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Sensors (Basel, Switzerland) May 2022Electronic Health Records (EHR) are the healthcare sector's core digital strategy meant to improve the quality of care provided to patients. Despite the benefits...
Electronic Health Records (EHR) are the healthcare sector's core digital strategy meant to improve the quality of care provided to patients. Despite the benefits afforded by this digital transformation initiative, adoption among healthcare organizations has been slower than desired. The sheer volume and sensitive nature of patient records compel these organizations to exercise a healthy amount of caution in implementing EHR. Cyberattacks have also increased the risks associated with non-optimal EHR implementations. An influx of high-profile data breaches has plagued the sector during the COVID-19 pandemic, which put the spotlight on EHR cybersecurity. One objective of this research project is to aid the acceleration of EHR adoption. Another objective is to ensure the robustness of the system to resist malicious attacks. For the former, a systematic review was used to unearth all the possible causes why the adoption of EHR has been anemic. In this paper, sixty-five existing proposed EHR solutions were analyzed and it was found that there are fourteen major challenges that need to be addressed to reduce friction and risk for health organizations. These were privacy, security, confidentiality, interoperability, access control, scalability, authentication, accessibility, availability, data storage, data ownership, data validity, data integrity, and ease of use. We propose EHRChain, a new framework that tackles all the listed challenges simultaneously to address the first objective while also being designed to achieve the second objective. It is enabled by dual-blockchains based on Hyperledger Sawtooth to allow patient data decentralization via a consortium blockchain and IPFS for distributed data storage.
Topics: Blockchain; COVID-19; Computer Security; Electronic Health Records; Humans; Pandemics
PubMed: 35684652
DOI: 10.3390/s22114032 -
The Cochrane Database of Systematic... Aug 2020Reducing the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global priority. Contact tracing identifies people who were recently in... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Reducing the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global priority. Contact tracing identifies people who were recently in contact with an infected individual, in order to isolate them and reduce further transmission. Digital technology could be implemented to augment and accelerate manual contact tracing. Digital tools for contact tracing may be grouped into three areas: 1) outbreak response; 2) proximity tracing; and 3) symptom tracking. We conducted a rapid review on the effectiveness of digital solutions to contact tracing during infectious disease outbreaks.
OBJECTIVES
To assess the benefits, harms, and acceptability of personal digital contact tracing solutions for identifying contacts of an identified positive case of an infectious disease.
SEARCH METHODS
An information specialist searched the literature from 1 January 2000 to 5 May 2020 in CENTRAL, MEDLINE, and Embase. Additionally, we screened the Cochrane COVID-19 Study Register.
SELECTION CRITERIA
We included randomised controlled trials (RCTs), cluster-RCTs, quasi-RCTs, cohort studies, cross-sectional studies and modelling studies, in general populations. We preferentially included studies of contact tracing during infectious disease outbreaks (including COVID-19, Ebola, tuberculosis, severe acute respiratory syndrome virus, and Middle East respiratory syndrome) as direct evidence, but considered comparative studies of contact tracing outside an outbreak as indirect evidence. The digital solutions varied but typically included software (or firmware) for users to install on their devices or to be uploaded to devices provided by governments or third parties. Control measures included traditional or manual contact tracing, self-reported diaries and surveys, interviews, other standard methods for determining close contacts, and other technologies compared to digital solutions (e.g. electronic medical records).
DATA COLLECTION AND ANALYSIS
Two review authors independently screened records and all potentially relevant full-text publications. One review author extracted data for 50% of the included studies, another extracted data for the remaining 50%; the second review author checked all the extracted data. One review author assessed quality of included studies and a second checked the assessments. Our outcomes were identification of secondary cases and close contacts, time to complete contact tracing, acceptability and accessibility issues, privacy and safety concerns, and any other ethical issue identified. Though modelling studies will predict estimates of the effects of different contact tracing solutions on outcomes of interest, cohort studies provide empirically measured estimates of the effects of different contact tracing solutions on outcomes of interest. We used GRADE-CERQual to describe certainty of evidence from qualitative data and GRADE for modelling and cohort studies.
MAIN RESULTS
We identified six cohort studies reporting quantitative data and six modelling studies reporting simulations of digital solutions for contact tracing. Two cohort studies also provided qualitative data. Three cohort studies looked at contact tracing during an outbreak, whilst three emulated an outbreak in non-outbreak settings (schools). Of the six modelling studies, four evaluated digital solutions for contact tracing in simulated COVID-19 scenarios, while two simulated close contacts in non-specific outbreak settings. Modelling studies Two modelling studies provided low-certainty evidence of a reduction in secondary cases using digital contact tracing (measured as average number of secondary cases per index case - effective reproductive number (R )). One study estimated an 18% reduction in R with digital contact tracing compared to self-isolation alone, and a 35% reduction with manual contact-tracing. Another found a reduction in R for digital contact tracing compared to self-isolation alone (26% reduction) and a reduction in R for manual contact tracing compared to self-isolation alone (53% reduction). However, the certainty of evidence was reduced by unclear specifications of their models, and assumptions about the effectiveness of manual contact tracing (assumed 95% to 100% of contacts traced), and the proportion of the population who would have the app (53%). Cohort studies Two cohort studies provided very low-certainty evidence of a benefit of digital over manual contact tracing. During an Ebola outbreak, contact tracers using an app found twice as many close contacts per case on average than those using paper forms. Similarly, after a pertussis outbreak in a US hospital, researchers found that radio-frequency identification identified 45 close contacts but searches of electronic medical records found 13. The certainty of evidence was reduced by concerns about imprecision, and serious risk of bias due to the inability of contact tracing study designs to identify the true number of close contacts. One cohort study provided very low-certainty evidence that an app could reduce the time to complete a set of close contacts. The certainty of evidence for this outcome was affected by imprecision and serious risk of bias. Contact tracing teams reported that digital data entry and management systems were faster to use than paper systems and possibly less prone to data loss. Two studies from lower- or middle-income countries, reported that contact tracing teams found digital systems simpler to use and generally preferred them over paper systems; they saved personnel time, reportedly improved accuracy with large data sets, and were easier to transport compared with paper forms. However, personnel faced increased costs and internet access problems with digital compared to paper systems. Devices in the cohort studies appeared to have privacy from contacts regarding the exposed or diagnosed users. However, there were risks of privacy breaches from snoopers if linkage attacks occurred, particularly for wearable devices.
AUTHORS' CONCLUSIONS
The effectiveness of digital solutions is largely unproven as there are very few published data in real-world outbreak settings. Modelling studies provide low-certainty evidence of a reduction in secondary cases if digital contact tracing is used together with other public health measures such as self-isolation. Cohort studies provide very low-certainty evidence that digital contact tracing may produce more reliable counts of contacts and reduce time to complete contact tracing. Digital solutions may have equity implications for at-risk populations with poor internet access and poor access to digital technology. Stronger primary research on the effectiveness of contact tracing technologies is needed, including research into use of digital solutions in conjunction with manual systems, as digital solutions are unlikely to be used alone in real-world settings. Future studies should consider access to and acceptability of digital solutions, and the resultant impact on equity. Studies should also make acceptability and uptake a primary research question, as privacy concerns can prevent uptake and effectiveness of these technologies.
Topics: Botswana; COVID-19; Cohort Studies; Contact Tracing; Coronavirus Infections; Disease Outbreaks; Hemorrhagic Fever, Ebola; Humans; Mobile Applications; Models, Theoretical; Patient Isolation; Privacy; Quarantine; Secondary Prevention; Sierra Leone; Tuberculosis; United States; Whooping Cough
PubMed: 33502000
DOI: 10.1002/14651858.CD013699 -
Frontiers in Medicine 2024The rapid spread of COVID-19 pandemic across the world has not only disturbed the global economy but also raised the demand for accurate disease detection models....
The rapid spread of COVID-19 pandemic across the world has not only disturbed the global economy but also raised the demand for accurate disease detection models. Although many studies have proposed effective solutions for the early detection and prediction of COVID-19 with Machine Learning (ML) and Deep learning (DL) based techniques, but these models remain vulnerable to data privacy and security breaches. To overcome the challenges of existing systems, we introduced Adaptive Differential Privacy-based Federated Learning (DPFL) model for predicting COVID-19 disease from chest X-ray images which introduces an innovative adaptive mechanism that dynamically adjusts privacy levels based on real-time data sensitivity analysis, improving the practical applicability of Federated Learning (FL) in diverse healthcare environments. We compared and analyzed the performance of this distributed learning model with a traditional centralized model. Moreover, we enhance the model by integrating a FL approach with an early stopping mechanism to achieve efficient COVID-19 prediction with minimal communication overhead. To ensure privacy without compromising model utility and accuracy, we evaluated the proposed model under various noise scales. Finally, we discussed strategies for increasing the model's accuracy while maintaining robustness as well as privacy.
PubMed: 38912338
DOI: 10.3389/fmed.2024.1409314 -
Sante Publique (Vandoeuvre-les-Nancy,...Pregnant women are heavy users of Internet and this has an impact on their medical follow-up. The purpose of this study is to highlight the ethical issues related to the...
INTRODUCTION
Pregnant women are heavy users of Internet and this has an impact on their medical follow-up. The purpose of this study is to highlight the ethical issues related to the use of the Internet by women in their medical care.Methode: Through a systematic literature review conducted on PubMed/Medline, Web of Science, CINAHL and Embase between June and July 2019, 10 670 results were obtained, and 79 articles were included in the post-selection study. A thematic analysis was conducted on these articles.
RESULTS
More than 90% of pregnant women use Internet, particularly to find medical information and social support, mainly on pregnancy and childbirth. This research allows them more equitable access to knowledge and develops their empowerment, which modifies the relationship between caregiver and patient, through the acquisition of greater autonomy for women and the development of experiential knowledge. This access offers a central and active role to pregnant women in their medical care. However, many authors also agree on the possible abuses of this use: misinformation, disproportionate information and the presence of judgment that undermine empowerment, but also digital divide and inequity in understanding information, stigmatization of women, and risks of privacy breaches on data acquired online.
CONCLUSION
In order to provide pregnant women with the central and active place they seek, the authors recommend involving caregivers in the referral to reliable sites, encouraging them to develop online content, and educating pregnant women in the search for health information on Internet.
Topics: Consumer Health Information; Female; Humans; Information Seeking Behavior; Internet; Patient Education as Topic; Pregnancy; Pregnant Women; Professional-Patient Relations; Social Support
PubMed: 32985833
DOI: 10.3917/spub.202.0171