BMC Medical Informatics and Decision Making最新文献

Background: Coronary artery disease is one of the leading causes of death and disability worldwide. Coronary angiography is a diagnostic procedure used to detect atherosclerosis. Patients typically experience anxiety and stress before and during the angiography procedure. Furthermore, self-care ability is crucial following angiography.

Aim: This study aims to describe the design and evaluation of a mobile application focusing on stress, anxiety, and self-care abilities in patients undergoing coronary angiography.

Method: The researchers developed a mobile application for patients undergoing angiography. The application provides information about angiography and tips for enhancing self-care following the procedure. An interventional study was conducted on 70 patients admitted to the angiography ward in hospitals in Kerman, Iran, between 2022 and 2023. The participants were randomly divided into two groups: control and intervention. The interventional group received the intervention application the night before angiography. Two groups completed the Anxiety and Stress Questionnaire (DAS) and Kearney-Flescher Self-Care Survey before the intervention. The researchers used questionnaires that had been prepared and previously utilized in other studies. The two groups completed the anxiety and stress questionnaire within three to six hours and the self-care questionnaire one month after angiography. SPSS 15 software was used for data analysis, with a significance level set at 0.05.

Results: The study found that the majority of participants were women. Before the study, there was no significant difference between the two groups in terms of anxiety, stress, and self-care scores. However, after the study, the intervention group showed a significant decrease in average anxiety and stress scores (p < 0.001). Additionally, compared to the control group, the intervention group demonstrated significant improvement in average self-care score (p < 0.001).

Conclusion: According to this study, AP can be effective in influencing the anxiety, stress levels, and self-care ability of patients who undergo coronary angiography. It can help to reduce stress and anxiety while increasing self-care. Instructive software is user-friendly, cost-effective, and can be recommended by nurses and doctors.

Background: Implementing an electronic system of service categorization and a referral system in healthcare is a strategic approach to improving overall health outcomes and optimizing resource use. This study aimed to investigate challenges experienced with the electronic patient referral system in Mashhad University of Medical Sciences (MUMS).

Methods: In this qualitative research, data were collected using semi-structured interviews. Participants included physicians, experts, and stakeholders working in the Family Physician Program and the referral system, selected through purposive sampling. The data were analyzed using a thematic analysis framework, in which a thematic framework was developed, and key themes were identified. Data analysis was performed using Atlas.ti8 software.

Results: According to the interviewees, the challenges of digitizing the referral system can be categorized into three main themes: structure, process, and outcomes. These themes include ten sub-themes, such as challenges related to Internet Infrastructure and the Sina System, Patients' Choice of Desired Specialists, Receiving Payment for Services, Appointment Scheduling, Interdepartmental Coordination, Recording Definitive Diagnosis Codes Before Referral, False Referrals, Dissatisfaction, Feedbacks, and Health Indicators.

Conclusion: To improve the e-referral in Iran's health system, several strategies can be implemented. These include sustainable resource allocation, designing consequence mechanisms within the referral system to motivate collaboration and improving appointment scheduling systems. Furthermore, addressing these challenges requires a collaborative approach involving healthcare providers, IT professionals, and patient representatives to ensure that the system is efficient, user-friendly, and effectively meets the needs of all parties involved. Not paying enough attention to these issues cause reform failure while solving them requires multi-dimensional, systematic and coordinated interventions with a deep understanding of the obstacles and challenges. Disregarding these factors may result in apathy over time, ultimately impacting both the quantity and, more importantly, the quality of services.

Background: Evaluating healthcare information systems, such as the Electronic Health Records (EHR), is both challenging and essential, especially in resource-limited countries. This study aims to psychometrically develop and validate an instrument (questionnaire) to assess the factors influencing the successful adoption of the EHR system by healthcare professionals in Moroccan university hospitals.

Methods: The questionnaire validation process occurred in two main stages. Initially, data collected from a pilot sample of 164 participants underwent analysis using exploratory factor analysis (EFA) to evaluate the validity and reliability of the retained factor structure. Subsequently, the validity of the overall measurement model was confirmed using confirmatory factor analysis (CFA) in a sample of 368 healthcare professionals.

Results: The structure of the modified HOT-fit model, comprising seven constructs (System Quality, Information Quality, Information technology Service Quality, User Satisfaction, Organization, Environment, and Clinical Performance), was confirmed through confirmatory factor analysis. Absolute, incremental, and parsimonious fit indices all indicated an appropriate level of acceptability, affirming the robustness of the measurement model. Additionally, the instrument demonstrated adequate reliability and convergent validity, with composite reliability values ranging from 0.75 to 0.89 and average variance extracted (AVE) values ranging from 0.51 to 0.63. Furthermore, the square roots of AVE values exceeded the correlations between different pairs of constructs, and the heterotrait-monotrait ratio of correlations (HTMT) was below 0.85, confirming suitable discriminant validity.

Conclusions: The resulting instrument, due to its rigorous development and validation process, can serve as a reliable and valid tool for assessing the success of information technologies in similar contexts.

Background: Measures in operations management are pivotal for monitoring and assessing various aspects of hospital performance. Existing literature highlights the importance of regularly updating key management measures to reflect changing trends and organizational goals. Advancements in machine learning (ML) have presented promising opportunities for enhancing the process of updating operations management measures. However, their specific application and performance remain relatively unexplored. We aimed to investigate the feasibility and effectiveness of using common ML techniques to identify and update key measures in hospital operations management.

Methods: Historical data on 43 measures on financial balance and quality of care under 4 categories were retrieved from the BI system of a regional health system in Central China. The dataset included 17 surgical and 15 non-surgical departments over 48 months. Four common ML techniques, linear models (LM), random forest (RF), partial least squares (PLS), and neural networks (NN), were used to identify the most important measures. Ordinary least square was employed to investigate the impact of the top 10 measures. A ground truth validation compared the ML-identified key measures against the humanly decided strategic measures from annual meeting minutes.

Results: For financial balancing, inpatient treatment revenue was an important measure in 3/4 years, followed by equipment depreciation costs. The measures identified using the same technique differed between years, though RF and PLS yielded relatively consistent results. For quality of care, none of the ML-identified measures repeated over the years. Those consistently important over four years differed almost entirely among four techniques. On ground truth validation, the 2016-2019 ML-identified measures were among the humanly identified measures, with the exception of equipment depreciation from the 2019 dataset. All the ML-identified measures for quality of care failed to coincide with the humanly decided measures.

Conclusions: Using ML to identify key hospital operational measures is viable but performance of ML techniques vary considerably. RF performs best among the four techniques in identifying key measures in financial balance. None of the ML techniques seem effective for identifying quality of care measures. ML is suggested as a decision support tool to remind and inspire decision-makers in certain aspects of hospital operations management.

Background: In clinical practice, the incidence of hypofibrinogenemia (HF) after tigecycline (TGC) treatment significantly exceeds the probability claimed by drug manufacturers.

Objective: We aimed to identify the risk factors for TGC-associated HF and develop prediction and survival models for TGC-associated HF and the timing of TGC-associated HF.

Methods: This single-center retrospective cohort study included 222 patients who were prescribed TGC. First, we used binary logistic regression to screen the independent factors influencing TGC-associated HF, which were used as predictors to train the extreme gradient boosting (XGBoost) model. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) were used to evaluate the performance of the model in the verification cohort. Subsequently, we conducted survival analysis using the random survival forest (RSF) algorithm. A consistency index (C-index) was used to evaluate the accuracy of the RSF model in the verification cohort.

Results: Binary logistic regression identified nine independent factors influencing TGC-associated HF, and the XGBoost model was constructed using these nine predictors. The ROC and calibration curves showed that the model had good discrimination (areas under the ROC curves (AUC) = 0.792 [95% confidence interval (CI), 0.668-0.915]) and calibration ability. In addition, DCA and CICA demonstrated good clinical practicability of this model. Notably, the RSF model showed good accuracy (C-index = 0.746 [95%CI, 0.652-0.820]) in the verification cohort. Stratifying patients treated with TGC based on the RSF model revealed a statistically significant difference in the mean survival time between the low- and high-risk groups.

Conclusions: The XGBoost model effectively predicts the risk of TGC-associated HF, whereas the RSF model has advantages in risk stratification. These two models have significant clinical practical value, with the potential to reduce the risk of TGC therapy.

Significant progress has been made recently with the contribution of technological advances in studies on brain cancer. Regarding this, identifying and correctly classifying tumors is a crucial task in the field of medical imaging. The disease-related tumor classification problem, on which deep learning technologies have also become a focus, is very important in the diagnosis and treatment of the disease. The use of deep learning models has shown promising results in recent years. However, the sparsity of ground truth data in medical imaging or inconsistent data sources poses a significant challenge for training these models. The utilization of StyleGANv2-ADA is proposed in this paper for augmenting brain MRI slices to enhance the performance of deep learning models. Specifically, augmentation is applied solely to the training data to prevent any potential leakage. The StyleGanv2-ADA model is trained with the Gazi Brains 2020, BRaTS 2021, and Br35h datasets using the researchers' default settings. The effectiveness of the proposed method is demonstrated on datasets for brain tumor classification, resulting in a notable improvement in the overall accuracy of the model for brain tumor classification on all the Gazi Brains 2020, BraTS 2021, and Br35h datasets. Importantly, the utilization of StyleGANv2-ADA on the Gazi Brains 2020 Dataset represents a novel experiment in the literature. The results show that the augmentation with StyleGAN can help overcome the challenges of working with medical data and the sparsity of ground truth data. Data augmentation employing the StyleGANv2-ADA GAN model yielded the highest overall accuracy for brain tumor classification on the BraTS 2021 and Gazi Brains 2020 datasets, together with the BR35H dataset, achieving 75.18%, 99.36%, and 98.99% on the EfficientNetV2S models, respectively. This study emphasizes the potency of GANs for augmenting medical imaging datasets, particularly in brain tumor classification, showcasing a notable increase in overall accuracy through the integration of synthetic GAN data on the used datasets.

Background: Hepatocellular Carcinoma (HCC) is a highly aggressive, prevalent, and deadly type of liver cancer. With the advent of deep learning techniques, significant advancements have been made in simplifying and optimizing the feature selection process.

Objective: Our scoping review presents an overview of the various deep learning models and algorithms utilized to address feature selection for HCC. The paper highlights the strengths and limitations of each approach, along with their potential applications in clinical practice. Additionally, it discusses the benefits of using deep learning to identify relevant features and their impact on the accuracy and efficiency of diagnosis, prognosis, and treatment of HCC.

Design: The review encompasses a comprehensive analysis of the research conducted in the past few years, focusing on the methodologies, datasets, and evaluation metrics adopted by different studies. The paper aims to identify the key trends and advancements in the field, shedding light on the promising areas for future research and development.

Results: The findings of this review indicate that deep learning techniques have shown promising results in simplifying feature selection for HCC. By leveraging large-scale datasets and advanced neural network architectures, these methods have demonstrated improved accuracy and robustness in identifying predictive features.

Conclusions: We analyze published studies to reveal the state-of-the-art HCC prediction and showcase how deep learning can boost accuracy and decrease false positives. But we also acknowledge the challenges that remain in translating this potential into clinical reality.

Aims: The primary goal of this study is to evaluate the capabilities of Large Language Models (LLMs) in understanding and processing complex medical documentation. We chose to focus on the identification of pathologic complete response (pCR) in narrative pathology reports. This approach aims to contribute to the advancement of comprehensive reporting, health research, and public health surveillance, thereby enhancing patient care and breast cancer management strategies.

Methods: The study utilized two analytical pipelines, developed with open-source LLMs within the healthcare system's computing environment. First, we extracted embeddings from pathology reports using 15 different transformer-based models and then employed logistic regression on these embeddings to classify the presence or absence of pCR. Secondly, we fine-tuned the Generative Pre-trained Transformer-2 (GPT-2) model by attaching a simple feed-forward neural network (FFNN) layer to improve the detection performance of pCR from pathology reports.

Results: In a cohort of 351 female breast cancer patients who underwent neoadjuvant chemotherapy (NAC) and subsequent surgery between 2010 and 2017 in Calgary, the optimized method displayed a sensitivity of 95.3% (95%CI: 84.0-100.0%), a positive predictive value of 90.9% (95%CI: 76.5-100.0%), and an F1 score of 93.0% (95%CI: 83.7-100.0%). The results, achieved through diverse LLM integration, surpassed traditional machine learning models, underscoring the potential of LLMs in clinical pathology information extraction.

Conclusions: The study successfully demonstrates the efficacy of LLMs in interpreting and processing digital pathology data, particularly for determining pCR in breast cancer patients post-NAC. The superior performance of LLM-based pipelines over traditional models highlights their significant potential in extracting and analyzing key clinical data from narrative reports. While promising, these findings highlight the need for future external validation to confirm the reliability and broader applicability of these methods.

Background: Wearable sensors have revolutionized cardiac health monitoring, with Seismocardiography (SCG) at the forefront due to its non-invasive nature. However, the substantial motion artefacts have hindered the translation of SCG-based medical applications, primarily induced by walking. In contrast, our innovative technique, Adaptive Bidirectional Filtering (ABF), surpasses these challenges by refining SCG signals more effectively than any motion-induced noise. ABF leverages a noise-cancellation algorithm, operating on the benefits of the Redundant Multi-Scale Wavelet Decomposition (RMWD) and the bidirectional filtering framework, to achieve optimal signal quality.

Methodology: The ABF technique is a two-stage process that diminishes the artefacts emanating from motion. The first step by RMWD is the identification of the heart-associated signals and the isolating samples with those related frequencies. Subsequently, the adaptive bidirectional filter operates in two dimensions: it uses Time-Frequency masking that eliminates temporal noise while engaging in non-negative matrix Decomposition to ensure spatial correlation and dorsoventral vibration reduction jointly. The main component that is altered from the other filters is the recursive structure that changes to the motion-adapted filter, which uses vertical axis accelerometer data to differentiate better between accurate SCG signals and motion artefacts.

Outcome: Our empirical tests demonstrate exceptional signal improvement with the application of our ABF approach. The accuracy in heart rate estimation reached an impressive r-squared value of 0.95 at - 20 dB SNR, significantly outperforming the baseline value, which ranged from 0.1 to 0.85. The effectiveness of the motion-artifact-reduction methodology is also notable at an SNR of - 22 dB. Consequently, ECG inputs are not required. This method can be seamlessly integrated into noisy environments, enhancing ECG filtering, automatic beat detection, and rhythm interpretation processes, even in highly variable conditions. The ABF method effectively filters out up to 97% of motion-related noise components within the SCG signal from implantable devices. This advancement is poised to become an integral part of routine patient monitoring.