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Women with hormone receptor-positive, early-stage breast cancer who adhere to adjuvant endocrine therapy (AET) reduce the risk of cancer recurrence and mortality.
AET, however, is associated with adverse symptoms that often result in poor adherence. We applied participatory action research (PAR) principles to conduct focus groups and interviews to refine and enhance a web-enabled app intervention that facilitates patient-provider communication about AET-related symptoms and other barriers to adherence.
THRIVE app content reflects researchers’ partnership with a racially diverse sample of breast cancer survivors and healthcare providers and adherence to participatory design by incorporating patient-requested app features, app aesthetics, and message content.
The app has the potential to improve AET adherence and quality of life among breast cancer survivors and reduce disparities in mortality rates for Black women by facilitating communication with healthcare providers.
Candra Satriya's curator insight,
October 7, 2021 9:30 PM
prinsip-prinsip pada participatory action research
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The pandemics of major infectious diseases often cause public health, economic, and social problems. Virtual reality (VR) and augmented reality (AR), as two novel technologies, have been used in many fields for emergency management of disasters. The objective of this paper was to review VR and AR applications in the emergency management of infectious outbreaks with an emphasis on the COVID-19 outbreak.
It appears that VR and AR technologies can play a positive role during infectious disease outbreaks.
VR and AR have been widely used in the prevention and response phases of emergency management during infectious disease pandemics, such as SARS and Ebola pandemics, especially for educating and training purposes for the public.
During the COVID-19 outbreak, these technologies have the potential to be used in various fields, including
1) clinical context (e.g., telehealth, drug discovery, patient assessment, mental health management),
2) entertainment (e.g., video call, meditation, gaming),
3) business and industry (e.g., holding meetings and conferences, marketing), and
4) education (e.g., in schools and universities, for healthcare providers, and VR-based content for improving public health).
These technologies can be used in the above-mentioned fields by providing their different features for facilitating the challenges of COVID-19.
However, to respond to COVID-19, all applications of VR and AR should be considered as a supportive approach alongside other information technologies.
We believe that VR and AR have a substantial potential to impact the emergency management of COVID-19 or any infectious disease pandemics; however, these potentials need to be studied in a more robust manner.
read the paper ta https://www.sciencedirect.com/science/article/pii/S2352914821000691
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Enabling bi-directional APIs is one way to offer speed, efficiency and security while preserving the most necessary components of human intervention. These closed-loop data retrieval processes will shape the future of ROI in healthcare for higher quality and faster intake and fulfillment.
Bi-directional APIs give healthcare providers maximum data visibility and control. Here’s how they’re shaping the future of release of information.
In recent years, a fire has been lit under healthcare’s use of application programming interfaces (APIs). Actually, it has been a FHIR [Fast Healthcare Interoperability Resources] as regulations encourage electronic medical record (EMR) vendors to continue to build its standards into their systems, expanding functionality and improving usability. But that does not mean the road to digitization and interoperability has been seamless, particularly as it relates to release of information (ROI).
more at https://www.healthcareittoday.com/2021/07/22/healthcare-apis-a-two-way-street/
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A team of researchers analyzed the genomes of more than 2,500 modern humans from 26 worldwide populations, to better understand how humans have adapted to historical coronavirus outbreaks.
The team used computational methods to uncover genetic traces of adaptation to coronaviruses, the family of viruses responsible for three major outbreaks in the last 20 years, including the ongoing COVID-19 pandemic.
Traces of the outbreak are evident in the genetic makeup of people from that area, they’ve found.
A coronavirus epidemic broke out in the East Asia region more than 20,000 years ago, as per their findings.
The discovery of a coronavirus outbreak from 20,000 years ago is "like finding fossilized dinosaur footprints instead of finding fossilized bones directly.
The work shows that over the course of the epidemic, selection favored certain variants of human genes involved in the virus-cell interactions that could have led to a less severe disease. Studying the “tracks” left by ancient viruses can help researchers better understand how the genomes of different human populations adapted to viruses that have emerged as important drivers of human evolution.
The study’s authors say their research could help identify viruses that have caused epidemics in the distant past and may do so in the future. Studies like theirs help researchers compile a list of potentially dangerous viruses and then develop diagnostics, vaccines, and drugs for the event of their return.
read the paper at https://www.cell.com/current-biology/fulltext/S0960-9822(21)00794-6
more at https://www.futurity.org/coronavirus-epidemic-viruses-2597742/
nrip's insight:
The promise of evolutionary genetic analyses as a new tool in fighting the outbreaks of the future
nrip's curator insight,
July 19, 2021 10:52 PM
The promise of evolutionary genetic analyses as a new tool in fighting the outbreaks of the future
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The COVID-19 outbreak exposed several problems faced by health systems worldwide, especially concerning the safe and rapid generation and sharing of health data. However, this pandemic scenario has also facilitated the rapid implementation and monitoring of technologies in the health field.
In view of the occurrence of the public emergency caused by SARS-CoV-2 in Brazil, the Department of Informatics of the Brazilian Unified Health System created a contingency plan. In this paper, we aim to report the digital health strategies applied in Brazil and the first results obtained during the fight against COVID-19. Conecte SUS, a platform created to store all the health data of an individual throughout their life, is the center point of the Brazilian digital strategy.
Access to the platform can be obtained through an app by the patient and the health professionals involved in the case. Health data sharing became possible due to the creation of the National Health Data Network (Rede Nacional de Dados em Saúde, RNDS).
A mobile app was developed to guide citizens regarding the need to go to a health facility and to assist in disseminating official news about the virus. The mobile app can also alert the user if they have had contact with an infected person. The official numbers of cases and available hospital beds are updated and published daily on a website containing interactive graphs.
These data are obtained due to creating a web-based notification system that uses the RNDS to share information about the cases.
Preclinical care through telemedicine has become essential to prevent overload in health facilities. The exchange of experiences between medical teams from large centers and small hospitals was made possible using telehealth.
Brazil took a giant step toward digital health adoption, creating and implementing important initiatives; however, these initiatives do not yet cover the entire health system. It is expected that the sharing of health data that are maintained and authorized by the patient will become a reality in the near future.
The intention is to obtain better clinical outcomes, cost reduction, and faster and better services in the public health network.
read more at https://publichealth.jmir.org/2021/6/e28643/
nrip's insight:
The whole context of health data sharing became evident and the need stronger than ever before amid the covid19 pandemic. Here daily updates of epidemiological data for control and decision-making by governments and health system were required.
The use of apps within healthcare (mHealth) is now a reality, and the number of mobile apps created has been increasing.
MHealth apps have been implemented for training, information sharing, risk assessment, self-management of symptoms, contact tracing, home monitoring, and decision making. They are considered valuable tools for citizens, health professionals, and decision-makers in facing critical challenges imposed by the pandemic.
The COVID-19 pandemic has demonstrated the importance and usefulness of digital health strategies and has allowed the insertion of these solutions into health care systems in the long term
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A new diagnostic technique that has the potential to identify opioid-addicted patients at risk for relapse could lead to better treatment and outcomes.
Using an algorithm that looks for patterns in brain structure and functional connectivity, researchers were able to distinguish prescription opioid users from healthy participants. If treatment is successful, their brains will resemble the brain of someone not addicted to opioids.
“People can say one thing, but brain patterns do not lie,” says lead researcher Suchismita Ray, an associate professor in the health informatics department at Rutgers School of Health Professions.
“The brain patterns that the algorithm identified from brain volume and functional connectivity biomarkers from prescription opioid users hold great promise to improve over current diagnosis.”
In the study in NeuroImage: Clinical, Ray and her colleagues used MRIs to look at the brain structure and function in people diagnosed with prescription opioid use disorder who were seeking treatment compared to individuals with no history of using opioids.
The scans looked at the brain network believed to be responsible for drug cravings and compulsive drug use. At the completion of treatment, if this brain network remains unchanged, the patient needs more treatment.
read the study at https://doi.org/10.1016/j.nicl.2021.102663
read the article at https://www.futurity.org/opioid-addiction-relapse-algorithm-2586182-2/
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The human brain is the most ridiculously complex computer that’s ever existed, and mapping this dense tangle of neurons, synapses and other cells is nigh on impossible. But engineers at Google and Harvard have given it the best shot yet, producing a browsable, searchable 3D map of a small section of human cerebral cortex.
A browsable 3D map of just one millionth of the cerebral cortex has been created using 225 million images and a whopping 1.4 petabytes of data, illustrating the immense complexity of the human brain.
With about 86 billion neurons connecting via 100 trillion synapses, it’s a Herculean task to figure out exactly what each of them does and how those connections form the basis of thought, emotion, memory, behavior and consciousness. Daunting as it may be, though, teams of scientists around the world are rolling up their sleeves and trying to build a wiring diagram for the human brain – a so-called “connectome.”
he researchers started with a sample taken from the temporal lobe of a human cerebral cortex, measuring just 1 mm3. This was stained for visual clarity, coated in resin to preserve it, and then cut into about 5,300 slices each about 30 nanometers (nm) thick. These were then imaged using a scanning electron microscope, with a resolution down to 4 nm. That created 225 million two-dimensional images, which were then stitched back together into one 3D volume.
Machine learning algorithms scanned the sample to identify the different cells and structures within. After a few passes by different automated systems, human eyes “proofread” some of the cells to ensure the algorithms were correctly identifying them.
The end result, which Google calls the H01 dataset, is one of the most comprehensive maps of the human brain ever compiled. It contains 50,000 cells and 130 million synapses, as well as smaller segments of the cells such axons, dendrites, myelin and cilia. But perhaps the most stunning statistic is that the whole thing takes up 1.4 petabytes of data – that’s more than a million gigabytes.
read more at https://newatlas.com/biology/google-harvard-human-brain-connectome/
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This week, the academic community provided a rather impressive example of the promise of neural implants. Using an implant, a paralyzed individual managed to type out roughly 90 characters per minute simply by imagining that he was writing those characters out by hand
Dreaming is doing
Previous attempts at providing typing capabilities to paralyzed people via implants have involved giving subjects a virtual keyboard and letting them maneuver a cursor with their mind. The process is effective but slow, and it requires the user's full attention, as the subject has to track the progress of the cursor and determine when to perform the equivalent of a key press. It also requires the user to spend the time to learn how to control the system.
But there are other possible routes to getting characters out of the brain and onto the page. Somewhere in our writing thought process, we form the intention of using a specific character, and using an implant to track this intention could potentially work. Unfortunately, the process is not especially well understood.
Downstream of that intention, a decision is transmitted to the motor cortex, where it's translated into actions. Again, there's an intent stage, where the motor cortex determines it will form the letter (by typing or writing, for example), which is then translated into the specific muscle motions required to perform the action. These processes are much better understood, and they're what the research team targeted for their new work.
Disclaimer: Not even a prototype
As the researchers themselves put it, this "is not yet a complete, clinically viable system." To begin with, it has only been used in a single individual, so we have no idea how well it might work for others. The simplified alphabet used here doesn't contain any digits, capital letters, or most forms of punctuation. And the behavior of the implants changes over time, perhaps because of minor shifts relative to the neurons they read or the build-up of scar tissue, so the system had to be recalibrated regularly—at least once per week to maintain a tolerable error rate
read the research at http://dx.doi.org/10.1038/s41586-021-03506-2
related code : https://github.com/fwillett/handwritingBCI
read the article in its complete and unedited form at https://arstechnica.com/science/2021/05/neural-implant-lets-paralyzed-person-type-by-imagining-writing/
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During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making.
We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables.
Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) when predicting deterioration within 96 hours.
The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.
In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.
read the open article at https://www.nature.com/articles/s41746-021-00453-0
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COVID-19 is impacting people worldwide and is currently a leading cause of death in many countries. Underlying factors, including Social Determinants of Health (SDoH), could contribute to these statistics. Our prior work has explored associations between SDoH and several adverse health outcomes (eg, asthma and obesity). Our findings reinforce the emerging consensus that SDoH factors should be considered when implementing intelligent public health surveillance solutions to inform public health policies and interventions.
Objective: This study sought to redefine the Healthy People 2030’s SDoH taxonomy to accommodate the COVID-19 pandemic. Furthermore, we aim to provide a blueprint and implement a prototype for the Urban Population Health Observatory (UPHO), a web-based platform that integrates classified group-level SDoH indicators to individual- and aggregate-level population health data.
Methods: The process of building the UPHO involves collecting and integrating data from several sources, classifying the collected data into drivers and outcomes, incorporating data science techniques for calculating measurable indicators from the raw variables, and studying the extent to which interventions are identified or developed to mitigate drivers that lead to the undesired outcomes.
Results: We generated and classified the indicators of social determinants of health, which are linked to COVID-19. To display the functionalities of the UPHO platform, we presented a prototype design to demonstrate its features. We provided a use case scenario for 4 different users.
Conclusions: UPHO serves as an apparatus for implementing effective interventions and can be adopted as a global platform for chronic and infectious diseases. The UPHO surveillance platform provides a novel approach and novel insights into immediate and long-term health policy responses to the COVID-19 pandemic and other future public health crises. The UPHO assists public health organizations and policymakers in their efforts in reducing health disparities, achieving health equity, and improving urban population health.
access the study at https://publichealth.jmir.org/2021/6/e28269/
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Contact tracing aims to avoid transmission by isolating, at an early stage, only those individuals who are infectious or potentially infectious, to minimize the societal costs associated with isolation. Considerable resources are therefore directed at improving surveillance capacities to allow efficient and rapid investigation and isolation of cases and their contacts. To enhance tracing capacities, the use of digital technologies has been proposed, leveraging the widespread use of smartphones. Therefore, proximity-sensing applications have been designed and made available to automatically trace contacts, notify users about potential exposure to COVID-19, and invite them to isolate.
The efficacy of digital contact tracing against coronavirus disease 2019 (COVID-19) epidemic is debated: Smartphone penetration is limited in many countries, with low coverage among the elderly, the most vulnerable to COVID-19.
Quantifying the impact of digital contact tracing is essential to envision this strategy within a wider response plan against the COVID-19 epidemic.
We modeled this intervention together with household isolation assuming a 50% detection of clinical cases. In a scenario of high transmissibility (R = 2.6), we found that household isolation by itself would produce a reduction in peak incidence of 27%, while the inclusion of digital contact tracing could increase this effect by 30% for a reasonably achievable app adoption (~20% of the population) and by 144% for a large-scale app adoption (~60%). At a moderate transmissibility level (R = 1.7), the app would substantially damp transmission (36 to 89% peak incidence reduction for increasing app adoption), bringing the epidemic to manageable levels if adopted by 32% of the population or more.
The app-based tracing and household isolation have different effects across settings, the first intervention efficiently preventing transmissions at work that are not well targeted by the second.
Moreover, app-based contact tracing also yields a protection for the elderly despite the lower penetration of smartphones in this age category.
These results may inform the inclusion of digital contact tracing within a COVID-19 response plan.
read the study at https://advances.sciencemag.org/content/7/15/eabd8750
nrip's insight:
Contact tracing works, if done right. In the early stages of the pandemic, a number of issues with contact tracing were seen. A majority of them were to do with the lack of knowledge/training about how to carry it out And the magnitude of the task it entailed for a set of untrained workers.
A contact tracing strategy must be built into public health surveillance systems and evolved to be with the times whenever needed, with the hope that it is not needed.
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Purdue University engineers have developed a method to transform existing cloth items into battery-free wearables resistant to laundry. These smart clothes are powered wirelessly through a flexible, silk-based coil sewn on the textile.
In the near future, all your clothes will become smart. These smart clothes will outperform conventional passive garments, thanks to their miniaturized electronic circuits and sensors, which will allow you to seamlessly communicate with your phone, computer, car and other machines.
This smart clothing will not only make you more productive but also check on your health status and even call for help if you suffer an accident. The reason why this smart clothing is not all over your closet yet is that the fabrication of this smart clothing is quite challenging, as clothes need to be periodically washed and electronics despise water.
Purdue engineers have developed a new spray/sewing method to transform any conventional cloth items into battery-free wearables that can be cleaned in the washing machine.
"By spray-coating smart clothes with highly hydrophobic molecules, we are able to render them repellent to water, oil and mud," said Ramses Martinez, an assistant professor in Purdue's School of Industrial Engineering and in the Weldon School of Biomedical Engineering in Purdue's College of Engineering. "These smart clothes are almost impossible to stain and can be used underwater and washed in conventional washing machines without damaging the electronic components sewn on their surface."
read the study at http://dx.doi.org/10.1016/j.nanoen.2021.106155
read the original and unedited version of the article at https://phys.org/news/2021-06-wearables-future-washable-smart-powered.html
Stephanie Chavarria's curator insight,
November 12, 2021 2:40 PM
this is interesting to buy it is beneficial for other's and for people that really need it for example this website helps and gives goods reasons why we should be this type of product it says it's foe your health and good and not needs.
Avidity Medical Design Consultants, LLC's comment,
January 28, 12:03 AM
Smart clothing is an excellent concept, especially being able to check on a person's health and call for help in the event of an accident. Thanks for sharing.
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Scientists propose a modeling framework that could predict how antibiotic resistance will evolve in response to different drug combinations.
A new framework may suggest which drug combinations would speed up, slow down, or even reverse antibiotic resistance.
The research could help doctors optimize the choice, timing, dose, and sequence of antibiotics used to treat common infections in order to help halt the growing threat of antibiotic resistance to modern medicine.
“Drug combinations are a particularly promising approach for slowing resistance, but the evolutionary impacts of combination therapy remain difficult to predict, especially in a clinical setting,” says Erida Gjini, a researcher at the University of Lisbon, Portugal, and first author of the paper in eLife.
read more at https://www.futurity.org/antibiotic-resistance-drug-combinations-2605182-2/
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Silicone wristbands are just as good as traditional testing methods at detecting chemicals in the air that can be harmful during pregnancy.
Inexpensive, convenient silicone wristbands can measure exposure to a class of chemicals that can be harmful during pregnancy, researchers report.
The researchers found that the wristbands, when used as passive samplers, have the ability to bind smaller molecular weight semi-volatile polycyclic aromatic hydrocarbons (PAHs) in a similar pattern as active sampling. PAHs are a class of chemicals that occur naturally in coal, crude oil, and gasoline and are produced when coal, oil, gas, wood, garbage, and tobacco are burned.
The use of wristbands is appealing because it is inexpensive and easy to wear,”
access the study at https://doi.org/10.1038/s41370-021-00348-y
read the original unedited article at https://www.futurity.org/silicone-wristbands-polycyclic-aromatic-hydrocarbons-chemicals-pregnancy-2602202/
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While the US debates the value of the modality, a South Korean survey finds that patients like to use audio-only telehealth platforms to connect with their care provider - but their providers aren’t so sure.
While telehealth is not legally allowed in South Korea, the Ministry of Health and Welfare temporarily permitted its use due to hospital closures at the start of the pandemic.
Between February 24 and March 7, 2020, 6,840 patients used audio-based telehealth.
Researchers sent surveys to patients and providers alike to gauge their satisfaction with the telehealth platform, which includes landline telephones and online services without video. They asked questions about ease-of-use, interaction quality, reliability, satisfaction, and future use.
Around 87 % of patients reported that they were satisfied with their provider interaction and felt they could effectively express their feelings during an audio-only telehealth visit.
Most patients (87.1 %) also responded that their telehealth visit was just as reliable as an in-person visit would have been.
Meanwhile, the providers’ opinions differed drastically.
Less than 10 % of doctors and nurses were satisfied with their ability to interact with patients through an audio-only telehealth visit compared to in-person visits (7.3 % and 9 %, respectively).
Only 14 % of providers felt that the visits were as reliable as an in-person visit.
Patients and providers also had differing opinions on the convenience of telehealth.
Nearly 80 % of patients were satisfied with the convenience of telehealth and found it easy to use.
Providers were not as satisfied, with only 38.2 % of doctors and 30 percent of nurses reporting that they found telehealth convenient.
Overall, providers felt the negatives outweighed the positives for audio-only telehealth. While 85.8 % of the doctors and nurses agreed that telehealth is appropriate for emergency situations such as a pandemic, only 27.7 said it would be appropriate at all times.
In contrast, 40 % of the doctors and nurses surveyed said telehealth would be appropriate if it involved an audio-visual platform, saying it would be easier to fully examine and diagnose a patient’s condition.
read more at https://mhealthintelligence.com/news/audio-only-telehealth-has-its-fans-patients-and-its-critics-providers
nrip's insight:
Its the age old conundrum plaguing digital health. Who is the benefactor of the solutions, the tools, the technologies? The doctor or the patient ?
The significant gap in the perceived value of tools and technologies has always existed within healthcare, which is why it has become critical for digital health firms to realize how to look at value provision to all those involved/actors/stakeholders/users/users or users
that will also determine how big a role telehealth plays post the pandemic.
Blue's curator insight,
July 24, 2021 7:44 PM
The idea of being able to literally have a check-up has jumped to the forefront in terms of convenience. While some believe Audio-only is a level of impersonal (providers), most whom needed to use the service (patients) believed it was very effect in terms of being diagnosed. Practitioners of the medical arts feel that this innovative idea can be expanded for a more accurate interaction with patients by also integrating visual/video with this program. With the rapid expansion of tech, it may not be long before you only need to actually go to the hospital when in need of critical care. This would lead to hospitals being able to take in more patients that actually need to be there vs ones taking up space because of the "convenience" of being closer physically. The one drawback I foresee may be the fact of availability of your actual doctor, though it could be be supplemented by other professionals in the field.
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Healthcare data is the number one target for cybercriminals and is 10 times more valuable than credit card data alone.
During the "Are your Medical Devices Cybersecure?" webinar on 14 July, moderator Andrew Pearce, Senior Digital Health Strategist of Analytics at HIMSS spoke with two subject matter experts on cybersecurity trends in healthcare, as they shared their recommendations on identifying and addressing gaps.
Contextualising the imminent threat of cybersecurity in healthcare, Richard Staynings, Chief Security Strategist of Cylera said, "These changes (in healthcare) have led to the emergence of a gap between advances in digital maturity and advances in security maturity, as digital transformation outpaces the industry’s ability to secure new technology."
Staynings pointed out that most healthcare providers might have "at best a poor inventory of IoT assets'', with few understanding the associated risks. He said that this creates “massive gaps in security risk management just waiting to be exploited".
Adding that providers cannot risk-assess what they do not know about, he shared that the industry needs better tools and processes to identify and assess growing IoT "connected" assets.
read more at https://www.healthcareitnews.com/news/apac/examining-cybersecurity-our-medical-health-devices
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The distribution of fat in the body can influence a person's risk of developing various diseases. The commonly used measure of body mass index (BMI) mostly reflects fat accumulation under the skin, rather than around the internal organs. In particular, there are suggestions that fat accumulation around the heart may be a predictor of heart disease, and has been linked to a range of conditions, including atrial fibrillation, diabetes, and coronary artery disease.
A team led by researchers from Queen Mary University of London has developed a new artificial intelligence (AI) tool that is able to automatically measure the amount of fat around the heart from MRI scan images.
Using the new tool, the team was able to show that a larger amount of fat around the heart is associated with significantly greater odds of diabetes, independent of a person's age, sex, and body mass index.
The research team invented an AI tool that can be applied to standard heart MRI scans to obtain a measure of the fat around the heart automatically and quickly, in under three seconds. This tool can be used by future researchers to discover more about the links between the fat around the heart and disease risk, but also potentially in the future, as part of a patient's standard care in hospital.
The research team tested the AI algorithm's ability to interpret images from heart MRI scans of more than 45,000 people, including participants in the UK Biobank, a database of health information from over half a million participants from across the UK.
The team found that the AI tool was accurately able to determine the amount of fat around the heart in those images, and it was also able to calculate a patient's risk of diabetes
read the research published at https://www.frontiersin.org/articles/10.3389/fcvm.2021.677574/full
read more at https://www.sciencedaily.com/releases/2021/07/210707112427.htm
also at the QMUL website https://www.qmul.ac.uk/media/news/2021/smd/ai-predicts-diabetes-risk-by-measuring-fat-around-the-heart-.html
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Until recently, clinicians didn’t have good tools for personalized genetic analysis.
But that’s changing, thanks to quantitative biology. The discipline merges mathematical, statistical, and computational methods to study living organisms.
Quantitative biologists develop algorithms that chew through big datasets and try to make sense of them. In case of rare genetic disorders, that means analyzing loads of data from multiple patients to understand how their genes work in tandem with each other.
Researchers hope to give clinicians a peek at what their patients’ genes are doing, helping devise personalized therapies.
In recent years, DNA-sequencing technologies have matured to the point where a smart algorithm can parse genetic data from multiple patients and their families—and find tale-telling trends much faster than experiments on rodents can
read the entire post at https://nautil.us/issue/102/hidden-truths/data-crunchers-to-the-rescue
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Electronic health records are overloading outpatient docs with info in "disparate files and folders rather than presenting comprehensive, actionable data in a context that gives meaning," say researchers in a VA-funded study
A primary care physician may care for 2,500 or more patients in a given year, and many of their patient encounters may last only 20 minutes – much of which is often spent at a computer with a back turned to the patient.
It's become a truism by now that electronic health records are often viewed askance by primary care docs, many of whom see them as detrimental to the patient encounter.
A new report from U.S. Department of Veterans Affairs, Regenstrief Institute and Indiana University details just how outpatient EHRs are often failing the physicians who use them.
Why it matters
EHRs "are not rising to the challenges faced by primary care physicians because EHRs have not been designed or tailored to their specific needs,"
The report draws on eight years of close study of EHR use patterns to argue for wider acceptance of "human factor approach for the design or redesign of EHR user interfaces."
many EHRs as currently configured make it too difficult for primary care docs to do their job in a streamlined and efficacious manner
– requiring navigation through multiple screens and tabs to find basic information, increasing redundancy and decreasing efficiency.
Something as simple as auto-save – a default capability for most online shopping, for instance – is missing from many EHR systems.
Roots of the problem
The study traces the roots of the challenge to the fact that many EHRs were initially designed for specialists and hospitals – without much attention to the specific needs of primary care physicians.
For Primary care physicians, effective decision-making is grounded in perception and comprehension of a patient's dynamic situation."
For example, they note, an outpatient doc's choice to stop a patient's use of a particular medication will usually be informed by trends in that patient's blood pressure or cholesterol numbers, or other medications taken over the course of a month – all holistic information with implications for the patient's future health trajectory, but data that isn't always readily seen on a single EHR screen.
Technology needs to adapt to humans' needs, abilities, and limitations in healthcare delivery as it has in other domains.
EHRs should be redesigned to improve situational awareness for busy primary care physicians and support their tasks including reviewing patient information, care coordination, and shared decision-making."
read more at https://www.healthcareitnews.com/news/regenstrief-study-shows-ehrs-underperforming-primary-care
nrip's insight:
An 8 year old study in a space where there is change every few months does make me wonder. I know clients make EMR vendors update a features every other quarter. So how did the study track that? Did it instead use the data from older period of time
That said, the crux of the study results -- EHRs "are not rising to the challenges faced by primary care physicians because EHRs have not been designed or tailored to their specific needs,"
Everyone agrees with this, and yet most vendors do little to change it. Most of the EMR's in the name of an upgrade get a makeover and look like the next generation of web solutions, with the same functionalities, same workflows, same mannerism in how they work. No good ...
For good software systems, the workflow must adapt and then enhance and move the users ahead.
Instead, in the last 16 years most EHRs are still happy figuring out how to adapt to workflows, and the newbies on the dock look like dashboard template from the coolest web design agency from Bucharest or Bangalore
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Once, the prospect of manipulating the human mind with brain implants and radio beams ignited public fears that curtailed this line of research for decades. But now there is a resurgence using even more advanced technology. Laser beams, ultrasound, electromagnetic pulses, mild alternating and direct current stimulation and other methods now allow access to, and manipulation of, electrical activity in the brain with far more sophistication than the needlelike electrodes Manuel Rodriguez Delgado stabbed into brains.
Billionaires Elon Musk of Tesla and Mark Zuckerberg of Facebook are leading the charge, pouring millions of dollars into developing brain-computer interface (BCI) technology. Musk says he wants to provide a “superintelligence layer” in the human brain to help protect us from artificial intelligence, and Zuckerberg reportedly wants users to upload their thoughts and emotions over the internet without the bother of typing.
But fact and fiction are easily blurred in these deliberations.
How does this technology actually work, and what is it capable of?
Today’s BCI devices work by analyzing data, in much the same way that Amazon tries to predict what book you might want next. Computers monitoring streams of electrical activity, picked up by a brain implant or a removable electrode cap, learn to recognize how the traffic pattern changes when a person makes an intended limb movement.
Advances in brain-computer interface technology are impressive, but we’re not close to anything resembling mind control.
read this excellent essay at https://www.quantamagazine.org/how-brain-computer-interface-technology-is-different-from-mind-control-20210517/
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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
read more at https://medicalxpress.com/news/2021-06-ai-app-hiv-accurately.html
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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The IMPACT (vIrtual-first Medical PrActice CollaboraTion) initiative, developed by the American Telemedicine Association and the Digital Medicine Society (DiMe), has unveiled a formal definition for virtual first care (V1C), along with some vignettes from providers who are only using virtual platforms to deliver emergency, respiratory, cardiac and sleep care.
As set forth on the IMPACT website, virtual first care is defined as “medical care for individuals or a community accessed through digital interactions where possible, guided by a clinician, and integrated into a person’s everyday life.”
The Boston-based initiative was borne out of the massive shift to telehealth during the coronavirus pandemic, and a resulting transition to hybrid care as COVID-19 eases off. In that landscape, some providers are thinking of either launching virtual-only care or transitioning their in-person services to virtual platforms.
“Virtual first care is digital health in practice,” IMPACT Co-Founder Don Jones, a former Qualcomm Life executive and former chief digital officer at the Scripps Research Translational Institute, said in the press release. “IMPACT uniquely convenes organizations from across the ecosystem that view virtual first care as their primary mission. Members of IMPACT are already demonstrating patient and provider satisfaction, as well as pathways to cost savings and improved outcomes.”
“With a clear definition for the field, we have paved the way for more fit-for-purpose reimbursement models and opportunities to demonstrate the value of virtual first in practice,” he added.
read more at https://mhealthintelligence.com/news/mhealth-collaborative-unveils-new-definition-resources-for-virtual-first-care
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Researchers have figured out a way to use images from a smartphone to identify potentially harmful bacteria on the skin and in the mouth.
A new method that uses smartphone-derived images can identify potentially harmful bacteria on the skin and in the mouth, research shows.
The approach can visually identify microbes on skin contributing to acne and slow wound healing, as well as bacteria in the oral cavity that can cause gingivitis and dental plaques.
Researchers combined a smartphone-case modification with image-processing methods to illuminate bacteria on images taken by a conventional smartphone camera. This approach yielded a relatively low-cost and quick method that could be used at home.
The team augmented a smartphone camera’s capabilities by attaching a small 3D-printed ring containing 10 LED black lights around a smartphone case’s camera opening. The researchers used the LED-augmented smartphone to take images of the oral cavity and skin on the face of two research subjects.
The LED lights ‘excite’ a class of bacteria-derived molecules called porphyrins, causing them to emit a red fluorescent signal that the smartphone camera can then pick up
Other components in the image—such as proteins or oily molecules our bodies produce, as well as skin, teeth, and gums—won’t glow red under LED. They’ll fluoresce in other colors.
The LED illumination gave the team enough visual information to computationally “convert” the RGB colors from the smartphone-derived images into other wavelengths in the visual spectrum. This generates a “pseudo-multispectral” image consisting of 15 different sections of the visual spectrum—rather than the three in the original RGB image.
Obtaining this visual information up front would have required expensive and cumbersome lights, rather than using the relatively inexpensive LED black lights
With their greater degree of visual discrimination, the pseudo-multispectral images clearly resolved porphyrin clusters on the skin and within the oral cavity. In addition, though they tailored this method to show porphyrin, researchers could modify the image-analysis pipeline to detect other bacterial signatures that also fluoresce under LED.
read the study at https://doi.org/10.1016/j.optlaseng.2021.106546
read the original unedited article at https://www.futurity.org/smartphone-images-skin-mouth-bacteria-2581642/
Richard Platt's curator insight,
June 18, 2021 12:54 PM
Researchers have figured out a way to use images from a smartphone to identify potentially harmful bacteria on the skin and in the mouth. A new method using smartphone-derived images can identify potentially harmful bacteria on the skin and in the mouth, research shows. The approach visually identifies microbes on the skin contributing to acne and slow wound healing, as well as bacteria in the oral cavity that can cause gingivitis and dental plaques. Researchers combined a smartphone-case modification with image-processing methods to illuminate bacteria on images taken by a conventional smartphone camera. This approach yielded a relatively low-cost and quick method that could be used at home. Augmenting a smartphone camera’s capabilities by attaching a small 3D-printed ring containing 10 LED black lights around a smartphone case’s camera opening. The researchers used the LED-augmented smartphone to take images of the oral cavity and skin on the face of two research subjects. The LED lights ‘excite’ a class of bacteria-derived molecules called porphyrins, causing them to emit a red fluorescent signal that the smartphone camera can then pick up.
Other components in the image—such as proteins or oily molecules our bodies produce, as well as skin, teeth, and gums—won’t glow red under LED. They’ll fluoresce in other colors.
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Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text.
Objective: This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats.
Methods: Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy.
Results: Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events.
Conclusions: Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems.
Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether.
The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus.
Such systems may aid future efforts to prevent and contain the spread of infectious diseases.
read the study at https://publichealth.jmir.org/2021/3/e26719
nrip's insight:
Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats. Using algorithms and/or learning models to extract travel related information from EHR's is not a novel concept but it has come into the spotlight(like most of digital health) in the past 18 months.
We should be adding short travel related questionnaires in patient intake forms going forward. The symptoms which trigger this sort of an intake form for a particular patient can change with time, month to month preferably, and be governed by a multi regional , multi national approach. What do you think?
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Pandemic SEIR and SEIRV modelling software and infrastructure for the Corona SARS-COV-2 COVID-19 disease with data from Johns-Hopkins-University CSSE, Robert Koch-Institute and vaccination data from Our World In Data.
The SARS-COV-2 pandemic has been affecting our lives for months. The effectiveness of measures against the pandemic can be tested and predicted by using epidemiological models. The Corona SEIR Workbench uses a SEIR model and combines a graphical output of the results with a simple parameter input for the model. Modelled data can be compared country by country with the SARS-COV-2 infection data of the Johns Hopkins University. Additionally, the R₀ values of the Robert Koch Institute can be displayed for Germany. Vaccination data is used from Our World In Data.
view on Github : https://github.com/logiclink/corona_seir_workbench
6 minutes pour en savoir plus !