Between 1975 and 2020, almost 6 million deaths from common cancer were averted through prevention, screening and improved treatments.

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At a Glance

• Most deaths avoided from common cancers between 1975 and 2020 could be traced to prevention and screening efforts.
• Improved treatments, however, accounted for most of the reduction in deaths from breast cancer.
• Understanding which strategies have been most effective in reducing cancer deaths can help guide their future use nationwide.

Over the last five decades, the number of deaths from many cancer types has dropped substantially in the U.S. A range of factors have played a role in this decrease. These include better treatments, prevention efforts such as smoking cessation, and nationwide screening campaigns to catch cancers—or precancerous growths—early.

It hasn’t been clear how much each of these factors has contributed to the overall drop in deaths observed since 1975. Understanding the largest contributors could help focus further research and promotion of the most effective strategies.

A research team led by Drs. Katrina Goddard and Philip Castle from NIH set out to better understand the contributions of prevention, screening, and treatment to the observed drop in cancer mortality. They used models developed by the Cancer Intervention and Surveillance Modeling Network (CISNET) through 2020.

The models examined deaths from five of the most common types of cancer: breast, cervical, colorectal, lung, and prostate cancer. They projected how cancer mortality rates changed under four different scenarios. These scenarios were: no advances in prevention, screening, or treatment; prevention and screening only; treatment advances only; and advances in prevention, screening, and treatment. Results were published on December 5, 2024, inJAMA Oncology.

The researchers estimated that, between 1975 and 2020, almost 6 million deaths from these cancers were averted through a combination of prevention, screening, and improved treatments. Together, prevention and screening averted about 4.75 million, or 80%, of the deaths.

The contributions of prevention and screening differed between cancer types. For example, 98% of lung cancer deaths were averted by efforts to help people stop smoking. Nearly all deaths averted from cervical cancer were by screening and the removal of pre-cancerous growths. In contrast, only 25% of breast cancer deaths averted were due to regular mammography. Rather, improved treatments were responsible for most of these averted deaths.

“Although many people may believe that treatment advances are the major driver of reductions in mortality from these five cancers combined, the surprise here is how much prevention and screening contribute to reductions in mortality,” Goddard says. “Eight out of 10 deaths from these five cancers that were averted over the past 45 years were due to advances in prevention and screening.”

“The impact of these interventions cannot be understated because they not only saved lives, but they also kept healthy people healthy,” Castle notes. He adds, “The success of these interventions also emphasizes the need for continued research to discover ways to prevent and screen for other cancers.”

More recent prevention and screening strategies, such as lung cancer screening and HPV vaccination to prevent cervical and other HPV-related cancers, were not in wide use during the study period and could further reduce cancer death rates.

The study did not address the potential harms of interventions, such as false-positive results and overdiagnosis during screening. It also didn’t measure other outcomes, such as quality of life. Further work will be needed to fine-tune and personalize screening recommendations.

This research summary was published by the National Institutes of Health onJanuary 14, 2025.

• #breast cancer
• #cancer screening
• #cancer treatment
• #cervical cancer
• #colorectal cancer
• #lung cancer
• #mammograms
• #mortality
• #prostate cancer
• #smoking

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Pitavastatin was associated with reduced artery plaque volume and changes in its composition.

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Taking a statin can help stabilize coronary artery plaque, reducing the risk that it will rupture and cause a heart attack or stroke, according to the latest findings from the REPRIEVE trial, published in JAMA Cardiology.

As people with HIV live longer thanks to effective antiretroviral treatment, cardiovascular disease (CVD) has become a leading cause of illness and death. Studies suggest that HIV-positive people have about a twofold higher risk for CVD compared with their HIV-negative peers, and people with HIV tend to experience cardiovascular complications at younger ages.

Results from the large REPRIEVE trial, presented at the 2023 International AIDS Society Conference on HIV Science and published in The New England Journal of Medicine, showed that middle-aged HIV-positive people with low to moderate CVD risk scores who took daily pitavastatin (Livalo) lowered their risk for heart attacks, strokes and other major cardiovascular events by 35%.

Based on these findings, federal treatment guidelines were updated in February 2024 to recommend statins for HIV-positive people ages 40 to 75 with low to intermediate 10-year CVD risk. People in this age group with high CVD risk and those who have already had a heart attack or related events have long been advised to take statins regardless of their HIV status.

The main effect of statins is lowering low-density lipoprotein (LDL) cholesterol, which contributes to the buildup of plaque in arteries, known as atherosclerosis. Unstable plaque can break off, causing clots that block arteries supplying the heart, leading to heart attack, or lodge in small blood vessels in the brain, leading to stroke. But statins also have secondary effects, including reduced inflammation. REPRIEVE showed that the benefits of pitavastatin were much greater than predicted by lowering LDL alone.

A REPRIEVE substudy found that pitavastatin reduced the volume of noncalcified plaque in arteries. Steven Grinspoon, MD, of Massachusetts General Hospital, and colleagues performed a post hoc analysis of the data to shed more light on proteins and gene pathways associated with plaque changes in people with HIV.

This analysis included 558 REPRIEVE participants. About 80% were men, and the mean age was 51 years. At baseline and after two years of follow-up, they underwent coronary computed tomography angiography scans, proteonomic analysis of 255 immune, inflammatory, cardiovascular and cardiometabolic proteins, and transcriptomic analysis of relevant gene expression.

People randomly assigned to take pitavastatin had higher levels of procollagen C-endopeptidase enhancer 1 (PCOLCE), an enzyme that controls deposition of collagen—a component of plaque that helps determine its stability. Changes in PCOLCE levels were associated with a reduction in noncalcified plaque volume and altered composition, such as reduced lipid content, suggesting plaque stabilization. These changes were independent of changes in LDL cholesterol levels.

Statin use was also associated with smaller changes in several other proteins. Notably, there was a decrease in MBL2, a lectin involved in complement activation and inflammation. What’s more, transcriptomic analysis showed that pitavastatin users had enhanced collagen gene expression and more active collagen synthesis pathways.

“Results of this secondary analysis of the REPRIEVE randomized clinical trial suggest that PCOLCE may be associated with the atherosclerotic plaque stabilization effects of statins by promoting collagen deposition in the extracellular matrix transforming vulnerable plaque phenotypes to more stable coronary lesions,” the study authors concluded.

These findings are promising because more stable artery plaque is less likely to rupture and block blood flow.

“[W]e identified a novel pathway that may be associated with the protective effects of statin therapy on coronary plaque,” the researchers wrote. “By increasing the abundance of the rate-limiting enzyme of collagen deposition, PCOLCE may play a role in the atherosclerotic plaque stabilization effects of statins by transforming lipid-rich components of noncalcified plaque to more stable fibrous and calcified components. Further studies are now needed to assess potential targeting of collagen pathways for reduction of vulnerable coronary plaque phenotypes.”

Click here to learn more about HIV and heart health.

• #atherosclerosis
• #cardiovascular
• #heart attack
• #HIV
• #REPRIEVE
• #stroke

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An up-close look at how patients respond to the drug at different times and doses adds nuance to a recent finding.

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An extended course of Paxlovid appears to help some patients with long COVID, according to a case series by UC San Francisco researchers that suggests this treatment option holds promise for some of those struggling with debilitating symptoms.

These results are at odds with recent research that has failed to show the antiviral can alleviate persistent symptoms of the disease. The authors said more study is needed to find out which patients may benefit from the drug and how long it should be given.

The Centers for Disease Control and Prevention (CDC) estimates that 17.6 million Americans, or 1 in 13 adults, have long COVID. These individuals experience symptoms for months or even years after their initial COVID-19 infection that range from brain fog and headaches to respiratory and cardiovascular issues.

“We are about five years into the pandemic, and yet there are not yet any federally-approved treatments for long COVID,” saidAlison Cohen, PhD, MPH, an assistant professor of epidemiology and biostatistics at UCSF and first author of the paper. “This is not a silver bullet, but it may help a lot of people in a meaningful way.”

The study appears January 6 in the Nature publicationCommunications Medicine.

Interviews with patients reveal the drug helps some

In June, a randomized controlled clinical trial of a 15-day course of nirmatrelvir/ritonavir, the drug combination marketed as Paxlovid, concluded that it was safe, although it did not lessen long COVID symptoms. The researchers noted that further research may show benefits at different doses or for people with specific symptoms.

The UCSF team collaborated with long COVID patients, some of whom are members of the Patient-Led Research Collaborative, a group of people with long COVID and other associated chronic conditions who are also researchers. The researchers took a closer look at what happened when people took a longer course of Paxlovid to see if it might work in people with specific symptoms or at different times in their disease. They also examined the experiences of people who contracted different strains of SARS-CoV-2.

They found that five of the 13 patients in the study experienced sustained improvements in their symptoms. Others experienced temporary reprieves; and some had no improvement. Treatment lengths varied, but many took Paxlovid for 15 days.

For example, a 56-year-old man who developed long COVID at the start of the pandemic was plagued for more than two years with fatigue, headaches, photosensitivity, brain fog, exercise intolerance, elevated heart rate and joint pain. He took Paxlovid for five days in September of 2022 with no change in his symptoms. Three months later, he took it for 15 days and reported that both his cognitive and his physical symptoms improved.

And a 45-year-old woman who developed long COVID in January of 2022 experienced fatigue, breathing difficulties, chest pain, weight loss and migraines for months after her infection. A five-day course of Paxlovid in October of 2022 gave her a three-day reprieve from her symptoms. But when she took a 15-day course the following month, it did not affect her symptoms.

With more than 200 symptoms ascribed to the condition, long COVID has remained difficult to define, diagnose or treat. Researchers are still trying to understand the biological mechanisms that underlie the disorder; and UCSF has launched theworld’s first long COVID tissue bank.

“If we’ve learned one thing over the last four years, it’s that long COVID is complex, and figuring out why some people benefit so remarkably from antiviral treatment while others don’t is one of the most important questions for the field,” saidMichael Peluso, MD, MD, principal investigator of the UCSF long COVID research program and an infectious disease researcher in the UCSF School of Medicine, who is a co-author of the study. “We are going to need to embrace that complexity to get answers for the millions of people suffering from this condition.”

This storywas published by UCSF on January 6, 2024. It is republished with permission. 

• #COVID-19
• #long COVID
• #Paxlovid
• #SARS-CoV-2

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The Department of Health and Aged Care has released the Aged Care Data and Digital Strategy and accompanying action plan.

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When able to access patient-specific medical data, an integrated chatbot was accurate, complete and as empathetic as human providers in its message replies – but also long-winded and more complex, according to research from NYU Langone.

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A new NYU Langone Health study shows that generative artificial intelligence compares favorably to provider responses when answering patient questions in electronic health record email baskets – and could help reduce the documentation burden for clinicians.

Led by researchers at the New York University Grossman School of Medicine, the study found that a genAI messaging tool not only drafted accurate responses to patient EHR queries, but its responses showed greater perceived empathy. 

“Our results suggest that chatbots could reduce the workload of care providers by enabling efficient and empathetic responses to patients’ concerns,” Dr. William Small, a clinical assistant professor in NYU Grossman Department of Medicine and the study’s lead author, said in an announcement Tuesday.

WHY IT MATTERS

It was common for physicians to receive more than 150 In Basket messages per day during the pandemic, representing a more than 30% annual increase in daily messages, according to Dr. Paul Testa, NYU Langone’s chief medical information officer.

NYU Langone has looked at how to address note bloat and other documentation burdens that are major sources of physician burnout. For this new study, medical researchers asked sixteen primary care physicians to rate 344 randomly assigned pairs of AI and human responses to patient messages on accuracy, relevance, completeness and tone.

The generative AI responses outperformed human providers in terms of understandability and tone by 9.5%, according to the researchers in the Large Language Model–Based Responses to Patients’ In-Basket Messages report, published by JAMA.

For the blinded study, the participating physicians indicated if they would use the AI response as a first draft, or have to start over. They did not know which responses humans or the AI tool had composed.

As a result, the AI responses were more than 125% more likely to be considered empathetic and 62% more likely to use language that conveyed positivity and affiliation, the researchers said.

However, they were 38% longer and 31% were more likely to contain complex language.

“While humans responded to patient queries at a 6th grade level, AI was writing at an 8th grade level, according to a standard measure of readability called the Flesch Kincaid score,” the researchers said.

They also noted that future studies are needed to confirm whether private data specifically improved AI tool performance.

THE LARGER TREND

In 2023 NYU Langone licensed GPT4 to let physicians experiment using real patient data within a secure environment..

Also last year, the health research organization encouraged teams of clinicians, educators and researchers to work together to test large language models in a no-experience-necessary Generative AI Prompt-A-Thon in Healthcare.

With the event, which engaged 70 participants and more than 500 others through a live webinar, the health system aimed to use observations generated to inform internal generative AI capacity building. 

Later, researchers used AI to look at transcripts from 820 individuals who received psychotherapy during the initial wave of COVID-19 in the United States and compare them to healthcare workers. 

That AI tool could detect distress in overburdened hospital workers – those who discussed sleep deprivation or mood issues during therapy sessions were more likely to receive diagnoses of anxiety and depression.

ON THE RECORD

“We found that EHR-integrated AI chatbots that use patient-specific data can draft messages similar in quality to human providers,” Small said in a statement.

“With this physician approval in place, GenAI message quality will be equal in the near future in quality, communication style, and usability, to responses generated by humans,” said corresponding author Dr. Devin Mann, senior director of informatics innovation in NYU Langone Medical Center Information Technology. 

Andrea Fox is senior editor of Healthcare IT News.Email: [email protected]Healthcare IT News is a HIMSS Media publication.

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The new Hospital AI Readiness Index from CB Insights assessed 25 health systems for how they’re innovating and executing on their artificial intelligence projects.

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Everyone in healthcare is talking about artificial intelligence, and most health organizations are doing something with it, even in pilots or small-scale use cases. But some hospitals and health systems are truly leading the pack.

WHY IT MATTERSCB Insights this week launched the Hospital AI Readiness Index. The scorecard is billed as a data-driven ranking that tracks leading-edge health systems on how creative they’re being with their AI projects – and how well they’re doing in putting them to work for better clinical and operational processes and outcomes.

The index uses CB Insights data to assess hospitals on two factors: innovation and execution.

For the former, authors explored what they’re doing to develop or acquire new AI and automation tools and whether, for instance, they have specialized AI research departments. For the latter, they looked at AI deployments – where they’re happening and what sort of real-world successes they’re enabling.

Perhaps unsurprisingly, three heavy-hitting health systems top the list. Mayo Clinic is tops, according to CB Insights, followed by Intermountain and Cleveland Clinic.

Mayo was cited, for example, for having filed more than 50 patents for AI-enabled tools in cardiology, oncology and elsewhere, and for having invested in AI startups tackling an array of clinical and operational use cases.

Intermountain and Cleveland Clinic, meanwhile were recognized, respectively, for the former’s homegrown development of an automated clinical decision support system, and the latter’s many productive AI-centric business relationships, such as a “partnership with PathAI focused on leveraging pathology algorithms to enhance translational research and clinical care.”

Here’s the full list of 25 health systems, according to CB Insights (note a tie at #17), linking to just some of our coverage of their AI work.

1. Mayo Clinic

2. Intermountain Health

3. Cleveland Clinic 

4. Kaiser Permanente

5. Mount Sinai

6. Providence

7. HCA Healthcare

8. Mass General Brigham

9. Community Health Systems

10. Johns Hopkins

11. CommonSpirit Health

12. Ascension 

13. UPMC 

14. Banner Health

15. AdventHealth 

16. Stanford Health Care

17. Baylor Scott & White Health

17. UCLA Health 

18. Tenet Health

19. CHI Memorial 

20. Endeavor Health 

21. Trinity Health

22. Lifepoint Health 

23. Houston Methodist 

24. Scripps Health

THE LARGER TRENDHealthcare IT News has been reporting for years with interviews and in-depth case studies about how leading-edge health systems have been innovating their care delivery with artificial intelligence and machine learning.

Recent collection pages feature articles showing how blue-chip provider organizations are pioneering their AI efforts, and success stories where IT and innovation investments are paying off with clinical and financial ROI.

Interviewees have included Mayo Clinic Platform President Dr. John Halamka on the potential of generative AI; Cleveland Clinic Chief Analytics Officer Albert Marinez, who offered advice on democratizing AI innovation and upskilling talent; and Intermountain Chief Digital and Information Officer Craig Richardville with tips for evaluating and selecting vendors.

ON THE RECORD“We looked at the top private-sector health systems in the US (by hospital count) and ranked them based on how prepared they are to adapt to a rapidly evolving AI landscape across two key pillars: innovation and execution,” said CB Insights researchers in announcing the new Hospital AI Readiness Index.

“The innovation score measures a health system’s track record of developing or acquiring novel AI capabilities,” they explained. “This score is based on CB Insights data including patents, acquisitions, and deal-making activity. It also considers the presence of an AI-dedicated research center.”

The execution score, meanwhile, “measures a health system’s ability to bring AI-powered products and services into clinical practice as well as deploy AI internally across business and back-office functions. This score is based on CB Insights data including business relationships, product launch media mentions, and earnings transcripts.”

Mike Miliard is executive editor of Healthcare IT NewsEmail the writer: [email protected]Healthcare IT News is a HIMSS publication.

The HIMSS AI in Healthcare Forum is scheduled to take place Sept. 5-6 in Boston. Learn more and register.

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“By relying on the AI technology to convert information into discrete data elements in our Epic EHR, we have significantly reduced the amount of manual entry required by our staff,” an inpatient pharmacy operations supervisor reports.

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Carle Health, an eight-hospital health system based in Illinois, is dedicated to giving patients the care they need at every point in their healthcare journey. Through collaboration and engagement, the health system aims to ensure the highest level of care by applying accurate data and innovative, evidence-based best practices.

THE PROBLEM

Medication reconciliation is a critical process that ensures patients’ medication lists are accurate and up to date during transitions of care, such as hospital admissions, transfers and discharges.

“To ensure accurate reconciliation, our pharmacy technicians need to know not only the medications a patient has been prescribed but what they are actually taking,” said Jason Tipton, inpatient pharmacy operations supervisor at Urbana, Illinois-based Carle Foundation Hospital. He holds a doctorate in pharmacy. “To verify that prescriptions – especially for high-risk medications – were being picked up, the pharmacy staff was spending a lot of time making phone calls to pharmacies.

“During the pandemic, that became a problem because pharmacies were overwhelmed by staffing shortages and closures,” he continued. “Our staff could be on hold for up to an hour for a conversation that lasted less than a minute, which was an inefficient way to make sure a patient’s medication history was up to date.”

The result was an increased burden on staff as well as unwelcome interruptions for the retail staff at local pharmacies. It also caused delays in medication history collection that could affect patient care.

“We were pulling medication history from a standard industry feed, but the information was often incomplete because it lacked detailed prescription instructions (sigs) and failed to capture all medication fill data, especially from local or independent pharmacies,” he noted.

PROPOSAL

Carle Health researched options for importing more medication history data directly into its Epic electronic health record system so staff could spend less time manually gathering data while improving patient safety.

“Vendor DrFirst delivers medication history from local and independent pharmacies in addition to the big box retailers,” Tipton explained. “Instead of showing only that a medication was prescribed, the DrFirst data would include a sold date so we could see when the medication was dispensed. That’s important because it helps our pharmacists see gaps in medication adherence.”

For example, a physician may prescribe an anticoagulant and provide the patient with a coupon at discharge for one month free. The prescription shows up on the patient’s medication history list but doesn’t reflect that they stopped taking it after the first month because it wasn’t affordable.

“The new technology would also give us more data in terms of specific medication information and prescription instructions such as dose and frequency,” Tipton said. “That would allow for our medication history staff to work more efficiently and give our staff pharmacists more accurate dose information for recent dispenses, thus improving recommendations during admission order reviews.”

MEETING THE CHALLENGE

At the end of 2021, Carle Health integrated the medication history system from DrFirst into the Epic EHR, making more data available to doctors, nurses and pharmacists directly within the electronic record without any changes to their standard clinical workflows.

“Since going live, we’ve seen an evolution in the AI function of the system, which translates prescription sigs such as dose and frequency into our system’s standard terminology,” Tipton explained. “Over time, the AI has improved so that it can accurately import data into the appropriate fields with less manual entry by our staff.

“Pharmacy techs now get access to the most up-to-date medication history for the patient, including dose and other prescription instructions, while eliminating a large proportion of phone calls to pharmacies,” he continued.

The new system also became key to the transitional care pharmacy program, which the health system had implemented in 2019. At admission, it’s important to get medication history to physicians quickly, especially for high-risk medications like anticoagulants.

Getting these medication details right boils down to patient safety. At discharge, pharmacy staff reviews medication lists to identify barriers that could prevent the patient from adhering to the physician’s orders. This gives pharmacy a more in-depth role in medication management and enables the organization to notify the provider in advance of possible issues.

RESULTS

“According to reporting data from DrFirst, our clinical staff is consulting the external medication history on 99% of our high-risk patients,” Tipton reported. “Considering that reviewing external medication history is a critical part of the best possible medication history (BPMH) process, specifically for high-risk patients, this puts our practices at a high level that is significantly above DrFirst’s peer average of 77%.

“By relying on the AI technology to convert information into discrete data elements in our Epic EHR, we have significantly reduced the amount of manual entry required by our staff,” he added.

ADVICE FOR OTHERS

It’s a challenging time for both retail and hospital pharmacies, industry observers say.

“New technology gives us the opportunity to take a more in-depth role in medication management while creating more efficient processes for things like gathering medication history and reconciling medication lists,” Tipton concluded. “By reducing the need for numerous phone calls and manual-based processes, we can provide better care to our patients and unburden our retail partners from frequent interruptions that take them away from their customers.”

Follow Bill’s HIT coverage on LinkedIn: Bill SiwickiEmail him: [email protected]Healthcare IT News is a HIMSS Media publication.

The HIMSS AI in Healthcare Forum is scheduled to take place Sept. 5-6 in Boston. Learn more and register.

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It recently digitised its registration and patient management systems.

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Bangkok Hospital, one of Thailand’s largest private healthcare providers, has moved away from the paper and manual process of registering and managing patient flow in its headquarters.

AI technologies provided by its partner, local startup Agnos Health, now underpin its patient registration and management systems. 

HOW IT WORKS

As part of its enhanced patient flow system, the tertiary hospital implemented self-service kiosks that run on multiple AI, integrating various registration steps. 

It features AI face recognition to identify and confirm patient identities; an AI symptom checker that initially screens a patient’s history to refer them to an appropriate department or clinic; and robotic process automation for automatic insurance verification. The system can also acquire a patient’s electronic consent for collecting personal data and issue e-visit slips to track service status.

Meanwhile, Bangkok Hospital’s check-up department, the Health Design Center (HDC), also transitioned from manual patient queue distribution to a digital system. 

Its nurses can now monitor and manage patient queues, the status of which are delivered to patients via mobile channels and the department’s television screen. The system has an AI that tracks service status and recommends repurposing rooms to relieve foot traffic in certain areas. 

The HDC can now send electronic forms that patients can fill out, even before going to the hospital. An AI assesses these for health risks and then recommends appropriate check-up packages. 

The department’s AI co-pilot processes patients’ check-up lists, wait times, appointments, and clinical priorities to recommend an optimal examination order. 

WHY IT MATTERS

Since augmenting its registration system with AI, Bangkok Hospital saw front-end and back-end procedures reduced, including taking patients’ medical histories. Wait times were also cut as registration steps were lessened by 45% from the previous system. 

The hospital claims patient queues in the check-up department are also now evenly distributed. The HDC manages between 200 and 300 patients daily, each with varied check-up packages, procedures, and time. With AI, staff spends less time informing patients about their wait times and next steps and answering oft-repeated questions. Wait times are down by half from the previous manual system.

THE LARGER TREND

Private hospital operators in Thailand have increasingly invested in and adopted data and digital technologies in recent years as part of their digital transformation initiatives. 

Srisawan Hospital, a private tertiary hospital in Nakhon Sawan, a city north of Bangkok, recently sought the assistance of GE HealthCare and InterSystems to digitise its asset and workforce resource management and install an integrated HIS at its Bangkok branch. 

To prove its maturity in the use of EMR technology, Princ Hospital Suvarnabhumi, part of the private Principle Healthcare Group, underwent the assessment for Stage 7 of the HIMSS Electronic Medical Record Adoption Model. It is currently the only hospital in Thailand to have achieved this highest validation, which it received early this year.

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Humberto Farias of Concepta Technologies discusses his new Machine Advancement and General Intelligence Center and offers AI advice for healthcare C-suite executives and other health IT decision makers.

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Humberto Farias has been watching the explosion of generative AI very closely.

Farias is cofounder and chairman of Concepta Technologies, a technology company specializing in software development and programming in the areas of mobile, web, digital transformation and artificial intelligence.

For example, he noticed that Apple is putting generative AI at the very center of the lives of hundreds of millions of iPhone-toting people. But with recent data leaks, patient privacy problems and other IT issues, he says he’s worried health IT teams will become prone to seeing AI as a threat rather than a tool.

The question becomes: How can health systems protect valuable patient data while still reaping the benefits of generative AI?

Farias has debuted the Concepta Machine Advancement and General Intelligence Center, or MAGIC, a collaborative research program, virtual incubator and service center for artificial intelligence and advanced technologies.

Healthcare IT News spoke recently with Farias to learn more about MAGIC and understand concerns he has heard from healthcare CTOs about implementing artificial intelligence. He offered tips and real-world examples to securely deploy AI and learning and described what he believes should be the primary focus for CIOs, CISOs and other security leaders at hospitals and health systems as AI and machine learning continue to transform healthcare.

Q. Please describe your new organization, MAGIC. What are your goals?

A. Our mission is to push the boundaries of AI research and development while providing practical applications and services that address real-world problems. At MAGIC, we aim to foster cutting-edge research for both fundamental technologies and applied solutions, support and nurture early-stage AI ventures, educate and train professionals in AI skills, provide consulting services, and build a network of collaboration.

Some of our initial partnerships include healthcare companies dedicated to improving healthcare for patients, hospitals and clinical teams. They combine assessments, analytics and education, and then measure it all to improve healthcare for everyone. Through our partnership, we are implementing AI to make programs run even more efficiently and cost-effectively for their teams.

We’re open to working with large health systems on some of the key issues they’re facing when it comes to AI implementation. We’ve worked with health systems like Advent Health on other software technology and are well-equipped to handle the unique regulatory and patient security issues healthcare faces.

Q. What are some of the concerns you have heard firsthand from healthcare CTOs about implementing AI into their business structures?

A. I’ve heard from healthcare CTOs that their main concerns regarding the implementation of AI into their business structures is still data privacy and security. Health executives want to ensure the privacy and security of sensitive patient data are a top priority, given the stringent regulations from HIPAA and other mandates.

There also is hesitation around how AI solutions can integrate with legacy systems and if they are compatible, as well as navigating the complex regulatory landscape to ensure AI solutions comply with all relevant laws and guidelines.

There also is a cost to implement AI, and many healthcare CTOs are uncertain about the return on investment this technology can provide. I’m always looking for ways to cut these costs by collaborating with peers and ensuring we don’t operate in a silo – learning from mistakes and building upon successes from other leaders in the industry.

In pairing with that, there is also a lack of skilled personnel to develop, implement and manage AI systems. Health systems already are on tight budgets and experiencing cutbacks, so working with an AI research program can fill this need and help advance the use of AI throughout their institutions.

We’re working to educate health systems on how AI can be used for simple things like minimizing repetitive admin tasks and large-scale projects that can improve workflows for providers and care with real patients.

Finally, there always are ethical concerns when it comes to AI, healthcare CTOs want to ensure AI is used ethically, particularly in decisions that directly affect patient care. The top concerns in this area are informed consent and data bias.

Patients must be aware AI is included in their care, as well as making sure data used to train AI algorithms does not result in biased healthcare decisions that exacerbate disparities in healthcare outcomes among different demographic groups.

Q. What are some tips and real-world examples you can offer to safely and securely deploy AI, especially considering sensitive medical data?

A. There are several ways healthcare executives can deploy AI safely and securely. One of those is through data encryption. It’s important always to encrypt sensitive medical data both in between networks and when filed in records systems to protect against unauthorized access.

Another tip is to implement robust access control mechanisms to ensure only authorized personnel can access sensitive data. Large healthcare centers should employ multi-factor authentication, role-based access controls and a 24/7 monitoring system. Conducting regular security audits is another way to ensure security and safety by continuous monitoring to detect and respond to potential threats promptly.

Regulating compliance is another tip to ensure trust; you would do this by aligning AI deployments with regulatory frameworks such as HIPAA and GDPR. Making a priority to develop and adhere to ethical guidelines for AI usage is another tip, making sure to focus on fairness, transparency and accountability.

For instance, Stanford Health Care has an ethics board that reviews AI projects for potential ethical issues.

Q. What would you say is the primary focus CIOs, CISOs, and other security leaders at hospitals and health systems should have as AI continues to explode in healthcare?

A. The use of AI is inevitable in healthcare, so the primary focus for CIOs, CISOs and other security leaders should be to continue to ensure data privacy and security and to protect patient data from breaches. The top priority should be making sure programs comply with regulations.

Healthcare leaders also should focus on the development of a scalable and secure IT infrastructure that can support AI applications without compromising performance or security. Then to support this system, provide ongoing training for staff at every level – from staff to providers to C-suite – on the latest AI technologies and security practices to mitigate risks associated with human error.

To ensure there’s a fail-safe plan, healthcare leaders should develop and maintain a comprehensive risk management strategy that includes regular assessments, incident response plans and continuous improvement.

Collaboration is key to creating the best team ready to handle the challenges of the world we live in, encouraging collaboration between IT, security and clinical teams to ensure AI solutions meet the needs of all stakeholders while maintaining security and compliance standards.

The HIMSS AI in Healthcare Forum is scheduled to take place September 5-6 in Boston. Learn more and register.

Follow Bill’s HIT coverage on LinkedIn: Bill SiwickiEmail him: [email protected]Healthcare IT News is a HIMSS Media publication.

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The companies will further develop speech-to-text technology that takes healthcare provider’s notes and can upload key excerpts to EHR-agnostic files, while the agency accepts feedback on the sole-source solicitations.

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The U.S. Department of Veterans Affairs plans to award new contracts to two winners of this year’s AI Tech Sprint program, designed to help reduce the burden of clinical documentation.

WHY IT MATTERS

The VA says it intends to award firm fixed-price contracts to Abridge AI and Nuance Communications to test and evaluate commercial, cloud-based, ambient scribe software in live VA environments.

The administration said it needs software-as-a-service tools to transcribe clinical encounters and generate notes in order to integrate with its electronic health record, and so that doctors can insert visit information “without copy and paste manual effort,” according to the July 11 notice.

Moving beyond storing and retrieving data more efficiently to impact people’s lives and solve important problems was the spirit behind the tech sprint – Documenting VA Clinical Encounters and Integrating Community Care Data – according to Charles Worthington, VA chief technology officer and chief artificial intelligence officer. 

While the VA has made great strides in cloud and mobile computing that have enabled more veterans to access their benefits and patient information, the agency is on the cusp of a major transition to a new way to deliver software that is about creating inferences or predictions, based on large models and complicated mathematics, Worthington said at an award ceremony on May 21, when the finalists were announced.

“This paradigm is quite different. I think all of the techniques we’ve used to get to this point we’re going to have to rethink because all of those techniques are necessarily applicable to a world where we’re generating inferences and using those inferences to deliver outcomes,” he said.

Other winners of the Ambient Dictation for Clinical Encounters track were Althea Health, ARETUM, Cognosante Military and Veterans Health, Commure, Contrast AI, Credence Management Solutions, DeepScribe, TranscribeMD AI, Knowtex, QuantumTechIT, Sourceree, Tali AI and Veterans EZ Info.

Any interested party that believes it could satisfy the requirements can respond to the VA notice, the agency said. 

THE LARGER TREND

The $1 million AI Tech Sprint initiative was first launched this past year with the goal of developing ambient dictation for clinical encounters, as well as an advanced document-processing system for its Community Care program. 

To address provider burdens, the agency is seeking “high fidelity, traceable records of provider encounters” from a platform that can also interoperate with VA health system information. 

Part of the challenge was incorporating advanced software features, like source vetting and an ability to extract structured CPT codes, SNOMED CT codes and/or LOINC codes into medical records and its Summit Data Platform Health Information Exchange.

“AI solutions can help us reduce the time that clinicians spend on non-clinical work, which will get our teams doing more of what they love most: caring for Veterans,” Dr. Shereef Elnahal, the VA undersecretary for health, said in a statement when the challenge was announced in November. “This effort will reduce burnout among our clinicians and improve veteran healthcare at the same time.”

Both tracks offered a first-place prize of $300,000, $150,000 for second place and $50,000 for third. More than 150 teams competed across the two tracks, and there were 25 finalists, according to the VA’s Office of Research and Development AI Tech Sprint website.

“We could all but eliminate the administrative load that has eroded the quality of doctor-patient conversations and has famously broken the spirit of many clinicians,” said Dr. Shiv Rao, a practicing cardiologist and CEO of Abridge, one of the AI contract awardees.

Generative AI can attract the next generation of healthcare workers by simplifying difficult and labor-intensive processes, he told Healthcare IT News last year.

Microsoft subsidiary Nuance, through its long-term partnership with Epic, advanced ambient documentation products with genAI last year. 

“For the first time, we can see how conversational understanding, generative AI, and clinical context can together generate high-quality documentation,” Sean Bina, Epic’s VP of patient experience, said in the announcement.

ON THE RECORD

“The challenge was a rigorous and competitive evaluation process authorized under the America COMPETES Act and aligned with the directives of Executive Order 14110,” said VA officials in its contract notice.

Andrea Fox is senior editor of Healthcare IT News.Email: [email protected]Healthcare IT News is a HIMSS Media publication.

The HIMSS AI in Healthcare Forum is scheduled to take place September 5-6 in Boston. Learn more and register.

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