healthcare technology

The past several decades have seen an alarming spike in communicable disease outbreaks worldwide. Given a confluence of host, virologic, environmental, and human factors, experts agree that the next pandemic could already be on the horizon.

 

 

In a globalized world, changes in how people use land and interact with their ecosystems—such as rapid deforestation and agricultural expansion—have resulted in humans and animals coming into more frequent and intense contact with one another, increasing opportunities for what is known as "zoonotic disease spillover."

 

 

In the past few years alone, numerous disease outbreaks have had suspected or confirmed zoonotic origin, including mpox (formerly known as monkeypox), Ebola virus disease, dengue fever, and COVID-19.

 

Experts also recognize the need to prepare for another possible Disease X, a term used to describe a currently unknown pathogen with pandemic potential.

 

To direct resources toward the most high-consequence pathogens, it is paramount that leaders have an accurate concept of pandemic risk—for individual viruses as well as viral families. Several institutions are developing disease rankings at national and global levels, including the Priority Zoonotic Diseases Lists facilitated by the U.S. Centers for Disease Control and Prevention and the Research and Development (R&D) Blueprint created by the World Health Organization. 

 

The original SpillOver risk ranking framework (SpillOver 1.0), an open-source webtool launched by researchers at the University of California, Davis One Health Institute, estimated the relative spillover potential of wildlife-origin viruses to humans based on a series of host, viral, and environmental risk factors determined via expert opinion and scientific evidence. 

 

Its next iteration, SpillOvers 2.0, has rebranded to better describe the diversity and frequency of virus spillovers to people. The new platform uses a One Health approach, which recognizes the interdependence of human, animal, and environmental health. It will expand to include domestic animal and vector-borne viruses and assess pandemic risk rather than just spillover risk for wildlife viruses.

 

 

 

Contact tracing apps are potentially useful tools for supporting national COVID-19 containment strategies. Various national apps with different technical design features have been commissioned and issued by governments worldwide.

Objective: Our goal was to develop and propose an item set that was suitable for describing and monitoring nationally issued COVID-19 contact tracing apps.

 

This item set could provide a framework for describing the key technical features of such apps and monitoring their use based on widely available information.

Methods: We used an open-source intelligence approach (OSINT) to access a multitude of publicly available sources and collect data and information regarding the development and use of contact tracing apps in different countries over several months (from June 2020 to January 2021). The collected documents were then iteratively analyzed via content analysis methods. During this process, an initial set of subject areas were refined into categories for evaluation (ie, coherent topics), which were then examined for individual features.

 

These features were paraphrased as items in the form of questions and applied to information materials from a sample of countries (ie, Brazil, China, Finland, France, Germany, Italy, Singapore, South Korea, Spain, and the United Kingdom [England and Wales]). This sample was purposefully selected; our intention was to include the apps of different countries from around the world and to propose a valid item set that can be relatively easily applied by using an OSINT approach.

Results: Our OSINT approach and subsequent analysis of the collected documents resulted in the definition of the following five main categories and associated subcategories:

 

(1) background information (open-source code, public information, and collaborators);

 

(2) purpose and workflow (secondary data use and warning process design);

 

(3) technical information (protocol, tracing technology, exposure notification system, and interoperability);

 

(4) privacy protection (the entity of trust and anonymity); and

 

(5) availability and use (release date and the number of downloads).

 

Based on this structure, a set of items that constituted the evaluation framework were specified. The application of these items to the 10 selected countries revealed differences, especially with regard to the centralization of the entity of trust and the overall transparency of the apps’ technical makeup.

Conclusions: We provide a set of criteria for monitoring and evaluating COVID-19 tracing apps that can be easily applied to publicly issued information. The application of these criteria might help governments to identify design features that promote the successful, widespread adoption of COVID-19 tracing apps among target populations and across national boundaries.

 

 read the study at https://mhealth.jmir.org/2021/3/e27232

 

 

The use of technology in health care has proven to increases care providers, the capabilities and to patients, access to improved quality of healthcare. During the GDD summit organized by BAO Systems, it occurred that some of the most beneficial countries include Nigeria.

 

Speaking at the summit, InStrat Global Health Solution (InStratGHS) CEO, Okey Okuzu, discussed the design, deployment, and impact of Artificial Intelligence (AI) tool – EWORS for TB case finding in Nigeria. EWORS is built on the MediXcel Digital Health Platform by Plus91

 

“Our goal is to overcome barriers to healthcare delivery in low resource settings presented by infrastructural challenges,” he noted. “We achieve this by leveraging mobile health technologies that allow for smooth flow of information across the health system for more effective patient health management or policy decisions regardless of physical location.”

 

The MediXcel-EWORS system is designed to drive actionable AI for public health intervention by sieving through a large volume of data to enable specific geospatial identification of disease cases and also predict the possibility of an outbreak based on historical data and set algorithm threshold.

 

This helps local surveillance personnel to make data-informed decisions to curb the spread of diseases. Field data, captured on android devices wirelessly transferred to a cloud server for storage and analysis.

 

“The EWORS engine conducts advanced analytics to detect unexpected elevations in indicator data and populates this information on real-time GIS heat maps, reports, and alert notifications,” he added.

 

The alert notifications are generated in form of emails/SMS and sent to designated individuals when data from local areas exceed set thresholds, indicating undetected community spread. Designated teams review alarms and mobilize to conduct mass screening outreach at Alarm locations.

 

Under a USAID-funded program led by the KNCV TB Foundation, and in partnership with Plus91 PVT, InStrat GHS deployed its EWORS predictive engine to 14 Nigerian states as a solution to find undetected TB cases in the country. The Analyses of the data from program inception to date demonstrate that prioritization of case finding outreach efforts, based on hotspot analytics and alarms, increases the yield of those efforts.

 

This strategy is crucial to finding missing TB cases and improving case-finding, especially in low resource settings,

 

read more at https://www.cio.co.ke/role-of-ai-in-healthcare-tb-prevalence-in-nigeria/

 

read about Instrats at https://instratghs.com/

 

read about Plus91 and MediXcel at https://plus91.in/about-us/

 

read about KCNV at https://kncvnigeria.org/

 

Web-based technology has dramatically improved our ability to detect communicable disease outbreaks, with the potential to reduce morbidity and mortality because of swift public health action.

 

Apps accessible through the internet and on mobile devices create an opportunity to enhance our traditional indicator-based surveillance systems, which have high specificity but issues with timeliness.

Objective: The aim of this study is to describe the literature on web-based apps for indicator-based surveillance and response to acute communicable disease outbreaks in the community with regard to their design, implementation, and evaluation.

Results: Apps were primarily designed to improve the early detection of disease outbreaks, targeted government settings, and comprised either complex algorithmic or statistical outbreak detection mechanisms or both.

 

We identified a need for these apps to have more features to support secure information exchange and outbreak response actions, with a focus on outbreak verification processes and staff and resources to support app operations.

 

Conclusions: Public health officials designing new or improving existing disease outbreak web-based apps should ensure that outbreak detection is automatic and signals are verified by users, the app is easy to use, and staff and resources are available to support the operations of the app and conduct rigorous and holistic evaluations.

 

read the study at https://publichealth.jmir.org/2021/4/e24330