'disease management' in healthcare technology

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AI app could help diagnose HIV more accurately

From medicalxpress.com - June 19, 2021 3:06 AM

More than 100 million HIV tests are performed around the world annually, meaning even a small improvement in quality assurance could impact the lives of millions of people by reducing the risk of false positives and negatives.

Academics from the London Center for Nanotechnology at UCL and AHRI used deep learning (artificial intelligence/AI) algorithms to improve health workers' ability to diagnose HIV using lateral flow tests in rural South Africa.

Their findings, published today in Nature Medicine, involve the first and largest study of field-acquired HIV test results, which have applied machine learning (AI) to help classify them as positive or negative.

By harnessing the potential of mobile phone sensors, cameras, processing power and data sharing capabilities, the team developed an app that can read test results from an image taken by end users on a mobile device. It may also be able to report results to public health systems for better data collection and ongoing care.

read the study at https://www.nature.com/articles/s41591-021-01384-9

nrip's insight:

The use of mobile tools for data capture and AI/ML algorithms for diagnostics and detections has been the inside story of digital health over the past 4 years. This is an excellent study and shows the promise of this combination of technologies in building the future of healthcare. HIV is a pandemic which must be eradicated.

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Tracing the Origin of the Covid Virus

From www.wired.com - December 7, 2020 1:57 PM

With cases soaring across the globe, the Covid-19 pandemic is nowhere near its end, but with three vaccines reporting trial data and two apparently nearing approval by the US FDA, it may be reaching a pivot point.

In what feels like a moment of drawing breath and taking stock, international researchers are turning their attention from the present back to the start of the pandemic, aiming to untangle its origin and asking what lessons can be learned to keep this from happening again.

Two efforts are happening in parallel. On November 5, the World Health Organization quietly published the rules of engagement for a long-planned and months-delayed mission that creates a multinational team of researchers who will pursue how the virus leaped species. Meanwhile, last week, a commission created by The Lancet and headed by the economist and policy expert Jeffrey Sachs announced the formation of its own international effort, a task force of 12 experts from nine countries who will undertake similar tasks.

Both groups will face the same complex problems. It has been approximately a year since the first cases of a pneumonia of unknown origin appeared in Wuhan, China, and about 11 months since the pneumonia’s cause was identified as a novel coronavirus, probably originating in bats.

The experts will have to retrace a chain of transmission—one or multiple leaps of the virus from the animal world into humans—using interviews, stored biological samples, lab assays, environmental surveys, genomic data, and the thousands of papers published since the pandemic began, all while following a trail that may have gone cold.

The point is not to look for patient zero, the first person infected—or even a hypothetical bat zero, the single animal from which the novel virus jumped.

It’s likely neither of those will ever be found. The goal instead is to elucidate the ecosystem—physical, but also viral—in which the spillover happened and ask what could make it likely to happen again.

more at WIRED : https://www.wired.com/story/two-global-efforts-try-to-trace-the-origin-of-the-covid-virus/?utm_source=pocket-newtab-intl-en

nrip's insight:

Back tracing the origins of an outbreak or an epidemic is way tougher than people expect it to be. So much changes during the period the epidemic ravages on, including the data from the time at which it was breaking out. Its high time, the world and health experts learn that the best way to manage and trace the roots of an outbreak is to prevent it, and if a break out happens, act fast towards containing its spread and studying it in parallel.

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Analyzing the Essential Attributes of Nationally Issued COVID-19 Contact Tracing Apps

From mhealth.jmir.org - May 4, 2021 4:03 AM

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

nrip's insight:

Where a lot of studies falter, is they dont focus on ease of use as a primary criteria of evaluation. Digital Health tools for far too long have faced criticism due to the ease of use factor.

It takes several iterations for any app/tool to become easy to use when the use cases contain a lot of data input. As such, contact tracing tools will do well by being built over surveillance and data collection platforms like MediXcel Lite and Commcare.

The data collection platforms must also focus on contact tracing as a type of app they generate along with the longitudinal and case based apps they currently allow.

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EHRs And Disease Prediction

From www.bsminfo.com - July 27, 2014 10:25 PM

Much of the chatter around electronic health records (EHRs) revolves around efficiency and cost cutting in clinical practice. There is even a bit of discussion about the use of EHRS to improve population health. But is there more benefit to be found in individual patient health?

Perhaps the greatest potential of the EHR, (and the concept applied to a broader application, the EMR) lies in the role it can play in predicting clinical outcomes around a range of diseases and conditions.

This application is still very much in its fledgling stage, but here are just a few examples of how data analytics, when applied to EHRs in mindful ways, can bring about positive changes in patient health.

Predicting Sepsis

One of the most recent examples we saw came out of UC Davis. Researchers there found that, by compiling and analyzing routine information — blood pressure, respiratory rate, temperature, and white blood cell count — as pulled from EHRs, they were able to predict early stages of sepsis, a condition that is a leading cause of hospitalization and death in the U.S. It took them only three measures — lactate level, blood pressure, and respiratory rate — to calculate the likelihood that a patient would die from the condition.

Progressing Kidney Disease

Data from EHRs has also played a key role in predicting the need for dialysis after a patient with chronic kidney disease progresses into kidney failure.

The Journal Of The American Medical Association in 2011 studied patients who were referred to nephrologists between April 1, 2001, and December 31, 2008, in an effort to develop and validate predictive models for the progression of chronic kidney disease.

According to the study, “Our models use laboratory data that are obtained routinely in patients with CKD and could be easily integrated into a laboratory information system or a clinic EHR.” It also notes that emerging literature suggests that the methods lead to “improved patient outcomes with individualized risk prediction and with advances in information technology that allow for easy implementation of risk prediction models as components of EHRs.”

All data for the study where pulled from nephrology clinic EHRs.

Cardiovascular Risk

EHRs have also been used to improve cardiovascular risk prediction. A study (available from the National Institutes Of Health), analyzed whether internal EHR data (using flexible, adaptive statistical methods) could improve clinical risk prediction. The study used the fact that EHRs have been extensively implemented in the VA system as an opportunity for exploration.

It found that, “despite the EHR lacking some risk factors and its imperfect data quality, health care systems may be able to substantially improve risk prediction for their patients by using internally developed EHR-derived models and flexible statistical methodology.”

Controlling Hypertension

Another prevalent health issue in the U.S., hypertension, has seen researchers apply predictive analytics using EHR data to gain more insight into the disease. This study, from the Journal Of Informatics In Health And Biomedicine, sought to identify transition points at which hypertension is brought in, as well as pushed out of, control, through the use of EHR data.

The study of 1294 patients with hypertension (who were enrolled in a chronic disease management program at the Vanderbilt University Medical Center) found that accurate prediction of transition points from a control status could be achieved

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