Impact of the COVID-19 Pandemic on Clinical Findings in Medical Imaging Exams in a Nationwide Israeli Health Organization: Observational Study.

BACKGROUND: The outbreak of the COVID-19 pandemic had a major effect on the consumption of health care services. Changes in the use of routine diagnostic exams, increased incidences of postacute COVID-19 syndrome (PCS), and other pandemic-related factors may have influenced detected clinical conditions. OBJECTIVE: This study aimed to analyze the impact of COVID-19 on the use of outpatient medical imaging services and clinical findings therein, specifically focusing on the time period after the launch of the Israeli COVID-19 vaccination campaign. In addition, the study tested whether the observed gains in abnormal findings may be linked to PCS or COVID-19 vaccination. METHODS: Our data set included 572,480 ambulatory medical imaging patients in a national health organization from January 1, 2019, to August 31, 2021. We compared different measures of medical imaging utilization and clinical findings therein before and after the surge of the pandemic to identify significant changes. We also inspected the changes in the rate of abnormal findings during the pandemic after adjusting for changes in medical imaging utilization. Finally, for imaging classes that showed increased rates of abnormal findings, we measured the causal associations between SARS-CoV-2 infection, COVID-19-related hospitalization (indicative of COVID-19 complications), and COVID-19 vaccination and future risk for abnormal findings. To adjust for a multitude of confounding factors, we used causal inference methodologies. RESULTS: After the initial drop in the utilization of routine medical imaging due to the first COVID-19 wave, the number of these exams has increased but with lower proportions of older patients, patients with comorbidities, women, and vaccine-hesitant patients. Furthermore, we observed significant gains in the rate of abnormal findings, specifically in musculoskeletal magnetic resonance (MR-MSK) and brain computed tomography (CT-brain) exams. These results also persisted after adjusting for the changes in medical imaging utilization. Demonstrated causal associations included the following: SARS-CoV-2 infection increasing the risk for an abnormal finding in a CT-brain exam (odds ratio [OR] 1.4, 95% CI 1.1-1.7) and COVID-19-related hospitalization increasing the risk for abnormal findings in an MR-MSK exam (OR 3.1, 95% CI 1.9-5.3). CONCLUSIONS: COVID-19 impacted the use of ambulatory imaging exams, with greater avoidance among patients at higher risk for COVID-19 complications: older patients, patients with comorbidities, and nonvaccinated patients. Causal analysis results imply that PCS may have contributed to the observed gains in abnormal findings in MR-MSK and CT-brain exams.

Use of Blockchain Technology to Protect Privacy in Electronic Health Records- A Review

Patients' medical files are electronically preserved and accessible through a network such as Electronic Health Records (EHRs). Numerous opportunities exist for EHRs to enhance patient care, clinical practice performance indicators, and potential future clinical research contributions. The techniques used to preserve EHRs have proved incredibly unsafe in the contemporary era of smart homes and urban areas. Data can be easily accessed by hackers and unauthorized third parties. Furthermore, the data is not accessible to patients or healthcare practitioners. These plans cannot balance the accessibility and security of the data. But with blockchain, these issues can be resolved. Any application created utilizing blockchain technology is secure and inaccessible to unauthorized parties thanks to the three critical characteristics of the technology: Security, Decentralization, and Transparency. In a blockchain network, it is nearly difficult to manipulate data. This research work utilizes blockchain technology to deploy EHRs and improve their security and privacy. With its decentralized structure and cryptographic techniques, blockchain technology will maintain control over who gets access to information. Furthermore, it will maintain a balance between accessing data and privacy. The advanced aspects of the EHR system are handled by this research using smart contracts. The comprehensive healthcare management solution across a network can incorporate several sectors, such as billing and transportation. A website program can be combined with it to increase interactivity. By adding pharmacists to the system as a participant, EHRs can help them track medical sales. © 2023 IEEE.

Children's Rates of BMI Change Prepandemic and During Two COVID-19 Pandemic Periods, IQVIA AEMR, January 2018-November 2021.

OBJECTIVE: Many U.S. youth experienced accelerated weight gain during the early COVID-19 pandemic. Using an ambulatory electronic health record dataset, we compared children's rates of BMI change in three periods: prepandemic (January 2018-February 2020), early pandemic (March-December 2020), and later pandemic (January-November 2021). METHODS: We used mixed-effects models to examine differences in rates of change in BMI, weight, and obesity prevalence among the three periods. Covariates included time as a continuous variable; a variable indicating in which period each BMI was taken; sex; age; and initial BMI category. RESULTS: In a longitudinal cohort of 241,600 children aged 2-19 years with ≥4 BMIs, the monthly rates of BMI change (kg/m2 ) were 0.056 (95%CI: 0.056, 0.057) prepandemic, 0.104 (95%CI: 0.102, 0.106) in the early pandemic, and 0.035 (95%CI: 0.033, 0.036) in the later pandemic. The estimated prevalence of obesity in this cohort was 22.5% by November 2021. CONCLUSIONS: In this large geographically-diverse cohort of U.S. youth, accelerated rates of BMI change observed during 2020 were largely attenuated in 2021. Positive rates indicate continued weight gain rather than loss, albeit at a slower rate. Childhood obesity prevalence remained high, which raises concern about long-term consequences of excess weight and underscores the importance of healthy lifestyle interventions. This article is protected by copyright. All rights reserved.

An Efficient Ensemble Model for Various Scale Medical Data

Electronic Health Records (EHRs) are the digital form of patients??? medical reports or records. EHRs facilitate advanced analytics and aid in better decision-making for clinical data. Medical data are very complicated and using one classification algorithm to reach good results is difficult. For this reason, we use a combination of classification techniques to reach an efficient and accurate classification model. This model combination is called the Ensemble model. We need to predict new medical data with a high accuracy value in a small processing time. We propose a new ensemble model MDRL which is efficient with different datasets. The MDRL gives the highest accuracy value. It saves the processing time instead of processing four different algorithms sequentially;it executes the four algorithms in parallel. We implement five different algorithms on five variant datasets which are Heart Disease, Health General, Diabetes, Heart Attack, and Covid-19 Datasets. The four algorithms are Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multi-layer Perceptron (MLP). In addition to MDRL (our proposed ensemble model) which includes MLP, DT, RF, and LR together. From our experiments, we conclude that our ensemble model has the best accuracy value for most datasets. We reach that the combination of the Correlation Feature Selection (CFS) algorithm and our ensemble model is the best for giving the highest accuracy value. The accuracy values for our ensemble model based on CFS are 98.86, 97.96, 100, 99.33, and 99.37 for heart disease, health general, Covid-19, heart attack, and diabetes datasets respectively.

A Covid-19 Vaccination Tracking and Control Platform in Santiago de Cali

The monitoring and control of epidemics is one of the most relevant topics in the field of smart health within smart cities. Smart health take advantage of a new generation of information technologies, such as big data, mobile internet, cloud computing and artificial intelligence, in order to transform the traditional medical system in a comprehensive way, making healthcare more efficient and personalized. From electronic Health records (EHR), diverse information about the epidemiological situation in institutions that provide health services can be extracted. This document describes the development of a platform to carry out the control and monitoring of vaccination process against Covid-19, which is based on cloud data storage technologies and make use of a existing platform designed for the registration of EHR emphasizing on data collection for structuring of epidemiological control strategies. The main goal is to identify and characterize patients who meet the prioritization criteria for Covid-19 vaccination according to stages defined by the Colombia Ministry of Health, execute the geocoding processes and identification of health conditions according to their previous EHR records, in order to accomplish an efficient and intelligent execution, monitoring and control of vaccination that impacts the epidemiological risk mitigation process. At the end of the document is described the use of the developed platform for the monitoring and control of the Covid-19 vaccination process in a Basic Health Services Unit called Medicips, which provides health services to approximately 90,000 people in the city of Santiago de Cali, Colombia. © 2022, Springer Nature Switzerland AG.

Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review.

PURPOSE: The advent of clinically adapted machine learning algorithms can solve numerous problems ranging from disease diagnosis and prognosis to therapy recommendations. This systematic review examines the performance of machine learning (ML) algorithms and evaluates the progress made to date towards their implementation in clinical practice. METHODS: Systematic searching of databases (PubMed, MEDLINE, Scopus, Google Scholar, Cochrane Library and WHO Covid-19 database) to identify original articles published between January 2011 and October 2021. Studies reporting ML techniques in clinical practice involving humans and ML algorithms with a performance metric were considered. RESULTS: Of 873 unique articles identified, 36 studies were eligible for inclusion. The XGBoost (extreme gradient boosting) algorithm showed the highest potential for clinical applications (n = 7 studies); this was followed jointly by random forest algorithm, logistic regression, and the support vector machine, respectively (n = 5 studies). Prediction of outcomes (n = 33), in particular Inflammatory diseases (n = 7) received the most attention followed by cancer and neuropsychiatric disorders (n = 5 for each) and Covid-19 (n = 4). Thirty-three out of the thirty-six included studies passed more than 50% of the selected quality assessment criteria in the TRIPOD checklist. In contrast, none of the studies could achieve an ideal overall bias rating of 'low' based on the PROBAST checklist. In contrast, only three studies showed evidence of the deployment of ML algorithm(s) in clinical practice. CONCLUSIONS: ML is potentially a reliable tool for clinical decision support. Although advocated widely in clinical practice, work is still in progress to validate clinically adapted ML algorithms. Improving quality standards, transparency, and interpretability of ML models will further lower the barriers to acceptability.

Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator.

BACKGROUND: The rapid global spread of the SARS-CoV-2 virus has provoked a spike in demand for hospital care. Hospital systems across the world have been over-extended, including in Northern Italy, Ecuador, and New York City, and many other systems face similar challenges. As a result, decisions on how to best allocate very limited medical resources and design targeted policies for vulnerable subgroups have come to the forefront. Specifically, under consideration are decisions on who to test, who to admit into hospitals, who to treat in an Intensive Care Unit (ICU), and who to support with a ventilator. Given today's ability to gather, share, analyze and process data, personalized predictive models based on demographics and information regarding prior conditions can be used to (1) help decision-makers allocate limited resources, when needed, (2) advise individuals how to better protect themselves given their risk profile, (3) differentiate social distancing guidelines based on risk, and (4) prioritize vaccinations once a vaccine becomes available. OBJECTIVE: To develop personalized models that predict the following events: (1) hospitalization, (2) mortality, (3) need for ICU, and (4) need for a ventilator. To predict hospitalization, it is assumed that one has access to a patient's basic preconditions, which can be easily gathered without the need to be at a hospital and hence serve citizens and policy makers to assess individual risk during a pandemic. For the remaining models, different versions developed include different sets of a patient's features, with some including information on how the disease is progressing (e.g., diagnosis of pneumonia). MATERIALS AND METHODS: National data from a publicly available repository, updated daily, containing information from approximately 91,000 patients in Mexico were used. The data for each patient include demographics, prior medical conditions, SARS-CoV-2 test results, hospitalization, mortality and whether a patient has developed pneumonia or not. Several classification methods were applied and compared, including robust versions of logistic regression, and support vector machines, as well as random forests and gradient boosted decision trees. RESULTS: Interpretable methods (logistic regression and support vector machines) perform just as well as more complex models in terms of accuracy and detection rates, with the additional benefit of elucidating variables on which the predictions are based. Classification accuracies reached 72 %, 79 %, 89 %, and 90 % for predicting hospitalization, mortality, need for ICU and need for a ventilator, respectively. The analysis reveals the most important preconditions for making the predictions. For the four models derived, these are: (1) for hospitalization:age, pregnancy, diabetes, gender, chronic renal insufficiency, and immunosuppression; (2) for mortality: age, immunosuppression, chronic renal insufficiency, obesity and diabetes; (3) for ICU need: development of pneumonia (if available), age, obesity, diabetes and hypertension; and (4) for ventilator need: ICU and pneumonia (if available), age, obesity, and hypertension.