Trend analysis of ambient air quality of Chattogram city

Several air quality parameters such as particulate matter (PM), ozone (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2), and carbon monoxide (CO) are considered as the major pollutants which can impose a significant threat to human health and surrounding environment. In this study, seasonal and temporal variations were analyzed for both gaseous air pollutants and particulate matter to investigate the trend analysis of ambient air quality of Chattogram city, a commercial hub of Bangladesh. Air quality data for six selected parameters (PM2.5, PM10, CO, SO2, NO2, and O3) were collected from Continuous Air Monitoring Stations (CAMS) during the period 2013 to 2021 for each pollutant. Air Quality Index (AQI) for each tested pollutant was determined as well as pollution level sharing among the pollutants was also investigated in this work. Results of this study showed that particulate matters (PM2.5 and PM10) were the most responsible pollutants that contributed significantly to air pollution levels in the city. The yearly average AQI was observed to be in the caution (unhealthy for sensitive groups) (100-150) category during the period from 2013 to 2021. Trend analysis showed that there is an ups and downs trend in the AQI level in the city that may be triggered by some interventions taken and Covid-19 pandemic situations. Overall, seasonal variation had a considerable effect on the concentration of pollutants. For each year, the highest concentration of PM2.5 and PM10 was recorded in winter season while the lowest was reported in monsoon season. This study will assist the researchers and policymakers in taking the required steps to take preventive measures in reducing air pollution levels for the studied area. © 2023 Author(s).

Emotional Labour During the COVID-19 Pandemic—Current Inquiry and Suggested Future Research Directions

The objective of this article is to examine the impact of macro-extreme emotional experience (MEEE) and the new societal norms during the COVID-19 pandemic on health and well-being and their situational consequences on emotional labour of frontline employees. The vast literature on emotional labour in the past has focused on several situational cues, and individual and organizational factors as antecedents. We did a systematic review of available literature on emotional labour, literature on sentiment analysis and emotional experience during the pandemic and analysed COVID-19 related blogs using Natural Language Processing (NLP) in RStudio. At the same time, we attempted to look at the possible intervention of individual factors of MEEEs and social aspects of the new societal norms as antecedents on emotion regulation process and its outcome and propose a conceptual framework for future research on emotional labour under the ‘new normal'. It was concluded that perceived risk, fear and anxiety are extreme emotions that individuals are experiencing during the pandemic. © 2023 XLRI Jamshedpur, School of Business Management & Human Resources.

Investigate the designing of eutectic mixture using DFT computation and evaluate their biological potency using molecular docking

Researchers are exploring the eutectic mixture because of their obvious great potential in various disciplines. Herein, authors have presented the DFT calculations, molecular docking and QSAR results for designed eutectic mixtures (EMs) using thiourea and resorcinol on taking different equivalent ratio. Authors have used Jakob et al. method to determine the melting temperature of the systems or EMs theoretically. Thermodynamic parameteres such as the free energy, enthalpy, and other energy of the EMs at room temperature are determined through DFT calculations using Gaussian. Authors have also calculated the physiochemical descriptors of various eutectic mixture based on DFT calculations. Further, molecular docking of the designed EMs is carried out to investigate their biological potential for inhibition of the Mpro of SARS-CoV-2. © 2023 Elsevier B.V.

Prediction of Unemployment using Machine Learning Approach

Unemployment is a circumstance which arises when people above a specific age are not engaged in any kind of activities which contribute to the economic welfare of the individual and country. Unemployment is becoming a rising concern which is making the daily life of people difficult. Unemployment causes poverty and depression among the citizens. Nowadays there are different opportunities in different sectors. But people are not aware of those opportunities. Different states are there where there is a lack of skilled labour whereas many states are there that have skilled labour but less opportunities. Another reason for unemployment since 2020 is the COVID-19 pandemic. We have selected this topic to spread awareness among the citizens. This work attempts to detect the states of India which are in serious need of increasing employment opportunities. We have applied the concept of Supervised Machine Learning algorithms to detect the states with the lowest employment rate. The data visualization gives a better picture of the trends in unemployment rate over years. There has been a use of different popular algorithms like Logistic Regression, Support Vector Machine, K-nearest neighbors (kNN) Algorithm and Decision Tree. In the end we have tried to find the algorithm which is going to give us more accuracy so that necessary steps can be taken for the employment of the eligible and deserving people. © 2022 IEEE.

Efficacy Evaluation of Silty-Sandy Soil and Chrysopogon zizanioides to Attenuate Doxycycline from Wastewater in a Constructed Wetland System

In recent years, the emergence of COVID-19 has created disastrous health effects worldwide. Doxycycline, a member of the tetracycline group, has been prescribed as a treatment companion for attending this catastrophe. Due to extensive use and high solubility, a significant amount of un-metabolized doxycycline has been found to reach water bodies within a short time, and consumption of this water may lead to the development of fatal resistance in organisms and create health problems. Therefore, it has become necessary to develop suitable technologies from a geoenvironmental point of view to remove these unwanted antibiotics from wastewater. In this context, locally obtainable silty-sandy soil was explored as a low-cost material in a constructed wetland with Chrysopogon zizanioides (vetiver sp.) for phytoremediation to mitigate doxycycline spiked wastewater. The obtained soil hydraulic conductivity was 1.63×10-7 m/s. Batch adsorption tests conducted on silty-sandy soil, vetiver leaf, and vetiver root provided maximum removal efficiencies of 90%, 72%, and 80% percent, respectively, at optimal sorbent doses of 10 g/L, 17 g/L, and 16 g/L, and contaminant concentrations of 25 mg/L, 20 mg/L, and 23 mg/L, with a 30-min time of contact. The Freundlich isotherm was the best fit, indicative of sufficient sorption capacity of all the adsorbents for doxycycline. The best match in the kinetic research was pseudo-second-order kinetics. A one dimensional vertical column test with the used soil on doxycycline revealed a 90% breakthrough in 24 h for a soil depth of 30 mm. Studies on a laboratory-scale wetland and numerically modeled yielded removal of around 92% by the selected soil and about 98% combined with Chrysopogon zizanioides for 25 mg/L of initial doxycycline concentration, which is considered quite satisfactory. Simulated results matched the laboratory tests very well. The study is expected to provide insight into remedies for similar practical problems. © 2023 American Society of Civil Engineers.

Analysis of the emergence and initial performance of REITs in India

Purpose: In 2014, real estate investment trust (REIT) emerged as a new alternative investment option in India. This research aims to give an empirical authentication of the Indian REITs performance from April 2019 to July 2022 across a range of investment variables. Design/methodology/approach: Using monthly total returns in Indian Rupee, risk-adjusted Indian REIT performance and investment portfolio characteristics are examined. Indian REITs' potential in a diversified multi-asset portfolio is analysed using the mean-variance analysis, asset allocation diagram and efficient frontier. Findings: During April 2019–July 2022, Indian REITs provided a lower return than stocks but outperformed bonds despite coronavirus disease 2019 (COVID-19) lockdowns, which hurt the traditional working from office concept. The study also examined REIT allocation to an Indian mixed-asset portfolio and the benefits of a diversified portfolio. Practical implications: Indian REITs provide a liquid, transparent alternative to direct property for investors seeking exposure to Indian real estate markets. Indian REITs gave real estate companies an extra funding source and investors an alternate asset. This paper explores Indian REITs' potential opportunities, given that domestic and foreign investors' demand for transparent property investment in India. The analysis found a positive early performance despite a challenging environment. Originality/value: This paper offers the first empirical performance validation of Indian REITs as a way to obtain exposure to commercial property in India and the REITs' role in a diversified asset portfolio. The authors' study improves investors' decision-making abilities by providing empirically validated, valuable and practicable property investing insights. © 2022, Emerald Publishing Limited.

Energy- efficient model "Inception V3 based on deep convolutional neural network" using cloud platform for detection of COVID-19 infected patients

Objectives: COVID-19 is frightening the health of billions of persons and speedily scattering worldwide. Medical studies have revealed that the majority of COVID-19 patients. X-ray of COVID-19 is extensively used because of their noticeably lower price than CT. This research article aims to spot the COVID-19 virus in the X-ray of the chest in less time and with better accuracy. Methods: We have used the inception-v3 available on the cloud platform transfer learning model to classify COVID-19 infection. The online Inception v3 model can be reliable and efficient for COVID-19 disease recognition. In this experiment, we collected images of COVID-19-infected patients, then applied the online inception-v3 model to automatically extract features, and used a softmax classifier to classify the COVID-19 images. Finally, the experiment shows inception v3 is significant for COVID-19 image classification. Results: Our results demonstrate that our proposed inception v3 model available on the cloud platform can detect 99.41% of COVID-19 cases between COVID-19 and Lung Mask diseases in 44 min only. We have also taken images of the normal chest for better outcomes. To estimate the computation power of the model, we collected 6018 COVID-19, Lung Masks, & Normal Chest images for experimentation. Our projected model offered a trustworthy COVID-19 classification by using chest X-rays. Conclusions: In this research paper, the inception v3 model available on the cloud platform is used to categorize COVID-19 infection by X-ray images. The Inception v3model available on the cloud platform is helpful to clinical experts to examine the enormous quantity of human chest X-ray images. Scientific and clinical experiments will be the subsequent objective of this paper. © 2023 Walter de Gruyter GmbH. All rights reserved.

Forecasting the Growth in Covid-19 Infection Rates

The recent times have seen the global rise in infection rates from the virus Covid-19, leading to a pandemic. The exponential rise in infections and deaths lead to panic and nation-wide lockdowns across the globe. Advancements in biotechnical and medical research have paved the way for the development and mass distribution of vaccines. To build an understanding of the current situation we did a comparative analysis of the rise in infection rates among citizens across the countries and also the growth in vaccinations in the pre-vaccination phase and the post-vaccination phase of the on-going pandemic to determine whether the rate of vaccination is more than the rate of infection or otherwise. Then, a comparison is done among two prediction models we built, one using polynomial regression and other using SVM to determine which model provides better prediction results of infection rates in a country. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analysing The Effectiveness of Stress Management Practices Post COVID-19 With A Modified Job Stress Scale

Post COVID-19, there is increased psychological stress and depression over the whole world population. Multiple factors like perception of safety, risk of contagion, confinement, stigma, social alienation, financial loss, and job insecurity create much stress for the working population. Top management at various companies implements various stress management policies to reduce the employees' fear and stress. Job stress scales used in many companies are incapable of measuring stress management practices post-COVID-19. This work proposes a new job stress scale for effective stress measurement in companies post COVID-19. The novel job stress scale's effectiveness is tested against a corporate company's pilot study to analyze the effectiveness of various stress management practices followed in the company post-COVID-19. The unprecedented impact of the COVID-19 pandemic has prompted action to address the global crisis, which is critical for psychiatry. As mental health professionals, we are on the front lines of providing psychological support to those affected by the pandemic. Empirical tools, such as validated scales and questionnaires, are essential for managing mental health issues. Such tools would help manage mental health occupational burden and other psychosocial issues and manage future uncertainty.

Analysis of the emergence and initial performance of REITs in India

Purpose: In 2014, real estate investment trust (REIT) emerged as a new alternative investment option in India. This research aims to give an empirical authentication of the Indian REITs performance from April 2019 to July 2022 across a range of investment variables. Design/methodology/approach: Using monthly total returns in Indian Rupee, risk-adjusted Indian REIT performance and investment portfolio characteristics are examined. Indian REITs' potential in a diversified multi-asset portfolio is analysed using the mean-variance analysis, asset allocation diagram and efficient frontier. Findings: During April 2019–July 2022, Indian REITs provided a lower return than stocks but outperformed bonds despite coronavirus disease 2019 (COVID-19) lockdowns, which hurt the traditional working from office concept. The study also examined REIT allocation to an Indian mixed-asset portfolio and the benefits of a diversified portfolio. Practical implications: Indian REITs provide a liquid, transparent alternative to direct property for investors seeking exposure to Indian real estate markets. Indian REITs gave real estate companies an extra funding source and investors an alternate asset. This paper explores Indian REITs' potential opportunities, given that domestic and foreign investors' demand for transparent property investment in India. The analysis found a positive early performance despite a challenging environment. Originality/value: This paper offers the first empirical performance validation of Indian REITs as a way to obtain exposure to commercial property in India and the REITs' role in a diversified asset portfolio. The authors' study improves investors' decision-making abilities by providing empirically validated, valuable and practicable property investing insights. © 2022, Emerald Publishing Limited.

Estimating the size of hard-to-reach subpopulations and segregation with the network scale-up method: lessons from a Hungarian survey

In this paper, we provide an empirical, descriptive analysis of the social networks of Hungarian society and illustrate how the network scale-up method estimates the size of hard-to-reach subpopulations and segregation of social groups. Based on a representative survey of 7000 respondents from Hungary (HS2021), we first estimate the average size of the respondents' personal networks. Then, we examine the social fault lines along various social groups and how accurately the network scale-up method estimates the size of these groups (e.g., unemployed, protesters, the Roma, Covid-infected). These estimates are then compared with data from other sources (census data, administrative data, surveys). Our results show that the network scale-up method estimates the size of visible social groups (e.g., the Roma, homeless people) quite well. The visibility of other social groups appears to be much lower. Social fault lines are greatest in the case of homeless people, protesters, and members of NGOs. Finally, we describe recent methodological advancements and summarize our suggestions for future research using this method. © 2022, Hungarian Sociological Association. All rights reserved.

Blueprint for impedance-based electrochemical biosensors as bioengineered tools in the field of nano-diagnostics

Electrochemical biosensors are analytical devices that hold a current across the surface of an electrode on which biological receptors are immobilized. These devices enable the conversion of physio-biochemical reactions by biological molecules into electron movements, so the output can be observed as the flow of charge across the electrode. These biosensing platforms detect changes in the reactive and resistive properties of the electrode surface when an alternating current (AC) or voltage is applied to output signals. Impedance-based electrochemical biosensors have advantages compared with other biosensors, such as high sensitivity, low cost, and ease of operation. In addition to uses as miniature detection tools, biosensors and microfluidics play vital roles in nano-diagnostics. Many sensors have been developed at the nanoscale by exploiting the greater conductivity across the electrodes and improved specificity for biorecognition element-receptor binding in biosensing devices. Several of these sensors have been assessed in trials and emerged as clinical products for detecting and diagnosing diseases, bacteria, viruses, deficiencies, and biofluid malfunctions in the human body. This review summarizes advances in impedance-based biosensors and their working principles and classifications, as well as providing relevant illustrations by focusing on the essential biorecognition elements, receptors, and target molecules during diagnosis. Copyright © 2022 The Authors

The aim of the study is to evaluate the impact of nutrition changes & programmed physical training on type 1 diabetic adolescents during COVID-19 lockdown

Introduction: According to WHO and IDF it is stated that healthy diet and regular physical activity and maintaining healthy weight is very effective for type 1 and type 2 diabetes. Objective(s): The main objectives of study was to schedule personalized healthy nutrition, to programmed physical training schedule & was to find out the impact of nutrition & physical activity in term of SMBG changes, weight, muscle mass, Hba1c. Method(s): Number of type 1 diabetic children & adolescents enrolled for the intervention were 15 (F = 9, M = 6). Number of subject completed the intervention was 13. The intervention conducted for 3 months during the lockdown from 1 April to 1 July 2020. All subjects were counseled and educated and followed up Through teleconsultation at baseline visit (Day 0), visit 1 on (day 15), visit -2 on (day 30) visit 3 on (day 60) visit 4 on (day 90). The parameters evaluated were anthropometric data,HBA1C & SMBG readings from baseline to end of the intervention. Result(s): Data showed significant improvement of hba1c of 0.8%, also improvement of glycemic control is seen. On evaluation of anthropometric data there was no significant changes in weight but increment seen in muscle mass. Conclusion(s): It has been concluded that MNT & physical activity comprise the basic pillars in treatment of diabetes. Especially for kids, young adults with a chronic condition following a strategic plan for nutrition & exercise can do in appropriate growth and development. In the real world, implementation of the MNT and exercise for the pediatric population still remains a challenge. This can be easily solved by including sports of the person's choice in their routine activities and healthy food options. These together can impact the glycemic status and the quality of life of young adults up to great extent.

Rough-Fuzzy CPD: a gradual change point detection algorithm

Changepoint detection is the problem of finding abrupt or gradual changes in time series data when the distribution of the time series changes significantly. There are many sophisticated statistical algorithms for solving changepoint detection problem, although there is not much work devoted towards gradual changepoints as compared to abrupt ones. Here we present a new approach to solve the changepoint detection problem using the fuzzy rough set theory which is able to detect such gradual changepoints. An expression for the rough-fuzzy estimate of changepoints is derived along with its mathematical properties concerning fast computation. In a statistical hypothesis testing framework, the asymptotic distribution of the proposed statistic on both single and multiple changepoints is derived under the null hypothesis enabling multiple changepoint detection. Extensive simulation studies have been performed to investigate how simple crude statistical measures of disparity can be subjected to improve their efficiency in the estimation of gradual changepoints. Also, the said rough-fuzzy estimate is robust to signal-to-noise ratio, a high degree of fuzziness in true changepoints, and also to hyperparameter values. Simulation studies reveal that the proposed method beats other methods of gradual changepoint detection (including MJPD, HSMUCE, fuzzy methods like FCP, FCMLCP etc) and also popular crisp methods like Binary Segmentation, PELT, and BOCD in detecting gradual changepoints. The applicability of the estimate is demonstrated using multiple real-life datasets including Covid-19. We have developed the python package roufcp for broader dissemination of the methods.

Forecasting the Growth in Covid-19 Infection Rates

The recent times have seen the global rise in infection rates from the virus Covid-19, leading to a pandemic. The exponential rise in infections and deaths lead to panic and nation-wide lockdowns across the globe. Advancements in biotechnical and medical research have paved the way for the development and mass distribution of vaccines. To build an understanding of the current situation we did a comparative analysis of the rise in infection rates among citizens across the countries and also the growth in vaccinations in the pre-vaccination phase and the post-vaccination phase of the on-going pandemic to determine whether the rate of vaccination is more than the rate of infection or otherwise. Then, a comparison is done among two prediction models we built, one using polynomial regression and other using SVM to determine which model provides better prediction results of infection rates in a country. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Effect of Diabetes Mellitus on Outcomes of Patients Admitted with COVID-19: A Single - Center Experience from a Tertiary Hospital in India.

Background and Objectives: Diabetes mellitus is associated with poor clinical outcomes in patients with coronavirus disease 2019 (COVID-19). This study aimed to explore the clinical characteristics of patients with type 2 diabetes with COVID-19, and to determine the impact of type 2 diabetes on clinical outcome of patients with COVID-19. Material and Methods: This single-center, retrospective, observational study enrolled patients admitted from March 2020 to June 2021 with COVID-19. The clinical and biochemical characteristics of patients with known type 2 diabetes, newly diagnosed diabetes, type 2 diabetes with comorbidities and those who succumbed to illness were analyzed. Results: Of 4,559 patients with COVID-19, 2,090 (45.8%) had type 2 diabetes. Patients with COVID-19 with diabetes were older, more likely to receive mechanical ventilation, had higher odds of mortality from COVID-19 as compared with patients without diabetes. In addition, patients with diabetes had significantly higher levels of serum creatinine, C-reactive protein, ferritin, lactate dehydrogenase, and D-dimer. Compared with previously diagnosed patients with diabetes, newly diagnosed patients had higher mortality (33% vs. 27%, P = 0.049). Among patients with COVID-19 and diabetes, nonsurvivors had significantly higher levels of inflammatory markers and had severe impairment of cardiac, renal, and coagulation parameters as opposed to survivors. Conclusion: Patients with COVID-19 with diabetes were more likely to have severe disease and had higher mortality. Presence of chronic kidney disease and coronary artery disease in patients with diabetes with COVID-19 was associated with adverse outcome. Patients with newly diagnosed diabetes had higher odds of severe disease at presentation and had higher mortality.

A BRIEF DESCRIPTION OF COVID-19 PULMONARY VIRAL INFECTION AND REPURPOSING OF DRUGS FOR ITS TREATMENT

A novel coronavirus disease, which is transmitted from human to human has quickly become the cause of the current worldwide health crisis. This virus is, also known as SARS coronavirus, belongs to the Coronaviridae family of viruses. The recent outbreak of acute respiratory disorders starting in Wuhan, China is found to be caused by this virus. The condition caused by it, known as COVID-19 has spread very rapidly all over the world, causing so many death. This led WHO on Mar 11, 2020, to designate it as a global pandemic. An update on the history, etiology, epidemiology, pathophysiology and preventive methods for COVID-19 such as masking, quarantine, and social distancing are discussed in this paper. Repurposed drugs, antibodies, corticosteroids, vaccination and plasma transfusion, are among the treatments explained in the study. Finally, the study discusses India’s COVID vaccination programme. The major aspects of this entire review are to describe COVID-19 infection, its prevention and treatment approach.

COVID-19 Disease Detection using Pre-Trained Deep Convolutional Neural Network (GoogleNet) on Cloud Platform

Background and Purpose: The COVID-19 epidemics are causing the main rash in more than 151 countries around the whole world.Covid-19 has a bad effect on human life worldwide. One of the critical steps in fighting COVID-19 is finding the contaminated patients early enough and putting these infected people under special care. Our main aim is to separate COVID-19 patients from other patients. Materials and Methods: In this research article, we used GoogleNet as a learning network. GoogleNet is a deep convolutional neural network of 22 layers deep. We have used a pre-trained version of the GoogleNet trained on ImageNet. The pre-trained GoogleNet image input size is 224 x 224.GoogleNet;the deep convolutional neural network model can analyze X-ray images to classify the patient’s condition of the affected disease. Result: Experiments and evaluation of the GoogleNet have been effectively done based on 80% of X-ray pictures for training and 20% of X-ray pictures for testing phases respectively. GoogleNet shows a good result for disease classification with 91.40% of accuracy in 2.49 minutes. Conclusion: In this research paper, we have used the deep CNN model to classify COVID-19 disease using X-ray images based on the projected GoogleNet. Scientific studies will be the next goal of this research article.

WE-Net: An Ensemble Deep Learning Model for Covid-19 Detection in Chest X-ray Images Using Segmentation and Classification

Amidst the increasing surge of Covid-19 infections worldwide, chest X-ray (CXR) imaging data have been found incredibly helpful for the fast screening of COVID-19 patients. This has been particularly helpful in resolving the overcapacity situation in the urgent care center and emergency department. An accurate Covid-19 detection algorithm can further aid this effort to reduce the disease burden. As part of this study, we put forward WE-Net, an ensemble deep learning (DL) framework for detecting pulmonary manifestations of COVID-19 from CXRs. We incorporated lung segmentation using U-Net to identify the thoracic Region of Interest (RoI), which was further utilized to train DL models to learn from relevant features. ImageNet based pre-trained DL models were fine-tuned, trained, and evaluated on the publicly available CXR collections. Ensemble methods like stacked generalization, voting, averaging, and the weighted average were used to combine predictions from best-performing models. The purpose of incorporating ensemble techniques is to overcome some of the challenges, such as generalization errors encountered due to noise and training on a small number of data sets. Experimental evaluations concluded on significant improvement in performance using the deep fusion neural network, i.e., the WE-Net model, which led to 99.02% accuracy and 0.989 area under the curve (AUC) in detecting COVID-19 from CXRs. The combined use of image segmentation, pre-trained DL models, and ensemble learning (EL) boosted the prediction results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Driving Impact in Claims Denial Management Using Artificial Intelligence

A healthcare provider’s ability to quickly and efficiently process claims and quantify denial rates is critical to ensure smooth revenue cycle management and medical reimbursement. But the hospitals and medical practitioners are receiving more claim denials from payers, with the average rate of denial steadily increasing year over year. The recent COVID-19 pandemic has further accelerated the denial rate. An accurate denial detection algorithm can help to reduce the burden on healthcare providers. In this study, we propose a boosting-based machine learning framework to predict the likelihood of claims being denied along with the reason code at a line level. Prediction at a line level provides a finer-grained explanation to the administrative staff by pointing out the specific line for corrections. The list of important features provides an interpretable solution to the healthcare providers which enables them to create the right edits and correct the claim before going out to the payer. This in turn helps the healthcare provider dramatically improve both net patient revenue and cash flow. They can also put a check on their costs, as fewer denials mean less rework, resources, and time devoted to appealing and recovering denied claims. The denial model showed good performance with Area Under the Curve (AUC) of 0.80 and 0.82 for professional and institutional claims respectively. According to our estimates, the model has the potential to save 15%–50% of the denial cost for a healthcare provider. This in turn would have a tremendous impact on the healthcare costs as well as help make the healthcare process smoother. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.