Category: IT in Healthcare

Categories : IT in HealthcareTags :

Is AI a Better Doctors’ Diagnostic Resource than Traditional Ones?

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With hospitals already deploying artificial intelligence (AI) to improve patient care, a new study has found that using Chat GPT Plus does not significantly improve the accuracy of doctors’ diagnoses when compared with the use of usual resources. 

The study, from UVA Health’s Andrew S. Parsons, MD, MPH and colleagues, enlisted 50 physicians in family medicine, internal medicine and emergency medicine to put Chat GPT Plus to the test. Half were randomly assigned to use Chat GPT Plus to diagnose complex cases, while the other half relied on conventional methods such as medical reference sites (for example, UpToDate©) and Google. The researchers then compared the resulting diagnoses, finding that the accuracy across the two groups was similar.

That said, Chat GPT alone outperformed both groups, suggesting that it still holds promise for improving patient care. Physicians, however, will need more training and experience with the emerging technology to capitalise on its potential, the researchers conclude. 

For now, Chat GPT remains best used to augment, rather than replace, human physicians, the researchers say.

“Our study shows that AI alone can be an effective and powerful tool for diagnosis,” said Parsons, who oversees the teaching of clinical skills to medical students at the University of Virginia School of Medicine and co-leads the Clinical Reasoning Research Collaborative. “We were surprised to find that adding a human physician to the mix actually reduced diagnostic accuracy though improved efficiency. These results likely mean that we need formal training in how best to use AI.”

Chat GPT for Disease Diagnosis

Chatbots called “large language models” that produce human-like responses are growing in popularity, and they have shown impressive ability to take patient histories, communicate empathetically and even solve complex medical cases. But, for now, they still require the involvement of a human doctor. 

Parsons and his colleagues were eager to determine how the high-tech tool can be used most effectively, so they launched a randomized, controlled trial at three leading-edge hospitals – UVA Health, Stanford and Harvard’s Beth Israel Deaconess Medical Center.

The participating docs made diagnoses for “clinical vignettes” based on real-life patient-care cases. These case studies included details about patients’ histories, physical exams and lab test results. The researchers then scored the results and examined how quickly the two groups made their diagnoses. 

The median diagnostic accuracy for the docs using Chat GPT Plus was 76.3%, while the results for the physicians using conventional approaches was 73.7%. The Chat GPT group members reached their diagnoses slightly more quickly overall – 519 seconds compared with 565 seconds.

The researchers were surprised at how well Chat GPT Plus alone performed, with a median diagnostic accuracy of more than 92%. They say this may reflect the prompts used in the study, suggesting that physicians likely will benefit from training on how to use prompts effectively. Alternately, they say, healthcare organisations could purchase predefined prompts to implement in clinical workflow and documentation.

The researchers also caution that Chat GPT Plus likely would fare less well in real life, where many other aspects of clinical reasoning come into play – especially in determining downstream effects of diagnoses and treatment decisions. They’re urging additional studies to assess large language models’ abilities in those areas and are conducting a similar study on management decision-making. 

“As AI becomes more embedded in healthcare, it’s essential to understand how we can leverage these tools to improve patient care and the physician experience,” Parsons said. “This study suggests there is much work to be done in terms of optimising our partnership with AI in the clinical environment.”

Following up on this groundbreaking work, the four study sites have also launched a bicoastal AI evaluation network called ARiSE (AI Research and Science Evaluation) to further evaluate GenAI outputs in healthcare. Find out more information at the ARiSE website.

Source: University of Virginia Health System

Categories : Expert Opinion IT in HealthcareTags :

Opinion Piece: Business Continuity and Data Management – a Life-or-death Situation in Healthcare

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Photo by Nahel Abdul on Unsplash

By Hemant Harie, Group CTO at DMP SA / Gabsten Technologies

Ransomware attacks are a growing concern for healthcare facilities worldwide, with attacks wreaking havoc, including encrypting complex patient records, cancelling appointments, delaying life-saving surgeries, and even rerouting ambulances. The critical nature of healthcare services, combined with the sensitive personal and medical data they handle, makes hospitals and healthcare providers a prime target for cybercriminals.

When these systems are compromised, the impact is severe, jeopardising patient safety, disrupting service delivery and causing financial strain. It has become imperative for healthcare facilities to adopt more robust cybersecurity measures, including effective data management strategies as part of an overall business continuity approach. Partnering with an expert third-party service provider can assist healthcare facilities in ensuring continuity of care and business operations even in the face of cyberattacks.

Attractive targets with unique vulnerabilities

Digital transformation within the healthcare space, while vital for improving patient care,  can also introduce significant cybersecurity risks. Many hospitals and healthcare facilities are at different stages in their digital transformation , and legacy infrastructure is a common challenge, alongside immature cybersecurity posture and processes, making them more susceptible to attacks.

Cybercriminals often target these systems because they handle vast amounts of sensitive data, including Personal Health Information (PHI), which is highly valuable on the black market. In addition, these facilities often lack the dedicated IT and cybersecurity specialists they need to adequately defend against or recover from ransomware incidents.

The nature of information housed within healthcare and the consequences of a breach mean the stakes are high. This, combined with the fact that healthcare facilities are legally bound by regulations such as the Protection of Personal Information Act (PoPIA), Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR) to protect this information, means potential breaches could have catastrophic consequences.

The impact of ransomware on healthcare

Ransomware attacks can have devastating effects on healthcare organisations, leading to significant downtime that directly threatens patient care. Operations may be postponed or cancelled, disrupting treatment schedules and putting patients’ lives at risk. Additionally, the exposure of PHI can result in severe legal and ethical repercussions, including costly regulatory fines and lawsuits. Financial losses also extend to ransom payments, the cost of recovery, and reputational damage, all of which can linger long after the attack is resolved.

Moreover, a ransomware attack on one healthcare facility can damage the reputation of the entire network, as trust is critical in healthcare. Patients may be less likely to seek care from a hospital they perceive as insecure, leading to long-term financial and operational challenges.

Data management mitigates ransomware risks

To effectively combat ransomware, healthcare organisations must prioritise data management and cyber resilience. This starts with classifying and understanding the types of data being processed and stored , such as medical records, surgical files, and other critical patient information. Once this data is properly categorised, healthcare facilities can implement security controls that ensure the integrity and availability of the data.

Regular, automated backups stored offline are essential for mitigating ransomware risks. These backups allow facilities to restore their systems quickly without paying a ransom, minimising downtime and ensuring continuity of care. In addition to regular backups, hospitals should adopt advanced security measures such as multi-factor authentication, firewalls, and intrusion detection systems to safeguard against unauthorised access.

An expert partner enhances data management and security

Third-party service providers offer critical expertise and comprehensive solutions that healthcare organisations may lack in-house. These providers specialise in data management, backup, and disaster recovery, ensuring that hospitals have access to the latest technologies and best practices for defending against cyber threats. These experts bring valuable experience from handling multiple cyber incidents across various sectors, which can inform and improve the healthcare facility’s own data management practices. In addition to providing technical expertise, third-party providers can offer ongoing education, helping healthcare staff stay informed about the latest cybersecurity threats and recovery processes.

One of the key services offered by third-party providers is automated backup and disaster recovery solutions. These services typically include offsite storage, secure cloud options, and regular backups, all of which are vital for restoring data and reducing downtime during a ransomware attack. Offsite storage and cloud solutions also protect data from physical threats like floods or fires, adding an extra layer of security. In addition to traditional backup services, advanced tools can enhance data protection by providing early warning systems and simulating real-time production environments, which allow healthcare facilities to detect and respond to potential threats before they can cause damage. For example, scanning tools can identify which versions of data are clean and free from malware, enabling faster and more effective recovery.

Partnering with a third-party provider ensures that healthcare organisations have access to continuous support and the latest innovations in data protection. These providers not only help mitigate ransomware risks but also assist in compliance with industry regulations and offer scalable solutions to meet the growing needs of healthcare facilities.

As ransomware threats continue to rise, healthcare organisations must take proactive steps to safeguard their systems and protect patient data. Effective data management, including regular backups and disaster recovery plans, is essential for mitigating these risks. By partnering with third-party service providers, healthcare facilities can leverage specialised expertise and advanced technologies to enhance their cybersecurity defences and maintain continuity of care, even in the face of growing cyber threats.

Categories : Genetics IT in HealthcareTags :

Researchers Find Persistent Problems with AI-assisted Genomic Studies

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Photo by Sangharsh Lohakare on Unsplash

In a paper published in Nature Genetics, researchers are warning that artificial intelligence tools gaining popularity in the fields of genetics and medicine can lead to flawed conclusions about the connection between genes and physical characteristics, including risk factors for diseases like diabetes.

The faulty predictions are linked to researchers’ use of AI to assist genome-wide association studies, according to the University of Wisconsin–Madison researchers. Such studies scan through hundreds of thousands of genetic variations across many people to hunt for links between genes and physical traits. Of particular interest are possible connections between genetic variations and certain diseases.

Genetics’ link to disease not always straightforward

Genetics play a role in the development of many health conditions. While changes in some individual genes are directly connected to an increased risk for diseases like cystic fibrosis, the relationship between genetics and physical traits is often more complicated.

Genome-wide association studies have helped to untangle some of these complexities, often using large databases of individuals’ genetic profiles and health characteristics, such as the National Institutes of Health’s All of Us project and the UK Biobank. However, these databases are often missing data about health conditions that researchers are trying to study.

“Some characteristics are either very expensive or labour-intensive to measure, so you simply don’t have enough samples to make meaningful statistical conclusions about their association with genetics,” says Qiongshi Lu, an associate professor in the UW–Madison Department of Biostatistics and Medical Informatics and an expert on genome-wide association studies.

The risks of bridging data gaps with AI

Researchers are increasingly attempting to work around this problem by bridging data gaps with ever more sophisticated AI tools.

“It has become very popular in recent years to leverage advances in machine learning, so we now have these advanced machine-learning AI models that researchers use to predict complex traits and disease risks with even limited data,” Lu says.

Now, Lu and his colleagues have demonstrated the peril of relying on these models without also guarding against biases they may introduce. In their paper, they show that a common type of machine learning algorithm employed in genome-wide association studies can mistakenly link several genetic variations with an individual’s risk for developing Type 2 diabetes.

“The problem is if you trust the machine learning-predicted diabetes risk as the actual risk, you would think all those genetic variations are correlated with actual diabetes even though they aren’t,” says Lu.

These “false positives” are not limited to these specific variations and diabetes risk, Lu adds, but are a pervasive bias in AI-assisted studies.

New statistical method can reduce false positives

In addition to identifying the problem with overreliance on AI tools, Lu and his colleagues propose a statistical method that researchers can use to guarantee the reliability of their AI-assisted genome-wide association studies. The method helps remove bias that machine learning algorithms can introduce when they’re making inferences based on incomplete information.

“This new strategy is statistically optimal,” Lu says, noting that the team used it to better pinpoint genetic associations with individuals’ bone mineral density.

AI not the only problem with some genome-wide association studies

While the group’s proposed statistical method could help improve the accuracy of AI-assisted studies, Lu and his colleagues also recently identified problems with similar studies that fill data gaps with proxy information rather than algorithms.

In another recently published paper appearing in Nature Genetics, the researchers sound the alarm about studies that over-rely on proxy information in an attempt to establish connections between genetics and certain diseases.

For instance, large health databases like the UK Biobank have a ton of genetic information about large populations, but they don’t have very much data regarding the incidence of diseases that tend to crop up later in life, like most neurodegenerative diseases.

For Alzheimer’s disease specifically, some researchers have attempted to bridge that gap with proxy data gathered through family health history surveys, where individuals can report a parent’s Alzheimer’s diagnosis.

The UW–Madison team found that such proxy-information studies can produce “highly misleading genetic correlation” between Alzheimer’s risk and higher cognitive abilities.

“These days, genomic scientists routinely work with biobank datasets that have hundreds of thousands of individuals; however, as statistical power goes up, biases and the probability of errors are also amplified in these massive datasets,” says Lu. “Our group’s recent studies provide humbling examples and highlight the importance of statistical rigor in biobank-scale research studies.”

Source: University of Wisconsin-Madison

Categories : IT in Healthcare OphthalmologyTags :

AI Eye to Eye with Ophthalmologists in Diagnosing Corneal Infections

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Photo by Victor Freitas on Pexels

A Birmingham-led study has found that AI-powered models match ophthalmologists in diagnosing infectious keratitis, offering promise for global eye care improvements.

Infectious keratitis (IK) is a leading cause of corneal blindness worldwide. This new study finds that deep learning models showed similar levels of accuracy in identifying infection.

In a meta-analysis study published in eClinicalMedicine, Dr Darren Ting from the University of Birmingham conducted a review with a global team of researchers analysing 35 studies that utilised Deep Learning (DL) models to diagnose infectious keratitis.

AI models in the study matched the diagnostic accuracy of ophthalmologists, exhibiting a sensitivity of 89.2% and specificity of 93.2%, compared to ophthalmologists’ 82.2% sensitivity and 89.6% specificity.

The models in the study had analysed a combined total of more than 136 000 corneal images, and the authors say that the results further demonstrate the potential use of artificial intelligence in clinical settings.

Dr Darren Ting, Senior author of the study, Birmingham Health Partners (BHP) Fellow and Consultant Ophthalmologist, University of Birmingham said: “Our study shows that AI has the potential to provide fast, reliable diagnoses, which could revolutionise how we manage corneal infections globally. This is particularly promising for regions where access to specialist eye care is limited, and can help to reduce the burden of preventable blindness worldwide.”

The AI models also proved effective at differentiating between healthy eyes, infected corneas, and the various underlying causes of IK, such as bacterial or fungal infections.

While these results highlight the potential of DL in healthcare, the study’s authors emphasised the need for more diverse data and further external validation to increase the reliability of these models for clinical use.

Infectious keratitis, an inflammation of the cornea, affects millions, particularly in low- and middle-income countries where access to specialist eye care is limited. As AI technology continues to grow and play a pivotal role in medicine, it may soon become a key tool in preventing corneal blindness globally.

Source: University of Birmingham

Categories : IT in HealthcareTags :

AI Tools Can’t Revolutionise Public Health if They Stick to Old Patterns

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As tools powered by artificial intelligence increasingly make their way into health care, the latest research from UC Santa Cruz Politics Department doctoral candidate Lucia Vitale takes stock of the current landscape of promises and anxieties. 

Proponents of AI envision the technology helping to manage health care supply chains, monitor disease outbreaks, make diagnoses, interpret medical images, and even reduce equity gaps in access to care by compensating for healthcare worker shortages. But others are sounding the alarm about issues like privacy rights, racial and gender biases in models, lack of transparency in AI decision-making processes that could lead to patient care mistakes, and even the potential for insurance companies to use AI to discriminate against people with poor health. 

Which types of impacts these tools ultimately have will depend upon the manner in which they are developed and deployed. In a paper for the journal Social Science & Medicine, Vitale and her coauthor, University of British Columbia doctoral candidate Leah Shipton, conducted an extensive literature analysis of AI’s current trajectory in health care. They argue that AI is positioned to become the latest in a long line of technological advances that ultimately have limited impact because they engage in a “politics of avoidance” that diverts attention away from, or even worsens, more fundamental structural problems in global public health. 

For example, like many technological interventions of the past, most AI being developed for health focuses on treating disease, while ignoring the underlying determinants of health. Vitale and Shipton fear that the hype over unproven AI tools could distract from the urgent need to implement low-tech but evidence-based holistic interventions, like community health workers and harm reduction programs. 

“We have seen this pattern before,” Vitale said. “We keep investing in these tech silver bullets that fail to actually change public health because they’re not dealing with the deeply rooted political and social determinants of health, which can range from things like health policy priorities to access to healthy foods and a safe place to live.”

AI is also likely to continue or exacerbate patterns of harm and exploitation that have historically been common in the biopharmaceutical industry. One example discussed in the paper is that the ownership of and profit from AI is currently concentrated in high-income countries, while low- to middle-income countries with weak regulations may be targeted for data extraction or experimentation with the deployment of potentially risky new technologies. 

The paper also predicts that lax regulatory approaches to AI will continue the prioritization of intellectual property rights and industry incentives over equitable and affordable public access to new treatments and tools. And since corporate profit motives will continue to drive product development, AI companies are also likely to follow the health technology sector’s long-term trend of overlooking the needs of the world’s poorest people when deciding which issues to target for investment in research and development. 

However, Vitale and Shipton did identify a bright spot. AI could potentially break the mold and create a deeper impact by focusing on improving the health care system itself. AI could be used to allocate resources more efficiently across hospitals and for more effective patient triage. Diagnostic tools could improve the efficiency and expand the capabilities of general practitioners in small rural hospitals without specialists. AI could even provide some basic yet essential health services to fill labor and specialization gaps, like providing prenatal check-ups in areas with growing maternity care deserts. 

All of these applications could potentially result in more equitable access to care. But that result is far from guaranteed. Depending on how and where these technologies are deployed, they could either successfully backfill gaps in care where there are genuine health worker shortages or lead to unemployment or precarious gig work for existing health care workers. And unless the underlying causes of health care worker shortages are addressed – including burnout and “brain drain” to high-income countries – AI tools could end up providing diagnosis or outbreak detection that is ultimately not useful because communities still lack the capacity to respond. 

To maximise benefits and minimise harms, Vitale and Shipton argue that regulation must be put in place before AI expands further into the health sector. The right safeguards could help to divert AI from following harmful patterns of the past and instead chart a new path that ensures future projects will align with the public interest.

“With AI, we have an opportunity to correct our way of governing new technologies,” Shipton said. “But we need a clear agenda and framework for the ethical governance of AI health technologies through the World Health Organization, major public-private partnerships that fund and deliver health interventions, and countries like the United States, India, and China that host tech companies. Getting that implemented is going to require continued civil society advocacy.”

Source: University of California – Santa Cruz

Categories : IT in HealthcareTags :

CareFirst Clinches Top Prize at Stuff Magazine App Awards

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In a remarkable achievement, CareFirst, the innovative healthcare web app developed by First Care Solutions, has secured 1st place in the Health Section at the 2024 Stuff Awards. Held annually by Stuff Magazine, these prestigious awards recognise excellence in digital innovation and technological advancement.

CareFirst has quickly emerged as a game-changer in South Africa’s healthcare landscape, offering users:

  • Instant medical consultations with qualified doctors, available 24/7 – 365
  • AI-powered Vital Scanning: Advanced technology to monitor key health metrics including blood pressure, respiratory rate, and heart rate.
  • Comprehensive Medical Services: Convenient access to doctors’ consultations, prescriptions, sick notes, and referrals.

‘’I’m really proud of what our team has created. CareFirst is a platform that can really transform the way people access healthcare,’’ says Dr Steve Holt, Chief Executive Officer – at First Care Solutions.

CareFirst represents a shift towards more accessible, efficient and personalised care, promising a future where quality care is available to everyone, regardless of their location or time constraint and this award is a testament to that.

Categories : IT in HealthcareTags :

Applying AI to EHRs Ensures Better Outcomes and Insights

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Photo by Christina Morillo

This week the GIBS, (Gordon Institute of Business Science), held an on-campus Healthcare Industry Insights Conference aimed at healthcare professionals and others with an interest in this field to hear from experts providing insightful discussion and frank debate. 

The sessions were each themed to different topics such as Innovation for Sustainable Access and Quality Care, Building a Skilled Workforce, navigating Public-Private Partnerships and Addressing Social Determinants. 

The day ended with a focus on Digital Transformation and advances in medical device manufacturing, were discussed. 

Dilip Naran, Vice President of Product Architecture at CompuGroup Medical South Africa, (an internationally leading MedTech provider), has over 25 years of dedicated service to the South African healthcare market, and was asked to share his thoughts on the next generation of digital health. 

Naran has been actively involved in shaping both billing and clinical applications and has been a key player in the creation of cutting-edge cloud-based solutions that have revolutionised the way healthcare professionals operate in South Africa. 

Improving workflow processes

The discussion focused on the AI and Electronic Health Records (EHRs), and how by harnessing the power of AI, healthcare providers can unlock unprecedented insights, enhance patient care and drive operational efficiencies.

The topical subject began by reminding the audience that AI has already improved the EHR data management. By extracting valuable insights from clinical notes, automation of repetitive tasks, analysing data to identify patterns and facilitating the seamless integration of multiple data sources. AI advances in HER and medical devices have reshaped the doctor / patient healthcare journey. 

To continue this growth, AI powered tools must be implemented in EHRs to enable functionality that enhance the Dr/Patient journey. Some benefits of AI powered EHRs include: 

  • Effective Clinical Decision Support 
  • Intelligent Automation. This includes improvement in workflow by automating certain tasks 
  • Smart Medication management . Ai can alert HCP to potential drug interactions and adverse effects 
  • Predictive Analytics that are personalised based on patient history 

Adoption in South Africa

Whilst some of the AI technologies are not yet available in South Africa, CGM’s recently launched Autoscriber solution which uses AI technologies such as Natural Language Processing NLP and a Large Language Model (LLM) has enabled South African HCPs to use this solution to create structured notes which includes diagnoses ICD10 and SNOMED coding. This assists the HCP in populating their HER without having to physically capture information. 

At the moment the adoption rate of EHR in practices is around 30% in the private sector, with oncology leading the way. 

With collaboration between government, private and public sector, existing technologies can forecast disease outbreaks, identify high-risk patients and optimise resource allocation. 

Dilip Naran concluded by saying: “The use of AI technologies and processes can facilitate the meaningful use of data in EHRs and lead to better patient outcomes” 

Categories : IT in Healthcare NeurologyTags :

New Brain–computer Interface Allows Man with ALS to ‘Speak’ Again

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A new brain-computer interface (BCI) developed at UC Davis Health translates brain signals into speech with up to 97% accuracy – the most accurate system of its kind. The researchers implanted sensors in the brain of a man with severely impaired speech due to amyotrophic lateral sclerosis (ALS). The man was able to communicate his intended speech within minutes of activating the system.

A study about this work was published in the New England Journal of Medicine.

ALS, also known as Lou Gehrig’s disease, affects the nerve cells that control movement throughout the body. The disease leads to a gradual loss of the ability to stand, walk and use one’s hands. It can also cause a person to lose control of the muscles used to speak, leading to a loss of understandable speech.

The new technology is being developed to restore communication for people who can’t speak due to paralysis or neurological conditions like ALS. It can interpret brain signals when the user tries to speak and turns them into text that is ‘spoken’ aloud by the computer.

“Our BCI technology helped a man with paralysis to communicate with friends, families and caregivers,” said UC Davis neurosurgeon David Brandman. “Our paper demonstrates the most accurate speech neuroprosthesis (device) ever reported.”

Brandman is the co-principal investigator and co-senior author of this study. He is an assistant professor in the UC Davis Department of Neurological Surgery and co-director of the UC Davis Neuroprosthetics Lab.

The new BCI breaks the communication barrier

When someone tries to speak, the new BCI device transforms their brain activity into text on a computer screen. The computer can then read the text out loud.

To develop the system, the team enrolled Casey Harrell, a 45-year-old man with ALS, in the BrainGate clinical trial. At the time of his enrolment, Harrell had weakness in his arms and legs (tetraparesis). His speech was very hard to understand (dysarthria) and required others to help interpret for him.

In July 2023, Brandman implanted the investigational BCI device. He placed four microelectrode arrays into the left precentral gyrus, a brain region responsible for coordinating speech. The arrays are designed to record the brain activity from 256 cortical electrodes.

“We’re really detecting their attempt to move their muscles and talk,” explained neuroscientist Sergey Stavisky. Stavisky is an assistant professor in the Department of Neurological Surgery. He is the co-director of the UC Davis Neuroprosthetics Lab and co-principal investigator of the study. “We are recording from the part of the brain that’s trying to send these commands to the muscles. And we are basically listening into that, and we’re translating those patterns of brain activity into a phoneme – like a syllable or the unit of speech – and then the words they’re trying to say.”

Casey Harrell with his personal assistant Emma Alaimo and UC Davis neuroscientist Sergey Stavisky

Faster training, better results

Despite recent advances in BCI technology, efforts to enable communication have been slow and prone to errors. This is because the machine-learning programs that interpreted brain signals required a large amount of time and data to perform.

“Previous speech BCI systems had frequent word errors. This made it difficult for the user to be understood consistently and was a barrier to communication,” Brandman explained. “Our objective was to develop a system that empowered someone to be understood whenever they wanted to speak.”

Harrell used the system in both prompted and spontaneous conversational settings. In both cases, speech decoding happened in real time, with continuous system updates to keep it working accurately.

The decoded words were shown on a screen. Amazingly, they were read aloud in a voice that sounded like Harrell’s before he had ALS. The voice was composed using software trained with existing audio samples of his pre-ALS voice.

At the first speech data training session, the system took 30 minutes to achieve 99.6% word accuracy with a 50-word vocabulary.

“The first time we tried the system, he cried with joy as the words he was trying to say correctly appeared on-screen. We all did,” Stavisky said.

In the second session, the size of the potential vocabulary increased to 125 000 words. With just an additional 1.4 hours of training data, the BCI achieved a 90.2% word accuracy with this greatly expanded vocabulary. After continued data collection, the BCI has maintained 97.5% accuracy.

“At this point, we can decode what Casey is trying to say correctly about 97% of the time, which is better than many commercially available smartphone applications that try to interpret a person’s voice,” Brandman said. “This technology is transformative because it provides hope for people who want to speak but can’t. I hope that technology like this speech BCI will help future patients speak with their family and friends.”

The study reports on 84 data collection sessions over 32 weeks. In total, Harrell used the speech BCI in self-paced conversations for over 248 hours to communicate in person and over video chat.

“Not being able to communicate is so frustrating and demoralising. It is like you are trapped,” Harrell said. “Something like this technology will help people back into life and society.”

“It has been immensely rewarding to see Casey regain his ability to speak with his family and friends through this technology,” said the study’s lead author, Nicholas Card. Card is a postdoctoral scholar in the UC Davis Department of Neurological Surgery.

“Casey and our other BrainGate participants are truly extraordinary. They deserve tremendous credit for joining these early clinical trials. They do this not because they’re hoping to gain any personal benefit, but to help us develop a system that will restore communication and mobility for other people with paralysis,” said co-author and BrainGate trial sponsor-investigator Leigh Hochberg. Hochberg is a neurologist and neuroscientist at Massachusetts General Hospital, Brown University and the VA Providence Healthcare System.

Brandman is the site-responsible principal investigator of the BrainGate2 clinical trial. The trial is enrolling participants. To learn more about the study, visit braingate.org or contact braingate@ucdavis.edu.

Source: UC Davis Health

Categories : IT in Healthcare Medical Research & TechnologyTags :

New, Inexpensive Medical Computers that Run on Air

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Closeup of the pneumatic logic sensing device. (William Grover/UCR)

Medical engineers have developed a new, air-powered computer sets off alarms when certain medical devices fail. The invention is a more reliable and lower-cost way to help prevent blood clots and strokes – all without electronic sensors. 

Described in a paper in the journal Device, the computer not only runs on air, but also uses air to issue warnings. It immediately blows a whistle when it detects a problem with the lifesaving compression machine it is designed to monitor.

Intermittent pneumatic compression (IPC) devices are pneumatic leg sleeves that periodically squeeze a patient’s legs to increase blood flow. This prevents clots that lead to blocked blood vessels, strokes, or death. Typically, these machines are powered and monitored by electronics.

“IPC devices can save lives, but all the electronics in them make them expensive. So, we wanted to develop a pneumatic device that gets rid of some of the electronics, to make these devices cheaper and safer,” said William Grover, associate professor of bioengineering at UC Riverside and corresponding paper author.

Pneumatics move compressed air from place to place. Emergency brakes on freight trains operate this way, as do bicycle pumps, tire pressure gauges, respirators, and IPC devices. It made sense to Grover and his colleagues to use one pneumatic logic device to control another and make it safer.

This type of device operates in a similar way to electronic circuits, by making parity bit calculations. “Let’s say I want to send a message in ones and zeroes, like 1-0-1, three bits,” Grover said. “Decades ago, people realized they could send these three bits with one additional piece of information to make sure the recipient got the right message.” 

That extra piece of information is called a parity bit. The bit is a number – 1 if the message contains an odd number of ones, and 0 if the message contains an even number of ones. Should the number one appear at the end of a message with an even number of bits, then it is clear the message was flawed. Many electronic computers send messages this way. 

An air-powered computer uses differences in air pressure flowing through 21 tiny valves to count the number of ones and zeroes. If no error in counting has occurred, then the whistle doesn’t blow. 

If it does blow, that’s a sign the machine requires repairs. Grover and his students, in a video demonstrating the air computer, are shown damaging an IPC device with a knife, rendering it unusable. Seconds later, the whistle blows.

“This device is about the size of a box of matches. It replaces a handful of sensors as well as a computer,” Grover said. “So, we can reduce costs while still detecting problems in a device. And it could also be used in high humidity or high temperature environments that aren’t ideal for electronics.”

The IPC device monitoring is only one application for air computing. For his next project, Grover would like to design a device that could eliminate the need for a job that kills people every year: moving around grain at the top of tall silos. 

Tall buildings full of corn or wheat, grain silos are a common sight in the Midwest. Often times, a human has to go inside with a shovel to break up the grains and even out the piles inside. 

“A remarkable number of deaths occur because the grain shifts and the person gets trapped. A robot could do this job instead of a person. However, these silos are explosive, and a single electric spark could blow a silo apart, so an electronic robot may not be the best choice,” Grover said. “I want to make an air-powered robot that could work in this explosive environment, not generate any sparks, and take humans out of danger.” 

Air-powered computing is an idea that has been around for at least a century. People used to make air-powered pianos that could play music from punched rolls of paper. After the rise of modern computing, engineers lost interest in pneumatic circuits.

“Once a new technology becomes dominant, we lose awareness of other solutions to problems,” Grover said. “One thing I like about this research is that it can show the world that there are situations today when 100-plus-year-old ideas can still be useful.”

Source: University of California Riverside

Categories : IT in HealthcareTags :

AI in Healthcare: From Hype to a Game Changing Reality  

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By Margot Brews, Head Health Risk Management Strategy at Momentum Health Solutions

The healthcare sector has been on the cusp of substantial reform for quite some time. However, the introduction and application of artificial intelligence (AI) across various healthcare disciplines will surely stand out as one of the most revolutionary eras in the industry.

This rapidly evolving field has been lauded as the key to unlocking greater quality in healthcare services, introducing more efficient protocols and treatment pathways, as well as considerably increasing access to healthcare across all demographics.

When we place this in a local context, taking into consideration that steps to implement the NHI are already in motion, AI will be critical in helping implement specific elements, such as public health interventions. If we are to refer to the Covid-19 pandemic and its immense scale, AI was leveraged across various countries around the globe to predict the spread of the virus. In doing so, this allowed governments to implement protocols to curb its spread, as well as provide citizens with critical information in an effort to decrease its proliferation.

Looking at the medical schemes sector in South Africa, the industry aims to ultimately improve health outcomes for members and in doing so, encourage and maintain a better quality of life. AI has assisted Momentum Health Solutions in evaluating the delivery of healthcare in the future with clear goals that include increasing access to quality healthcare, and utilising the unmatched innovation that AI offers in assessing member profiles more comprehensively. This is to ensure that we are not only providing a service, but actually understanding in the broadest terms possible what type of care a member requires and partnering with them on that journey.

An example of this is closely analysing commonalities within a member’s treatment pathway. When we review clinical data such as doctors’ consultations, the discipline of the doctor and their particular field of expertise, along with the medication prescribed, we can more timeously start to see patterns developing. This indicates and therefore informs us that the member may have a more serious illness or chronic disease that requires clinical support on a more extensive scale, which we can then discuss with the member and facilitate.

To ensure we are providing tailored healthcare solutions that steer away from offering members generic benefits, we have partnered with Amazon Web Services (AWS), which provides the most comprehensive services, tools, and resources in artificial intelligence today. Through this partnership we have been able to provide members with unique services and individualised care that ultimately ensures their healthcare is a priority.

While AI is indeed key to creating a more efficient healthcare system, ethical considerations remain a concern for many when evaluating factors such as the protection and privacy of data and its ownership, as well as the accuracy of its outputs and conclusions. Having said this, risk mitigation protocols have been implemented to ensure that personal data is protected, and ethical standards are maintained at the highest level.

AI is certainly the most effective solution in the 21st century when investigating ways to solve the ongoing healthcare crisis, particularly in South Africa, where immense disparity exists between the public and private sectors. Leveraging AI, both from a medical scheme provider perspective and more broadly, will not only empower current and future workforces within the sector, but will also create greater opportunity for improved healthcare services that can be sustained. When implemented and utilised for the benefit of all, AI has the potential to be South Africa’s healthcare redeemer.