Dermatology News

TOPLINE:

Intact fish skin grafts, sourced from Atlantic cod, show superior and faster healing than standard wound care practices in patients with deep and penetrating diabetic foot ulcers.

METHODOLOGY:

• Standard wound care for diabetic foot ulcers involves vascular assessment, surgical debridement, use of appropriate dressings, infection management, and glycemic control; however, standard care is typically associated with poor outcomes.
• Researchers conducted a multicenter clinical trial in 15 tertiary care centers with diabetic foot units across France, Italy, Germany, and Sweden to evaluate the efficacy and safety of intact fish skin grafts over standard-of-care practices in treating complex diabetic foot ulcers.
• A total of 255 patients aged 18 years or older with diabetes and lower limb wounds penetrating to the tendon, capsule, bone, or joint were randomly assigned to receive either an intact fish skin graft or standard wound care for 14 weeks.
• The primary endpoint was the percentage of wounds achieving complete closure by 16 weeks.
• Wound healing was also assessed at 20 and 24 weeks.

TAKEAWAY:

• The proportion of wounds healed at 16 weeks was higher with intact fish skin grafts than with standard-of-care (44.0% vs 26.4% adjusted odds ratio [aOR], 2.58; 95% CI, 1.48-4.56).
• The fish skin grafts continued to be more effective than standard wound care practices at weeks 20 (aOR, 2.15; 95% CI, 1.27–3.70) and 24 (aOR, 2.19; 95% CI, 1.31–3.70).
• The mean time to healing was 17.31 weeks for the intact fish skin graft group and 19.37 weeks for the standard-of-care group; intact fish skin grafts were also associated with faster healing times than standard wound care (hazard ratio, 1.59; 95% CI, 1.07-2.36).
• Target wound infections were the most common adverse events, occurring in a similar number of patients in both the groups.

IN PRACTICE:

“Our trial demonstrated treatment of complex diabetic foot ulcers with intact fish skin grafts achieved a significantly greater proportion of diabetic foot ulcers healed at 16 weeks than standard of care, and was associated with increased healing at 20 and 24 weeks. That these results were achieved in non-superficial UT [University of Texas diabetic wound classification system] grade 2 and 3 diabetic foot ulcers and included ischemic and/or infected diabetic foot ulcers is of importance,” the authors wrote.

SOURCE:

The study was led by Dured Dardari, MD, PhD, Center Hospitalier Sud Francilien, Corbeil-Essonnes, France, and was published online in NEJM Evidence.

LIMITATIONS:

No limitations were discussed for this study.

DISCLOSURES:

The study was funded by European Commission Fast Track to Innovation Horizon 2020 and Kerecis. Two authors reported being employees with or without stock options at Kerecis, and other authors reported having ties with many sources including Kerecis.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article first appeared on Medscape.com.

TOPLINE:

Intact fish skin grafts, sourced from Atlantic cod, show superior and faster healing than standard wound care practices in patients with deep and penetrating diabetic foot ulcers.

METHODOLOGY:

• Standard wound care for diabetic foot ulcers involves vascular assessment, surgical debridement, use of appropriate dressings, infection management, and glycemic control; however, standard care is typically associated with poor outcomes.
• Researchers conducted a multicenter clinical trial in 15 tertiary care centers with diabetic foot units across France, Italy, Germany, and Sweden to evaluate the efficacy and safety of intact fish skin grafts over standard-of-care practices in treating complex diabetic foot ulcers.
• A total of 255 patients aged 18 years or older with diabetes and lower limb wounds penetrating to the tendon, capsule, bone, or joint were randomly assigned to receive either an intact fish skin graft or standard wound care for 14 weeks.
• The primary endpoint was the percentage of wounds achieving complete closure by 16 weeks.
• Wound healing was also assessed at 20 and 24 weeks.

TAKEAWAY:

• The proportion of wounds healed at 16 weeks was higher with intact fish skin grafts than with standard-of-care (44.0% vs 26.4% adjusted odds ratio [aOR], 2.58; 95% CI, 1.48-4.56).
• The fish skin grafts continued to be more effective than standard wound care practices at weeks 20 (aOR, 2.15; 95% CI, 1.27–3.70) and 24 (aOR, 2.19; 95% CI, 1.31–3.70).
• The mean time to healing was 17.31 weeks for the intact fish skin graft group and 19.37 weeks for the standard-of-care group; intact fish skin grafts were also associated with faster healing times than standard wound care (hazard ratio, 1.59; 95% CI, 1.07-2.36).
• Target wound infections were the most common adverse events, occurring in a similar number of patients in both the groups.

IN PRACTICE:

“Our trial demonstrated treatment of complex diabetic foot ulcers with intact fish skin grafts achieved a significantly greater proportion of diabetic foot ulcers healed at 16 weeks than standard of care, and was associated with increased healing at 20 and 24 weeks. That these results were achieved in non-superficial UT [University of Texas diabetic wound classification system] grade 2 and 3 diabetic foot ulcers and included ischemic and/or infected diabetic foot ulcers is of importance,” the authors wrote.

SOURCE:

The study was led by Dured Dardari, MD, PhD, Center Hospitalier Sud Francilien, Corbeil-Essonnes, France, and was published online in NEJM Evidence.

LIMITATIONS:

No limitations were discussed for this study.

DISCLOSURES:

The study was funded by European Commission Fast Track to Innovation Horizon 2020 and Kerecis. Two authors reported being employees with or without stock options at Kerecis, and other authors reported having ties with many sources including Kerecis.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article first appeared on Medscape.com.

TOPLINE:

Intact fish skin grafts, sourced from Atlantic cod, show superior and faster healing than standard wound care practices in patients with deep and penetrating diabetic foot ulcers.

METHODOLOGY:

• Standard wound care for diabetic foot ulcers involves vascular assessment, surgical debridement, use of appropriate dressings, infection management, and glycemic control; however, standard care is typically associated with poor outcomes.
• Researchers conducted a multicenter clinical trial in 15 tertiary care centers with diabetic foot units across France, Italy, Germany, and Sweden to evaluate the efficacy and safety of intact fish skin grafts over standard-of-care practices in treating complex diabetic foot ulcers.
• A total of 255 patients aged 18 years or older with diabetes and lower limb wounds penetrating to the tendon, capsule, bone, or joint were randomly assigned to receive either an intact fish skin graft or standard wound care for 14 weeks.
• The primary endpoint was the percentage of wounds achieving complete closure by 16 weeks.
• Wound healing was also assessed at 20 and 24 weeks.

TAKEAWAY:

• The proportion of wounds healed at 16 weeks was higher with intact fish skin grafts than with standard-of-care (44.0% vs 26.4% adjusted odds ratio [aOR], 2.58; 95% CI, 1.48-4.56).
• The fish skin grafts continued to be more effective than standard wound care practices at weeks 20 (aOR, 2.15; 95% CI, 1.27–3.70) and 24 (aOR, 2.19; 95% CI, 1.31–3.70).
• The mean time to healing was 17.31 weeks for the intact fish skin graft group and 19.37 weeks for the standard-of-care group; intact fish skin grafts were also associated with faster healing times than standard wound care (hazard ratio, 1.59; 95% CI, 1.07-2.36).
• Target wound infections were the most common adverse events, occurring in a similar number of patients in both the groups.

IN PRACTICE:

“Our trial demonstrated treatment of complex diabetic foot ulcers with intact fish skin grafts achieved a significantly greater proportion of diabetic foot ulcers healed at 16 weeks than standard of care, and was associated with increased healing at 20 and 24 weeks. That these results were achieved in non-superficial UT [University of Texas diabetic wound classification system] grade 2 and 3 diabetic foot ulcers and included ischemic and/or infected diabetic foot ulcers is of importance,” the authors wrote.

SOURCE:

The study was led by Dured Dardari, MD, PhD, Center Hospitalier Sud Francilien, Corbeil-Essonnes, France, and was published online in NEJM Evidence.

LIMITATIONS:

No limitations were discussed for this study.

DISCLOSURES:

The study was funded by European Commission Fast Track to Innovation Horizon 2020 and Kerecis. Two authors reported being employees with or without stock options at Kerecis, and other authors reported having ties with many sources including Kerecis.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article first appeared on Medscape.com.

In seconds, Ravi Parikh, MD, an oncologist at the Emory University School of Medicine in Atlanta, had a summary of his patient’s entire medical history. Normally, Parikh skimmed the cumbersome files before seeing a patient. However, the artificial intelligence (AI) tool his institution was testing could list the highlights he needed in a fraction of the time.

“On the whole, I like it ... it saves me time,” Parikh said of the tool. “But I’d be lying if I told you it was perfect all the time. It’s interpreting the [patient] history in some ways that may be inaccurate,” he said.

Within the first week of testing the tool, Parikh started to notice that the large language model (LLM) made a particular mistake in his patients with prostate cancer. If their prostate-specific antigen test results came back slightly elevated — which is part of normal variation — the LLM recorded it as disease progression. Because Parikh reviews all his notes — with or without using an AI tool — after a visit, he easily caught the mistake before it was added to the chart. “The problem, I think, is if these mistakes go under the hood,” he said.

In the data science world, these mistakes are called hallucinations. And a growing body of research suggests they’re happening more frequently than is safe for healthcare. The industry promised LLMs would alleviate administrative burden and reduce physician burnout. But so far, studies show these AI-tool mistakes often create more work for doctors, not less. To truly help physicians and be safe for patients, some experts say healthcare needs to build its own LLMs from the ground up. And all agree that the field desperately needs a way to vet these algorithms more thoroughly.
 

Prone to Error

Right now, “I think the industry is focused on taking existing LLMs and forcing them into usage for healthcare,” said Nigam H. Shah, MBBS, PhD, chief data scientist for Stanford Health. However, the value of deploying general LLMs in the healthcare space is questionable. “People are starting to wonder if we’re using these tools wrong,” he told this news organization.

In 2023, Shah and his colleagues evaluated seven LLMs on their ability to answer electronic health record–based questions. For realistic tasks, the error rate in the best cases was about 35%, he said. “To me, that rate seems a bit high ... to adopt for routine use.”

A study earlier this year by the UC San Diego School of Medicine showed that using LLMs to respond to patient messages increased the time doctors spent on messages. And this summer, a study by the clinical AI firm Mendel found that when GPT-4o or Llama-3 were used to summarize patient medical records, almost every summary contained at least one type of hallucination.

“We’ve seen cases where a patient does have drug allergies, but the system says ‘no known drug allergies’ ” in the medical history summary, said Wael Salloum, PhD, cofounder and chief science officer at Mendel. “That’s a serious hallucination.” And if physicians have to constantly verify what the system is telling them, that “defeats the purpose [of summarization],” he said.
 

A Higher Quality Diet

Part of the trouble with LLMs is that there’s just not enough high-quality information to feed them. The algorithms are insatiable, requiring vast swaths of data for training. GPT-3.5, for instance, was trained on 570 GB of data from the internet, more than 300 billion words. And to train GPT-4o, OpenAI reportedly transcribed more than 1 million hours of YouTube content.

However, the strategies that built these general LLMs don’t always translate well to healthcare. The internet is full of low-quality or misleading health information from wellness sites and supplement advertisements. And even data that are trustworthy, like the millions of clinical studies and the US Food and Drug Administration (FDA) statements, can be outdated, Salloum said. And “an LLM in training can’t distinguish good from bad,” he added.

The good news is that clinicians don’t rely on controversial information in the real world. Medical knowledge is standardized. “Healthcare is a domain rich with explicit knowledge,” Salloum said. So there’s potential to build a more reliable LLM that is guided by robust medical standards and guidelines.

It’s possible that healthcare could use small language models, which are LLM’s pocket-sized cousins, and perform tasks needing only bite-sized datasets requiring fewer resources and easier fine-tuning, according to Microsoft’s website. Shah said training these smaller models on real medical data might be an option, like an LLM meant to respond to patient messages that could be trained with real messages sent by physicians.

Several groups are already working on databases of standardized human medical knowledge or real physician responses. “Perhaps that will work better than using LLMs trained on the general internet. Those studies need to be done,” Shah said.

Jon Tamir, assistant professor of electrical and computer engineering and co-lead of the AI Health Lab at The University of Texas at Austin, said, “The community has recognized that we are entering a new era of AI where the dataset itself is the most important aspect. We need training sets that are highly curated and highly specialized.

“If the dataset is highly specialized, it will definitely help reduce hallucinations,” he said.
 

Cutting Overconfidence

A major problem with LLM mistakes is that they are often hard to detect. Hallucinations can be highly convincing even if they’re highly inaccurate, according to Tamir.

When Shah, for instance, was recently testing an LLM on de-identified patient data, he asked the LLM which blood test the patient last had. The model responded with “complete blood count [CBC].” But when he asked for the results, the model gave him white blood count and other values. “Turns out that record did not have a CBC done at all! The result was entirely made up,” he said.

Making healthcare LLMs safer and more reliable will mean training AI to acknowledge potential mistakes and uncertainty. Existing LLMs are trained to project confidence and produce a lot of answers, even when there isn’t one, Salloum said. They rarely respond with “I don’t know” even when their prediction has low confidence, he added.

Healthcare stands to benefit from a system that highlights uncertainty and potential errors. For instance, if a patient’s history shows they have smoked, stopped smoking, vaped, and started smoking again. The LLM might call them a smoker but flag the comment as uncertain because the chronology is complicated, Salloum said.

Tamir added that this strategy could improve LLM and doctor collaboration by honing in on where human expertise is needed most.
 

Too Little Evaluation

For any improvement strategy to work, LLMs — and all AI-assisted healthcare tools — first need a better evaluation framework. So far, LLMs have “been used in really exciting ways but not really well-vetted ways,” Tamir said.

While some AI-assisted tools, particularly in medical imaging, have undergone rigorous FDA evaluations and earned approval, most haven’t. And because the FDA only regulates algorithms that are considered medical devices, Parikh said that most LLMs used for administrative tasks and efficiency don’t fall under the regulatory agency’s purview.

But these algorithms still have access to patient information and can directly influence patient and doctor decisions. Third-party regulatory agencies are expected to emerge, but it’s still unclear who those will be. Before developers can build a safer and more efficient LLM for healthcare, they’ll need better guidelines and guardrails. “Unless we figure out evaluation, how would we know whether the healthcare-appropriate large language models are better or worse?” Shah asked.
 

A version of this article appeared on Medscape.com.

In seconds, Ravi Parikh, MD, an oncologist at the Emory University School of Medicine in Atlanta, had a summary of his patient’s entire medical history. Normally, Parikh skimmed the cumbersome files before seeing a patient. However, the artificial intelligence (AI) tool his institution was testing could list the highlights he needed in a fraction of the time.

“On the whole, I like it ... it saves me time,” Parikh said of the tool. “But I’d be lying if I told you it was perfect all the time. It’s interpreting the [patient] history in some ways that may be inaccurate,” he said.

Within the first week of testing the tool, Parikh started to notice that the large language model (LLM) made a particular mistake in his patients with prostate cancer. If their prostate-specific antigen test results came back slightly elevated — which is part of normal variation — the LLM recorded it as disease progression. Because Parikh reviews all his notes — with or without using an AI tool — after a visit, he easily caught the mistake before it was added to the chart. “The problem, I think, is if these mistakes go under the hood,” he said.

In the data science world, these mistakes are called hallucinations. And a growing body of research suggests they’re happening more frequently than is safe for healthcare. The industry promised LLMs would alleviate administrative burden and reduce physician burnout. But so far, studies show these AI-tool mistakes often create more work for doctors, not less. To truly help physicians and be safe for patients, some experts say healthcare needs to build its own LLMs from the ground up. And all agree that the field desperately needs a way to vet these algorithms more thoroughly.
 

Prone to Error

Right now, “I think the industry is focused on taking existing LLMs and forcing them into usage for healthcare,” said Nigam H. Shah, MBBS, PhD, chief data scientist for Stanford Health. However, the value of deploying general LLMs in the healthcare space is questionable. “People are starting to wonder if we’re using these tools wrong,” he told this news organization.

In 2023, Shah and his colleagues evaluated seven LLMs on their ability to answer electronic health record–based questions. For realistic tasks, the error rate in the best cases was about 35%, he said. “To me, that rate seems a bit high ... to adopt for routine use.”

A study earlier this year by the UC San Diego School of Medicine showed that using LLMs to respond to patient messages increased the time doctors spent on messages. And this summer, a study by the clinical AI firm Mendel found that when GPT-4o or Llama-3 were used to summarize patient medical records, almost every summary contained at least one type of hallucination.

“We’ve seen cases where a patient does have drug allergies, but the system says ‘no known drug allergies’ ” in the medical history summary, said Wael Salloum, PhD, cofounder and chief science officer at Mendel. “That’s a serious hallucination.” And if physicians have to constantly verify what the system is telling them, that “defeats the purpose [of summarization],” he said.
 

A Higher Quality Diet

Part of the trouble with LLMs is that there’s just not enough high-quality information to feed them. The algorithms are insatiable, requiring vast swaths of data for training. GPT-3.5, for instance, was trained on 570 GB of data from the internet, more than 300 billion words. And to train GPT-4o, OpenAI reportedly transcribed more than 1 million hours of YouTube content.

However, the strategies that built these general LLMs don’t always translate well to healthcare. The internet is full of low-quality or misleading health information from wellness sites and supplement advertisements. And even data that are trustworthy, like the millions of clinical studies and the US Food and Drug Administration (FDA) statements, can be outdated, Salloum said. And “an LLM in training can’t distinguish good from bad,” he added.

The good news is that clinicians don’t rely on controversial information in the real world. Medical knowledge is standardized. “Healthcare is a domain rich with explicit knowledge,” Salloum said. So there’s potential to build a more reliable LLM that is guided by robust medical standards and guidelines.

It’s possible that healthcare could use small language models, which are LLM’s pocket-sized cousins, and perform tasks needing only bite-sized datasets requiring fewer resources and easier fine-tuning, according to Microsoft’s website. Shah said training these smaller models on real medical data might be an option, like an LLM meant to respond to patient messages that could be trained with real messages sent by physicians.

Several groups are already working on databases of standardized human medical knowledge or real physician responses. “Perhaps that will work better than using LLMs trained on the general internet. Those studies need to be done,” Shah said.

Jon Tamir, assistant professor of electrical and computer engineering and co-lead of the AI Health Lab at The University of Texas at Austin, said, “The community has recognized that we are entering a new era of AI where the dataset itself is the most important aspect. We need training sets that are highly curated and highly specialized.

“If the dataset is highly specialized, it will definitely help reduce hallucinations,” he said.
 

Cutting Overconfidence

A major problem with LLM mistakes is that they are often hard to detect. Hallucinations can be highly convincing even if they’re highly inaccurate, according to Tamir.

When Shah, for instance, was recently testing an LLM on de-identified patient data, he asked the LLM which blood test the patient last had. The model responded with “complete blood count [CBC].” But when he asked for the results, the model gave him white blood count and other values. “Turns out that record did not have a CBC done at all! The result was entirely made up,” he said.

Making healthcare LLMs safer and more reliable will mean training AI to acknowledge potential mistakes and uncertainty. Existing LLMs are trained to project confidence and produce a lot of answers, even when there isn’t one, Salloum said. They rarely respond with “I don’t know” even when their prediction has low confidence, he added.

Healthcare stands to benefit from a system that highlights uncertainty and potential errors. For instance, if a patient’s history shows they have smoked, stopped smoking, vaped, and started smoking again. The LLM might call them a smoker but flag the comment as uncertain because the chronology is complicated, Salloum said.

Tamir added that this strategy could improve LLM and doctor collaboration by honing in on where human expertise is needed most.
 

Too Little Evaluation

For any improvement strategy to work, LLMs — and all AI-assisted healthcare tools — first need a better evaluation framework. So far, LLMs have “been used in really exciting ways but not really well-vetted ways,” Tamir said.

While some AI-assisted tools, particularly in medical imaging, have undergone rigorous FDA evaluations and earned approval, most haven’t. And because the FDA only regulates algorithms that are considered medical devices, Parikh said that most LLMs used for administrative tasks and efficiency don’t fall under the regulatory agency’s purview.

But these algorithms still have access to patient information and can directly influence patient and doctor decisions. Third-party regulatory agencies are expected to emerge, but it’s still unclear who those will be. Before developers can build a safer and more efficient LLM for healthcare, they’ll need better guidelines and guardrails. “Unless we figure out evaluation, how would we know whether the healthcare-appropriate large language models are better or worse?” Shah asked.
 

A version of this article appeared on Medscape.com.

In seconds, Ravi Parikh, MD, an oncologist at the Emory University School of Medicine in Atlanta, had a summary of his patient’s entire medical history. Normally, Parikh skimmed the cumbersome files before seeing a patient. However, the artificial intelligence (AI) tool his institution was testing could list the highlights he needed in a fraction of the time.

“On the whole, I like it ... it saves me time,” Parikh said of the tool. “But I’d be lying if I told you it was perfect all the time. It’s interpreting the [patient] history in some ways that may be inaccurate,” he said.

Within the first week of testing the tool, Parikh started to notice that the large language model (LLM) made a particular mistake in his patients with prostate cancer. If their prostate-specific antigen test results came back slightly elevated — which is part of normal variation — the LLM recorded it as disease progression. Because Parikh reviews all his notes — with or without using an AI tool — after a visit, he easily caught the mistake before it was added to the chart. “The problem, I think, is if these mistakes go under the hood,” he said.

In the data science world, these mistakes are called hallucinations. And a growing body of research suggests they’re happening more frequently than is safe for healthcare. The industry promised LLMs would alleviate administrative burden and reduce physician burnout. But so far, studies show these AI-tool mistakes often create more work for doctors, not less. To truly help physicians and be safe for patients, some experts say healthcare needs to build its own LLMs from the ground up. And all agree that the field desperately needs a way to vet these algorithms more thoroughly.
 

Prone to Error

Right now, “I think the industry is focused on taking existing LLMs and forcing them into usage for healthcare,” said Nigam H. Shah, MBBS, PhD, chief data scientist for Stanford Health. However, the value of deploying general LLMs in the healthcare space is questionable. “People are starting to wonder if we’re using these tools wrong,” he told this news organization.

In 2023, Shah and his colleagues evaluated seven LLMs on their ability to answer electronic health record–based questions. For realistic tasks, the error rate in the best cases was about 35%, he said. “To me, that rate seems a bit high ... to adopt for routine use.”

A study earlier this year by the UC San Diego School of Medicine showed that using LLMs to respond to patient messages increased the time doctors spent on messages. And this summer, a study by the clinical AI firm Mendel found that when GPT-4o or Llama-3 were used to summarize patient medical records, almost every summary contained at least one type of hallucination.

“We’ve seen cases where a patient does have drug allergies, but the system says ‘no known drug allergies’ ” in the medical history summary, said Wael Salloum, PhD, cofounder and chief science officer at Mendel. “That’s a serious hallucination.” And if physicians have to constantly verify what the system is telling them, that “defeats the purpose [of summarization],” he said.
 

A Higher Quality Diet

Part of the trouble with LLMs is that there’s just not enough high-quality information to feed them. The algorithms are insatiable, requiring vast swaths of data for training. GPT-3.5, for instance, was trained on 570 GB of data from the internet, more than 300 billion words. And to train GPT-4o, OpenAI reportedly transcribed more than 1 million hours of YouTube content.

However, the strategies that built these general LLMs don’t always translate well to healthcare. The internet is full of low-quality or misleading health information from wellness sites and supplement advertisements. And even data that are trustworthy, like the millions of clinical studies and the US Food and Drug Administration (FDA) statements, can be outdated, Salloum said. And “an LLM in training can’t distinguish good from bad,” he added.

The good news is that clinicians don’t rely on controversial information in the real world. Medical knowledge is standardized. “Healthcare is a domain rich with explicit knowledge,” Salloum said. So there’s potential to build a more reliable LLM that is guided by robust medical standards and guidelines.

It’s possible that healthcare could use small language models, which are LLM’s pocket-sized cousins, and perform tasks needing only bite-sized datasets requiring fewer resources and easier fine-tuning, according to Microsoft’s website. Shah said training these smaller models on real medical data might be an option, like an LLM meant to respond to patient messages that could be trained with real messages sent by physicians.

Several groups are already working on databases of standardized human medical knowledge or real physician responses. “Perhaps that will work better than using LLMs trained on the general internet. Those studies need to be done,” Shah said.

Jon Tamir, assistant professor of electrical and computer engineering and co-lead of the AI Health Lab at The University of Texas at Austin, said, “The community has recognized that we are entering a new era of AI where the dataset itself is the most important aspect. We need training sets that are highly curated and highly specialized.

“If the dataset is highly specialized, it will definitely help reduce hallucinations,” he said.
 

Cutting Overconfidence

A major problem with LLM mistakes is that they are often hard to detect. Hallucinations can be highly convincing even if they’re highly inaccurate, according to Tamir.

When Shah, for instance, was recently testing an LLM on de-identified patient data, he asked the LLM which blood test the patient last had. The model responded with “complete blood count [CBC].” But when he asked for the results, the model gave him white blood count and other values. “Turns out that record did not have a CBC done at all! The result was entirely made up,” he said.

Making healthcare LLMs safer and more reliable will mean training AI to acknowledge potential mistakes and uncertainty. Existing LLMs are trained to project confidence and produce a lot of answers, even when there isn’t one, Salloum said. They rarely respond with “I don’t know” even when their prediction has low confidence, he added.

Healthcare stands to benefit from a system that highlights uncertainty and potential errors. For instance, if a patient’s history shows they have smoked, stopped smoking, vaped, and started smoking again. The LLM might call them a smoker but flag the comment as uncertain because the chronology is complicated, Salloum said.

Tamir added that this strategy could improve LLM and doctor collaboration by honing in on where human expertise is needed most.
 

Too Little Evaluation

For any improvement strategy to work, LLMs — and all AI-assisted healthcare tools — first need a better evaluation framework. So far, LLMs have “been used in really exciting ways but not really well-vetted ways,” Tamir said.

While some AI-assisted tools, particularly in medical imaging, have undergone rigorous FDA evaluations and earned approval, most haven’t. And because the FDA only regulates algorithms that are considered medical devices, Parikh said that most LLMs used for administrative tasks and efficiency don’t fall under the regulatory agency’s purview.

But these algorithms still have access to patient information and can directly influence patient and doctor decisions. Third-party regulatory agencies are expected to emerge, but it’s still unclear who those will be. Before developers can build a safer and more efficient LLM for healthcare, they’ll need better guidelines and guardrails. “Unless we figure out evaluation, how would we know whether the healthcare-appropriate large language models are better or worse?” Shah asked.
 

A version of this article appeared on Medscape.com.

From the largest healthcare companies to solo practices, just every organization in medicine faces a risk for costly cyberattacks. In recent years, hackers have threatened to release the personal information of patients and employees — or paralyze online systems — unless they’re paid a ransom.

Should companies pay? It’s not an easy answer, a pair of experts told colleagues in an American Medical Association (AMA) cybersecurity webinar on October 18. It turns out that each choice — pay or don’t pay — can end up being costly.

This is just one of the new challenges facing the American medical system on the cybersecurity front, the speakers said. Others include the possibility that hackers will manipulate patient data — turning a medical test negative, for example, when it’s actually positive — and take advantage of the powers of artificial intelligence (AI).

The AMA held the webinar to educate physicians about cybersecurity risks and defenses, an especially hot topic in the wake of February’s Change Healthcare hack, which cost UnitedHealth Group an estimated $2.5 billion — so far — and deeply disrupted the American healthcare system.

Cautionary tales abound. Greg Garcia, executive director for cybersecurity of the Health Sector Coordinating Council, a coalition of medical industry organizations, pointed to a Pennsylvania clinic that refused to pay a ransom to prevent the release of hundreds of images of patients with breast cancer undressed from the waist up. Garcia told webinar participants that the ransom was $5 million.
 

Risky Choices

While the Federal Bureau of Investigation recommends against paying a ransom, this can be a risky choice, Garcia said. Hackers released the images, and the center has reportedly agreed to settle a class-action lawsuit for $65 million. “They traded $5 million for $60 million,” Garcia added, slightly misstating the settlement amount.

Health systems have been cagey about whether they’ve paid ransoms to prevent private data from being made public in cyberattacks. If a ransom is demanded, “it’s every organization for itself,” Garcia said.

He highlighted the case of a chain of psychiatry practices in Finland that suffered a ransomware attack in 2020. The hackers “contacted the patients and said: ‘Hey, call your clinic and tell them to pay the ransom. Otherwise, we’re going to release all your psychiatric notes to the public.’ ”

Cyberattacks continue. In October, Boston Children’s Health Physicians announced that it had suffered a “ recent security incident” involving data — possibly including Social Security numbers and treatment information — regarding patients and employees. A hacker group reportedly claimed responsibility and wants the system, which boasts more than 300 clinicians, to pay a ransom or else it will release the stolen information.
 

Should Paying Ransom Be a Crime?

Christian Dameff, MD, MS, an emergency medicine physician and director of the Center for Healthcare Cybersecurity at the University of California (UC), San Diego, noted that there are efforts to turn paying ransom into a crime. “If people aren’t paying ransoms, then ransomware operators will move to something else that makes them money.”

Dameff urged colleagues to understand we no longer live in a world where clinicians only bother to think of technology when they call the IT department to help them reset their password.

New challenges face clinicians, he said. “How do we develop better strategies, downtime procedures, and safe clinical care in an era where our vital technology may be gone, not just for an hour or 2, but as is the case with these ransomware attacks, sometimes weeks to months.”

Garcia said “cybersecurity is everybody’s responsibility, including frontline clinicians. Because you’re touching data, you’re touching technology, you’re touching patients, and all of those things combine to present some vulnerabilities in the digital world.”
 

Next Frontier: Hackers May Manipulate Patient Data

Dameff said future hackers may use AI to manipulate individual patient data in ways that threaten patient health. AI makes this easier to accomplish.

“What if I delete your allergies in your electronic health record, or I manipulate your chest x-ray, or I change your lab values so it looks like you’re in diabetic ketoacidosis when you’re not so a clinician gives you insulin when you don’t need it?”

Garcia highlighted another new threat: Phishing efforts that are harder to ignore thanks to AI.

“One of the most successful way that hackers get in, disrupt systems, and steal data is through email phishing, and it’s only going to get better because of artificial intelligence,” he said. “No longer are you going to have typos in that email written by a hacking group in Nigeria or in China. It’s going to be perfect looking.”

What can practices and healthcare systems do? Garcia highlighted federal health agency efforts to encourage organizations to adopt best practices in cybersecurity.

“If you’ve got a data breach, and you can show to the US Department of Health & Human Services [HHS] you have implemented generally recognized cybersecurity controls over the past year, that you have done your best, you did the right thing, and you still got hit, HHS is directed to essentially take it easy on you,” he said. “That’s a positive incentive.”
 

Ransomware Guide in the Works

Dameff said UC San Diego’s Center for Healthcare Cybersecurity plans to publish a free cybersecurity guide in 2025 that will include specific information about ransomware attacks for medical specialties such as cardiology, trauma surgery, and pediatrics.

“Then, should you ever be ransomed, you can pull out this guide. You’ll know what’s going to kind of happen, and you can better prepare for those effects.”

Will the future president prioritize healthcare cybersecurity? That remains to be seen, but crises do have the capacity to concentrate the mind, experts said.

The nation’s capital “has a very short memory, a short attention span. The policymakers tend to be reactive,” Dameff said. “All it takes is yet another Change Healthcare–like attack that disrupts 30% or more of the nation’s healthcare system for the policymakers to sit up, take notice, and try to come up with solutions.”

In addition, he said, an estimated two data breaches/ransomware attacks are occurring per day. “The fact is that we’re all patients, up to the President of the United States and every member of the Congress is a patient.”

There’s a “very existential, very palpable understanding that cyber safety is patient safety and cyber insecurity is patient insecurity,” Dameff said.

A version of this article appeared on Medscape.com.

From the largest healthcare companies to solo practices, just every organization in medicine faces a risk for costly cyberattacks. In recent years, hackers have threatened to release the personal information of patients and employees — or paralyze online systems — unless they’re paid a ransom.

Should companies pay? It’s not an easy answer, a pair of experts told colleagues in an American Medical Association (AMA) cybersecurity webinar on October 18. It turns out that each choice — pay or don’t pay — can end up being costly.

This is just one of the new challenges facing the American medical system on the cybersecurity front, the speakers said. Others include the possibility that hackers will manipulate patient data — turning a medical test negative, for example, when it’s actually positive — and take advantage of the powers of artificial intelligence (AI).

The AMA held the webinar to educate physicians about cybersecurity risks and defenses, an especially hot topic in the wake of February’s Change Healthcare hack, which cost UnitedHealth Group an estimated $2.5 billion — so far — and deeply disrupted the American healthcare system.

Cautionary tales abound. Greg Garcia, executive director for cybersecurity of the Health Sector Coordinating Council, a coalition of medical industry organizations, pointed to a Pennsylvania clinic that refused to pay a ransom to prevent the release of hundreds of images of patients with breast cancer undressed from the waist up. Garcia told webinar participants that the ransom was $5 million.
 

Risky Choices

While the Federal Bureau of Investigation recommends against paying a ransom, this can be a risky choice, Garcia said. Hackers released the images, and the center has reportedly agreed to settle a class-action lawsuit for $65 million. “They traded $5 million for $60 million,” Garcia added, slightly misstating the settlement amount.

Health systems have been cagey about whether they’ve paid ransoms to prevent private data from being made public in cyberattacks. If a ransom is demanded, “it’s every organization for itself,” Garcia said.

He highlighted the case of a chain of psychiatry practices in Finland that suffered a ransomware attack in 2020. The hackers “contacted the patients and said: ‘Hey, call your clinic and tell them to pay the ransom. Otherwise, we’re going to release all your psychiatric notes to the public.’ ”

Cyberattacks continue. In October, Boston Children’s Health Physicians announced that it had suffered a “ recent security incident” involving data — possibly including Social Security numbers and treatment information — regarding patients and employees. A hacker group reportedly claimed responsibility and wants the system, which boasts more than 300 clinicians, to pay a ransom or else it will release the stolen information.
 

Should Paying Ransom Be a Crime?

Christian Dameff, MD, MS, an emergency medicine physician and director of the Center for Healthcare Cybersecurity at the University of California (UC), San Diego, noted that there are efforts to turn paying ransom into a crime. “If people aren’t paying ransoms, then ransomware operators will move to something else that makes them money.”

Dameff urged colleagues to understand we no longer live in a world where clinicians only bother to think of technology when they call the IT department to help them reset their password.

New challenges face clinicians, he said. “How do we develop better strategies, downtime procedures, and safe clinical care in an era where our vital technology may be gone, not just for an hour or 2, but as is the case with these ransomware attacks, sometimes weeks to months.”

Garcia said “cybersecurity is everybody’s responsibility, including frontline clinicians. Because you’re touching data, you’re touching technology, you’re touching patients, and all of those things combine to present some vulnerabilities in the digital world.”
 

Next Frontier: Hackers May Manipulate Patient Data

Dameff said future hackers may use AI to manipulate individual patient data in ways that threaten patient health. AI makes this easier to accomplish.

“What if I delete your allergies in your electronic health record, or I manipulate your chest x-ray, or I change your lab values so it looks like you’re in diabetic ketoacidosis when you’re not so a clinician gives you insulin when you don’t need it?”

Garcia highlighted another new threat: Phishing efforts that are harder to ignore thanks to AI.

“One of the most successful way that hackers get in, disrupt systems, and steal data is through email phishing, and it’s only going to get better because of artificial intelligence,” he said. “No longer are you going to have typos in that email written by a hacking group in Nigeria or in China. It’s going to be perfect looking.”

What can practices and healthcare systems do? Garcia highlighted federal health agency efforts to encourage organizations to adopt best practices in cybersecurity.

“If you’ve got a data breach, and you can show to the US Department of Health & Human Services [HHS] you have implemented generally recognized cybersecurity controls over the past year, that you have done your best, you did the right thing, and you still got hit, HHS is directed to essentially take it easy on you,” he said. “That’s a positive incentive.”
 

Ransomware Guide in the Works

Dameff said UC San Diego’s Center for Healthcare Cybersecurity plans to publish a free cybersecurity guide in 2025 that will include specific information about ransomware attacks for medical specialties such as cardiology, trauma surgery, and pediatrics.

“Then, should you ever be ransomed, you can pull out this guide. You’ll know what’s going to kind of happen, and you can better prepare for those effects.”

Will the future president prioritize healthcare cybersecurity? That remains to be seen, but crises do have the capacity to concentrate the mind, experts said.

The nation’s capital “has a very short memory, a short attention span. The policymakers tend to be reactive,” Dameff said. “All it takes is yet another Change Healthcare–like attack that disrupts 30% or more of the nation’s healthcare system for the policymakers to sit up, take notice, and try to come up with solutions.”

In addition, he said, an estimated two data breaches/ransomware attacks are occurring per day. “The fact is that we’re all patients, up to the President of the United States and every member of the Congress is a patient.”

There’s a “very existential, very palpable understanding that cyber safety is patient safety and cyber insecurity is patient insecurity,” Dameff said.

A version of this article appeared on Medscape.com.

From the largest healthcare companies to solo practices, just every organization in medicine faces a risk for costly cyberattacks. In recent years, hackers have threatened to release the personal information of patients and employees — or paralyze online systems — unless they’re paid a ransom.

Should companies pay? It’s not an easy answer, a pair of experts told colleagues in an American Medical Association (AMA) cybersecurity webinar on October 18. It turns out that each choice — pay or don’t pay — can end up being costly.

This is just one of the new challenges facing the American medical system on the cybersecurity front, the speakers said. Others include the possibility that hackers will manipulate patient data — turning a medical test negative, for example, when it’s actually positive — and take advantage of the powers of artificial intelligence (AI).

The AMA held the webinar to educate physicians about cybersecurity risks and defenses, an especially hot topic in the wake of February’s Change Healthcare hack, which cost UnitedHealth Group an estimated $2.5 billion — so far — and deeply disrupted the American healthcare system.

Cautionary tales abound. Greg Garcia, executive director for cybersecurity of the Health Sector Coordinating Council, a coalition of medical industry organizations, pointed to a Pennsylvania clinic that refused to pay a ransom to prevent the release of hundreds of images of patients with breast cancer undressed from the waist up. Garcia told webinar participants that the ransom was $5 million.
 

Risky Choices

While the Federal Bureau of Investigation recommends against paying a ransom, this can be a risky choice, Garcia said. Hackers released the images, and the center has reportedly agreed to settle a class-action lawsuit for $65 million. “They traded $5 million for $60 million,” Garcia added, slightly misstating the settlement amount.

Health systems have been cagey about whether they’ve paid ransoms to prevent private data from being made public in cyberattacks. If a ransom is demanded, “it’s every organization for itself,” Garcia said.

He highlighted the case of a chain of psychiatry practices in Finland that suffered a ransomware attack in 2020. The hackers “contacted the patients and said: ‘Hey, call your clinic and tell them to pay the ransom. Otherwise, we’re going to release all your psychiatric notes to the public.’ ”

Cyberattacks continue. In October, Boston Children’s Health Physicians announced that it had suffered a “ recent security incident” involving data — possibly including Social Security numbers and treatment information — regarding patients and employees. A hacker group reportedly claimed responsibility and wants the system, which boasts more than 300 clinicians, to pay a ransom or else it will release the stolen information.
 

Should Paying Ransom Be a Crime?

Christian Dameff, MD, MS, an emergency medicine physician and director of the Center for Healthcare Cybersecurity at the University of California (UC), San Diego, noted that there are efforts to turn paying ransom into a crime. “If people aren’t paying ransoms, then ransomware operators will move to something else that makes them money.”

Dameff urged colleagues to understand we no longer live in a world where clinicians only bother to think of technology when they call the IT department to help them reset their password.

New challenges face clinicians, he said. “How do we develop better strategies, downtime procedures, and safe clinical care in an era where our vital technology may be gone, not just for an hour or 2, but as is the case with these ransomware attacks, sometimes weeks to months.”

Garcia said “cybersecurity is everybody’s responsibility, including frontline clinicians. Because you’re touching data, you’re touching technology, you’re touching patients, and all of those things combine to present some vulnerabilities in the digital world.”
 

Next Frontier: Hackers May Manipulate Patient Data

Dameff said future hackers may use AI to manipulate individual patient data in ways that threaten patient health. AI makes this easier to accomplish.

“What if I delete your allergies in your electronic health record, or I manipulate your chest x-ray, or I change your lab values so it looks like you’re in diabetic ketoacidosis when you’re not so a clinician gives you insulin when you don’t need it?”

Garcia highlighted another new threat: Phishing efforts that are harder to ignore thanks to AI.

“One of the most successful way that hackers get in, disrupt systems, and steal data is through email phishing, and it’s only going to get better because of artificial intelligence,” he said. “No longer are you going to have typos in that email written by a hacking group in Nigeria or in China. It’s going to be perfect looking.”

What can practices and healthcare systems do? Garcia highlighted federal health agency efforts to encourage organizations to adopt best practices in cybersecurity.

“If you’ve got a data breach, and you can show to the US Department of Health & Human Services [HHS] you have implemented generally recognized cybersecurity controls over the past year, that you have done your best, you did the right thing, and you still got hit, HHS is directed to essentially take it easy on you,” he said. “That’s a positive incentive.”
 

Ransomware Guide in the Works

Dameff said UC San Diego’s Center for Healthcare Cybersecurity plans to publish a free cybersecurity guide in 2025 that will include specific information about ransomware attacks for medical specialties such as cardiology, trauma surgery, and pediatrics.

“Then, should you ever be ransomed, you can pull out this guide. You’ll know what’s going to kind of happen, and you can better prepare for those effects.”

Will the future president prioritize healthcare cybersecurity? That remains to be seen, but crises do have the capacity to concentrate the mind, experts said.

The nation’s capital “has a very short memory, a short attention span. The policymakers tend to be reactive,” Dameff said. “All it takes is yet another Change Healthcare–like attack that disrupts 30% or more of the nation’s healthcare system for the policymakers to sit up, take notice, and try to come up with solutions.”

In addition, he said, an estimated two data breaches/ransomware attacks are occurring per day. “The fact is that we’re all patients, up to the President of the United States and every member of the Congress is a patient.”

There’s a “very existential, very palpable understanding that cyber safety is patient safety and cyber insecurity is patient insecurity,” Dameff said.

A version of this article appeared on Medscape.com.

On October 1, 2024, International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for central centrifugal cicatricial alopecia (CCCA) and frontal fibrosing alopecia (FFA) took effect for the first time, a development characterized as a critical advancement in the field of hair loss disorders.

“CCCA and FFA are conditions that require early diagnosis and intervention to prevent irreversible hair loss,” Maria Hordinsky, MD, professor of dermatology at the University of Minnesota, Minneapolis, and a member of the Board of Directors, Scarring Alopecia Foundation (SAF), said in an interview.

Ms. Jefferson

For her part, Jefferson said that she will continue to advocate for patients with scarring alopecia as a medical student and when she becomes a physician. “I hope in the near future we will see an externally led FDA Patient-Focused Drug Development meeting for both CCCA and FFA, further advancing care and research for these conditions,” she said in an interview.

McMichael, Hordinsky, and Jefferson had no relevant disclosures to report.

A version of this article appeared on Medscape.com.

On October 1, 2024, International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for central centrifugal cicatricial alopecia (CCCA) and frontal fibrosing alopecia (FFA) took effect for the first time, a development characterized as a critical advancement in the field of hair loss disorders.

“CCCA and FFA are conditions that require early diagnosis and intervention to prevent irreversible hair loss,” Maria Hordinsky, MD, professor of dermatology at the University of Minnesota, Minneapolis, and a member of the Board of Directors, Scarring Alopecia Foundation (SAF), said in an interview.

Ms. Jefferson

For her part, Jefferson said that she will continue to advocate for patients with scarring alopecia as a medical student and when she becomes a physician. “I hope in the near future we will see an externally led FDA Patient-Focused Drug Development meeting for both CCCA and FFA, further advancing care and research for these conditions,” she said in an interview.

McMichael, Hordinsky, and Jefferson had no relevant disclosures to report.

A version of this article appeared on Medscape.com.

On October 1, 2024, International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for central centrifugal cicatricial alopecia (CCCA) and frontal fibrosing alopecia (FFA) took effect for the first time, a development characterized as a critical advancement in the field of hair loss disorders.

“CCCA and FFA are conditions that require early diagnosis and intervention to prevent irreversible hair loss,” Maria Hordinsky, MD, professor of dermatology at the University of Minnesota, Minneapolis, and a member of the Board of Directors, Scarring Alopecia Foundation (SAF), said in an interview.

Ms. Jefferson

For her part, Jefferson said that she will continue to advocate for patients with scarring alopecia as a medical student and when she becomes a physician. “I hope in the near future we will see an externally led FDA Patient-Focused Drug Development meeting for both CCCA and FFA, further advancing care and research for these conditions,” she said in an interview.

McMichael, Hordinsky, and Jefferson had no relevant disclosures to report.

A version of this article appeared on Medscape.com.

As a physician, you might be contacted by the media to provide your professional opinion and advice. Or you might be looking for media interview opportunities to market your practice or side project. And if you do research, media interviews can be an effective way to spread the word. It’s important to prepare for a media interview so that you achieve the outcome you are looking for. Here are six tips I learned from writing health articles, interviewing experts, and being interviewed myself. 

Keep your message simple. When you are a subject expert, you might think that the basics are obvious or even boring, and that the nuances are more important. However, most of the audience is looking for big-picture information that they can apply to their lives. Consider a few key takeaways, keeping in mind that your interview is likely to be edited to short sound bites or a few quotes. It may help to jot down notes so that you cover the fundamentals clearly. You could even write and rehearse a script beforehand. If there is something complicated or subtle that you want to convey, you can preface it by saying, “This is confusing but very important …” to let the audience know to give extra consideration to what you are about to say.

Avoid extremes and hyperbole. Sometimes, exaggerated statements make their way into medical discussions. Statements such as “it doesn’t matter how many calories you consume — it’s all about the quality” are common oversimplifications. But you might be upset to see your name next to a comment like this because it is not actually correct. Check the phrasing of your key takeaways to avoid being stuck defending or explaining an inaccurate statement when your patients ask you about it later. 

Ask the interviewers what they are looking for. Many medical topics have some controversial element, so it is good to know what you’re getting into. Find out the purpose of the article or interview before you decide whether it is right for you. It could be about another doctor in town who is being sued; if you don’t want to be associated with that story, it might be best to decline the interview. 

Explain your goals. You might accept or pursue an interview to raise awareness about an underrecognized condition. You might want the public to identify and get help for early symptoms, or you might want to create empathy for people coping with a disease you treat. Consider why you are participating in an interview, and communicate that to the interviewer to ensure that your objective can be part of the final product. 

Know whom you’re dealing with. It is good to learn about the publication/media channel before you agree to participate. It may have a political bias, or perhaps the interview is intended to promote a specific product. If you agree with and support their purposes, then you may be happy to lend your opinion. But learning about the “voice” of the publication in advance allows you to make an informed decision about whether you want to be identified with a particular political ideology or product endorsement.

Ask to see your quotes before publication. It’s good to have the opportunity to make corrections in case you are accidentally misquoted or misunderstood. It is best to ask to see quotes before you agree to the interview. Some reporters may agree to (or even prefer) a written question-and-answer format so that they can directly quote your responses without rephrasing your words. You could suggest this, especially if you are too busy for a call or live meeting.

As a physician, your insights and advice can be highly beneficial to others. You can also use media interviews to propel your career forward. Doing your homework can ensure that you will be pleased with the final product and how your words were used. 
 

Dr. Moawad, Clinical Assistant Professor, Department of Medical Education, Case Western Reserve University School of Medicine, Cleveland, Ohio, has disclosed no relevant financial relationships.

A version of this article appeared on Medscape.com.

As a physician, you might be contacted by the media to provide your professional opinion and advice. Or you might be looking for media interview opportunities to market your practice or side project. And if you do research, media interviews can be an effective way to spread the word. It’s important to prepare for a media interview so that you achieve the outcome you are looking for. Here are six tips I learned from writing health articles, interviewing experts, and being interviewed myself. 

Keep your message simple. When you are a subject expert, you might think that the basics are obvious or even boring, and that the nuances are more important. However, most of the audience is looking for big-picture information that they can apply to their lives. Consider a few key takeaways, keeping in mind that your interview is likely to be edited to short sound bites or a few quotes. It may help to jot down notes so that you cover the fundamentals clearly. You could even write and rehearse a script beforehand. If there is something complicated or subtle that you want to convey, you can preface it by saying, “This is confusing but very important …” to let the audience know to give extra consideration to what you are about to say.

Avoid extremes and hyperbole. Sometimes, exaggerated statements make their way into medical discussions. Statements such as “it doesn’t matter how many calories you consume — it’s all about the quality” are common oversimplifications. But you might be upset to see your name next to a comment like this because it is not actually correct. Check the phrasing of your key takeaways to avoid being stuck defending or explaining an inaccurate statement when your patients ask you about it later. 

Ask the interviewers what they are looking for. Many medical topics have some controversial element, so it is good to know what you’re getting into. Find out the purpose of the article or interview before you decide whether it is right for you. It could be about another doctor in town who is being sued; if you don’t want to be associated with that story, it might be best to decline the interview. 

Explain your goals. You might accept or pursue an interview to raise awareness about an underrecognized condition. You might want the public to identify and get help for early symptoms, or you might want to create empathy for people coping with a disease you treat. Consider why you are participating in an interview, and communicate that to the interviewer to ensure that your objective can be part of the final product. 

Know whom you’re dealing with. It is good to learn about the publication/media channel before you agree to participate. It may have a political bias, or perhaps the interview is intended to promote a specific product. If you agree with and support their purposes, then you may be happy to lend your opinion. But learning about the “voice” of the publication in advance allows you to make an informed decision about whether you want to be identified with a particular political ideology or product endorsement.

Ask to see your quotes before publication. It’s good to have the opportunity to make corrections in case you are accidentally misquoted or misunderstood. It is best to ask to see quotes before you agree to the interview. Some reporters may agree to (or even prefer) a written question-and-answer format so that they can directly quote your responses without rephrasing your words. You could suggest this, especially if you are too busy for a call or live meeting.

As a physician, your insights and advice can be highly beneficial to others. You can also use media interviews to propel your career forward. Doing your homework can ensure that you will be pleased with the final product and how your words were used. 
 

Dr. Moawad, Clinical Assistant Professor, Department of Medical Education, Case Western Reserve University School of Medicine, Cleveland, Ohio, has disclosed no relevant financial relationships.

A version of this article appeared on Medscape.com.

As a physician, you might be contacted by the media to provide your professional opinion and advice. Or you might be looking for media interview opportunities to market your practice or side project. And if you do research, media interviews can be an effective way to spread the word. It’s important to prepare for a media interview so that you achieve the outcome you are looking for. Here are six tips I learned from writing health articles, interviewing experts, and being interviewed myself. 

Keep your message simple. When you are a subject expert, you might think that the basics are obvious or even boring, and that the nuances are more important. However, most of the audience is looking for big-picture information that they can apply to their lives. Consider a few key takeaways, keeping in mind that your interview is likely to be edited to short sound bites or a few quotes. It may help to jot down notes so that you cover the fundamentals clearly. You could even write and rehearse a script beforehand. If there is something complicated or subtle that you want to convey, you can preface it by saying, “This is confusing but very important …” to let the audience know to give extra consideration to what you are about to say.

Avoid extremes and hyperbole. Sometimes, exaggerated statements make their way into medical discussions. Statements such as “it doesn’t matter how many calories you consume — it’s all about the quality” are common oversimplifications. But you might be upset to see your name next to a comment like this because it is not actually correct. Check the phrasing of your key takeaways to avoid being stuck defending or explaining an inaccurate statement when your patients ask you about it later. 

Ask the interviewers what they are looking for. Many medical topics have some controversial element, so it is good to know what you’re getting into. Find out the purpose of the article or interview before you decide whether it is right for you. It could be about another doctor in town who is being sued; if you don’t want to be associated with that story, it might be best to decline the interview. 

Explain your goals. You might accept or pursue an interview to raise awareness about an underrecognized condition. You might want the public to identify and get help for early symptoms, or you might want to create empathy for people coping with a disease you treat. Consider why you are participating in an interview, and communicate that to the interviewer to ensure that your objective can be part of the final product. 

Know whom you’re dealing with. It is good to learn about the publication/media channel before you agree to participate. It may have a political bias, or perhaps the interview is intended to promote a specific product. If you agree with and support their purposes, then you may be happy to lend your opinion. But learning about the “voice” of the publication in advance allows you to make an informed decision about whether you want to be identified with a particular political ideology or product endorsement.

Ask to see your quotes before publication. It’s good to have the opportunity to make corrections in case you are accidentally misquoted or misunderstood. It is best to ask to see quotes before you agree to the interview. Some reporters may agree to (or even prefer) a written question-and-answer format so that they can directly quote your responses without rephrasing your words. You could suggest this, especially if you are too busy for a call or live meeting.

As a physician, your insights and advice can be highly beneficial to others. You can also use media interviews to propel your career forward. Doing your homework can ensure that you will be pleased with the final product and how your words were used. 
 

Dr. Moawad, Clinical Assistant Professor, Department of Medical Education, Case Western Reserve University School of Medicine, Cleveland, Ohio, has disclosed no relevant financial relationships.

A version of this article appeared on Medscape.com.

US veterans were nearly three times more likely to develop skin cancer than the general population, according to a large cross-sectional analysis of recent national data.

“US veterans are known to have increased risk of cancers and cancer morbidity compared to the general US population,” one of the study authors, Sepideh Ashrafzadeh, MD, a third-year dermatology resident at Massachusetts General Hospital, Boston, told this news organization following the annual meeting of the American Society for Dermatologic Surgery, where the results were presented. “There have been several studies that have shown that US veterans have an increased prevalence of melanoma compared to nonveterans,” she said, noting, however, that no study has investigated the prevalence of nonmelanoma skin cancers (NMSCs), which include basal cell carcinomas and squamous cell carcinomas, compared with the general population.

Dr. Ashrafzadeh

To address this knowledge gap, the researchers performed a national cross-sectional study of adults aged 18 years or older from the 2019-2023 National Health Interview Surveys to examine the prevalence of melanoma and NMSCs among veterans compared with the general US population. They aggregated and tabulated the data by veteran status, defined as having served at any point in the US armed forces, reserves, or national guard, and by demographic and socioeconomic status variables. Next, they performed multivariate logistic regression for skin cancer risk adjusted for age, sex, race, ethnicity, urbanicity, and disability status.

The study population consisted of 14,301 veterans and 209,936 nonveterans. Compared with nonveterans, veterans were more likely to have been diagnosed with skin cancer at some point in their lives (7% vs 2.4%; P < .001); had a higher mean age of skin cancer diagnosis (61.1 vs 55.8 years; P < .001); were more likely to have been diagnosed with melanoma (2.8% vs 0.9%; P < .001), and were more likely to have been diagnosed with NMSC (4.4% vs 1.6%; P < .001).

The researchers found that older age, White race, non-Hispanic ethnicity, and veteran status were all associated with higher odds of developing NMSCs, even after adjusting for relevant covariates. Specifically, veterans had 1.23 higher odds of developing NMSC than the general population, while two factors were protective for developing NMSCs: Living in a rural setting (adjusted odds ratio [aOR], 0.78) and receiving supplemental security income or disability income (aOR, 0.69).

In another part of the study, the researchers evaluated demographic and socioeconomic variables associated with developing melanoma among veterans. These included the following: Male (aOR, 1.16), older age (50-64 years: aOR, 6.82; 65-74 years: aOR, 12.55; and 75 years or older: aOR, 16.16), White race (aOR, 9.24), and non-Hispanic ethnicity (aOR, 7.15).

“Veterans may have occupational risks such as sun and chemical exposure, as well as behavioral habits for sun protection, that may contribute to their elevated risk of melanoma and NMSCs,” Ashrafzadeh said. “Therefore, US veterans would benefit from targeted and regular skin cancer screenings, sun protective preventative resources such as hats and sunscreen, and access to medical and surgical care for diagnosis and treatment of skin cancers.”

Christine Ko, MD, professor of dermatology and pathology at Yale University, New Haven, Connecticut, who was asked to comment on the findings, said that a key strength of the study is that it drew from a nationally representative sample. “A limitation is that skin cancer was self-reported rather than based on documented medical histories,” Ko said. “The study confirms that skin cancer risk is higher in older individuals (> 75 as compared to < 50) and in individuals of self-reported white race and non-Hispanic ethnicity,” she added.

Neither the researchers nor Ko reported having relevant disclosures.
 

A version of this article first appeared on Medscape.com.

US veterans were nearly three times more likely to develop skin cancer than the general population, according to a large cross-sectional analysis of recent national data.

“US veterans are known to have increased risk of cancers and cancer morbidity compared to the general US population,” one of the study authors, Sepideh Ashrafzadeh, MD, a third-year dermatology resident at Massachusetts General Hospital, Boston, told this news organization following the annual meeting of the American Society for Dermatologic Surgery, where the results were presented. “There have been several studies that have shown that US veterans have an increased prevalence of melanoma compared to nonveterans,” she said, noting, however, that no study has investigated the prevalence of nonmelanoma skin cancers (NMSCs), which include basal cell carcinomas and squamous cell carcinomas, compared with the general population.

Dr. Ashrafzadeh

To address this knowledge gap, the researchers performed a national cross-sectional study of adults aged 18 years or older from the 2019-2023 National Health Interview Surveys to examine the prevalence of melanoma and NMSCs among veterans compared with the general US population. They aggregated and tabulated the data by veteran status, defined as having served at any point in the US armed forces, reserves, or national guard, and by demographic and socioeconomic status variables. Next, they performed multivariate logistic regression for skin cancer risk adjusted for age, sex, race, ethnicity, urbanicity, and disability status.

The study population consisted of 14,301 veterans and 209,936 nonveterans. Compared with nonveterans, veterans were more likely to have been diagnosed with skin cancer at some point in their lives (7% vs 2.4%; P < .001); had a higher mean age of skin cancer diagnosis (61.1 vs 55.8 years; P < .001); were more likely to have been diagnosed with melanoma (2.8% vs 0.9%; P < .001), and were more likely to have been diagnosed with NMSC (4.4% vs 1.6%; P < .001).

The researchers found that older age, White race, non-Hispanic ethnicity, and veteran status were all associated with higher odds of developing NMSCs, even after adjusting for relevant covariates. Specifically, veterans had 1.23 higher odds of developing NMSC than the general population, while two factors were protective for developing NMSCs: Living in a rural setting (adjusted odds ratio [aOR], 0.78) and receiving supplemental security income or disability income (aOR, 0.69).

In another part of the study, the researchers evaluated demographic and socioeconomic variables associated with developing melanoma among veterans. These included the following: Male (aOR, 1.16), older age (50-64 years: aOR, 6.82; 65-74 years: aOR, 12.55; and 75 years or older: aOR, 16.16), White race (aOR, 9.24), and non-Hispanic ethnicity (aOR, 7.15).

“Veterans may have occupational risks such as sun and chemical exposure, as well as behavioral habits for sun protection, that may contribute to their elevated risk of melanoma and NMSCs,” Ashrafzadeh said. “Therefore, US veterans would benefit from targeted and regular skin cancer screenings, sun protective preventative resources such as hats and sunscreen, and access to medical and surgical care for diagnosis and treatment of skin cancers.”

Christine Ko, MD, professor of dermatology and pathology at Yale University, New Haven, Connecticut, who was asked to comment on the findings, said that a key strength of the study is that it drew from a nationally representative sample. “A limitation is that skin cancer was self-reported rather than based on documented medical histories,” Ko said. “The study confirms that skin cancer risk is higher in older individuals (> 75 as compared to < 50) and in individuals of self-reported white race and non-Hispanic ethnicity,” she added.

Neither the researchers nor Ko reported having relevant disclosures.
 

A version of this article first appeared on Medscape.com.

US veterans were nearly three times more likely to develop skin cancer than the general population, according to a large cross-sectional analysis of recent national data.

“US veterans are known to have increased risk of cancers and cancer morbidity compared to the general US population,” one of the study authors, Sepideh Ashrafzadeh, MD, a third-year dermatology resident at Massachusetts General Hospital, Boston, told this news organization following the annual meeting of the American Society for Dermatologic Surgery, where the results were presented. “There have been several studies that have shown that US veterans have an increased prevalence of melanoma compared to nonveterans,” she said, noting, however, that no study has investigated the prevalence of nonmelanoma skin cancers (NMSCs), which include basal cell carcinomas and squamous cell carcinomas, compared with the general population.

Dr. Ashrafzadeh

To address this knowledge gap, the researchers performed a national cross-sectional study of adults aged 18 years or older from the 2019-2023 National Health Interview Surveys to examine the prevalence of melanoma and NMSCs among veterans compared with the general US population. They aggregated and tabulated the data by veteran status, defined as having served at any point in the US armed forces, reserves, or national guard, and by demographic and socioeconomic status variables. Next, they performed multivariate logistic regression for skin cancer risk adjusted for age, sex, race, ethnicity, urbanicity, and disability status.

The study population consisted of 14,301 veterans and 209,936 nonveterans. Compared with nonveterans, veterans were more likely to have been diagnosed with skin cancer at some point in their lives (7% vs 2.4%; P < .001); had a higher mean age of skin cancer diagnosis (61.1 vs 55.8 years; P < .001); were more likely to have been diagnosed with melanoma (2.8% vs 0.9%; P < .001), and were more likely to have been diagnosed with NMSC (4.4% vs 1.6%; P < .001).

The researchers found that older age, White race, non-Hispanic ethnicity, and veteran status were all associated with higher odds of developing NMSCs, even after adjusting for relevant covariates. Specifically, veterans had 1.23 higher odds of developing NMSC than the general population, while two factors were protective for developing NMSCs: Living in a rural setting (adjusted odds ratio [aOR], 0.78) and receiving supplemental security income or disability income (aOR, 0.69).

In another part of the study, the researchers evaluated demographic and socioeconomic variables associated with developing melanoma among veterans. These included the following: Male (aOR, 1.16), older age (50-64 years: aOR, 6.82; 65-74 years: aOR, 12.55; and 75 years or older: aOR, 16.16), White race (aOR, 9.24), and non-Hispanic ethnicity (aOR, 7.15).

“Veterans may have occupational risks such as sun and chemical exposure, as well as behavioral habits for sun protection, that may contribute to their elevated risk of melanoma and NMSCs,” Ashrafzadeh said. “Therefore, US veterans would benefit from targeted and regular skin cancer screenings, sun protective preventative resources such as hats and sunscreen, and access to medical and surgical care for diagnosis and treatment of skin cancers.”

Christine Ko, MD, professor of dermatology and pathology at Yale University, New Haven, Connecticut, who was asked to comment on the findings, said that a key strength of the study is that it drew from a nationally representative sample. “A limitation is that skin cancer was self-reported rather than based on documented medical histories,” Ko said. “The study confirms that skin cancer risk is higher in older individuals (> 75 as compared to < 50) and in individuals of self-reported white race and non-Hispanic ethnicity,” she added.

Neither the researchers nor Ko reported having relevant disclosures.
 

A version of this article first appeared on Medscape.com.

SAN FRANCISCO — While the physical toll of cancer is well documented, the financial toll can also be severe and lasting.

Overall, patients with cancer tend to face higher rates of debt collection, medical collections, and bankruptcies, as well as lower credit scores, according to two new studies presented at the American College of Surgeons Clinical Congress 2024.

“These are the first studies to provide numerical evidence of financial toxicity among cancer survivors,” Benjamin C. James, MD, with Beth Israel Deaconess Medical Center and Harvard Medical School, both in Boston, Massachusetts, who worked on both studies, said in a statement. “Previous data on this topic largely relies on subjective survey reviews.”

In one study, researchers used the Massachusetts Cancer Registry to identify 99,175 patients diagnosed with cancer between 2010 and 2019 and matched them with 188,875 control individuals without cancer. Researchers then assessed financial toxicity using Experian credit bureau data for participants.

Overall, patients with cancer faced a range of financial challenges that often lasted years following their diagnosis.

Patients were nearly five times more likely to experience bankruptcy and had average credit scores nearly 80 points lower than control individuals without cancer. The drop in credit scores was more pronounced for survivors of bladder, liver, lung, and colorectal cancer (CRC) and persisted for up to 9.5 years.

For certain cancer types, in particular, “we are looking years after a diagnosis, and we see that the credit score goes down and it never comes back up,” James said.

The other study, which used a sample of 7227 patients with CRC from Massachusetts, identified several factors that correlated with lower credit scores.

Compared with patients who only had surgery, peers who underwent radiation only experienced a 62-point drop in their credit score after their diagnosis, while those who had chemotherapy alone had just over a 14-point drop in their credit score. Among patients who had combination treatments, those who underwent both surgery and radiation experienced a nearly 16-point drop in their credit score and those who had surgery and chemoradiation actually experienced a 2.59 bump, compared with those who had surgery alone.

Financial toxicity was worse for patients younger than 62 years, those identifying as Black or Hispanic individuals, unmarried individuals, those with an annual income below $52,000, and those living in deprived areas.

The studies add to findings from the 2015 North American Thyroid Cancer Survivorship Study, which reported that 50% of thyroid cancer survivors encountered financial toxicity because of their diagnosis.

James said the persistent financial strain of cancer care, even in a state like Massachusetts, which mandates universal healthcare, underscores the need for “broader policy changes and reforms, including reconsidering debt collection practices.”

“Financial security should be a priority in cancer care,” he added.

The studies had no specific funding. The authors have disclosed no relevant conflict of interest.

A version of this article first appeared on Medscape.com.

SAN FRANCISCO — While the physical toll of cancer is well documented, the financial toll can also be severe and lasting.

Overall, patients with cancer tend to face higher rates of debt collection, medical collections, and bankruptcies, as well as lower credit scores, according to two new studies presented at the American College of Surgeons Clinical Congress 2024.

“These are the first studies to provide numerical evidence of financial toxicity among cancer survivors,” Benjamin C. James, MD, with Beth Israel Deaconess Medical Center and Harvard Medical School, both in Boston, Massachusetts, who worked on both studies, said in a statement. “Previous data on this topic largely relies on subjective survey reviews.”

In one study, researchers used the Massachusetts Cancer Registry to identify 99,175 patients diagnosed with cancer between 2010 and 2019 and matched them with 188,875 control individuals without cancer. Researchers then assessed financial toxicity using Experian credit bureau data for participants.

Overall, patients with cancer faced a range of financial challenges that often lasted years following their diagnosis.

Patients were nearly five times more likely to experience bankruptcy and had average credit scores nearly 80 points lower than control individuals without cancer. The drop in credit scores was more pronounced for survivors of bladder, liver, lung, and colorectal cancer (CRC) and persisted for up to 9.5 years.

For certain cancer types, in particular, “we are looking years after a diagnosis, and we see that the credit score goes down and it never comes back up,” James said.

The other study, which used a sample of 7227 patients with CRC from Massachusetts, identified several factors that correlated with lower credit scores.

Compared with patients who only had surgery, peers who underwent radiation only experienced a 62-point drop in their credit score after their diagnosis, while those who had chemotherapy alone had just over a 14-point drop in their credit score. Among patients who had combination treatments, those who underwent both surgery and radiation experienced a nearly 16-point drop in their credit score and those who had surgery and chemoradiation actually experienced a 2.59 bump, compared with those who had surgery alone.

Financial toxicity was worse for patients younger than 62 years, those identifying as Black or Hispanic individuals, unmarried individuals, those with an annual income below $52,000, and those living in deprived areas.

The studies add to findings from the 2015 North American Thyroid Cancer Survivorship Study, which reported that 50% of thyroid cancer survivors encountered financial toxicity because of their diagnosis.

James said the persistent financial strain of cancer care, even in a state like Massachusetts, which mandates universal healthcare, underscores the need for “broader policy changes and reforms, including reconsidering debt collection practices.”

“Financial security should be a priority in cancer care,” he added.

The studies had no specific funding. The authors have disclosed no relevant conflict of interest.

A version of this article first appeared on Medscape.com.

SAN FRANCISCO — While the physical toll of cancer is well documented, the financial toll can also be severe and lasting.

Overall, patients with cancer tend to face higher rates of debt collection, medical collections, and bankruptcies, as well as lower credit scores, according to two new studies presented at the American College of Surgeons Clinical Congress 2024.

“These are the first studies to provide numerical evidence of financial toxicity among cancer survivors,” Benjamin C. James, MD, with Beth Israel Deaconess Medical Center and Harvard Medical School, both in Boston, Massachusetts, who worked on both studies, said in a statement. “Previous data on this topic largely relies on subjective survey reviews.”

In one study, researchers used the Massachusetts Cancer Registry to identify 99,175 patients diagnosed with cancer between 2010 and 2019 and matched them with 188,875 control individuals without cancer. Researchers then assessed financial toxicity using Experian credit bureau data for participants.

Overall, patients with cancer faced a range of financial challenges that often lasted years following their diagnosis.

Patients were nearly five times more likely to experience bankruptcy and had average credit scores nearly 80 points lower than control individuals without cancer. The drop in credit scores was more pronounced for survivors of bladder, liver, lung, and colorectal cancer (CRC) and persisted for up to 9.5 years.

For certain cancer types, in particular, “we are looking years after a diagnosis, and we see that the credit score goes down and it never comes back up,” James said.

The other study, which used a sample of 7227 patients with CRC from Massachusetts, identified several factors that correlated with lower credit scores.

Compared with patients who only had surgery, peers who underwent radiation only experienced a 62-point drop in their credit score after their diagnosis, while those who had chemotherapy alone had just over a 14-point drop in their credit score. Among patients who had combination treatments, those who underwent both surgery and radiation experienced a nearly 16-point drop in their credit score and those who had surgery and chemoradiation actually experienced a 2.59 bump, compared with those who had surgery alone.

Financial toxicity was worse for patients younger than 62 years, those identifying as Black or Hispanic individuals, unmarried individuals, those with an annual income below $52,000, and those living in deprived areas.

The studies add to findings from the 2015 North American Thyroid Cancer Survivorship Study, which reported that 50% of thyroid cancer survivors encountered financial toxicity because of their diagnosis.

James said the persistent financial strain of cancer care, even in a state like Massachusetts, which mandates universal healthcare, underscores the need for “broader policy changes and reforms, including reconsidering debt collection practices.”

“Financial security should be a priority in cancer care,” he added.

The studies had no specific funding. The authors have disclosed no relevant conflict of interest.

A version of this article first appeared on Medscape.com.