A Hand Gesture-Based Contact-less Interface for Electronic Health Records

Clinical data monitoring and storing are essential components of continuous and preventive healthcare systems. Data such as blood pressure, pulse rate, temperature, etc., can be recorded by the hospital staff daily for in-patient subjects. The usual way of noting them down is to check different parameters using various medical instruments and write it on paper with the corresponding patient's details (e.g., name, patient-id, or government identity card number). However, after the outbreak of COVID-19, there is a set of World Health Organization (WHO) guidelines to behave in public places. Ordinary people and professionals feel hesitant to touch any media even if they have some protection such as gloves and sanitizer. In this crisis, there is a natural demand for contact-less activities instead of touch-based traditional ways. Gesture-based activities might be one of the low-cost alternatives to some sensor-based systems. This paper uses a profound learning-based finger point gesture to capture writing in the air and realize it on the screen through a predictive model. Here, the proposed framework has been demonstrated as a proof of concept to record blood pressure data for multiple patients without touching any electronic screen or paper. The proposed architecture is developed based on the gesture recognition and metric learning, which have been used to recognize different digits trained from the MNIST digit dataset. The mean test accuracy is reached 99.47% on the same dataset. © 2022 IEEE.

Pandemetrics: systematically assessing, monitoring, and controlling the evolution of a pandemic.

The still ongoing pandemic of SARS-CoV-2 virus and COVID-19 disease, affecting the population worldwide, has demonstrated the need of more accurate methodologies for assessing, monitoring, and controlling an outbreak of such devastating proportions. Authoritative attempts have been made in traditional fields of medicine (epidemiology, virology, infectiology) to address these shortcomings, mainly by relying on mathematical and statistical modeling. However, here, we propose approaching the methodological work from a different, and to some extent alternative, standpoint. Applied systematically, the concepts and tools of statistical engineering and quality management, developed not only in healthcare settings, but also in other scientific contexts, can be very useful in assessing, monitoring, and controlling pandemic events. We propose a methodology based on a set of tools and techniques, formulas, graphs, and tables to support the decision-making concerning the management of a pandemic like COVID-19. This methodological body is hereby named Pandemetrics. This name intends to emphasize the peculiarity of our approach to measuring, and graphically presenting the unique context of the COVID-19 pandemic.

Impact of COVID-19 on energy consumption in a residential complex in Hyderabad, India

When the Indian government declared the first lockdown on 25 March 2020 to control the increasing number of COVID-19 cases, people were forced to stay and work from home. The aim of this study is to quantify the impact of stay-at-home orders on residential Air Conditioning (AC) energy and household electricity consumption (excluding AC energy). This was done using monitored data from 380 homes in a group of five buildings in Hyderabad, India. We gathered AC energy and household electricity consumption data at a 30-min interval for each home individually in April 2019 and April 2020. Descriptive and inferential statistical analysis was done on this data. To offset the difference in temperatures for the month of April in 2019 and 2020, only those weekdays were selected where the average temperature in 2019 was same as the average temperature in 2020. The study establishes that the average number of hours the AC was used per day in each home increased in the range 4.90–7.45% depending on the temperature for the year 2020. Correspondingly, the overall AC consumption increased in the range 3.60–4.5%, however the daytime (8:00 AM to 8:00 PM) AC energy consumption increased in the range 22–26% and nighttime (8:00 PM to 8:00 AM) AC energy consumption decreased by 5–7% in the year 2020. The study showed a rise in household electricity consumption of about 15% for the entire day in the year 2020. The household electricity consumption increased during daytime by 22- 27.50% and 1.90- 6.6% during the nighttime. It was observed that the morning household electricity peak demand shifted from 7:00 AM in 2019 to 9:00 AM in 2020. Conversely, the evening peak demand shifted from 9:00 PM in 2019 to 7:00 PM in 2020. An additional peak was observed during afternoon hours in the lockdown. © 2022, The Author(s).

Investigating the impacts of COVID-19 on aviation safety based on occurrences captured through flight data monitoring.

The COVID-19 pandemic led to growing concerns about pilots' proficiency due to the significant decrease in flight operations. The objective of this research is to provide a proactive approach to mitigate potential risks in flight operations associated with the impact of the COVID-19 pandemic using flight data monitoring (FDM). The results demonstrated significant associations between the pandemic impacts and FDM exceedance categories, flight phases and fleets. Manual flying skill decay, lack of practice effects on use of standard operating procedures and knowledge of flight deck automation should be considered by airlines when preparing for the return to normal operations. An FDM Programme allows prediction of the probability and severity of occurrences for developing an effective SMS within an airline. To mitigate the impacts of the pandemic, tailored training sessions must be implemented, and airlines should strive to avoid additional optional procedures where practicable. Practitioner summary: The COVID-19 pandemic has raised concerns regarding pilot proficiency due to lack of practice effects. Results from the Flight Data Monitoring Programme show significant associations between the pandemic impacts and occurrence categories, fleets, and flight phases. FDM can be applied to mitigate the probability and severity of occurrences for airlines developing effective safety management systems.HIGHLIGHTSThere is a significant association between the COVID-19 pandemic stages and FDM events in different flight phases, FDM categories, and aircraft typesThe COVID-19 pandemic led to a significant increase in FDM exceedances, especially for precursors on runway excursion and go-aroundsAirlines should carefully plan training sessions for pilots as the disruptions due to the pandemic led to a lack of practice effect in flight operationsReviewing FDM data may have contributions to establish proactive SMS and mitigate COVID-19 impacts to aviation safety.

Breakthrough infections, hospital admissions, and mortality after major COVID-19 vaccination profiles: A prospective cohort study.

Background: Several COVID-19 vaccination rollout strategies are implemented. Real-world data from the large-scale, government-mandated Central Vaccination Center (CVC), Thailand, could be used for comparing the breakthrough infection, across all available COVID-19 vaccination profiles. Methods: This prospective cohort study combined the vaccine profiles from the CVC registry with three nationally validated outcome datasets to assess the breakthrough COVID-19 infection, hospitalization, and death among Thais individuals who received at least one dose of the COVID-19 vaccine. The outcomes were analyzed by comparing vaccine profiles to investigate the shot effect and homologous effect. Findings: Of 2,407,315 Thais who had at least one dose of COVID-19 vaccine, 63,469 (2.75%) had breakthrough infection, 42,001 (1.79%) had been hospitalized, and 431 (0.02%) died. Per one vaccination shot added, there was an 18% risk reduction of breakthrough infection (adjusted hazard ratio [HR] 0.82, 95% confidence interval [CI] 0.80-0.82), a 25% risk reduction of hospitalization (HR 0.75, 95% CI 0.73-0.76), and a 96% risk reduction of mortality (HR 0.04, 95% CI 0.03-0.06). The heterologous two-shot vaccine profiles had a higher protective effect against infection, hospitalization, and mortality compared to the homologous counterparts. Interpretation: COVID-19 breakthrough infection, hospitalization, and death differ across vaccination profiles that had a different number of shots and types of vaccines. Funding: This study did not involve any funding.

Learning to Learn HVAC Failures: Layering ML Experiments in the Absence of Ground Truth

Passenger comfort systems such as Heating, Ventilation, and Air-Conditioning units (HVACs) usually lack the data monitoring quality enjoyed by mission-critical systems in trains. But climate change, in addition to the high ventilation standards enforced by authorities due to the COVID pandemic, have increased the importance of HVACs worldwide. We propose a machine learning (ML) approach to the challenge of failure detection from incomplete data, consisting of two steps: 1. human-annotation bootstrapping, on a fraction of temperature data, to detect ongoing functional loss and build an artificial ground truth (AGT);2. failure prediction from digital-data, using the AGT to train an ML model based on failure diagnose codes to foretell functional loss. We exercise our approach in trains of Dutch Railways, showing its implementation, ML-predictive capabilities (the ML model for the AGT can detect HVAC malfunctions online), limitations (we could not foretell failures from our digital data), and discussing its application to other assets. © 2022, Springer Nature Switzerland AG.

Effects of the COVID-19 Pandemic on the Air Quality of the Metropolitan Region of São Paulo: Analysis Based on Satellite Data, Monitoring Stations and Records of Annual Average Daily Traffic Volumes on the Main Access Roads to the City

This paper presents an analysis of the effects of the COVID-19 pandemic on the air quality of the Metropolitan Region of São Paulo (MRSP). The effects of social distancing are still recent in the society;however, it was possible to observe patterns of environmental changes in places that had adhered transportation measures to combat the spread of the coronavirus. Thus, from the analysis of the traffic volumes made on some of the main access highways to the MRSP, as well as the monitoring of the levels of fine particulate matter (PM2.5), carbon monoxide (CO) and nitrogen dioxide (NO2), directly linked to atmospheric emissions from motor vehicles–which make up about 95% of air polluting agents in the region in different locations–we showed relationships between the improvement in air quality and the decrease in vehicles that access the MRSP. To improve the data analysis, therefore, the isolation index parameter was evaluated to provide daily information on the percentage of citizens in each municipality of the state that was effectively practicing social distancing. The intersection of these groups of data determined that the COVID-19 pandemic reduced the volume of vehicles on the highways by up to 50% of what it was in 2019, with the subsequent recovery of the traffic volume, even surpassing the values from the baseline year. Thus, the isolation index showed a decline of up to 20% between its implementation in March 2020 and December 2020. These data and the way they varied during 2020 allowed to observe an improvement of up to 50% in analyzed periods of the pollutants PM2.5, CO and NO2 in the MRSP. The main contribution of this study, alongside the synergistic use of data from different sources, was to perform traffic flow analysis separately for light and heavy duty vehicles (LDVs and HDVs). The relationships between traffic volume patterns and COVID-19 pollution were analyzed based on time series. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

The Essential Role of Data and Safety Monitoring Boards (DSMBs) in Ensuring the Ethics of Global Vaccine Trials to Address Coronavirus Disease 2019 (COVID-19O).

Coronavirus disease 2019 (COVID-19) vaccines are being developed and implemented with unprecedented speed. Accordingly, trials considered ethical at their inception may quickly become concerning. We provide recommendations for Data and Safety Monitoring Boards (DSMBs) on monitoring the ethical acceptability of COVID-19 vaccine trials, focusing on placebo-controlled trials in low- and middle-income countries.

Novel Digital Technologies for Blood Pressure Monitoring and Hypertension Management.

PURPOSE OF REVIEW: Hypertension is common, impacting an estimated 108 million US adults, and deadly, responsible for the deaths of one in six adults annually. Optimal management includes frequent blood pressure monitoring and antihypertensive medication titration, but in the traditional office-based care delivery model, patients have their blood pressure measured only intermittently and in a way that is subject to misdiagnosis with white coat or masked hypertension. There is a growing opportunity to leverage our expanding repository of digital technology to reimagine hypertension care delivery. This paper reviews existing and emerging digital tools available for hypertension management, as well as behavioral economic insights that could supercharge their impact. RECENT FINDINGS: Digitally connected blood pressure monitors offer an alternative to office-based blood pressure monitoring. A number of cuffless blood pressure monitors are in development but require further validation before they can be deployed for widespread clinical use. Patient-facing hubs and applications offer a means to transmit blood pressure data to clinicians. Though artificial intelligence could allow for curation of this data, its clinical use for hypertension remains limited to assessing risk factors at this time. Finally, text-based and telemedicine platforms are increasingly being employed to translate hypertension data into clinical outcomes with promising results. SUMMARY: The digital management of hypertension shows potential as an avenue for increasing patient engagement and improving clinical efficiency and outcomes. It is important for clinicians to understand the benefits, limitations, and future directions of digital health to optimize management of hypertension.

Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference.

Often when biological entities are measured in multiple ways, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other information is hard-to-obtain information (HI) and can only be gathered on some. We propose building a model to make probabilistic predictions of HI using EI. Our feature mapping GAN (FMGAN), based on the conditional GAN framework, uses an embedding network to process conditions as part of the conditional GAN training to create manifold structure when it is not readily present in the conditions. We experiment on generating RNA sequencing of cell lines perturbed with a drug conditioned on the drug's chemical structure and generating FACS data from clinical monitoring variables on a cohort of COVID-19 patients, effectively describing their immune response in great detail.

Risk-based centralized data monitoring of clinical trials at the time of COVID-19 pandemic.

OBJECTIVES: COVID-19 pandemic caused several alarming challenges for clinical trials. On-site source data verification (SDV) in the multicenter clinical trial became difficult due to travel ban and social distancing. For multicenter clinical trials, centralized data monitoring is an efficient and cost-effective method of data monitoring. Centralized data monitoring reduces the risk of COVID-19 infections and provides additional capabilities compared to on-site monitoring. The key steps for on-site monitoring include identifying key risk factors and thresholds for the risk factors, developing a monitoring plan, following up the risk factors, and providing a management plan to mitigate the risk. METHODS: For analysis purposes, we simulated data similar to our clinical trial data. We classified the data monitoring process into two groups, such as the Supervised analysis process, to follow each patient remotely by creating a dashboard and an Unsupervised analysis process to identify data discrepancy, data error, or data fraud. We conducted several risk-based statistical analysis techniques to avoid on-site source data verification to reduce time and cost, followed up with each patient remotely to maintain social distancing, and created a centralized data monitoring dashboard to ensure patient safety and maintain the data quality. CONCLUSION: Data monitoring in clinical trials is a mandatory process. A risk-based centralized data review process is cost-effective and helpful to ignore on-site data monitoring at the time of the pandemic. We summarized how different statistical methods could be implemented and explained in SAS to identify various data error or fabrication issues in multicenter clinical trials.

Leveraging analytics to assure quality during the Covid-19 pandemic - The COVACTA clinical study example.

The world has seen a shift in the ways of working during the Covid-19 pandemic. Routine activities performed at the clinical investigator sites (e.g. on-site audits) that are a part of Quality Assurance (QA) have not been feasible at this time. Analytics has played a huge role in contributing to our continued efforts of ensuring quality during the conduct of a clinical trial. Decisions driven through data, now more than ever, heavily contribute to the efficiency of QA activities. In this report, we share the approach we took to conduct QA activities for the COVACTA study (to treat Covid-19 pneumonia) by leveraging analytics.

Operational Experiences in China and Statistical Issues on the Conduct of Clinical Trials During the COVID-19 Pandemic.

The COVID-19 outbreak is impacting clinical trials in many ways, such as patient recruitment, data collection and data analysis. To proceed in this difficult time, the adoption of new technologies and new approaches for conducting clinical trials needs to be accelerated. Simultaneously, regulatory agencies such as the US FDA and EMA have issued guidance to help the pharmaceutical industry conduct clinical trials of medical products during the COVID-19 pandemic. In this article, we will address some statistical issues and operational experiences in the conduction of clinical trials during the COVID-19 pandemic. Specifically, we will share experiences in the applications of remote clinical trials in China. Statistical issues related to protocol modifications caused by COVID-19 will be raised.

[Data monitoring committees-state of the art and perspectives for the future]. / Das Data Monitoring Committee ­ State of the Art und Perspektiven für die Zukunft.

BACKGROUND: The classic randomized and controlled clinical trial is facing new challenges with complex study designs and disease interception concepts. For this reason, data monitoring committees (DMCs) can take on a central function if professionally integrated into the methodical procedure of clinical trials. On this basis, the responsible competent authority and the responsible ethics committee have to verify the substantial charter document in the implicit/explicit approval procedure reflecting the working process of the independent committee. OBJECTIVES: The frequencies and conditions under which DMCs are used in clinical trials was investigated. METHODS: The database of the Federal Institute for Drugs and Medical Devices (BfArM) was the basis for statistical analysis concerning the frequency of implementation of data monitoring committees with different criteria over an observation period of more than 15 years. RESULTS: In total, 4152 DMCs have been used in 14,135 clinical trials with drugs. The independent expert committee was mostly integrated by commercial sponsors in phase III of the clinical development. The ethics committees were involved with different absolute frequencies. DISCUSSION: Sponsors demonstrate an increasing willingness to integrate DMCs in the methodical conduct of clinical trials especially in the case of new study designs. DMCs could be an important scientific aid in order to assess the implications of coronavirus SARS-CoV­2 on clinical trials.

Drug Information Association (DIA) 2020 Virtual Global Annual Meeting (June 14-18, 2020).

At the 56th Global Annual Meeting of the Drug Information Association (DIA), attendees met virtually during the height of the global COVID-19 pandemic for "rapid cross-stakeholder, cross-border collaboration" to support health worldwide. Sessions included presenters and speakers from regulatory, patient advocacy and academia sectors, with patients at the forefront of those discussions. This report covers various presentations and panel discussions from the 4-day meeting that focus on COVID-19, innovative trial designs spurred by a need to adapt amid a pandemic, digital health, novel products inspiring new regulatory standards, clinical trials, data collection and management, the need for more and better data and the ever-increasing importance of the patient perspective.

Achieving effective informed oversight by DMCs in COVID clinical trials.

Best practices of data monitoring committees (DMCs) in randomized clinical trials are well established. Independent oversight provided by DMCs is particularly important in trials conducted in public health emergencies, such as in HIV/AIDS or coronavirus epidemics. Special considerations are needed to enable DMCs to effectively address novel circumstances they face in such settings. In the COVID-19 pandemic, these include the remarkable speed in which data regarding benefits and risks of interventions are accumulated. DMCs must hold frequent virtual meetings, using state-of-the-art communication software that protects against risk for security breaches. Data capture and DMC reports should be focused on the most informative measures about benefits and risks. Because numerous clinical trials are being concurrently conducted in the COVID-19 setting, often addressing closely related scientific questions, structures for DMC oversight should be efficient and adequately informative. When these concurrently conducted trials are evaluating related regimens in related clinical settings, often individually underpowered for safety and having separate DMCs, processes should be implemented enabling these DMCs to share with each other emerging confidential evidence to better assess risks and benefits. Ideally a single DMC would monitor a portfolio of clinical trials or a trial with multiple arms, such as a platform trial.