Using Information Technology to Manage the COVID-19 Pandemic: Development of a Technical Framework Based on Practical Experience in China

Personal Information Protection and Privacy Policy Compliance of Health Code Apps in China: Scale Development and Content Analysis

Background

Digital technologies, especially contact tracing apps, have been crucial in monitoring and tracing the transmission of COVID-19 worldwide. China developed health code apps as an emergency response to the pandemic with plans to use them for broader public health services. However, potential problems within privacy policies may compromise personal information (PI) protection.

Objective

We aimed to evaluate the compliance of the privacy policies of 30 health code apps in the mainland of China with the Personal Information Protection Law (PIPL) and related specifications.

Methods

We reviewed and assessed the privacy policies of 30 health code apps between August 26 and September 6, 2023. We used a 3-level indicator scale based on the information life cycle as provided in the PIPL and related specifications. The scale comprised 7 level-1 indicators, 26 level-2 indicators, and 71 level-3 indicators.

Results

The mean compliance score of the 30 health code apps was 59.9% (SD 22.6%). A total of 13 (43.3%) apps scored below this average, and 6 apps scored below 40%. Level-1 indicator scores included the following: general attributes (mean 85.6%, SD 23.3%); PI collection and use (mean 66.2%, SD 22.7%); PI storage and protection (mean 63.3%, SD 30.8%); PI sharing, transfer, disclosure, and transmission (mean 57.2%, SD 27.3%); PI deletion (mean 52.2%, SD 29.4%); individual rights (mean 59.3%, SD 25.7%); and PI processor duties (mean 43.7%, SD 23.8%). Sensitive PI protection compliance (mean 51.4%, SD 26.0%) lagged behind general PI protection (mean 83.3%, SD 24.3%), with only 1 app requiring separate consent for sensitive PI processing. Additionally, 46.7% (n=14) of the apps needed separate consent for subcontracting activities, while fewer disclosed PI recipient information (n=13, 43.3%), safety precautions (n=11, 36.7%), and rules of PI transfer during specific events (n=10, 33.3%). Most privacy policies specified the PI retention period (n=23, 76.7%) and postperiod deletion or anonymization (n=22, 73.3%), but only 6.7% (n=2) were committed to prompt third-party PI deletion. Most apps delineated various individual rights: the right to inquire (n=25, 83.3%), correct (n=24, 80%), and delete PI (n=24, 80%); cancel their account (n=21, 70%); withdraw consent (n=20, 60%); and request privacy policy explanations (n=24, 80%). Only a fraction addressed the rights to obtain copies (n=4, 13.3%) or refuse advertisement of automated decision-making (n=1, 3.3%). The mean compliance rate of PI processor duties was only 43.7% (SD 23.8%), with significant deficiencies in impact assessments (mean 5.0%, SD 19.8%), PI protection officer appointment (mean 6.7%, SD 24.9%), regular compliance audits (mean 6.7%, SD 24.9%), and complaint management (mean 37.8%, SD 39.2%).

Conclusions

Our analysis revealed both strengths and significant shortcomings in the compliance of privacy policies of health code apps with the PIPL and related specifications considering the information life cycle. As China contemplates the future extended use of health code apps, it should articulate the legitimacy of the apps' normalization and ensure that users provide informed consent. Meanwhile, China should raise the compliance level of relevant privacy policies and fortify its enforcement mechanisms.

Applying the digital data and the bioinformatics tools in SARS-CoV-2 research

Bioinformatics has been playing a crucial role in the scientific progress to fight against the pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The advances in novel algorithms, mega data technology, artificial intelligence and deep learning assisted the development of novel bioinformatics tools to analyze daily increasing SARS-CoV-2 data in the past years. These tools were applied in genomic analyses, evolutionary tracking, epidemiological analyses, protein structure interpretation, studies in virus-host interaction and clinical performance. To promote the in-silico analysis in the future, we conducted a review which summarized the databases, web services and software applied in SARS-CoV-2 research. Those digital resources applied in SARS-CoV-2 research may also potentially contribute to the research in other coronavirus and non-coronavirus viruses.

Perception towards the implementation of telemedicine during COVID-19 pandemic: a cross-sectional study

BACKGROUND

The COVID-19 pandemic has led to a surge in the use of telemedicine as a means of delivering healthcare services remotely. Healthcare providers play a key role in the adoption and implementation of telemedicine for its effectiveness. Despite its benefits, there have been unclear concerns about its effectiveness and acceptance in the process of implementing telemedicine. The objective of the study was to assess health professionals' perceptions towards the implementation of telemedicine during the COVID-19 pandemic.

METHODS

A cross-sectional study design was conducted among eight hundred forty-five study participants from December 2020 to February 2021. A pre-test was performed on 5% of the total sample size, and the quality of the data was ensured by checking its completeness and consistency. Descriptive statistics and bivariable and multivariable logistic regression were used. The Variables with a P-value equal to or less than 0.25 in bivariable logistic regression were entered into a multivariable logistic regression, and model fitness was assessed.

RESULT

The study revealed that 60.9% of professionals had a good perception toward telemedicine implementation, with an 87.2% response rate. Health professionals with IT support staff, ICT training, who use social media platforms regularly, and availability of computer or smartphone within/outside their health facility were 4.7, 3.3, 3.7, and 13.2 times more likely to have a positive association towards telemedicine implementation respectively.

CONCLUSION

More than half of the health professionals had a good perception of telemedicine. Social media use, ICT training, computer accessibility, and the presence of IT support staff were all found to have positive associations with the telemedicine perception. In the era of the COVID-19 pandemic, the government should take the initiative to strengthen opportunities for health professionals to learn and apply telemedicine in their medical practice by providing ICT training, IT infrastructure and support staff, improving computer access, and recommending health professionals' positive use of social media in the health facility.

Automated Electrodermal Activity and Facial Expression Analysis for Continuous Pain Intensity Monitoring on the X-ITE Pain Database

This study focuses on improving healthcare quality by introducing an automated system that continuously monitors patient pain intensity. The system analyzes the Electrodermal Activity (EDA) sensor modality modality, compares the results obtained from both EDA and facial expressions modalities, and late fuses EDA and facial expressions modalities. This work extends our previous studies of pain intensity monitoring via an expanded analysis of the two informative methods. The EDA sensor modality and facial expression analysis play a prominent role in pain recognition; the extracted features reflect the patient's responses to different pain levels. Three different approaches were applied: Random Forest (RF) baseline methods, Long-Short Term Memory Network (LSTM), and LSTM with the sample-weighting method (LSTM-SW). Evaluation metrics included Micro average F1-score for classification and Mean Squared Error (MSE) and intraclass correlation coefficient (ICC [3, 1]) for both classification and regression. The results highlight the effectiveness of late fusion for EDA and facial expressions, particularly in almost balanced datasets (Micro average F1-score around 61%, ICC about 0.35). EDA regression models, particularly LSTM and LSTM-SW, showed superiority in imbalanced datasets and outperformed guessing (where the majority of votes indicate no pain) and baseline methods (RF indicates Random Forest classifier (RFc) and Random Forest regression (RFr)). In conclusion, by integrating both modalities or utilizing EDA, they can provide medical centers with reliable and valuable insights into patients' pain experiences and responses.

Economic Efficiency of an Open-Source National Medical Lab Software in Canada

Although the Canada federal government has invested over $3.1 billion developing health information technology (HIT), all 10 provinces still have their own separate HIT systems, which are non-interoperable, expensive, and inconsistent. After first reviewing how these systems operate, this paper analyzes the costs and savings of integrating the common billing, lab results, and diagnostic imaging (BLD) functions of these separate systems using free and open-source software and proposes a system for this, HermesAPI. Currently, 8 provincial governments representing over 95% of Canada's population allow private companies to create their own electronic medical records (EMR) system and integrate with provincial BLD systems. This study found the cost to develop and maintain HermesAPI would be between CAD$610,000 to CAD$740,000, but would prevent CAD$120,000 per company per province in development costs for a total savings of $6.4 million. HermesAPI would lower barriers to entry for the HIT industry to increase competition, improve the quality of HIT products, and ultimately patient care. The proposed open-source approach of the HermesAPI is one option towards building a more interoperable, less expensive, and more consistent HIT system for Canada.

Comparative characteristics of developing morphofunctional features of schoolchildren from different climatic and geographical regions

OBJECTIVES

Adaptation processes in body of schoolchildren, along with study load and social factors, are determined by influence of climatic and geographical factors of residence region. This research has been undertaken to study the morphofunctional characteristics of schoolchildren at the age of 7-17 years.

METHODS

The research involved 880 schoolchildren of both sexes in age from 7 to 17 years, studying in secondary school №22 in Pavlodar (Kazakhstan) and secondary school №7 in Kyzylorda (Kazakhstan). The Quetelet index of northern schoolchildren was within the norm but higher compared to southern peers.

RESULTS

The Quetelet index was within the normal range for all schoolchildren, but higher among northerners. The indicator of the life index in the boys of the south was higher up to 10 years, then the adaptive possibilities of breathing decreased; in the girls in the north this indicator was higher in all periods, except for 8-9 years. The physical performance index (PWC170/kg) was higher in children from the southern region.

CONCLUSIONS

This study revealed significant geographical differences in the morphofunctional development of children. The obtained data served allowed establishing the "Electronic map of schoolchildren's health" and indicated the need for systematic accounting of schoolchildren's health indicators for effective development of differentiated prevention programs.

Digitalization impacts the COVID-19 pandemic and the stringency of government measures

COVID-19 poses a significant burden to populations worldwide. Although the pandemic has accelerated digital transformation, little is known about the influence of digitalization on pandemic developments. Therefore, this country-level study aims to explore the impact of pre-pandemic digital adoption on COVID-19 outcomes and government measures. Using the Digital Adoption Index (DAI), we examined the association between countries' digital preparedness levels and COVID-19 cases, deaths, and stringency indices (SI) of government measures until March 2021. Gradient Tree Boosting based algorithm pinpointed essential features related to COVID-19 trends, such as digital adoption, populations' smoker fraction, age, and poverty. Subsequently, regression analyses indicated that higher DAI was associated with significant declines in new cases (β = - 362.25/pm; p < 0.001) and attributed deaths (β = - 5.53/pm; p < 0.001) months after the peak. When plotting DAI against the SI normalized for the starting day, countries with higher DAI adopted slightly more stringent government measures (β = 4.86; p < 0.01). Finally, a scoping review identified 70 publications providing valuable arguments for our findings. Countries with higher DAI before the pandemic show a positive trend in handling the pandemic and facilitate the implementation of more decisive governmental measures. Further distribution of digital adoption may have the potential to attenuate the impact of COVID-19 cases and deaths.

The complementary and substitutive value of online health information

The Internet plays a significant role in health information searching, sharing and emotional support. However, scholars have devoted little attention to the complementary and substitute value of online health information from diseases, especially chronic diseases, health insurance, barriers to health resources and their interaction effects with income. This research uses data from the 2020 Health Information National Trends Survey (HINTS 2020), the latest HINTS survey that includes seeking online health information questions critical to this research. This paper proposes that the factors contributing to seeking online health information can be categorized into two modalities - complementary and substitutive. Concerning the complementary value, I argue that individuals with certain health conditions use online health information as a complementary health resource in addition to traditional health resources such as doctors to understand their health issues better. Online health information also functions as substitute information sources for individuals who have experienced more barriers to typical health information resources.

Development of neural network models for prediction of the outcome of COVID-19 hospitalized patients based on initial laboratory findings, demographics, and comorbidities

Background

During the process of the treatment of COVID-19 hospitalized patients, physicians still face a lot of unknowns and problems. Despite the application of the treatment protocol, it is still unknown why the medical status of a certain number of patients worsens and ends with death. Many factors were analyzed for the prediction of the clinical outcome of the patients using different methods. The aim of this paper was to develop a prediction model based on initial laboratory blood test results, accompanying comorbidities, and demographics to help physicians to better understand the medical state of patients with respect to possible clinical outcomes using neural networks, hypothesis testing, and confidence intervals.

Methods

The research had retrospective-prospective, descriptive, and analytical character. As inputs for this research, 12 components of laboratory blood test results, six accompanying comorbidities, and demographics (age and gender) data were collected from hospital information system in Sarajevo for each patient from a sample of 634 hospitalized patients. Clinical outcome of the hospitalized patients, survival or death, was recorded 30 days after admission to the hospital. The prediction model was designed using a neural network. In addition, formal hypothesis tests were performed to investigate whether there were significant differences in laboratory blood test results and age between patients who died and those who survived, including the construction of 95% confidence intervals.

Results

In this paper, 11 neural networks were developed with different threshold values to determine the optimal neural network with the highest prediction performance. The performances of the neural networks were evaluated by accuracy, precision, sensitivity, and specificity. Optimal neural network model evaluation metrics are: accuracy = 87.78%, precision = 96.37%, sensitivity = 90.07%, and specificity = 62.16%. Significantly higher values (P < 0.05) of blood laboratory result components and age were detected in patients who died.

Conclusion

Optimal neural network model, results of hypothesis tests, and confidence intervals could help to predict, analyze, and better understand the medical state of COVID-19 hospitalized patients and thus reduce the mortality rate.

Data analytics and knowledge management approach for COVID-19 prediction and control

The Coronavirus Disease (COVID-19) caused by SARS-CoV-2, continues to be a global threat. The major global concern among scientists and researchers is to develop innovative digital solutions for prediction and control of infection and to discover drugs for its cure. In this paper we developed a strategic technical solution for surveillance and control of COVID-19 in Delhi-National Capital Region (NCR). This work aims to elucidate the Delhi COVID-19 Data Management Framework, the backend mechanism of integrated Command and Control Center (iCCC) with plugged-in modules for various administrative, medical and field operations. Based on the time-series data extracted from iCCC repository, the forecasting of COVID-19 spread has been carried out for Delhi using the Auto-Regressive Integrated Moving Average (ARIMA) model as it can effectively predict the logistics requirements, active cases, positive patients, and death rate. The intelligence generated through this research has paved the way for the Government of National Capital Territory Delhi to strategize COVID-19 related policies formulation and implementation on real time basis. The outcome of this innovative work has led to the drastic reduction in COVID-19 positive cases and deaths in Delhi-NCR.

COVID-19 Mobile Health Apps: An Overview of Mobile Applications in Indonesia

Background

Mobile health applications (mHealth apps) have been widely used for various purposes for mitigating the COVID-19 pandemic, such as self-assessment, contact tracing, disseminating information, minimizing exposure, and reducing face-to-face health consultation. The objective of this study is to systematically review COVID-19 related mHealth apps and highlight gaps to inform the development of future mHealth initiatives in Indonesia.

Methods

A systematic search strategy using a PRISMA flowchart was used to identify mHealth apps available in Google Play and Apple Play stores. We searched mHealth apps using certain specific terms related to COVID-19 outbreaks. The inclusion criteria were apps-based smartphone users related to COVID-19 using local language, free of cost, available in the Google Play and Apple Play Stores, and supported by the Indonesian government. We excluded games, apps on infectious diseases unrelated to COVID-19 specifically, and apps with non-Bahasa Indonesia (Indonesian language). The selected mHealth apps were assessed based on two measures: (1) the WHO guidelines on digital health intervention and (2) the four dimensions of the mHealth technology fit framework. In addition, user feedback from experienced and non-experienced users was conducted to evaluate four dimensions of the apps.

Results

A total of 339 mHealth apps were generated from the initial search, remaining seven selected apps that met inclusion criteria. The results highlighted that mHealth apps reviewed had still not been widely used by the general public. The applications were purposed to disseminate information, conduct a self-risk assessment, provide an online community forum, and telemedicine or teleconsultation regarding COVID-19. Data services, including data storage, aggregation, and data exchange, are available in most apps. The rarest function found was contact tracing and assisting health management and health workers, such as the availability of testing facilities, reporting test results, and prescribing medication. The main issues reported were the lack of data security and data privacy protection, integration and infrastructures, usability, and usefulness.

Conclusion

Our study highlighted the necessity to improve mHealth apps' functions related to assisting health workers and the function of digital contact tracing. An effort to increase public awareness regarding the use of mHealth is also necessary to streamline the function of this innovation. Policymakers must consider usefulness, usability, integration, and infrastructure issues to improve their mHealth function.

Using a 5G network in hospitals to reduce nosocomial infection during the COVID-19 pandemic

Wen et al. discuss how implementing a 5G network in hospitals can be used to reduce nosocomial infections. Such systems can reduce the spread of COVID-19.

Predicting the number of COVID-19 infections and deaths in USA

BACKGROUND

Uncertainties surrounding the 2019 novel coronavirus (COVID-19) remain a major global health challenge and requires attention. Researchers and medical experts have made remarkable efforts to reduce the number of cases and prevent future outbreaks through vaccines and other measures. However, there is little evidence on how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection entropy can be applied in predicting the possible number of infections and deaths. In addition, more studies on how the COVID-19 infection density contributes to the rise in infections are needed. This study demonstrates how the SARS-COV-2 daily infection entropy can be applied in predicting the number of infections within a given period. In addition, the infection density within a given population attributes to an increase in the number of COVID-19 cases and, consequently, the new variants.

RESULTS

Using the COVID-19 initial data reported by Johns Hopkins University, World Health Organization (WHO) and Global Initiative on Sharing All Influenza Data (GISAID), the result shows that the original SAR-COV-2 strain has R0<1 with an initial infection growth rate entropy of 9.11 bits for the United States (U.S.). At close proximity, the average infection time for an infected individual to infect others within a susceptible population is approximately 7 minutes. Assuming no vaccines were available, in the U.S., the number of infections could range between 41,220,199 and 82,440,398 in late March 2022 with approximately, 1,211,036 deaths. However, with the available vaccines, nearly 48 Million COVID-19 cases and 706, 437 deaths have been prevented.

CONCLUSION

The proposed technique will contribute to the ongoing investigation of the COVID-19 pandemic and a blueprint to address the uncertainties surrounding the pandemic.

Technology in the Era of COVID-19: A Systematic Review of Current Evidence

BACKGROUND

During the COVID-19 pandemic, the use of technology-based services has been incremental by the care providers for patients scheduling, regulatory considerations, resource allocation, thus enabling virus exposure prevention while maintaining effective patient care. This study aims to review the currently available evidence to identify available technology solutions in the era of COVID-19.

METHODS

A systematic review in July 2020 using the PubMed, Scopus, Embase, Science Direct, and Web of Science databases has been carried out. After evaluating the title and abstract to select the most relevant studies based on inclusion and exclusion criteria, the selected articles underwent quality assessment. The full text of selected articles was then thoroughly evaluated to extract the essential findings.

RESULTS

In this study, 20 technology-based approaches have been identified for provision of healthcare services to patients with COVID-19. These methods included telemedicine, virtual visits, e-consult, tele-consulting, video conference, virtual healthcare, mobile-based self-care, social media, tele ICU, 3D printing technology, telemonitoring, teleradiology, telesurgical, and cloud-based service.

CONCLUSION

Due to the rapid spread of the coronavirus, the use of technology-based methods for the provision of remote healthcare services can help control the disease. The effectiveness of each of these approaches can be investigated in future research.

Public Policy Measures to Increase Anti-SARS-CoV-2 Vaccination Rate in Russia

The total vaccination rate remains relatively low in Russia as of March 2022 (around 55%, with around 20% in some regions). In the paper, we study the reasons for it. We communicate the results of our survey aimed at detecting reasons for the relatively low anti-SARS-CoV-2 vaccination rate in Russia (47.1% as of mid-January 2022) and suggest potential measures to increase the level of confidence in the Russian vaccination campaign. A total of 14,310 users exhibited interest to participate in the research (16.84% of the total number of invitations sent in the Russian social network VKontakte). After the sample set repair, only 5822 (40.68% of those who agreed to participate) responses were suitable for the research, and they composed the final set. The age range of the respondents was 16-51 years old (y.o.) with a mean of 29.1 ± 10.6 y.o. The proportion of the female gender in responses was 44.23%. A total of 2454 persons (42.15%) expressed their hesitant, cautious, or negative attitude towards vaccine uptake. Of the 2454 persons with cautious attitude towards vaccination, only 928 (37.82%) were concerned about the quality of the Russian vaccines. A total of 1323 individuals (53.91%) supported one or more conspiracy beliefs. A total of 5064 (86.98% of the whole set) showed cautious or negative attitude towards the planned introduction of a nationwide system of vaccination certification/verification based on QR codes. The main social factors that hinder the Russian vaccination campaign are: vexation over the lack of desire of officials to receive feedback from the general population regarding vaccination, wide support for conspiracy beliefs, and controversy over the QR code-based digital system. To elevate the vaccination rate in Russia, the following steps may be taken: social encouragement of those who support vaccination, increase in transparency of the vaccination campaign, acceptance of both digital and paper vaccination certificates, increase in participation of society in vaccination-related discussions, public disclosure of vaccine composition, and avoidance of excessive digitalization of data in the vaccination campaign.

A framework for information technology-based management against COVID-19 in Iran

BACKGROUND

The COVID-19 pandemic has become a global concern. Iran is one of the countries affected most by the SARS-CoV-2 outbreak. As a result, the use of information technology (IT) has a variety of applications for pandemic management. The purpose of this study was to develop a conceptual framework for responding to the COVID-19 pandemic via IT management, based on extensive literature review and expert knowledge.

METHODS

The conceptual framework is developed in three stages: (1) a literature review to gather practical experience with IT applications for managing the COVID-19 pandemic, (2) a study of Iranian documents and papers that present Iran's practical experience with COVID-19, and (3) developing a conceptual framework based on the previous steps and validating it through a Delphi approach in two rounds, and by 13 experts.

RESULTS

The proposed conceptual framework demonstrates that during pandemics, 22 different types of technologies were used for various purposes, including virtual education, early warning, rapid screening and diagnosis of infected individuals, and data management. These objectives were classified into six categories, with the following applications highlighted: (1) Prevention (M-health, Internet search queries, telehealth, robotics, Internet of things (IoT), Artificial Intelligence (AI), big data, Virtual Reality (VR), social media); (2) Diagnosis (M-health, drones, telehealth, IoT, Robotics, AI, Decision Support System (DSS), Electronic Health Record (EHR)); (3) Treatment (Telehealth, M-health, AI, Robotic, VR, IoT); (4) Follow-up (Telehealth, M-health, VR), (5) Management & planning (Geographic information system, M-health, IoT, blockchain), and (6) Protection (IoT, AI, Robotic and automatic vehicles, Augmented Reality (AR)). In Iran, the use of IT for prevention has been emphasized through M-health, internet search queries, social media, video conferencing, management and planning objectives using databases, health information systems, dashboards, surveillance systems, and vaccine coverage.

CONCLUSIONS

IT capabilities were critical during the COVID-19 outbreak. Practical experience demonstrates that various aspects of information technologies were overlooked. To combat this pandemic, the government and decision-makers of this country should consider strategic planning that incorporates successful experiences against COVID-19 and the most advanced IT capabilities.

Topic Evolution of Chinese COVID-19 Policies Based on Co-Occurrence Clustering Network Analysis

This study aims to explore the changes of Chinese coronavirus disease-2019 (COVID-19) policy topics in the eclipse, outbreak, and convalescent stage of COVID-19 based on 4982 textual policies. By using the co-occurrence clustering network method, we find that the strict prevention and control of the epidemic is the only topic of policies in the eclipse stage. In the outbreak stage, strict epidemic prevention and control is still the most important policy topic. The policies of resuming work of “essential” enterprises and stabilizing market prices are important support and guarantee for fighting against COVID-19. In the convalescent stage, as the prevention and control of COVID-19 has become regular, promoting and ensuring the resumption of work in all sectors of society is the most important topic of the policies. Moreover, the success of Wuhan City’s fight against COVID-19 reflects China’s governance characteristics of “concentrating power to do a major event”. Finally, the possible improvements for Chinese COVID-19 policies are discussed, which can provide practical suggestions for government departments on how to effectively respond to public health emergencies.

CN, F.V. J, P. V, K. J. Mobile technologies for contact tracing and prevention of COVID-19 positive cases: a cross- sectional study

Purpose

The purpose of this paper is to review the techniques for versatile advancements in contact tracing for the coronavirus disease 2019 (COVID-19) positive cases in this pandemic and to introduce the way of using the mobile location information collected within the country India. As the method, an exploratory review of current measures was conducted for confirmed COVID-19 contact tracing after understanding the current situation of the world. This paper has examined the way of using free locational information in an innovative way to reduce the spread of COVID-19 spread.

Design/methodology/approach

COVID-19 pandemic is the utmost global economic and health challenge of the century. One powerful and consistent procedure to slow down the spread and decrease the effect of COVID-19 is to track the essential and auxiliary contacts of confirmed COVID-19 positive cases by using contact-tracing innovation.

Findings

Although it takes the information from various clients, there are numerous odds in the information. The sincere measures were taken by the authors to avoid the abuse of information by any kind. A portion of the tips for keeping information from getting abused is on the whole, the information ought to be with just higher specialists, and they ought not to have the authorization to impart information to anybody.

Originality/value

This paper helps to track the COVID-19 positive cases as of now by using the field information assortment and outbreak examination stages. At the same time, mobile location information used inside the current guideline, rules for information handlers must incorporate measures to reduce the abusing of information.

Wearable body sensor network: SDGs panacea for an holistic SARS-CoV-2 mitigation, diagnostic, therapeutic, and health informatics interventions

The COVID-19 pandemic offers enormous potential for digital health solutions, with social distancing being the only current approach that has thus far been established to reduce the risk. Wearable Activity Tracker (WATs) is an appealing, successful, and inexpensive choice in this context. This technology is of vital significance in ensuring the well-being of both patients and healthcare professionals following sustainable development goals. The application of technological interventions will in no doubt help in tackling the debilitating effect of health emergencies in the world population. However, there is a shortage of literature explaining the programming resources needed to effectively treat this novel infection which is briefly addressed in this review. This study prominently addressed the potential of wearable Activity Tracker as a technological solution to healthcare disaster management.

Digital SARS-CoV-2 Detection Among Hospital Employees: Participatory Surveillance Study

Background

The implementation of novel techniques as a complement to traditional disease surveillance systems represents an additional opportunity for rapid analysis.

Objective

The objective of this work is to describe a web-based participatory surveillance strategy among health care workers (HCWs) in two Swiss hospitals during the first wave of COVID-19.

Methods

A prospective cohort of HCWs was recruited in March 2020 at the Cantonal Hospital of St. Gallen and the Eastern Switzerland Children’s Hospital. For data analysis, we used a combination of the following techniques: locally estimated scatterplot smoothing (LOESS) regression, Spearman correlation, anomaly detection, and random forest.

Results

From March 23 to August 23, 2020, a total of 127,684 SMS text messages were sent, generating 90,414 valid reports among 1004 participants, achieving a weekly average of 4.5 (SD 1.9) reports per user. The symptom showing the strongest correlation with a positive polymerase chain reaction test result was loss of taste. Symptoms like red eyes or a runny nose were negatively associated with a positive test. The area under the receiver operating characteristic curve showed favorable performance of the classification tree, with an accuracy of 88% for the training data and 89% for the test data. Nevertheless, while the prediction matrix showed good specificity (80.0%), sensitivity was low (10.6%).

Conclusions

Loss of taste was the symptom that was most aligned with COVID-19 activity at the population level. At the individual level—using machine learning–based random forest classification—reporting loss of taste and limb/muscle pain as well as the absence of runny nose and red eyes were the best predictors of COVID-19.

Health Information Systems in the COVID-19 Pandemic: A Short Survey of Experiences and Lessons Learned From the European Region

Introduction: The COVID-19 crisis provides an opportunity to reflect on what worked during the pandemic, what could have been done differently, and what innovations should become part of an enhanced health information system in the future. Methods: An online qualitative survey was designed and administered online in November 2020 to all the 37 Member States that are part of the WHO European Health Information Initiative and the WHO Central Asian Republics Information Network. Results: Nineteen countries responded to the survey (Austria, Belgium, Croatia, Czech Republic, Finland, Greece, Iceland, Ireland, Israel, Italy, Kazakhstan, Latvia, Lithuania, Romania, Russian Federation, Sweden, Turkey, United Kingdom, and Uzbekistan). The COVID-19 pandemic required health information systems (HIS) to rapidly adapt to identify, collect, store, manage, and transmit accurate and timely COVID-19 related data. HIS stakeholders have been put to the test, and valuable experience has been gained. Despite critical gaps such as under-resourced public health services, obsolete health information technologies, and lack of interoperability, most countries believed that their information systems had worked reasonably well in addressing the needs arising during the COVID-19 pandemic. Conclusion: Strong enabling environments and advanced and digitized health information systems are vital to controlling epidemics. Sustainable finance and government support are required for the continued implementation and enhancement of HIS. It is important to promote digital solutions beyond the COVID-19 pandemic. Now is the time to discuss potential solutions to obtain timely, accurate, and reliable health information and steer policy-making while protecting privacy rights and meeting the highest ethical standards.

5G Use in Healthcare: The Future is Present

Background:

Most healthcare providers are unaware of the extraordinary opportunities for implementation in healthcare which can be enabled by 5G wireless networks. 5G created enormous opportunities for a myriad of new technologies, resulting in an integrated through 5G ‘ecosystem’. Although the new opportunities in healthcare are immense, medicine is slow to change, as manifest by the paucity of new, innovative applications based upon this ecosystem. Thus, emerges the need to “avoid technology surprise” - both laparoscopic and robotic assisted minimally invasive surgery were delayed for years because the surgical community was either unaware or unaccepting of a new technology.

Database:

PubMed (Medline) and Scopus (Elsevier) databases were searched and all published studies regarding clinical applications of 5G were retrieved. From a total of 40 articles, 13 were finally included in our review.

Discussion:

The important transformational properties of 5G communications and other innovative technologies are described and compared to healthcare needs, looking for opportunities, limitations, and challenges to implementation of 5G and the ecosystem it has spawned. Furthermore, the needs in the clinical applications, education and research in medicine and surgery, in addition to the administrative infrastructure are addressed. Additionally, we explore the nontechnical challenges, that either support or oppose this new healthcare renovation. Based upon proven advantages of these innovative technologies, current scientific evidence is analyzed for future trends for the transformation of healthcare. By providing awareness of these opportunities and their advantages for patients, it will be possible to decrease the prolonged timeframe for acceptance and implementation for patients.

Telemedicine maybe an effective solution for management of chronic disease during the COVID-19 epidemic

Aim:

Based on the development of telemedicine and the experience of using it during the COVID-19 epidemic, we aimed to explore its convenience and shortcomings to provide a reference for the further improvement of telemedicine.

Background:

Traditional healthcare has been significantly affected by the outbreak of COVID-19, which has increased fear in patients with chronic diseases and increased the difficulty of obtaining hospitalized treatment.

Methods:

This is a conceptual article. The literature search is based on Pubmed, including articles published between January 2015 and December 2020. The purpose was to determine whether telemedicine is effective in the management of chronic diseases in the epidemic situation and to develop telemedicine and chronic disease management for long-term epidemic situations in the future.

Findings:

Telemedicine has demonstrated its advantages during the COVID-19 epidemic and can provide diversified clinical care services for patients with chronic diseases; these services have played a vital role in epidemic prevention and control, greatly alleviated the shortage of medical resources, increased the utilization level of medical resources, and reduced the cross-infection risk during treatment in hospitals. Furthermore, the epidemic situation presents opportunities for the development of diagnosis and treatment methods via the internet and active health management modalities.

A survey of COVID-19 contact-tracing apps

Recently, the sudden outbreak of the COVID-19 virus caused a major health crisis by affecting masses around the world. The virus, which is known to be highly contagious, has forced the research community and governments to fight the disease and take prompt actions by applying various strategies to keep the numbers under control. These strategies range from imposing strict social distancing measures, isolating infected cases, and enforcing either a partial or a full lockdown, to mathematical modeling and contact-tracing applications. In this work, we survey the current contact-tracing apps and organize them based on underlying technologies such as Bluetooth, Wi-Fi, GPS, geofencing, and Quick Response (QR) codes. We compare the main features of 22 existing applications and highlight each of the pros and cons associated with these different technologies.

Pandemics: Historically Slow "Learning Curve" Leading to Biomedical Informatics and Vaccine Breakthroughs

BACKGROUND

The worldwide tragedy of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic vividly demonstrates just how inadequate mitigation and control of the spread of infectious diseases can be when faced with a new microorganism with unknown pathogenic effects. Responses by governments in charge of public health, and all other involved organizations, have proved largely wanting. Data infrastructure and the information and communication systems needed to deal with the pandemic have likewise not been up to the task. Nevertheless, after a year of the worldwide outbreak, hope arises from this being the first major pandemic event in history where genomic and related biosciences - relying on biomedical informatics - have been essential in decoding the viral sequence data and producing the mRNA and other biotechnologies that unexpectedly rapidly have led to investigation, design, development, and testing of useful vaccines. Medical informatics may also help support public health actions and clinical interventions - but scalability and impact will depend on overcoming ingrained human shortcomings to deal with complex socio-economic, political, and technological disruptions together with the many ethical challenges presented by pandemics.

OBJECTIVES

The principal goal is to review the history of biomedical information and healthcare practices related to past pandemics in order to illustrate just how exceptional and dependent on biomedical informatics are the recent scientific insights into human immune responses to viral infection, which are enabling rapid antiviral vaccine development and clinical management of severe cases - despite the many societal challenges ahead.

METHODS

This paper briefly reviews some of the key historical antecedents leading up to modern insights into epidemic and pandemic processes with their biomedical and healthcare information intended to guide practitioners, agencies, and the lay public in today's ongoing pandemic events.

CONCLUSIONS

Poor scientific understanding and excessively slow learning about infectious disease processes and mitigating behaviors have stymied effective treatment until the present time. Advances in insights about immune systems, genomes, proteomes, and all the other -omes, became a reality thanks to the key sequencing technologies and biomedical informatics that enabled the Human Genome Project, and only now, 20 years later, are having an impact in ameliorating devastating zoonotic infectious pandemics, including the present SARS-CoV-2 event through unprecedently rapid vaccine development. In the future these advances will hopefully also enable more targeted prevention and treatment of disease. However, past and present shortcomings of most of the COVID-19 pandemic responses illustrate just how difficult it is to persuade enough people - and especially political leaders - to adopt societally beneficial risk-avoidance behaviors and policies, even as these become better understood.

Mapping the Role of Digital Health Technologies in Prevention and Control of COVID-19 Pandemic: Review of the Literature

BACKGROUND

Coronavirus Disease (COVID-19) is currently spreading exponentially around the globe. Various digital health technologies are currently being used as weapons in the fight against the pandemic in different ways by countries. The main objective of this review is to explore the role of digital health technologies in the fight against the COVID-19 pandemic and address the gaps in the use of these technologies for tackling the pandemic.

METHODS

We conducted a scoping review guided by the Joanna Briggs Institute guidelines. The articles were searched using electronic databases including MEDLINE (PubMed), Cochrane Library, and Hinari. In addition, Google and Google scholar were searched. Studies that focused on the application of digital health technologies on COVID-19 prevention and control were included in the review. We characterized the distribution of technological applications based on geographical locations, approaches to apply digital health technologies and main findings. The study findings from the existing literature were presented using thematic content analysis.

RESULTS

A total of 2,601 potentially relevant studies were generated from the initial search and 22 studies were included in the final review. The review found that telemedicine was used most frequently, followed by electronic health records and other digital technologies such as artificial intelligence, big data, and the internet of things (IoT). Digital health technologies were used in multiple ways in response to the COVID-19 pandemic, including screening and management of patients, methods to minimize exposure, modelling of disease spread, and supporting overworked providers.

CONCLUSION

Digital health technologies like telehealth, mHealth, electronic medical records, artificial intelligence, the internet of things, and big data/internet were used in different ways for the prevention and control of the COVID-19 pandemic in different settings using multiple approaches. For more effective deployment of digital health tools in times of pandemics, development of a guiding policy and standard on the development, deployment, and use of digital health tools in response to a pandemic is recommended.

The Clinical Information Systems Response to the COVID-19 Pandemic

OBJECTIVE

The year 2020 was predominated by the coronavirus disease 2019 (COVID-19) pandemic. The objective of this article is to review the areas in which clinical information systems (CIS) can be and have been utilized to support and enhance the response of healthcare systems to pandemics, focusing on COVID-19.

METHODS

PubMed/MEDLINE, Google Scholar, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies pertaining to CIS, pandemics, and COVID-19 through October 2020. The most informative and detailed studies were highlighted, while many others were referenced.

RESULTS

CIS were heavily relied upon by health systems and governmental agencies worldwide in response to COVID-19. Technology-based screening tools were developed to assist rapid case identification and appropriate triaging. Clinical care was supported by utilizing the electronic health record (EHR) to onboard frontline providers to new protocols, offer clinical decision support, and improve systems for diagnostic testing. Telehealth became the most rapidly adopted medical trend in recent history and an essential strategy for allowing safe and effective access to medical care. Artificial intelligence and machine learning algorithms were developed to enhance screening, diagnostic imaging, and predictive analytics - though evidence of improved outcomes remains limited. Geographic information systems and big data enabled real-time dashboards vital for epidemic monitoring, hospital preparedness strategies, and health policy decision making. Digital contact tracing systems were implemented to assist a labor-intensive task with the aim of curbing transmission. Large scale data sharing, effective health information exchange, and interoperability of EHRs remain challenges for the informatics community with immense clinical and academic potential. CIS must be used in combination with engaged stakeholders and operational change management in order to meaningfully improve patient outcomes.

CONCLUSION

Managing a pandemic requires widespread, timely, and effective distribution of reliable information. In the past year, CIS and informaticists made prominent and influential contributions in the global response to the COVID-19 pandemic.

Home testing for COVID-19 and other virus outbreaks: The complex system of translating to communities

Home testing is an emerging innovation that can enable nations and health care systems to safely and efficiently test large numbers of patients to manage COVID-19 and other viral outbreaks.  In this position paper, we explore the process of moving home testing across the translational continuum from labs to households, and ultimately into practice and communities for optimal public health impact. We focus on the four translational science drivers to accelerate the implementation of systems-wide home testing programmes 1) collaboration and team science, 2) technology, 3) multilevel interventions, and 4) knowledge integration. We use the Socio Ecological Model (SEM) as a framework to illustrate our vision for the ideal future state of a comprehensive system of stakeholders utilising tech-enabled home testing for COVID-19 and other virus outbreaks, and we suggest SEM as a tool to address key translational readiness and response questions.

Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

Beyond contact-tracing: The public value of eHealth application in a pandemic

This study adopts a public value perspective to examine the eHealth services deployed by national and regional governments to contain the coronavirus (Covid-19) pandemic, including symptoms checkers, information portals and contact-tracing applications. We analyse 50 cases of eHealth applications adopted in 25 European Economic Area (EEA) and outline how these systems and technologies map against four dimensions of public value: user orientation, participation, legality and equity. Our findings reveal that the public value of the eHealth applications adopted in the context of the current pandemic is affected by both endogenous and exogenous factors that undermine their ability to improve the quality of healthcare services and social wellbeing. We conclude by suggesting areas for further research to address such factors and the trade-offs emerging between different dimensions of public value.

Macro level factors influencing strategic responses to emergent pandemics: A scoping review

BACKGROUND

Strategic planning is critical for successful pandemic management. This study aimed to identify and review the scope and analytic depth of situation analyses conducted to understand their utility, and capture the documented macro-level factors impacting pandemic management.

METHODS

To synthesise this disparate body of literature, we adopted a two-step search and review process. A systematic search of the literature was conducted to identify all studies since 2000, that have 1) employed a situation analysis; and 2) examined contextual factors influencing pandemic management. The included studies are analysed using a seven-domain systems approach from the discipline of strategic management.

RESULTS

Nineteen studies were included in the final review ranging from single country (6) to regional, multi-country studies (13). Fourteen studies had a single disease focus, with 5 studies evaluating responses to one or more of COVID-19, Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), Influenza A (H1N1), Ebola virus disease, and Zika virus disease pandemics. Six studies examined a single domain from political, economic, sociological, technological, ecological or wider industry (PESTELI), 5 studies examined two to four domains, and 8 studies examined five or more domains. Methods employed were predominantly literature reviews. The recommendations focus predominantly on addressing inhibitors in the sociological and technological domains with few recommendations articulated in the political domain. Overall, the legislative domain is least represented.

CONCLUSIONS

Ex-post analysis using the seven-domain strategic management framework provides further opportunities for a planned systematic response to pandemics which remains critical as the current COVID-19 pandemic evolves.

Development, Status Quo, and Challenges to China's Health Informatization During COVID-19: Evaluation and Recommendations

By applying advanced health information technology to the health care field, health informatization helps optimize health resource allocation, improve health care services, and realize universal health coverage. COVID-19 has tested the status quo of China's health informatization, revealing challenges to the health care system. This viewpoint evaluates the development, status quo, and practice of China's health informatization, especially during COVID-19, and makes recommendations to address the health informatization challenges. We collected, assessed, and evaluated data on the development of China's health informatization from five perspectives-health information infrastructure, information technology (IT) applications, financial and intellectual investment, health resource allocation, and standard system-and discussed the status quo of the internet plus health care service pattern during COVID-19. The main data sources included China's policy documents and national plans on health informatization, commercial and public welfare sources and websites, public reports, institutional reports, and academic papers. In particular, we extracted data from the 2019 National Health Informatization Survey released by the National Health Commission in China. We found that China developed its health information infrastructure and IT applications, made significant financial and intellectual informatization investments, and improved health resource allocations. Tested during COVID-19, China's current health informatization system, especially the internet plus health care system, has played a crucial role in monitoring and controlling the pandemic and allocating medical resources. However, an uneven distribution of health resources and insufficient financial and intellectual investment continue to challenge China's health informatization. China's rapid development of health informatization played a crucial role during COVID-19, providing a reference point for global pandemic prevention and control. To further promote health informatization, China's health informatization needs to strengthen top-level design, increase investment and training, upgrade the health infrastructure and IT applications, and improve internet plus health care services.

State of the Art in Adoption of Contact Tracing Apps and Recommendations Regarding Privacy Protection and Public Health: Systematic Review

BACKGROUND

During the COVID-19 pandemic, contact tracing apps have received a lot of public attention. The ongoing debate highlights the challenges of the adoption of data-driven innovation. We reflect on how to ensure an appropriate level of protection of individual data and how to maximize public health benefits that can be derived from the collected data.

OBJECTIVE

The aim of the study was to analyze available COVID-19 contact tracing apps and verify to what extent public health interests and data privacy standards can be fulfilled simultaneously in the process of the adoption of digital health technologies.

METHODS

A systematic review of PubMed and MEDLINE databases, as well as grey literature, was performed to identify available contact tracing apps. Two checklists were developed to evaluate (1) the apps' compliance with data privacy standards and (2) their fulfillment of public health interests. Based on both checklists, a scorecard with a selected set of minimum requirements was created with the goal of estimating whether the balance between the objective of data privacy and public health interests can be achieved in order to ensure the broad adoption of digital technologies.

RESULTS

Overall, 21 contact tracing apps were reviewed. In total, 11 criteria were defined to assess the usefulness of each digital technology for public health interests. The most frequently installed features related to contact alerting and governmental accountability. The least frequently installed feature was the availability of a system of medical or organizational support. Only 1 app out of 21 (5%) provided a threshold for the population coverage needed for the digital solution to be effective. In total, 12 criteria were used to assess the compliance of contact tracing apps with data privacy regulations. Explicit user consent, voluntary use, and anonymization techniques were among the most frequently fulfilled criteria. The least often implemented criteria were provisions of information about personal data breaches and data gathered from children. The balance between standards of data protection and public health benefits was achieved best by the COVIDSafe app and worst by the Alipay Health Code app.

CONCLUSIONS

Contact tracing apps with high levels of compliance with standards of data privacy tend to fulfill public health interests to a limited extent. Simultaneously, digital technologies with a lower level of data privacy protection allow for the collection of more data. Overall, this review shows that a consistent number of apps appear to comply with standards of data privacy, while their usefulness from a public health perspective can still be maximized.

Nosocomial infection of COVID‑19: A new challenge for healthcare professionals (Review)

Nosocomial infections, also known as hospital-acquired infections, pose a serious challenge to healthcare professionals globally during the Coronavirus disease 2019 (COVID‑19) pandemic. Nosocomial infection of COVID‑19 directly impacts the quality of life of patients, as well as results in extra expenditure to hospitals. It has been shown that COVID‑19 is more likely to transmit via close, unprotected contact with infected patients. Additionally, current preventative and containment measures tend to overlook asymptomatic individuals and superspreading events. Since the mode of transmission and real origin of COVID‑19 in hospitals has not been fully elucidated yet, minimizing nosocomial infection in hospitals remains a difficult but urgent task for healthcare professionals. Healthcare professionals globally should form an alliance against nosocomial COVID‑19 infections. The fight against COVID‑19 may provide valuable lessons for the future prevention and control of nosocomial infections. The present review will discuss some of the key strategies to prevent and control hospital‑based nosocomial COVID‑19 infections.

IoT MEMS: IoT-Based Paradigm for Medical Equipment Management Systems of ICUs in Light of COVID-19 Outbreak

Recently, COVID-19 has infected a lot of people around the world. The healthcare systems are overwhelmed because of this virus. The intensive care unit (ICU) as a part of the healthcare sector has faced several challenges due to the poor information quality provided by current ICUs' medical equipment management. IoT has raised the ability for vital data transfer in the healthcare sector of the new century. However, most of the existing paradigms have adopted IoT technology to track patients' health statuses. Therefore, there is a lack of understanding on how to utilize such technology for ICUs' medical equipment management. This paper proposes a novel IoT-based paradigm called IoT Based Paradigm for Medical Equipment Management Systems (IoT MEMS) to manage medical equipment of ICUs efficiently. It employs IoT technology to enhance the information flow between medical equipment management systems (THIS) and ICUs during the COVID-19 outbreak to ensure the highest level of transparency and fairness in reallocating medical equipment. We described in detail the theoretical and practical aspects of IoT MEMS. Adopting IoT MEMS will enhance hospital capacity and capability in mitigating COVID-19 efficiently. It will also positively influence the information quality of (THIS) and strengthen trust and transparency among the stakeholders.

Suggesting a framework for preparedness against the pandemic outbreak based on medical informatics solutions: a thematic analysis

Background

When an outbreak emerged, each country needs a coherent and preventive plan to deal with epidemics. In the era of technology, adopting informatics‐based solutions is essential. The main objective of this study is to propose a conceptual framework to provide a rapid and responsive surveillance system against pandemics.

Methods

A three‐step approach was employed in this research to develop a conceptual framework. These three steps comprise (1) literature review, (2) extracting and coding concepts, and determining main themes based on thematic analysis using ATLAS.ti® software, and (3) mapping concepts. Later, all of the results synthesized under expert consultation to design a conceptual framework based on the main themes and identified strategies related to medical informatics.

Results

In the literature review phase, 65 articles were identified as eligible studies for analysis. Through line by line coding in thematic analysis, more than 46 themes were extracted as potential foremost themes. Based on the key themes and strategies were employed by studies, the proposed framework designed in three main components. The most appropriate strategies that can be used in each section were identified based on the demands of each part and the available solutions. These solutions were employed in the final framework.

Conclusion

The presented model in this study can be the first step for a better understanding of the potential of medical informatics solutions in promoting epidemic disease management. It can be applied as a reference model for designing intelligent surveillance systems to prepare for probable future pandemics.

Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review

BACKGROUND

The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches.

OBJECTIVE

Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented.

METHODS

We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions.

RESULTS

We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases.

CONCLUSIONS

We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).

The Internet of Things: Impact and Implications for Health Care Delivery

The Internet of Things (IoT) is a system of wireless, interrelated, and connected digital devices that can collect, send, and store data over a network without requiring human-to-human or human-to-computer interaction. The IoT promises many benefits to streamlining and enhancing health care delivery to proactively predict health issues and diagnose, treat, and monitor patients both in and out of the hospital. Worldwide, government leaders and decision makers are implementing policies to deliver health care services using technology and more so in response to the novel COVID-19 pandemic. It is now becoming increasingly important to understand how established and emerging IoT technologies can support health systems to deliver safe and effective care. The aim of this viewpoint paper is to provide an overview of the current IoT technology in health care, outline how IoT devices are improving health service delivery, and outline how IoT technology can affect and disrupt global health care in the next decade. The potential of IoT-based health care is expanded upon to theorize how IoT can improve the accessibility of preventative public health services and transition our current secondary and tertiary health care to be a more proactive, continuous, and coordinated system. Finally, this paper will deal with the potential issues that IoT-based health care generates, barriers to market adoption from health care professionals and patients alike, confidence and acceptability, privacy and security, interoperability, standardization and remuneration, data storage, and control and ownership. Corresponding enablers of IoT in current health care will rely on policy support, cybersecurity-focused guidelines, careful strategic planning, and transparent policies within health care organizations. IoT-based health care has great potential to improve the efficiency of the health system and improve population health.

The influences of the COVID-19 pandemic on medical service behaviors

The outbreak of the novel coronavirus (COVID-19) has greatly impacted medical services worldwide. In addition to changing the processes used by hospital medical services, it has also changed the behaviors of medical staff, resulting in a completely different appearance. Fear of being infected with COVID-19 makes patients fear entering hospitals, and hospitals must repeatedly screen patients prior to entry in order to confirm that they are not infected. Patients are then separated according to their symptoms and travel, occupation, contact and cluster histories (TOCC), which seriously affects them. In addition, hospitals have invested a lot of money into the whole visiting process and into the equipment required to prevent the spread or lessen the impact of COVID-19.

Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis

BACKGROUND

Accurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks.

OBJECTIVE

The aim of this study is to investigate whether excess visits where the word "cough" was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years.

METHODS

A retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons.

RESULTS

The percentage of patients presenting with an EHR reason for visit containing the word "cough" to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure.

CONCLUSIONS

A significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.