR posts using SNS data. After analyzing drug name-ADR patterns, we filtered high-quality data by extracting posts, including known ADR words based on the analysis. Based on these data, we developed a model that classifies ADR posts. This confirmed that a model that can leverage social data to monitor ADRs automatically is feasible.