A Fit between Clinical Workflow and Health Care Information Systems: Not waiting for Godot but making the journey

Health care has long suffered from inefficiencies due to the fragmentation of patient care information and the lack of coordination between health professionals [1]. Health care information systems (HISs) have been lauded as tools to remedy such inefficiencies [2, 3]. The primary idea behind the support of their implementation in health care is that these systems support clinical workflow and thereby decrease medical errors [2]. However, their introduction to health care settings have been accompanied by a transformation of the way their primary users, care providers, carry out clinical tasks and establish or maintain work relationships [4]. Studies have shown that these transformations have not always been productive [5, 6]

Enhancing coronary artery diseases screening:A comprehensive assessment of machine learning approaches using routine clinical and laboratory data

Introduction: Coronary artery disease (CAD) stands among the leading global causes of mortality, underscoring the critical necessity for early detection to facilitate effective treatment. Although Coronary Angiography (CA) serves as the gold standard for diagnosis, its limitations for screening, including side effects and cost, necessitate alternative approaches. This study focuses on the development and comparison of machine learning techniques as substitutes for CA in CAD screening, leveraging routine clinical and laboratory data. Material and Methods: Various machine learning classification algorithms—decision tree, k-nearest neighbor, artificial neural network, support vector machine, logistic regression, and stacked ensemble learning were employed to differentiate CAD and healthy subjects. Feature selection algorithms, namely LASSO and ReliefF, were utilized to prioritize relevant features. A range of evaluation metrics, including accuracy, precision, sensitivity, specificity, AUC, F1 score, ROC curve, and NPV, were applied. The SHAP technique was employed to elucidate and interpret the artificial neural network model. Results: The artificial neural network, support vector machine, and stacked ensemble learning models demonstrated excellent results in a 10-fold cross-validation evaluation using features selected by LASSO and ReliefF. With the LASSO feature selection algorithm, these models achieved accuracies of 90.38%, 90.07%, and 90.39%, sensitivities of 94.43%, 93.03%, and 93.96%, and specificities of 80.27%, 82.77%, and 81.52%, respectively. Using ReliefF, the accuracies were 88.79%, 88.77%, and 90.06%, sensitivities were 92.12%, 91.66%, and 93.98%, and specificities were 80.13%, 81.38%, and 80.13%, respectively. The SHAP technique revealed that typical and atypical chest pain, hypertension, diabetes mellitus, T inversion, and age were the most influential features in the neural network model. Conclusion: The machine learning models developed in this study exhibit high potential for non-invasive screening and diagnosis of CAD in the Z-Alizadeh Sani dataset. However, further studies are essential to validate and apply these models in real-world and clinical settings.</p

Unlocking therapeutic symphonies:Innovations in clinical decision support for drug-disease interactions in kidney transplantation

Introduction: Drug-disease interactions (DDSIs) are associated with increasing morbidity, mortality, and healthcare costs. These interactions are preventable if recognized and managed properly. Medication safety is critical in kidney transplant patients due to polypharmacy, co-morbidities, and susceptibility to adverse events. Clinical decision support systems (CDSSs) can play a key role therein. Therefore, this study aims to report on the process of developing an innovative, patient-centered, context-aware CDSS for managing DDSIs in kidney recipients. Material and Methods: Clinically important DDSIs were identified in the medications of patients at a kidney transplant outpatient clinic. Subsequently, rules for their detection and management were extracted based on pharmacology references and clinical expertise. A CDSS was developed and piloted following recommendations on medication CDSS design principles. Results: The knowledge base for this CDSS was developed with clinical context sensitivity. We defined priority levels for alerts, established associated display rules, and determined necessary actions based on the transplantation clinical workflow. The DDSI-CDSS correctly detected 37 DDSIs and displayed nine warnings and 28 cautionary alerts for the medications of 113 study patients (32.7% DDSI rate). The system fired three warnings for diltiazem in bradyarrhythmia, and two for each of the following medications and underlying diseases: aspirin in asthma, erythropoietin alfa in hypertension, and gemfibrozil in gall bladder disease. The potential consequences of the identified DDSIs were GI complications (17%), deterioration of the existing disease/condition (6.1%), and an increased risk of arrhythmias (2.6%), thrombosis (2.6%), and hypertension (1.7%). Complying with system alerts and recommendations would potentially prevent all these DDSIs. Conclusion: This study delineates the process of developing an evidence-based DDSI-CDSS for kidney transplantation, laying the groundwork for future advancements. Our results underscore the clinical significance of these interactions and emphasize the imperative for their accurate and timely detection, particularly in these vulnerable patients.</p

THE STUDY OF THE EPIDEMIOLOGY OF INFECTIOUS DISEASES BASED ON ULTRASOUND (SONOGRAPHY) RESULTS OBTAINED FROM CHILDREN WITH CALCULUS STONE IN ONE OF THE MEDICAL CENTERS OF TEHRAN PROVINCE

Abstract. Background and Goal: urinary infections are invasions to any urinary systems by microbial factors. This infection is the most common urinary infection and is the second most commonly reported infection in children. The aim of this study was to investigate the epidemiology of infectious diseases based on ultrasound (sonography) results obtained from children with calculus stone in a medical center in Tehran. Methods: This cross-sectional study was conducted in children who were in the Mofid hospital due to problems with calculus stone in 1397. First, specifications of all patients admitted with calculus stone were recorded. All children with calculus stones were under ultrasound (sonography) of the kidneys and urinary system, and the number, condition and dispersion of the stones were recorded andexamined. Findings: 31 children in hospital in the Nephrology department were studied which were 58.68% girl and 41.49% boy and on all of them ultrasound (sonography) was done. Blood and kidney ultrasound (sonography) findings (48.38%) and (22.58%) respectively, were abnormal. In the study of ultrasound (sonography) findings, the most common results were 21/21% remaining urine volume and 30/80% increase in bladder thickness, after which the stasis in the pilocalysis system was 9.26%, 9% kidney anomalies and 4.5% stones. Conclusion: The epidemiology of urinary infections was 32% of girls (form 58.6% female) and 22% male (from 41.94% male), which indicates a high level of urinaryinfections, especially in the female population.Keywords: Calculus Stones, Infectious Diseases, Urinary Infections, Ultrasound (sonography), Kidney Stones

Potentially inappropriate medication prescribing based on 2019 Beers criteria and the impact of pharmacist intervention in elderly patients with kidney diseases:A report from Iran

Background and Aims: A potentially inappropriate medication (PIM) is a pharmaceutical agent that poses a greater risk of harm than potential benefit to elderly patients. This study aimed to detect PIMs and their risk factors in hospitalized elderly patients with kidney disease. Methods: This cross-sectional study assessed medication orders of elderly patients (≥65 years old) with kidney diseases admitted to the hospital. In the first 6 months, we retrospectively evaluated all medications to identify PIMs according to the 2019 Beers criteria. In the second phase, a clinical pharmacist prospectively evaluated all medications and suggested modifications as needed. Data were analyzed to determine risk factors for prescribing PIMs. Results: Based on our evaluation of 258 patients, we observed that the utilization of PIMs was prevalent among the study population. Of the total patients evaluated, 273 instances of PIM use were identified, with only 23.3% of patients not having any PIMs. Notably, proton pump inhibitors and benzodiazepines were the most frequently prescribed PIMs. The risk of experiencing a PIM was significantly amplified by a higher degree of polypharmacy, with odds approximately 2.68 times higher (p &lt; 0.01). Several factors were found to be associated with an increased likelihood of having a PIM, including being male, undergoing hemodialysis, having chronic kidney disease or other comorbidities, and having an extended hospital stay. The second phase of study, in terms of addressing these issues, physicians adhered to 67.5% of the 120 recommendations made by pharmacists regarding the discontinuation of PIM usage. Conclusion: High prevalence of PIMs was detected in our study population. Preventing medication-associated harms in the elderly can reduce the financial burden imposed on healthcare systems. Therefore, routine evaluation of medications with clinical pharmacists and/or implementation of computerized medication decision support systems is recommended to prevent PIMs use.</p

Potentially inappropriate medication prescribing based on 2019 Beers criteria and the impact of pharmacist intervention in elderly patients with kidney diseases:A report from Iran

Background and Aims: A potentially inappropriate medication (PIM) is a pharmaceutical agent that poses a greater risk of harm than potential benefit to elderly patients. This study aimed to detect PIMs and their risk factors in hospitalized elderly patients with kidney disease. Methods: This cross-sectional study assessed medication orders of elderly patients (≥65 years old) with kidney diseases admitted to the hospital. In the first 6 months, we retrospectively evaluated all medications to identify PIMs according to the 2019 Beers criteria. In the second phase, a clinical pharmacist prospectively evaluated all medications and suggested modifications as needed. Data were analyzed to determine risk factors for prescribing PIMs. Results: Based on our evaluation of 258 patients, we observed that the utilization of PIMs was prevalent among the study population. Of the total patients evaluated, 273 instances of PIM use were identified, with only 23.3% of patients not having any PIMs. Notably, proton pump inhibitors and benzodiazepines were the most frequently prescribed PIMs. The risk of experiencing a PIM was significantly amplified by a higher degree of polypharmacy, with odds approximately 2.68 times higher (p &lt; 0.01). Several factors were found to be associated with an increased likelihood of having a PIM, including being male, undergoing hemodialysis, having chronic kidney disease or other comorbidities, and having an extended hospital stay. The second phase of study, in terms of addressing these issues, physicians adhered to 67.5% of the 120 recommendations made by pharmacists regarding the discontinuation of PIM usage. Conclusion: High prevalence of PIMs was detected in our study population. Preventing medication-associated harms in the elderly can reduce the financial burden imposed on healthcare systems. Therefore, routine evaluation of medications with clinical pharmacists and/or implementation of computerized medication decision support systems is recommended to prevent PIMs use.</p

Using machine learning algorithms in determining the stage of breast cancer from pathology reports

Introduction: After a cancer diagnosis, the most important thing is to determine the stage and grade of the cancer. Pathology reports are the main source for cancer staging, but they do not contain all the information needed for the staging. However, the text of these reports is sometimes the only available information. We were interested in knowing whether text mining methods can be used to predict staging only from pathology reports. Material and Methods: A total of 698 pathology reports of breast cancer cases and their TNM staging collected from multiple centers in West Azerbaijan Province, Iran were used for this study. After preparing the semi-structured reports, the texts of the reports were imported into a program written by Python V3. Three machine learning algorithms of Logistic Regression, SVM, and Naïve Bayes and a simple pipeline were used for the purpose of text mining. The performance of the algorithms was evaluated in terms of accuracy, precision, recall, and F1 score. Results: The Naïve Bayes algorithm achieved excellent results and a value rate of higher than 91% in all evaluation criteria (accuracy, precision, recall and F1 score). This means that the Naïve Bayes algorithm could classify the reports with high efficiency and its predictions were more correct than the other two algorithms. Naïve Bayes also outperformed SVM and Logistic Regression in terms of accuracy, recall and F1 score. In addition, Naïve-Bayes showed faster inference due to its simplicity and lower computational and training time. Conclusion: We suggest using the proposed design in this study for predicting breast cancer staging, where there is a need but not all necessary information except pathology reports. This method may not be a useful for clinical management of cancer patients, but it can be safely used for epidemiological estimations.</p