Original Research

To the Editor:

Online resources that are convenient and affordable play a crucial role in mitigating health inequality and improving patient access to health care information; however, the benefits are limited by the quality of information available, as medical misinformation can lead to patients engaging in harmful practices, making dangerous decisions, and even avoiding safe and effective treatments. In this study, we aimed to assess and compare the quality of patient guidance on dermatologic care generated by ChatGPT vs Reddit based on accuracy, appropriateness, and safety. It is essential to assess the quality and reliability of online health information to support patients in making informed decisions about their health.

The emergence and advancement of artificial intelligence and large language models such as ChatGPT present a new method for patients to access health care advice. ChatGPT can engage in conversation by accessing information from existing publicly available data on the internet, including books and websites, up to the year 2023 and providing humanlike responses with context.¹ ChatGPT’s access to a breadth of online evidence-based literature ensures the dissemination of quality information that is quick and without inherent bias, offering the potential to more closely align with health care professionals. ChatGPT’s use in dermatology by patients has shown efficacy, with a 98.87% approval rate by dermatologists scoring its ability to recommend appropriate medication for common dermatologic conditions.² However, ChatGPT has limitations when providing health care advice and has been observed to misunderstand health care standards, lack personalization, and offer incorrect references; currently, the latest publicly available version (ChatGPT 3.5) also is unable to analyze clinical images.^3,4

Reddit is an online social media forum that allows users to post questions and photographs to which anyone can reply and offer advice. Patients may find comfort in online communities where they can connect with others facing similar challenges related to their diagnosis. Within these communities, the responses often share users’ own lived experiences and offer support based on what has and has not worked for them. Prior research found that users intentionally seeking health information via Reddit are likely to implement the advice they receive even without verification of its credibility, suggesting a trust and receptibility to ideas offered on the platform.⁵ Furthermore, a study analyzing the dermatologic content of 17 dermatology related subreddits that had 1000 or more subscribers found that 70.6% of posts fell under the category of “seeking health/cosmetic advice.”⁶ Reddit users thus are vulnerable to receiving advice based on personal bias and exposing their health information to the public.

We hypothesized that ChatGPT would provide users with guidance that was more closely aligned with typical dermatologists’ advice due to its thorough analysis and compilation of diverse sources and recommendations available on the internet. We expected Reddit to yield recommendations of lesser quality and a diminished safety score, primarily due to the absence of credibility-vetting mechanisms and the influence of personal biases within the advice shared.

User-submitted posts to large dermatologic community Reddit forums representing a few of the most common skin conditions (r/eczema, r/acne, r/Folliculitis, r/SebDerm, r/Hidradenitis, r/keratosis, and r/Psoriasis) were retrospectively reviewed from January 2024 to March 2024. The most popular posts that did not include photographs were included in our study. Posts with photographs were excluded, as clinical images were not able to be uploaded to the publicly available ChatGPT 3.5. We collected real user questions about common skin conditions from Reddit forums and then asked ChatGPT to answer those same questions. We compared ChatGPT’s responses to the most upvoted Reddit comments to see how they matched up (eTable).

Each ChatGPT response and the top-rated Reddit comment were independently evaluated by a board certified dermatologist (S.A.) and a dermatology resident (A.H.K.). The quality of the ChatGPT and Reddit responses were determined by scoring the accuracy, appropriateness, safety consideration, and specificity on a 5-point Likert scale (1=low, 5=high). The 2 evaluators’ mean scores for each of the 4 categories were calculated based on adequate interrater reliability, which was tested using Cohen’s κ coefficient. Related-samples sign tests were used to compare ChatGPT and Reddit responses for each of the 4 categories. Analysis was completed using SPSS statistics software version 29.0 (IBM). The evaluators also were asked to provide qualitative feedback on the strengths and weaknesses of each response.

Our retrospective review yielded 20 total questions: 5 (25%) on atopic dermatitis, 4 (20%) on acne, 4 (20%) on hidradenitis suppurativa, 4 (20%) on psoriasis, 1 (5%) on folliculitis, 1 (5%) on keratosis pilaris, and 1 (5%) on seborrheic dermatitis. The number of posts was limited to 20 due to the extensive time required for grading each response. These 20 questions were selected from a larger pool of eligible posts based on factors such as clarity and relevance to common skin conditions. With regard to the types of questions that were asked, 6 (30%) were related to general management of a diagnosis, 5 (25%) were on treatment recommendations for symptom relief, 3 (15%) were on optimal utilization of current treatment regimens, 2 (10%) were on prescription side effects, 2 (10%) were on diagnosis presentation, 1 (5%) was on potential triggers of the diagnosis, and 1 (5%) was on natural treatment recommendations.

Mean (SD) evaluator scores for accuracy were significantly higher among ChatGPT responses compared with Reddit (4.63 [0.60] vs 2.60 [0.98])(P<.001). ChatGPT responses also were significantly higher for appropriateness compared with Reddit (4.55 [0.71] vs 2.58 [1.02])(P<.001) and safety consideration (4.88 [0.56] vs 2.80[0.97])(P <.001). There was no significant difference in mean specificity scores between ChatGPT and Reddit (4.25[1.02] vs 3.80 [0.70])(P=.096)(Figure).

For the Reddit responses, the weighted Cohen’s κ coefficient between the 2 evaluators was 0.200 (95% CI, –.089 to .489) for accuracy, 0.255 (95% CI, .014-.497) for appropriateness, 0.385 (95% CI, .176-.594) for safety consideration, and –0.024 (95% CI, –.177 to .129) for specificity. For the ChatGPT responses, the weighted Cohen’s κ coefficient between the 2 evaluators was 0.426 (95% CI, .122-.730) for accuracy, 0.571 (95% CI, .294-.849) for appropriateness, 0.655 (95% CI, .632-.678) for safety consideration, and 0.313 (95% CI, .043-.584) for specificity.

The strengths and weaknesses of the responses also were qualitatively analyzed. One commonly observed strength was ChatGPT’s frequent and appropriate recommendation for users to consult a dermatologist. In the case of atopic dermatitis—one of the more frequently asked about conditions—ChatGPT consistently emphasized evidence-based strategies such as gentle skin care and moisturization, reflecting alignment with clinical guidelines. Additionally, a common weakness of both ChatGPT and Reddit responses generally was the lack of personalized guidance and comprehensive discussion of the risks and benefits of specific treatments. It also was noted that neither platform consistently explored differential diagnoses—for example, distinguishing atopic dermatitis from conditions such as allergic contact dermatitis—limiting the diagnostic depth of the responses.

ChatGPT and Reddit can provide patients with quick and accessible health information for various dermatologic concerns. The results of our study demonstrated a significantly higher level of accuracy, appropriateness, and safety of responses generated by ChatGPT compared with human-generated responses on Reddit (P<.001). Both platforms offered similarly specific responses to user inquiries, demonstrating ChatGPT’s ability to comprehend user questions and draw from publicly available texts and Reddit users’ contributing insights based on their own first-hand experiences.

Reddit’s dermatologic forums often feature personal anecdotes and unique treatments described by individual users. Although specific to particular dermatologic concerns, such advice lacks an evidence-based standard of care. With the noted inherent trust of patients seeking guidance within Reddit communities, patients may follow unhelpful or potentially dangerous medical advice.⁵ A study examining 300 user-submitted posts on popular Reddit dermatology forums during the COVID-19 pandemic found that the mean scores for top-rated comments’ potential to be misleading or dangerous was 2.33 out of 5 on a Likert scale (95% CI, 2.18- 2.48).⁷ Dermatologists should be aware of the potential risks associated with dermatologic advice offered on Reddit and should caution patients against relying solely on this information without consulting a qualified dermatologist first.

Reddit’s open-forum design provides licensed dermatologists with the opportunity to disseminate evidence based information regarding dermatologic conditions. Currently, there is a subreddit (r/AskDocs) that allows users to post medical questions that can be answered by moderator-verified physicians. Participation from dermatologists in online communities such as this can improve the quality of dermatologic information shared online, combat misinformation, and promote safe skin care practices.

ChatGPT offers more accurate, appropriate, and safe information compared to Reddit responses, but its answers lack personalization. In a clinical setting, a personalized treatment plan from a physician can be tailored with a comprehensive discussion of the risks and benefits. Further, clinical settings allow for diagnosis and confirmation via biopsy and meticulous history taking to ensure that the diagnosis and treatment plan are accurate. While ChatGPT may be an option for seeking basic advice on dermatologic conditions, a licensed dermatologist should always be consulted for proper medical advice. Services such as telehealth may be another option to for patients with limited access to care.

Since ChatGPT 3.5 does not support the ability to upload images, our study acknowledges a limitation regarding the inclusion of Reddit posts containing photographs. Images can improve the response quality from both Reddit users and ChatGPT. While the updated ChatGPT 4o is capable of processing images, it requires a monthly subscription fee. The free version was chosen for use in this study, as this may reflect the most likely version that patients of low socioeconomic status would utilize to access dermatologic care; however, there is potential for growth and improvement of ChatGPT’s capability in providing medical advice.

This study compared the strengths and limitations of ChatGPT’s and Reddit’s responses to common dermatologic inquiries. ChatGPT and Reddit both show potential to be helpful sources of dermatologic health information; however, their current versions have many limitations and require caution and careful examination by patients of the guidance provided. Clinicians should be aware of these limitations when advising patients and emphasize the importance of consulting a licensed dermatologist for personalized, evidence-based care. For the best medical advice, it is always advisable to consult with a licensed dermatologist.

To the Editor:

Online resources that are convenient and affordable play a crucial role in mitigating health inequality and improving patient access to health care information; however, the benefits are limited by the quality of information available, as medical misinformation can lead to patients engaging in harmful practices, making dangerous decisions, and even avoiding safe and effective treatments. In this study, we aimed to assess and compare the quality of patient guidance on dermatologic care generated by ChatGPT vs Reddit based on accuracy, appropriateness, and safety. It is essential to assess the quality and reliability of online health information to support patients in making informed decisions about their health.

The emergence and advancement of artificial intelligence and large language models such as ChatGPT present a new method for patients to access health care advice. ChatGPT can engage in conversation by accessing information from existing publicly available data on the internet, including books and websites, up to the year 2023 and providing humanlike responses with context.¹ ChatGPT’s access to a breadth of online evidence-based literature ensures the dissemination of quality information that is quick and without inherent bias, offering the potential to more closely align with health care professionals. ChatGPT’s use in dermatology by patients has shown efficacy, with a 98.87% approval rate by dermatologists scoring its ability to recommend appropriate medication for common dermatologic conditions.² However, ChatGPT has limitations when providing health care advice and has been observed to misunderstand health care standards, lack personalization, and offer incorrect references; currently, the latest publicly available version (ChatGPT 3.5) also is unable to analyze clinical images.^3,4

Reddit is an online social media forum that allows users to post questions and photographs to which anyone can reply and offer advice. Patients may find comfort in online communities where they can connect with others facing similar challenges related to their diagnosis. Within these communities, the responses often share users’ own lived experiences and offer support based on what has and has not worked for them. Prior research found that users intentionally seeking health information via Reddit are likely to implement the advice they receive even without verification of its credibility, suggesting a trust and receptibility to ideas offered on the platform.⁵ Furthermore, a study analyzing the dermatologic content of 17 dermatology related subreddits that had 1000 or more subscribers found that 70.6% of posts fell under the category of “seeking health/cosmetic advice.”⁶ Reddit users thus are vulnerable to receiving advice based on personal bias and exposing their health information to the public.

We hypothesized that ChatGPT would provide users with guidance that was more closely aligned with typical dermatologists’ advice due to its thorough analysis and compilation of diverse sources and recommendations available on the internet. We expected Reddit to yield recommendations of lesser quality and a diminished safety score, primarily due to the absence of credibility-vetting mechanisms and the influence of personal biases within the advice shared.

User-submitted posts to large dermatologic community Reddit forums representing a few of the most common skin conditions (r/eczema, r/acne, r/Folliculitis, r/SebDerm, r/Hidradenitis, r/keratosis, and r/Psoriasis) were retrospectively reviewed from January 2024 to March 2024. The most popular posts that did not include photographs were included in our study. Posts with photographs were excluded, as clinical images were not able to be uploaded to the publicly available ChatGPT 3.5. We collected real user questions about common skin conditions from Reddit forums and then asked ChatGPT to answer those same questions. We compared ChatGPT’s responses to the most upvoted Reddit comments to see how they matched up (eTable).

Each ChatGPT response and the top-rated Reddit comment were independently evaluated by a board certified dermatologist (S.A.) and a dermatology resident (A.H.K.). The quality of the ChatGPT and Reddit responses were determined by scoring the accuracy, appropriateness, safety consideration, and specificity on a 5-point Likert scale (1=low, 5=high). The 2 evaluators’ mean scores for each of the 4 categories were calculated based on adequate interrater reliability, which was tested using Cohen’s κ coefficient. Related-samples sign tests were used to compare ChatGPT and Reddit responses for each of the 4 categories. Analysis was completed using SPSS statistics software version 29.0 (IBM). The evaluators also were asked to provide qualitative feedback on the strengths and weaknesses of each response.

Our retrospective review yielded 20 total questions: 5 (25%) on atopic dermatitis, 4 (20%) on acne, 4 (20%) on hidradenitis suppurativa, 4 (20%) on psoriasis, 1 (5%) on folliculitis, 1 (5%) on keratosis pilaris, and 1 (5%) on seborrheic dermatitis. The number of posts was limited to 20 due to the extensive time required for grading each response. These 20 questions were selected from a larger pool of eligible posts based on factors such as clarity and relevance to common skin conditions. With regard to the types of questions that were asked, 6 (30%) were related to general management of a diagnosis, 5 (25%) were on treatment recommendations for symptom relief, 3 (15%) were on optimal utilization of current treatment regimens, 2 (10%) were on prescription side effects, 2 (10%) were on diagnosis presentation, 1 (5%) was on potential triggers of the diagnosis, and 1 (5%) was on natural treatment recommendations.

Mean (SD) evaluator scores for accuracy were significantly higher among ChatGPT responses compared with Reddit (4.63 [0.60] vs 2.60 [0.98])(P<.001). ChatGPT responses also were significantly higher for appropriateness compared with Reddit (4.55 [0.71] vs 2.58 [1.02])(P<.001) and safety consideration (4.88 [0.56] vs 2.80[0.97])(P <.001). There was no significant difference in mean specificity scores between ChatGPT and Reddit (4.25[1.02] vs 3.80 [0.70])(P=.096)(Figure).

For the Reddit responses, the weighted Cohen’s κ coefficient between the 2 evaluators was 0.200 (95% CI, –.089 to .489) for accuracy, 0.255 (95% CI, .014-.497) for appropriateness, 0.385 (95% CI, .176-.594) for safety consideration, and –0.024 (95% CI, –.177 to .129) for specificity. For the ChatGPT responses, the weighted Cohen’s κ coefficient between the 2 evaluators was 0.426 (95% CI, .122-.730) for accuracy, 0.571 (95% CI, .294-.849) for appropriateness, 0.655 (95% CI, .632-.678) for safety consideration, and 0.313 (95% CI, .043-.584) for specificity.

The strengths and weaknesses of the responses also were qualitatively analyzed. One commonly observed strength was ChatGPT’s frequent and appropriate recommendation for users to consult a dermatologist. In the case of atopic dermatitis—one of the more frequently asked about conditions—ChatGPT consistently emphasized evidence-based strategies such as gentle skin care and moisturization, reflecting alignment with clinical guidelines. Additionally, a common weakness of both ChatGPT and Reddit responses generally was the lack of personalized guidance and comprehensive discussion of the risks and benefits of specific treatments. It also was noted that neither platform consistently explored differential diagnoses—for example, distinguishing atopic dermatitis from conditions such as allergic contact dermatitis—limiting the diagnostic depth of the responses.

ChatGPT and Reddit can provide patients with quick and accessible health information for various dermatologic concerns. The results of our study demonstrated a significantly higher level of accuracy, appropriateness, and safety of responses generated by ChatGPT compared with human-generated responses on Reddit (P<.001). Both platforms offered similarly specific responses to user inquiries, demonstrating ChatGPT’s ability to comprehend user questions and draw from publicly available texts and Reddit users’ contributing insights based on their own first-hand experiences.

Reddit’s dermatologic forums often feature personal anecdotes and unique treatments described by individual users. Although specific to particular dermatologic concerns, such advice lacks an evidence-based standard of care. With the noted inherent trust of patients seeking guidance within Reddit communities, patients may follow unhelpful or potentially dangerous medical advice.⁵ A study examining 300 user-submitted posts on popular Reddit dermatology forums during the COVID-19 pandemic found that the mean scores for top-rated comments’ potential to be misleading or dangerous was 2.33 out of 5 on a Likert scale (95% CI, 2.18- 2.48).⁷ Dermatologists should be aware of the potential risks associated with dermatologic advice offered on Reddit and should caution patients against relying solely on this information without consulting a qualified dermatologist first.

Reddit’s open-forum design provides licensed dermatologists with the opportunity to disseminate evidence based information regarding dermatologic conditions. Currently, there is a subreddit (r/AskDocs) that allows users to post medical questions that can be answered by moderator-verified physicians. Participation from dermatologists in online communities such as this can improve the quality of dermatologic information shared online, combat misinformation, and promote safe skin care practices.

ChatGPT offers more accurate, appropriate, and safe information compared to Reddit responses, but its answers lack personalization. In a clinical setting, a personalized treatment plan from a physician can be tailored with a comprehensive discussion of the risks and benefits. Further, clinical settings allow for diagnosis and confirmation via biopsy and meticulous history taking to ensure that the diagnosis and treatment plan are accurate. While ChatGPT may be an option for seeking basic advice on dermatologic conditions, a licensed dermatologist should always be consulted for proper medical advice. Services such as telehealth may be another option to for patients with limited access to care.

Since ChatGPT 3.5 does not support the ability to upload images, our study acknowledges a limitation regarding the inclusion of Reddit posts containing photographs. Images can improve the response quality from both Reddit users and ChatGPT. While the updated ChatGPT 4o is capable of processing images, it requires a monthly subscription fee. The free version was chosen for use in this study, as this may reflect the most likely version that patients of low socioeconomic status would utilize to access dermatologic care; however, there is potential for growth and improvement of ChatGPT’s capability in providing medical advice.

This study compared the strengths and limitations of ChatGPT’s and Reddit’s responses to common dermatologic inquiries. ChatGPT and Reddit both show potential to be helpful sources of dermatologic health information; however, their current versions have many limitations and require caution and careful examination by patients of the guidance provided. Clinicians should be aware of these limitations when advising patients and emphasize the importance of consulting a licensed dermatologist for personalized, evidence-based care. For the best medical advice, it is always advisable to consult with a licensed dermatologist.

To the Editor:

Online resources that are convenient and affordable play a crucial role in mitigating health inequality and improving patient access to health care information; however, the benefits are limited by the quality of information available, as medical misinformation can lead to patients engaging in harmful practices, making dangerous decisions, and even avoiding safe and effective treatments. In this study, we aimed to assess and compare the quality of patient guidance on dermatologic care generated by ChatGPT vs Reddit based on accuracy, appropriateness, and safety. It is essential to assess the quality and reliability of online health information to support patients in making informed decisions about their health.

The emergence and advancement of artificial intelligence and large language models such as ChatGPT present a new method for patients to access health care advice. ChatGPT can engage in conversation by accessing information from existing publicly available data on the internet, including books and websites, up to the year 2023 and providing humanlike responses with context.¹ ChatGPT’s access to a breadth of online evidence-based literature ensures the dissemination of quality information that is quick and without inherent bias, offering the potential to more closely align with health care professionals. ChatGPT’s use in dermatology by patients has shown efficacy, with a 98.87% approval rate by dermatologists scoring its ability to recommend appropriate medication for common dermatologic conditions.² However, ChatGPT has limitations when providing health care advice and has been observed to misunderstand health care standards, lack personalization, and offer incorrect references; currently, the latest publicly available version (ChatGPT 3.5) also is unable to analyze clinical images.^3,4

Reddit is an online social media forum that allows users to post questions and photographs to which anyone can reply and offer advice. Patients may find comfort in online communities where they can connect with others facing similar challenges related to their diagnosis. Within these communities, the responses often share users’ own lived experiences and offer support based on what has and has not worked for them. Prior research found that users intentionally seeking health information via Reddit are likely to implement the advice they receive even without verification of its credibility, suggesting a trust and receptibility to ideas offered on the platform.⁵ Furthermore, a study analyzing the dermatologic content of 17 dermatology related subreddits that had 1000 or more subscribers found that 70.6% of posts fell under the category of “seeking health/cosmetic advice.”⁶ Reddit users thus are vulnerable to receiving advice based on personal bias and exposing their health information to the public.

We hypothesized that ChatGPT would provide users with guidance that was more closely aligned with typical dermatologists’ advice due to its thorough analysis and compilation of diverse sources and recommendations available on the internet. We expected Reddit to yield recommendations of lesser quality and a diminished safety score, primarily due to the absence of credibility-vetting mechanisms and the influence of personal biases within the advice shared.

User-submitted posts to large dermatologic community Reddit forums representing a few of the most common skin conditions (r/eczema, r/acne, r/Folliculitis, r/SebDerm, r/Hidradenitis, r/keratosis, and r/Psoriasis) were retrospectively reviewed from January 2024 to March 2024. The most popular posts that did not include photographs were included in our study. Posts with photographs were excluded, as clinical images were not able to be uploaded to the publicly available ChatGPT 3.5. We collected real user questions about common skin conditions from Reddit forums and then asked ChatGPT to answer those same questions. We compared ChatGPT’s responses to the most upvoted Reddit comments to see how they matched up (eTable).

Each ChatGPT response and the top-rated Reddit comment were independently evaluated by a board certified dermatologist (S.A.) and a dermatology resident (A.H.K.). The quality of the ChatGPT and Reddit responses were determined by scoring the accuracy, appropriateness, safety consideration, and specificity on a 5-point Likert scale (1=low, 5=high). The 2 evaluators’ mean scores for each of the 4 categories were calculated based on adequate interrater reliability, which was tested using Cohen’s κ coefficient. Related-samples sign tests were used to compare ChatGPT and Reddit responses for each of the 4 categories. Analysis was completed using SPSS statistics software version 29.0 (IBM). The evaluators also were asked to provide qualitative feedback on the strengths and weaknesses of each response.

Our retrospective review yielded 20 total questions: 5 (25%) on atopic dermatitis, 4 (20%) on acne, 4 (20%) on hidradenitis suppurativa, 4 (20%) on psoriasis, 1 (5%) on folliculitis, 1 (5%) on keratosis pilaris, and 1 (5%) on seborrheic dermatitis. The number of posts was limited to 20 due to the extensive time required for grading each response. These 20 questions were selected from a larger pool of eligible posts based on factors such as clarity and relevance to common skin conditions. With regard to the types of questions that were asked, 6 (30%) were related to general management of a diagnosis, 5 (25%) were on treatment recommendations for symptom relief, 3 (15%) were on optimal utilization of current treatment regimens, 2 (10%) were on prescription side effects, 2 (10%) were on diagnosis presentation, 1 (5%) was on potential triggers of the diagnosis, and 1 (5%) was on natural treatment recommendations.

Mean (SD) evaluator scores for accuracy were significantly higher among ChatGPT responses compared with Reddit (4.63 [0.60] vs 2.60 [0.98])(P<.001). ChatGPT responses also were significantly higher for appropriateness compared with Reddit (4.55 [0.71] vs 2.58 [1.02])(P<.001) and safety consideration (4.88 [0.56] vs 2.80[0.97])(P <.001). There was no significant difference in mean specificity scores between ChatGPT and Reddit (4.25[1.02] vs 3.80 [0.70])(P=.096)(Figure).

For the Reddit responses, the weighted Cohen’s κ coefficient between the 2 evaluators was 0.200 (95% CI, –.089 to .489) for accuracy, 0.255 (95% CI, .014-.497) for appropriateness, 0.385 (95% CI, .176-.594) for safety consideration, and –0.024 (95% CI, –.177 to .129) for specificity. For the ChatGPT responses, the weighted Cohen’s κ coefficient between the 2 evaluators was 0.426 (95% CI, .122-.730) for accuracy, 0.571 (95% CI, .294-.849) for appropriateness, 0.655 (95% CI, .632-.678) for safety consideration, and 0.313 (95% CI, .043-.584) for specificity.

The strengths and weaknesses of the responses also were qualitatively analyzed. One commonly observed strength was ChatGPT’s frequent and appropriate recommendation for users to consult a dermatologist. In the case of atopic dermatitis—one of the more frequently asked about conditions—ChatGPT consistently emphasized evidence-based strategies such as gentle skin care and moisturization, reflecting alignment with clinical guidelines. Additionally, a common weakness of both ChatGPT and Reddit responses generally was the lack of personalized guidance and comprehensive discussion of the risks and benefits of specific treatments. It also was noted that neither platform consistently explored differential diagnoses—for example, distinguishing atopic dermatitis from conditions such as allergic contact dermatitis—limiting the diagnostic depth of the responses.

ChatGPT and Reddit can provide patients with quick and accessible health information for various dermatologic concerns. The results of our study demonstrated a significantly higher level of accuracy, appropriateness, and safety of responses generated by ChatGPT compared with human-generated responses on Reddit (P<.001). Both platforms offered similarly specific responses to user inquiries, demonstrating ChatGPT’s ability to comprehend user questions and draw from publicly available texts and Reddit users’ contributing insights based on their own first-hand experiences.

Reddit’s dermatologic forums often feature personal anecdotes and unique treatments described by individual users. Although specific to particular dermatologic concerns, such advice lacks an evidence-based standard of care. With the noted inherent trust of patients seeking guidance within Reddit communities, patients may follow unhelpful or potentially dangerous medical advice.⁵ A study examining 300 user-submitted posts on popular Reddit dermatology forums during the COVID-19 pandemic found that the mean scores for top-rated comments’ potential to be misleading or dangerous was 2.33 out of 5 on a Likert scale (95% CI, 2.18- 2.48).⁷ Dermatologists should be aware of the potential risks associated with dermatologic advice offered on Reddit and should caution patients against relying solely on this information without consulting a qualified dermatologist first.

Reddit’s open-forum design provides licensed dermatologists with the opportunity to disseminate evidence based information regarding dermatologic conditions. Currently, there is a subreddit (r/AskDocs) that allows users to post medical questions that can be answered by moderator-verified physicians. Participation from dermatologists in online communities such as this can improve the quality of dermatologic information shared online, combat misinformation, and promote safe skin care practices.

ChatGPT offers more accurate, appropriate, and safe information compared to Reddit responses, but its answers lack personalization. In a clinical setting, a personalized treatment plan from a physician can be tailored with a comprehensive discussion of the risks and benefits. Further, clinical settings allow for diagnosis and confirmation via biopsy and meticulous history taking to ensure that the diagnosis and treatment plan are accurate. While ChatGPT may be an option for seeking basic advice on dermatologic conditions, a licensed dermatologist should always be consulted for proper medical advice. Services such as telehealth may be another option to for patients with limited access to care.

Since ChatGPT 3.5 does not support the ability to upload images, our study acknowledges a limitation regarding the inclusion of Reddit posts containing photographs. Images can improve the response quality from both Reddit users and ChatGPT. While the updated ChatGPT 4o is capable of processing images, it requires a monthly subscription fee. The free version was chosen for use in this study, as this may reflect the most likely version that patients of low socioeconomic status would utilize to access dermatologic care; however, there is potential for growth and improvement of ChatGPT’s capability in providing medical advice.

This study compared the strengths and limitations of ChatGPT’s and Reddit’s responses to common dermatologic inquiries. ChatGPT and Reddit both show potential to be helpful sources of dermatologic health information; however, their current versions have many limitations and require caution and careful examination by patients of the guidance provided. Clinicians should be aware of these limitations when advising patients and emphasize the importance of consulting a licensed dermatologist for personalized, evidence-based care. For the best medical advice, it is always advisable to consult with a licensed dermatologist.

Excessive alcohol use is one of the leading preventable causes of death in the United States, responsible for about 178,000 deaths annually and an average of 488 daily deaths in 2020 and 2021.¹Alcohol-related deaths increased by 49% between 2006 and 2019.² This trend continued during the COVID-19 pandemic, with death certificates that listed alcohol increasing by > 25% from 2019 to 2020, and another 10% in 2021.³ This increase of alcohol-related deaths includes those as a direct result of chronic alcohol use, such as alcoholic cardiomyopathy, alcoholic hepatitis and cirrhosis, and alcohol-induced pancreatitis, as well as a result of acute use such as alcohol poisoning, suicide by exposure to alcohol, and alcohol-impaired driving fatalities.⁴

Excessive alcohol consumption poses other serious risks, including cases when intake is abruptly reduced without proper management. Alcohol withdrawal syndrome (AWS) can vary in severity, with potentially life-threatening complications such as hallucinations, seizures, and delirium tremens.⁵

These risks highlight the importance of professional intervention and support, not only to mitigate risks associated with AWS, but provide a pathway towards recovery from alcohol use disorder (AUD).

According to the 2022 National Survey on Drug Use and Health, 28.8 million US adults had AUD in the prior year, yet only 7.6% of these individuals received treatment and an even smaller group (2.2%) received medication-assisted treatment for alcohol.^6,7 This is despite American Psychiatric Association guidelines for the pharmacological treatment of patients with AUD, including the use of naltrexone, acamprosate, disulfiram, topiramate, or gabapentin, depending on therapy goals, past medication trials, medication contraindications, and patient preference.⁸ Several of these medications are approved by the US Food and Drug Administration (FDA) for the treatment of AUD and have support for effectiveness from randomized controlled trials and meta-analyses.^9-11

Clinical practice guidelines for the management of substance use disorders (SUDs) from the US Department of Veterans Affairs (VA) and US Department of Defense have strong recommendations for naltrexone and topiramate as first-line pharmacotherapies for moderate to severe AUD. Acamprosate and disulfiram are weak recommendations as alternative options. Gabapentin is a weak recommendation for cases where first-line treatments are contraindicated or ineffective. The guidelines emphasize the importance of a comprehensive approach to AUD treatment, including psychosocial interventions in addition to pharmacotherapy.¹²

A 2023 national survey found veterans reported higher alcohol consumption than nonveterans.¹³ At the end of fiscal year 2023, > 4.4 million veterans—6% of Veterans Health Administration patients—had been diagnosed with AUD.14 However, > 87% of these patients nationally, and 88% of Veterans Integrated Service Network (VISN) 21 patients, were not receiving naltrexone, acamprosate, disulfiram, or topiramate as part of their treatment. The VA Academic Detailing Service (ADS) now includes AUD pharmacotherapy as a campaign focus, highlighting its importance. The ADS is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote aligning prescribing behavior with best practices. Academic detailing methods include speaking with health care practitioners (HCPs), and direct-to-consumer (DTC) patient education.

ADS campaigns include DTC educational handouts. Past ADS projects and research using DTC have demonstrated a significant improvement in outcomes and positively influencing patients’ pharmacotherapy treatment. ^15,16 A VA quality improvement project found a positive correlation between the initiation of AUD pharmacotherapy and engagement with mental health care following the distribution of AUD DTC patient education. ¹⁷ This project aimed to apply the same principles of prior research to explore the use of DTC across multiple facilities within VISN 21 to increase AUD pharmacotherapy. VISN 21 includes VA facilities and clinics across the Pacific Islands, Nevada, and California and serves about 350,000 veterans.

METHODS

A prospective cohort of VISN 21 veterans with or at high risk for AUD was identified using the VA ADS AUD Dashboard. The cohort included those not on acamprosate, disulfiram, naltrexone, topiramate, or gabapentin for treatment of AUD and had an elevated Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) score of ≥ 6 (high risk) with an AUD diagnosis or ≥ 8 (severe risk) without a diagnosis. The AUDIT-C scores used in the dashboard are supported by the VA AUD clinician guide as the minimum scores when AUD pharmacotherapy should be offered to patients.¹⁸ Prescriptions filled outside the VA were not included in this dashboard.

Data and patient information were collected using the VA Corporate Data Warehouse. To be eligible, veterans needed a valid mailing address within the VISN 21 region and a primary care, mental health, or SUD clinician prescriber visit scheduled between October 1, 2023, and January 31, 2024. Veterans were excluded if they were in hospice, had a 1-year mortality risk score > 50% based on their Care Assessment Need (CAN) score, or facility leadership opted out of project involvement. Patients with both severe renal and hepatic impairments were excluded because they were ineligible for AUD pharmacotherapy. However, veterans with either renal or hepatic impairment (but not both) were included, as they could be potential candidates for ≥ 1 AUD pharmacotherapy option.

Initial correspondence with facilities was initiated through local academic detailers. A local champion was identified for the 1 facility without an academic detailer. Facilities could opt in or out of the project. Approval was provided by the local pharmacy and therapeutics committee, pharmacy, primary care, or psychiatry leadership. Approval process and clinician involvement varied by site.

Education

The selected AUD patient education was designed and approved by the national VA ADS (eappendix). The DTC patient education provided general knowledge about alcohol, including what constitutes a standard amount of alcohol, what is considered heavy drinking, risks of heavy drinking, creating a plan with a clinician to reduce and manage withdrawal symptoms, and additional resources. The DTC was accompanied by a cover letter that included a local facility contact number.

A centralized mailing facility was used for all materials. VA Northern California Health Care System provided the funding to cover the cost of postage. The list of veterans to be contacted was updated on a rolling basis and DTC education was mailed 2 weeks prior to their scheduled prescriber visit.

The eligible cohort of 1260 veterans received DTC education. A comparator group of 2048 veterans that did not receive DTC education was obtained retrospectively by using the same inclusion and exclusion criteria with a scheduled primary care, mental health, or SUD HCP visit from October 1, 2022, to January 31, 2023. The outcomes assessed were within 30 days of the scheduled visit, with the primary outcome as the initiation of AUD-related pharmacotherapy and the secondary outcome as the placement of a consultation for mental health or SUD services. Any consultations sent to Behavioral Health, Addiction, Mental Health, Psychiatric, and SUD services following the HCP visit, within the specified time frame, were used for the secondary outcome.

Matching and Analysis

A 1-to-1 nearest neighbor propensity score (PS) matching without replacement was used to pair the 1260 veterans from the intervention group with similarly scored comparator group veterans for a PS-matched final dataset of 2520 veterans. The PS model was a multivariate logistic regression with the outcome being exposure and comparator group status. Baseline characteristics used in the PS model were age, birth sex, race, facility of care, baseline AUDIT-C score, and days between project start and scheduled appointment. Covariate imbalance for the PS-matched sample was assessed to ensure the standardized mean difference for all covariates fell under a 0.1 threshold (Figure).¹⁹

A frequency table was provided to compare the discrete distributions of the baseline characteristics in the intervention and comparator groups. Logistic regression analysis was performed to evaluate the association between DTC education exposure and pharmacotherapy initiation, while controlling for potential confounders. Univariate and multivariate P value results for each variable included in the model were reported along with the multivariate odds ratios (ORs) and their associated 95% CIs. Logistic regression analyses were run for both outcomes. Each model included the exposure and comparator group status as well as the baseline characteristics included in the PS model. Statistical significance was set at P < .05. All statistical analyses were performed with R version 4.2.1.

RESULTS

Two of 7 VISN 21 sites did not participate, and 3 had restrictions on participation. DTC education was mailed about 2 weeks prior to scheduled visit for 1260 veterans; 53.6% identified as White, 37.6% were aged 41 to 60 years, and 79.2% had an AUDIT-C ≥ 8 (Table 1). Of those mailed education, there were 173 no-show appointments (13.7%). Thirty-two veterans (2.5%) in the DTC group and 33 veterans (2.6%) in the comparator group received an AUD-related pharmacotherapy prescription (P = .88) (Table 2). One hundred seventy-one veterans (13.6%) in the DTC group and 160 veterans (12.7%) in the comparator group had a consult placed for mental health or SUD services within 30 days of their appointment (P = .59) (Table 3).

DISCUSSION

This project did not yield statistically significant differences in either the primary or secondary outcomes within the 30-day follow-up window and found limited impact from the DTC educational outreach to veterans. The percentage of veterans that received AUD-related pharmacotherapy or consultations for mental health or SUD services was similarly low in the DTC and comparator groups. These findings suggest that although DTC education may raise awareness, it may not be sufficient on its own to drive changes in prescribing behavior or referral patterns without system-level support.

Addiction is a complex disease faced with stigma and requiring readiness by both the HCP and patient to move forward in support and treatment. The consequences of stigma can be severe: the more stigma perceived by a person with AUD, the less likely they are to seek treatment.²⁰ Stigma may exist even within HCPs and may lead to compromised care including shortened visits, less engagement, and less empathy.¹⁹ Cultural attitude towards alcohol use and intoxication can also be influenced through a wide range of sources including social media, movies, music, and television. Studies have shown targeted alcohol marketing may result in the development of positive beliefs about drinking and expand environments where alcohol use is socially acceptable and encouraged.²¹ These factors can impact drinking behavior, including the onset of drinking, binge drinking, and increased alcohol consumption.²²

Three VISN 21 sites in this study had restrictions on or excluded primary care from participation. Leadership at some of these facilities were concerned that primary care teams did not have the bandwidth to take on additional items and/or there was variable primary care readiness for initiating AUD pharmacotherapy. Further attempts should be made to integrate primary care into the process of initiating AUD treatment as significant research suggests that integrated care models for AUD may be associated with improved process and outcome measures of care.²³

There are several differences between this quality improvement project and prior research investigating the impact of DTC education for other conditions, such as the EMPOWER randomized controlled trial and VISN 22 project, which both demonstrated effectiveness of DTC education for reducing benzodiazepine use in geriatric veterans. ^15,16 These studies focused on reducing or stopping pharmacotherapy use, whereas this project sought to promote the initiation of AUD pharmacotherapy. These studies evaluated outcomes at least 6 months postindex date, whereas this project evaluated outcomes within 30 days postappointment. Furthermore, the educational content varied significantly. Other projects provided patients with information focused on specific medications and interventions, such as benzodiazepine tapering, while this project mailed general information on heavy drinking, its risks, and strategies for cutting back, without mentioning pharmacotherapy. The DTC material used in this project was chosen because it was a preapproved national VA ADS resource, which expedited the project timeline by avoiding the need for additional approvals at each participating site. These differences may impact the observed effectiveness of DTC education in this project, especially regarding the primary outcome.

Strengths and Limitations

This quality improvement project sent a large sample of veterans DTC education in a clinical setting across multiple sites. Additionally, PS matching methods were used to balance covariates between the comparator and DTC education group, thereby simulating a randomized controlled trial and reducing selection bias. The project brought attention to the VISN 21 AUD treatment rates, stimulated conversation across sites about available treatments and resources for AUD, and sparked collaboration between academic detailing, mental health, and primary care services. The time frame for visits was selected during the winter; the National Institute on Alcohol Abuse and Alcoholism notes this is a time when people may be more likely to engage in excessive alcohol consumption than at other times of the year.²⁴

The 30-day time frame for outcomes may have been too short to observe changes in prescribing or referral patterns. Additionally, the comparator group was comprised of veterans seen from October 1, 2022, to January 31, 2023, where seasonal timing may have influenced alcohol consumption behaviors and skewed the results. There were also no-show appointments in the DTC education group (13.7%), though it is likely some patients rescheduled and still received AUD pharmacotherapy within 30 days of the original appointment. Finally, it was not possible to confirm whether a patient opened and read the education that was mailed to them. This may be another reason to explore electronic distribution of DTC education. This all may have contributed to the lack of statistically significant differences in both the primary and secondary outcomes.

There was a high level of variability between facility participation in the project. Two of 7 sites did not participate, and 3 sites restricted primary care engagement. This represents a significant limitation, particularly for the secondary outcome of placing consultations for MH or SUD services. Facilities that only included mental health or SUD HCPs may have resulted in lower consultation rates due to their inherent specialization, reducing the likelihood of self-referrals.

The project may overestimate prescribed AUD pharmacotherapy in the primary outcome due to potential misclassification of medications. While the project adhered to the national VA ADS AUD dashboard’s definition of AUD pharmacotherapy, including acamprosate, disulfiram, naltrexone, topiramate, and gabapentin, some of these medications have multiple indications. For example, gabapentin is commonly prescribed for peripheral neuropathy, and topiramate is used to treat migraines and seizures. The multipurpose use adds uncertainty about whether they were prescribed specifically for AUD treatment, especially in cases where the HCP is responsible for treating a broad range of disease states, as in primary care.

CONCLUSIONS

Results of this quality improvement project did not show a statistically significant difference between patients sent DTC education and the comparator group for the initiation of AUD pharmacotherapy or placement of a consult to mental health or SUD services within 30 days of their scheduled visit. Future studies may seek to implement stricter criteria to confirm the intended use of topiramate and gabapentin, such as looking for keywords in the prescription instructions for use, performing chart reviews, and/or only including these medications if prescribed by a mental health or SUD HCP. Alternatively, future studies may consider limiting the analysis to only FDA-approved AUD medications: acamprosate, disulfiram, and naltrexone. It is vital to continue to enhance primary care HCP readiness to treat AUD, given the existing relationships and trust they often have with patients. Electronic methods for distributing DTC education could also be advantageous, as these methods may have the ability to track whether a message has been opened and read. Despite a lack of statistical significance, this project sparked crucial conversations and collaboration around AUD, available treatments, and addressing potential barriers to connecting patients to care within VISN 21.

Excessive alcohol use is one of the leading preventable causes of death in the United States, responsible for about 178,000 deaths annually and an average of 488 daily deaths in 2020 and 2021.¹Alcohol-related deaths increased by 49% between 2006 and 2019.² This trend continued during the COVID-19 pandemic, with death certificates that listed alcohol increasing by > 25% from 2019 to 2020, and another 10% in 2021.³ This increase of alcohol-related deaths includes those as a direct result of chronic alcohol use, such as alcoholic cardiomyopathy, alcoholic hepatitis and cirrhosis, and alcohol-induced pancreatitis, as well as a result of acute use such as alcohol poisoning, suicide by exposure to alcohol, and alcohol-impaired driving fatalities.⁴

Excessive alcohol consumption poses other serious risks, including cases when intake is abruptly reduced without proper management. Alcohol withdrawal syndrome (AWS) can vary in severity, with potentially life-threatening complications such as hallucinations, seizures, and delirium tremens.⁵

These risks highlight the importance of professional intervention and support, not only to mitigate risks associated with AWS, but provide a pathway towards recovery from alcohol use disorder (AUD).

According to the 2022 National Survey on Drug Use and Health, 28.8 million US adults had AUD in the prior year, yet only 7.6% of these individuals received treatment and an even smaller group (2.2%) received medication-assisted treatment for alcohol.^6,7 This is despite American Psychiatric Association guidelines for the pharmacological treatment of patients with AUD, including the use of naltrexone, acamprosate, disulfiram, topiramate, or gabapentin, depending on therapy goals, past medication trials, medication contraindications, and patient preference.⁸ Several of these medications are approved by the US Food and Drug Administration (FDA) for the treatment of AUD and have support for effectiveness from randomized controlled trials and meta-analyses.^9-11

Clinical practice guidelines for the management of substance use disorders (SUDs) from the US Department of Veterans Affairs (VA) and US Department of Defense have strong recommendations for naltrexone and topiramate as first-line pharmacotherapies for moderate to severe AUD. Acamprosate and disulfiram are weak recommendations as alternative options. Gabapentin is a weak recommendation for cases where first-line treatments are contraindicated or ineffective. The guidelines emphasize the importance of a comprehensive approach to AUD treatment, including psychosocial interventions in addition to pharmacotherapy.¹²

A 2023 national survey found veterans reported higher alcohol consumption than nonveterans.¹³ At the end of fiscal year 2023, > 4.4 million veterans—6% of Veterans Health Administration patients—had been diagnosed with AUD.14 However, > 87% of these patients nationally, and 88% of Veterans Integrated Service Network (VISN) 21 patients, were not receiving naltrexone, acamprosate, disulfiram, or topiramate as part of their treatment. The VA Academic Detailing Service (ADS) now includes AUD pharmacotherapy as a campaign focus, highlighting its importance. The ADS is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote aligning prescribing behavior with best practices. Academic detailing methods include speaking with health care practitioners (HCPs), and direct-to-consumer (DTC) patient education.

ADS campaigns include DTC educational handouts. Past ADS projects and research using DTC have demonstrated a significant improvement in outcomes and positively influencing patients’ pharmacotherapy treatment. ^15,16 A VA quality improvement project found a positive correlation between the initiation of AUD pharmacotherapy and engagement with mental health care following the distribution of AUD DTC patient education. ¹⁷ This project aimed to apply the same principles of prior research to explore the use of DTC across multiple facilities within VISN 21 to increase AUD pharmacotherapy. VISN 21 includes VA facilities and clinics across the Pacific Islands, Nevada, and California and serves about 350,000 veterans.

METHODS

A prospective cohort of VISN 21 veterans with or at high risk for AUD was identified using the VA ADS AUD Dashboard. The cohort included those not on acamprosate, disulfiram, naltrexone, topiramate, or gabapentin for treatment of AUD and had an elevated Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) score of ≥ 6 (high risk) with an AUD diagnosis or ≥ 8 (severe risk) without a diagnosis. The AUDIT-C scores used in the dashboard are supported by the VA AUD clinician guide as the minimum scores when AUD pharmacotherapy should be offered to patients.¹⁸ Prescriptions filled outside the VA were not included in this dashboard.

Data and patient information were collected using the VA Corporate Data Warehouse. To be eligible, veterans needed a valid mailing address within the VISN 21 region and a primary care, mental health, or SUD clinician prescriber visit scheduled between October 1, 2023, and January 31, 2024. Veterans were excluded if they were in hospice, had a 1-year mortality risk score > 50% based on their Care Assessment Need (CAN) score, or facility leadership opted out of project involvement. Patients with both severe renal and hepatic impairments were excluded because they were ineligible for AUD pharmacotherapy. However, veterans with either renal or hepatic impairment (but not both) were included, as they could be potential candidates for ≥ 1 AUD pharmacotherapy option.

Initial correspondence with facilities was initiated through local academic detailers. A local champion was identified for the 1 facility without an academic detailer. Facilities could opt in or out of the project. Approval was provided by the local pharmacy and therapeutics committee, pharmacy, primary care, or psychiatry leadership. Approval process and clinician involvement varied by site.

Education

The selected AUD patient education was designed and approved by the national VA ADS (eappendix). The DTC patient education provided general knowledge about alcohol, including what constitutes a standard amount of alcohol, what is considered heavy drinking, risks of heavy drinking, creating a plan with a clinician to reduce and manage withdrawal symptoms, and additional resources. The DTC was accompanied by a cover letter that included a local facility contact number.

A centralized mailing facility was used for all materials. VA Northern California Health Care System provided the funding to cover the cost of postage. The list of veterans to be contacted was updated on a rolling basis and DTC education was mailed 2 weeks prior to their scheduled prescriber visit.

The eligible cohort of 1260 veterans received DTC education. A comparator group of 2048 veterans that did not receive DTC education was obtained retrospectively by using the same inclusion and exclusion criteria with a scheduled primary care, mental health, or SUD HCP visit from October 1, 2022, to January 31, 2023. The outcomes assessed were within 30 days of the scheduled visit, with the primary outcome as the initiation of AUD-related pharmacotherapy and the secondary outcome as the placement of a consultation for mental health or SUD services. Any consultations sent to Behavioral Health, Addiction, Mental Health, Psychiatric, and SUD services following the HCP visit, within the specified time frame, were used for the secondary outcome.

Matching and Analysis

A 1-to-1 nearest neighbor propensity score (PS) matching without replacement was used to pair the 1260 veterans from the intervention group with similarly scored comparator group veterans for a PS-matched final dataset of 2520 veterans. The PS model was a multivariate logistic regression with the outcome being exposure and comparator group status. Baseline characteristics used in the PS model were age, birth sex, race, facility of care, baseline AUDIT-C score, and days between project start and scheduled appointment. Covariate imbalance for the PS-matched sample was assessed to ensure the standardized mean difference for all covariates fell under a 0.1 threshold (Figure).¹⁹

A frequency table was provided to compare the discrete distributions of the baseline characteristics in the intervention and comparator groups. Logistic regression analysis was performed to evaluate the association between DTC education exposure and pharmacotherapy initiation, while controlling for potential confounders. Univariate and multivariate P value results for each variable included in the model were reported along with the multivariate odds ratios (ORs) and their associated 95% CIs. Logistic regression analyses were run for both outcomes. Each model included the exposure and comparator group status as well as the baseline characteristics included in the PS model. Statistical significance was set at P < .05. All statistical analyses were performed with R version 4.2.1.

RESULTS

Two of 7 VISN 21 sites did not participate, and 3 had restrictions on participation. DTC education was mailed about 2 weeks prior to scheduled visit for 1260 veterans; 53.6% identified as White, 37.6% were aged 41 to 60 years, and 79.2% had an AUDIT-C ≥ 8 (Table 1). Of those mailed education, there were 173 no-show appointments (13.7%). Thirty-two veterans (2.5%) in the DTC group and 33 veterans (2.6%) in the comparator group received an AUD-related pharmacotherapy prescription (P = .88) (Table 2). One hundred seventy-one veterans (13.6%) in the DTC group and 160 veterans (12.7%) in the comparator group had a consult placed for mental health or SUD services within 30 days of their appointment (P = .59) (Table 3).

DISCUSSION

This project did not yield statistically significant differences in either the primary or secondary outcomes within the 30-day follow-up window and found limited impact from the DTC educational outreach to veterans. The percentage of veterans that received AUD-related pharmacotherapy or consultations for mental health or SUD services was similarly low in the DTC and comparator groups. These findings suggest that although DTC education may raise awareness, it may not be sufficient on its own to drive changes in prescribing behavior or referral patterns without system-level support.

Addiction is a complex disease faced with stigma and requiring readiness by both the HCP and patient to move forward in support and treatment. The consequences of stigma can be severe: the more stigma perceived by a person with AUD, the less likely they are to seek treatment.²⁰ Stigma may exist even within HCPs and may lead to compromised care including shortened visits, less engagement, and less empathy.¹⁹ Cultural attitude towards alcohol use and intoxication can also be influenced through a wide range of sources including social media, movies, music, and television. Studies have shown targeted alcohol marketing may result in the development of positive beliefs about drinking and expand environments where alcohol use is socially acceptable and encouraged.²¹ These factors can impact drinking behavior, including the onset of drinking, binge drinking, and increased alcohol consumption.²²

Three VISN 21 sites in this study had restrictions on or excluded primary care from participation. Leadership at some of these facilities were concerned that primary care teams did not have the bandwidth to take on additional items and/or there was variable primary care readiness for initiating AUD pharmacotherapy. Further attempts should be made to integrate primary care into the process of initiating AUD treatment as significant research suggests that integrated care models for AUD may be associated with improved process and outcome measures of care.²³

There are several differences between this quality improvement project and prior research investigating the impact of DTC education for other conditions, such as the EMPOWER randomized controlled trial and VISN 22 project, which both demonstrated effectiveness of DTC education for reducing benzodiazepine use in geriatric veterans. ^15,16 These studies focused on reducing or stopping pharmacotherapy use, whereas this project sought to promote the initiation of AUD pharmacotherapy. These studies evaluated outcomes at least 6 months postindex date, whereas this project evaluated outcomes within 30 days postappointment. Furthermore, the educational content varied significantly. Other projects provided patients with information focused on specific medications and interventions, such as benzodiazepine tapering, while this project mailed general information on heavy drinking, its risks, and strategies for cutting back, without mentioning pharmacotherapy. The DTC material used in this project was chosen because it was a preapproved national VA ADS resource, which expedited the project timeline by avoiding the need for additional approvals at each participating site. These differences may impact the observed effectiveness of DTC education in this project, especially regarding the primary outcome.

Strengths and Limitations

This quality improvement project sent a large sample of veterans DTC education in a clinical setting across multiple sites. Additionally, PS matching methods were used to balance covariates between the comparator and DTC education group, thereby simulating a randomized controlled trial and reducing selection bias. The project brought attention to the VISN 21 AUD treatment rates, stimulated conversation across sites about available treatments and resources for AUD, and sparked collaboration between academic detailing, mental health, and primary care services. The time frame for visits was selected during the winter; the National Institute on Alcohol Abuse and Alcoholism notes this is a time when people may be more likely to engage in excessive alcohol consumption than at other times of the year.²⁴

The 30-day time frame for outcomes may have been too short to observe changes in prescribing or referral patterns. Additionally, the comparator group was comprised of veterans seen from October 1, 2022, to January 31, 2023, where seasonal timing may have influenced alcohol consumption behaviors and skewed the results. There were also no-show appointments in the DTC education group (13.7%), though it is likely some patients rescheduled and still received AUD pharmacotherapy within 30 days of the original appointment. Finally, it was not possible to confirm whether a patient opened and read the education that was mailed to them. This may be another reason to explore electronic distribution of DTC education. This all may have contributed to the lack of statistically significant differences in both the primary and secondary outcomes.

There was a high level of variability between facility participation in the project. Two of 7 sites did not participate, and 3 sites restricted primary care engagement. This represents a significant limitation, particularly for the secondary outcome of placing consultations for MH or SUD services. Facilities that only included mental health or SUD HCPs may have resulted in lower consultation rates due to their inherent specialization, reducing the likelihood of self-referrals.

The project may overestimate prescribed AUD pharmacotherapy in the primary outcome due to potential misclassification of medications. While the project adhered to the national VA ADS AUD dashboard’s definition of AUD pharmacotherapy, including acamprosate, disulfiram, naltrexone, topiramate, and gabapentin, some of these medications have multiple indications. For example, gabapentin is commonly prescribed for peripheral neuropathy, and topiramate is used to treat migraines and seizures. The multipurpose use adds uncertainty about whether they were prescribed specifically for AUD treatment, especially in cases where the HCP is responsible for treating a broad range of disease states, as in primary care.

CONCLUSIONS

Results of this quality improvement project did not show a statistically significant difference between patients sent DTC education and the comparator group for the initiation of AUD pharmacotherapy or placement of a consult to mental health or SUD services within 30 days of their scheduled visit. Future studies may seek to implement stricter criteria to confirm the intended use of topiramate and gabapentin, such as looking for keywords in the prescription instructions for use, performing chart reviews, and/or only including these medications if prescribed by a mental health or SUD HCP. Alternatively, future studies may consider limiting the analysis to only FDA-approved AUD medications: acamprosate, disulfiram, and naltrexone. It is vital to continue to enhance primary care HCP readiness to treat AUD, given the existing relationships and trust they often have with patients. Electronic methods for distributing DTC education could also be advantageous, as these methods may have the ability to track whether a message has been opened and read. Despite a lack of statistical significance, this project sparked crucial conversations and collaboration around AUD, available treatments, and addressing potential barriers to connecting patients to care within VISN 21.

Excessive alcohol use is one of the leading preventable causes of death in the United States, responsible for about 178,000 deaths annually and an average of 488 daily deaths in 2020 and 2021.¹Alcohol-related deaths increased by 49% between 2006 and 2019.² This trend continued during the COVID-19 pandemic, with death certificates that listed alcohol increasing by > 25% from 2019 to 2020, and another 10% in 2021.³ This increase of alcohol-related deaths includes those as a direct result of chronic alcohol use, such as alcoholic cardiomyopathy, alcoholic hepatitis and cirrhosis, and alcohol-induced pancreatitis, as well as a result of acute use such as alcohol poisoning, suicide by exposure to alcohol, and alcohol-impaired driving fatalities.⁴

Excessive alcohol consumption poses other serious risks, including cases when intake is abruptly reduced without proper management. Alcohol withdrawal syndrome (AWS) can vary in severity, with potentially life-threatening complications such as hallucinations, seizures, and delirium tremens.⁵

These risks highlight the importance of professional intervention and support, not only to mitigate risks associated with AWS, but provide a pathway towards recovery from alcohol use disorder (AUD).

According to the 2022 National Survey on Drug Use and Health, 28.8 million US adults had AUD in the prior year, yet only 7.6% of these individuals received treatment and an even smaller group (2.2%) received medication-assisted treatment for alcohol.^6,7 This is despite American Psychiatric Association guidelines for the pharmacological treatment of patients with AUD, including the use of naltrexone, acamprosate, disulfiram, topiramate, or gabapentin, depending on therapy goals, past medication trials, medication contraindications, and patient preference.⁸ Several of these medications are approved by the US Food and Drug Administration (FDA) for the treatment of AUD and have support for effectiveness from randomized controlled trials and meta-analyses.^9-11

Clinical practice guidelines for the management of substance use disorders (SUDs) from the US Department of Veterans Affairs (VA) and US Department of Defense have strong recommendations for naltrexone and topiramate as first-line pharmacotherapies for moderate to severe AUD. Acamprosate and disulfiram are weak recommendations as alternative options. Gabapentin is a weak recommendation for cases where first-line treatments are contraindicated or ineffective. The guidelines emphasize the importance of a comprehensive approach to AUD treatment, including psychosocial interventions in addition to pharmacotherapy.¹²

A 2023 national survey found veterans reported higher alcohol consumption than nonveterans.¹³ At the end of fiscal year 2023, > 4.4 million veterans—6% of Veterans Health Administration patients—had been diagnosed with AUD.14 However, > 87% of these patients nationally, and 88% of Veterans Integrated Service Network (VISN) 21 patients, were not receiving naltrexone, acamprosate, disulfiram, or topiramate as part of their treatment. The VA Academic Detailing Service (ADS) now includes AUD pharmacotherapy as a campaign focus, highlighting its importance. The ADS is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote aligning prescribing behavior with best practices. Academic detailing methods include speaking with health care practitioners (HCPs), and direct-to-consumer (DTC) patient education.

ADS campaigns include DTC educational handouts. Past ADS projects and research using DTC have demonstrated a significant improvement in outcomes and positively influencing patients’ pharmacotherapy treatment. ^15,16 A VA quality improvement project found a positive correlation between the initiation of AUD pharmacotherapy and engagement with mental health care following the distribution of AUD DTC patient education. ¹⁷ This project aimed to apply the same principles of prior research to explore the use of DTC across multiple facilities within VISN 21 to increase AUD pharmacotherapy. VISN 21 includes VA facilities and clinics across the Pacific Islands, Nevada, and California and serves about 350,000 veterans.

METHODS

A prospective cohort of VISN 21 veterans with or at high risk for AUD was identified using the VA ADS AUD Dashboard. The cohort included those not on acamprosate, disulfiram, naltrexone, topiramate, or gabapentin for treatment of AUD and had an elevated Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) score of ≥ 6 (high risk) with an AUD diagnosis or ≥ 8 (severe risk) without a diagnosis. The AUDIT-C scores used in the dashboard are supported by the VA AUD clinician guide as the minimum scores when AUD pharmacotherapy should be offered to patients.¹⁸ Prescriptions filled outside the VA were not included in this dashboard.

Data and patient information were collected using the VA Corporate Data Warehouse. To be eligible, veterans needed a valid mailing address within the VISN 21 region and a primary care, mental health, or SUD clinician prescriber visit scheduled between October 1, 2023, and January 31, 2024. Veterans were excluded if they were in hospice, had a 1-year mortality risk score > 50% based on their Care Assessment Need (CAN) score, or facility leadership opted out of project involvement. Patients with both severe renal and hepatic impairments were excluded because they were ineligible for AUD pharmacotherapy. However, veterans with either renal or hepatic impairment (but not both) were included, as they could be potential candidates for ≥ 1 AUD pharmacotherapy option.

Initial correspondence with facilities was initiated through local academic detailers. A local champion was identified for the 1 facility without an academic detailer. Facilities could opt in or out of the project. Approval was provided by the local pharmacy and therapeutics committee, pharmacy, primary care, or psychiatry leadership. Approval process and clinician involvement varied by site.

Education

The selected AUD patient education was designed and approved by the national VA ADS (eappendix). The DTC patient education provided general knowledge about alcohol, including what constitutes a standard amount of alcohol, what is considered heavy drinking, risks of heavy drinking, creating a plan with a clinician to reduce and manage withdrawal symptoms, and additional resources. The DTC was accompanied by a cover letter that included a local facility contact number.

A centralized mailing facility was used for all materials. VA Northern California Health Care System provided the funding to cover the cost of postage. The list of veterans to be contacted was updated on a rolling basis and DTC education was mailed 2 weeks prior to their scheduled prescriber visit.

The eligible cohort of 1260 veterans received DTC education. A comparator group of 2048 veterans that did not receive DTC education was obtained retrospectively by using the same inclusion and exclusion criteria with a scheduled primary care, mental health, or SUD HCP visit from October 1, 2022, to January 31, 2023. The outcomes assessed were within 30 days of the scheduled visit, with the primary outcome as the initiation of AUD-related pharmacotherapy and the secondary outcome as the placement of a consultation for mental health or SUD services. Any consultations sent to Behavioral Health, Addiction, Mental Health, Psychiatric, and SUD services following the HCP visit, within the specified time frame, were used for the secondary outcome.

Matching and Analysis

A 1-to-1 nearest neighbor propensity score (PS) matching without replacement was used to pair the 1260 veterans from the intervention group with similarly scored comparator group veterans for a PS-matched final dataset of 2520 veterans. The PS model was a multivariate logistic regression with the outcome being exposure and comparator group status. Baseline characteristics used in the PS model were age, birth sex, race, facility of care, baseline AUDIT-C score, and days between project start and scheduled appointment. Covariate imbalance for the PS-matched sample was assessed to ensure the standardized mean difference for all covariates fell under a 0.1 threshold (Figure).¹⁹

A frequency table was provided to compare the discrete distributions of the baseline characteristics in the intervention and comparator groups. Logistic regression analysis was performed to evaluate the association between DTC education exposure and pharmacotherapy initiation, while controlling for potential confounders. Univariate and multivariate P value results for each variable included in the model were reported along with the multivariate odds ratios (ORs) and their associated 95% CIs. Logistic regression analyses were run for both outcomes. Each model included the exposure and comparator group status as well as the baseline characteristics included in the PS model. Statistical significance was set at P < .05. All statistical analyses were performed with R version 4.2.1.

RESULTS

Two of 7 VISN 21 sites did not participate, and 3 had restrictions on participation. DTC education was mailed about 2 weeks prior to scheduled visit for 1260 veterans; 53.6% identified as White, 37.6% were aged 41 to 60 years, and 79.2% had an AUDIT-C ≥ 8 (Table 1). Of those mailed education, there were 173 no-show appointments (13.7%). Thirty-two veterans (2.5%) in the DTC group and 33 veterans (2.6%) in the comparator group received an AUD-related pharmacotherapy prescription (P = .88) (Table 2). One hundred seventy-one veterans (13.6%) in the DTC group and 160 veterans (12.7%) in the comparator group had a consult placed for mental health or SUD services within 30 days of their appointment (P = .59) (Table 3).

DISCUSSION

This project did not yield statistically significant differences in either the primary or secondary outcomes within the 30-day follow-up window and found limited impact from the DTC educational outreach to veterans. The percentage of veterans that received AUD-related pharmacotherapy or consultations for mental health or SUD services was similarly low in the DTC and comparator groups. These findings suggest that although DTC education may raise awareness, it may not be sufficient on its own to drive changes in prescribing behavior or referral patterns without system-level support.

Addiction is a complex disease faced with stigma and requiring readiness by both the HCP and patient to move forward in support and treatment. The consequences of stigma can be severe: the more stigma perceived by a person with AUD, the less likely they are to seek treatment.²⁰ Stigma may exist even within HCPs and may lead to compromised care including shortened visits, less engagement, and less empathy.¹⁹ Cultural attitude towards alcohol use and intoxication can also be influenced through a wide range of sources including social media, movies, music, and television. Studies have shown targeted alcohol marketing may result in the development of positive beliefs about drinking and expand environments where alcohol use is socially acceptable and encouraged.²¹ These factors can impact drinking behavior, including the onset of drinking, binge drinking, and increased alcohol consumption.²²

Three VISN 21 sites in this study had restrictions on or excluded primary care from participation. Leadership at some of these facilities were concerned that primary care teams did not have the bandwidth to take on additional items and/or there was variable primary care readiness for initiating AUD pharmacotherapy. Further attempts should be made to integrate primary care into the process of initiating AUD treatment as significant research suggests that integrated care models for AUD may be associated with improved process and outcome measures of care.²³

There are several differences between this quality improvement project and prior research investigating the impact of DTC education for other conditions, such as the EMPOWER randomized controlled trial and VISN 22 project, which both demonstrated effectiveness of DTC education for reducing benzodiazepine use in geriatric veterans. ^15,16 These studies focused on reducing or stopping pharmacotherapy use, whereas this project sought to promote the initiation of AUD pharmacotherapy. These studies evaluated outcomes at least 6 months postindex date, whereas this project evaluated outcomes within 30 days postappointment. Furthermore, the educational content varied significantly. Other projects provided patients with information focused on specific medications and interventions, such as benzodiazepine tapering, while this project mailed general information on heavy drinking, its risks, and strategies for cutting back, without mentioning pharmacotherapy. The DTC material used in this project was chosen because it was a preapproved national VA ADS resource, which expedited the project timeline by avoiding the need for additional approvals at each participating site. These differences may impact the observed effectiveness of DTC education in this project, especially regarding the primary outcome.

Strengths and Limitations

This quality improvement project sent a large sample of veterans DTC education in a clinical setting across multiple sites. Additionally, PS matching methods were used to balance covariates between the comparator and DTC education group, thereby simulating a randomized controlled trial and reducing selection bias. The project brought attention to the VISN 21 AUD treatment rates, stimulated conversation across sites about available treatments and resources for AUD, and sparked collaboration between academic detailing, mental health, and primary care services. The time frame for visits was selected during the winter; the National Institute on Alcohol Abuse and Alcoholism notes this is a time when people may be more likely to engage in excessive alcohol consumption than at other times of the year.²⁴

The 30-day time frame for outcomes may have been too short to observe changes in prescribing or referral patterns. Additionally, the comparator group was comprised of veterans seen from October 1, 2022, to January 31, 2023, where seasonal timing may have influenced alcohol consumption behaviors and skewed the results. There were also no-show appointments in the DTC education group (13.7%), though it is likely some patients rescheduled and still received AUD pharmacotherapy within 30 days of the original appointment. Finally, it was not possible to confirm whether a patient opened and read the education that was mailed to them. This may be another reason to explore electronic distribution of DTC education. This all may have contributed to the lack of statistically significant differences in both the primary and secondary outcomes.

There was a high level of variability between facility participation in the project. Two of 7 sites did not participate, and 3 sites restricted primary care engagement. This represents a significant limitation, particularly for the secondary outcome of placing consultations for MH or SUD services. Facilities that only included mental health or SUD HCPs may have resulted in lower consultation rates due to their inherent specialization, reducing the likelihood of self-referrals.

The project may overestimate prescribed AUD pharmacotherapy in the primary outcome due to potential misclassification of medications. While the project adhered to the national VA ADS AUD dashboard’s definition of AUD pharmacotherapy, including acamprosate, disulfiram, naltrexone, topiramate, and gabapentin, some of these medications have multiple indications. For example, gabapentin is commonly prescribed for peripheral neuropathy, and topiramate is used to treat migraines and seizures. The multipurpose use adds uncertainty about whether they were prescribed specifically for AUD treatment, especially in cases where the HCP is responsible for treating a broad range of disease states, as in primary care.

CONCLUSIONS

Results of this quality improvement project did not show a statistically significant difference between patients sent DTC education and the comparator group for the initiation of AUD pharmacotherapy or placement of a consult to mental health or SUD services within 30 days of their scheduled visit. Future studies may seek to implement stricter criteria to confirm the intended use of topiramate and gabapentin, such as looking for keywords in the prescription instructions for use, performing chart reviews, and/or only including these medications if prescribed by a mental health or SUD HCP. Alternatively, future studies may consider limiting the analysis to only FDA-approved AUD medications: acamprosate, disulfiram, and naltrexone. It is vital to continue to enhance primary care HCP readiness to treat AUD, given the existing relationships and trust they often have with patients. Electronic methods for distributing DTC education could also be advantageous, as these methods may have the ability to track whether a message has been opened and read. Despite a lack of statistical significance, this project sparked crucial conversations and collaboration around AUD, available treatments, and addressing potential barriers to connecting patients to care within VISN 21.

Store-and-forward teledermatology (SFT) allows clinical images and information to be sent to a dermatologist for evaluation. In fiscal year (FY) 2018, 117,780 SFT consultations were completed in the Veterans Health Administration. Continued growth is expected since SFT has proven to be an effective method for improving access to face-to-face (FTF) dermatology care.¹ In the same period, the US Department of Veterans Affairs (VA) Puget Sound Health Care System (VAPSHCS) completed 12,563 consultations in a mean 1.1 days from entry into episode of care (EEC), according to data reported by VA Teledermatology Program Administrator Chris Foster.

Obtaining a prompt consultation is reported to be an overwhelming advantage of using SFT.^2-5 Rapid turnaround may appear to make SFT specialist care more accessible to veterans, yet this is an oversimplification. The process of delivering care (rather than consultation) through SFT is more complex than reading the images and reporting the findings. When a skin condition is identified by a primary care clinician and that person decides to request an SFT consultation, a complex set of tasks and handoffs is set into motion. A swim-lane diagram illustrates the numerous steps and handoffs that go into delivering care to a patient with a malignant melanoma on the SFT platform compared to FTF care, which requires fewer handoffs (Figure).

This process improvement project examined whether handoffs necessitated by SFT care lengthened the timeline of care for biopsy-proven primary cutaneous malignant melanoma. The stakes of delay in care are high. A 2018 study using the National Cancer Database found that a delay of > 30 days from biopsy to definitive excision (the date definitive surgical procedure for the condition is performed) resulted in a measurable increase in melanoma-related mortality. ⁶ This study sought to identify areas where the SFT timeline of care could be shortened.

Methods

This retrospective cohort study was approved by the VAPSHCS Institutional Review Board. The study drew from secondary data obtained from VistA, the VA Corporate Data Warehouse, the Veterans Integrated Service Network (VISN) 20 database, the American Academy of Dermatology Teledermatology Program database, and the VA Computerized Patient Record System.

Patients registered for ≥ 1 year at VAPSHCS with a diagnosis of primary cutaneous malignant melanoma by the Pathology service between January 1, 2006, and December 31, 2013, were included. Patients with metastatic or recurrent melanoma were excluded.

Cases were randomly selected from a melanoma database previously validated and used for another quality improvement project.⁷ There were initially 115 patient cases extracted from this database for both the FTF and SFT groups. Eighty-seven SFT and 107 FTF cases met inclusion criteria. To further analyze these groups, we split the FTF group into 2 subgroups: FTF dermatology (patients whose melanomas were entered into care in a dermatology clinic) and FTF primary care (patients whose melanomas were entered into care in primary care or a nondermatology setting).

The timeline of care was divided into 2 major time intervals: (1) entry into episode of care (EEC; the date a lesion was first documented in the electronic health record) to biopsy; and (2) biopsy to definitive excision. The SFT process was divided into the following intervals: EEC to imaging request (the date a clinician requested imaging); imaging request to imaging completion (the date an imager photographed a patient’s lesion); imaging completion to SFT consultation request (the date the SFT consultation was requested); SFT consultation request to consultation completion (the date an SFT reader completed the consultation request for a patient); and SFT consultation completion to biopsy. Mean and median interval lengths were compared between groups and additional analyses identified steps that may have contributed to delays in care.

To address potential bias based on access to care for rural veterans, SFT and FTF primary care cases were categorized into groups based on their location: (1) EEC and biopsy conducted at the same facility; (2) EEC and biopsy conducted at different facilities within the same health care system (main health care facility and its community-based outpatient clinics); and (3) EEC and biopsy conducted at different health care systems.

Statistics

Means, medians, and SDs were calculated in Excel. The Mann-Whitney U test was used to compare SFT medians to the FTF data and X² test was used to compare proportions for secondary analyses.

Results

The median (mean) interval from EEC to definitive excision was 73 days (85) for SFT and 58 days (73) for FTF (P = .004) (Table). To understand this difference, the distribution of intervals from EEC to biopsy and biopsy to definitive excision were calculated. Only 38% of SFT cases were biopsied within 20 days compared to 65% of FTF cases (P < .001). The difference in time from biopsy to definitive excision distributions were not statistically significant, suggesting that the difference is actually a reflection of the differences seen in the period between EEC and biopsy.

EEC and biopsy occurred at the same facility in 85% and 82% of FTF primary care and SFT cases, respectively. EEC and biopsy occurred at different facilities within the same health care system in 15% and 16% of FTF primary care and SFT cases, respectively. EEC and biopsy occurred at different health care systems in 0% and 2% of FTF primary care and SFT cases, respectively. Geographic bias did not impact results for either group of veterans.

The interval between EEC and biopsy was shorter for FTF dermatology cases than for FTF primary care cases. For FTF dermatology cases, 96% were biopsied within 20 days compared with 34% of FTF primary care cases (P < .001).

To further analyze the difference in the EEC to biopsy interval duration between SFT and FTF primary care the timeline was divided into smaller steps: EEC to imaging completion, imaging completion to SFT consult completion, and SFT consult completion to biopsy. From EEC to SFT consult completion, SFT cases took a median of 6.0 days and a mean of 12.3 days, reflecting the administrative handoffs that must occur in SFT. A total of 82% of FTF primary care cases were entered into care and consultation was requested on the same day, while this was true for only 1% of SFT cases.

Since mortality data were not collected, the frequency of in situ melanomas and invasive melanomas (pathologic stage pT1a or greater) was used as a proxy for comparing outcomes. No significant difference was found in the frequency of in situ vs invasive melanomas in the SFT and FTF dermatology groups; however, there was a much higher frequency of invasive melanomas in the FTF primary care group (P = .007).

Discussion

This study compared the time to treatment for SFT vs FTF and identified important differences. The episode of care for melanomas diagnosed by SFT was statistically significantly longer (15 days) than those diagnosed by FTF. The interval between biopsy and definitive excision was a median of 34 and 38 days, and a mean of 48 and 44 days for SFT and FTF, respectively, which were not statistically significant. The difference in the total duration of the interval between EEC and definitive excision was accounted for by the duration of the interval from EEC to biopsy. When excluding dermatology clinic cases from the FTF group, there was no difference in the interval between EEC and biopsy for SFT and FTF primary care. The handoffs in SFT accounted for a median of 6 days and mean of 12 days, a significant portion of the timeline, and is a target for process improvement. The delay necessitated by handoffs did not significantly affect the distribution of in situ and invasive melanomas in the SFT and FTF dermatology groups. This suggests that SFT may have better outcomes than FTF primary care.

There has been extensive research on the timeline from the patient initially noticing a lesion to the EEC.^8-11 There is also a body of research on the timeline from biopsy to definitive excision. ^6,12-16 However, there has been little research on the timeline between EEC and biopsy, which comprises a large portion of the overall timeline of both SFT care and FTF care. This study analyzed the delays that can occur in this interval. When patients first enter FTF dermatology care, this timeline is quite short because lesions are often biopsied on the same day. When patients enter into care with their primary or nondermatology clinician, there can be significant delays.

Since the stakes are high when it comes to treating melanoma, it is important to minimize the overall timeline. A 6-day median and 12-day mean were established as targets for teledermatology handoffs. Ideally, a lesion should be entered into an episode of care, imaged, and sent for consultation on the same day. To help further understand delays in administrative handoffs, we stratified the SFT cases by VISN 20 sites and spoke with an administrator at a top performing site. Between 2006 and 2013, this site had a dedicated full-time imager as well as a backup imager that ensured images were taken quickly, usually on the same day the lesion was entered into care. Unfortunately, this is not the standard at all VISN 20 sites and certainly contributes to the overall delay in care in SFT

Minimizing the timeline of care is possible, as shown by the Danish health system, which developed a fast-track referral system after recognizing the need to minimize delays between the presentation, diagnosis, and treatment of cutaneous melanomas. In Denmark, a patient who presents to a general practitioner with a suspicious lesion is referred to secondary care for excision biopsy within 6 days. Diagnosis is made within 2 weeks, and, if necessary, definitive excision is offered within 9 days of the diagnosis. This translates into a maximum 20-day EEC to biopsy timeline and maximum 29-day EEC to definitive excision timeline. Although an intervention such as this may be difficult to implement in the United States due to its size and decentralized health care system, it would, however, be more realistic within the VA due to its centralized structure. The Danish system shows that with appropriate resource allocation and strict timeframes for treatment referrals, the timeline can be minimized.¹⁷

Despite the delay in the SFT timeline, this study found no significant difference between the distribution of in situ vs invasive melanomas in FTF dermatology and SFT groups. One possible explanation for this is that SFT increases access to dermatologist care, meaning clinicians may be more willing to consult SFT for less advanced– appearing lesions.

The finding that SFT diagnosed a larger proportion of in situ melanomas than FTF primary care is consistent with the findings of Ferrándiz et al, who reported that the mean Breslow thickness was significantly lower among patients in an SFT group compared to patients in an FTF group consisting of general practitioners. ¹⁸ However, the study population was not randomized and the results may have been impacted by ascertainment bias. Ferrándiz et al hypothesized that clinicians may have a lower threshold for consulting teledermatology, resulting in lower mean Breslow thicknesses.¹⁸ Karavan et al found the opposite results, with a higher mean Breslow thickness in SFT compared to a primary care FTF group.¹⁹ The data presented here suggest that SFT has room for process improvement yet is essentially equivalent to FTF dermatology in terms of outcomes.

Limitations

The majority of patients in this study were aged > 50 years, White, and male. The results may not be representative for other populations. The study was relatively small compared to studies that looked at other aspects of the melanoma care timeline. The study was not powered to ascertain mortality, the most important metric for melanoma.

Conclusions

The episode of care was significantly longer for melanomas diagnosed by SFT than those diagnosed by FTF; however, timelines were not statistically different when FTF lesions entered into care in dermatology were excluded. A median 6-day and mean 12.3-day delay in administrative handoffs occurred at the beginning of the SFT process and is a target for process improvement. Considering the high stakes of melanoma, the SFT timeline could be reduced if EEC, imaging, and SFT consultation all happened in the same day.

Store-and-forward teledermatology (SFT) allows clinical images and information to be sent to a dermatologist for evaluation. In fiscal year (FY) 2018, 117,780 SFT consultations were completed in the Veterans Health Administration. Continued growth is expected since SFT has proven to be an effective method for improving access to face-to-face (FTF) dermatology care.¹ In the same period, the US Department of Veterans Affairs (VA) Puget Sound Health Care System (VAPSHCS) completed 12,563 consultations in a mean 1.1 days from entry into episode of care (EEC), according to data reported by VA Teledermatology Program Administrator Chris Foster.

Obtaining a prompt consultation is reported to be an overwhelming advantage of using SFT.^2-5 Rapid turnaround may appear to make SFT specialist care more accessible to veterans, yet this is an oversimplification. The process of delivering care (rather than consultation) through SFT is more complex than reading the images and reporting the findings. When a skin condition is identified by a primary care clinician and that person decides to request an SFT consultation, a complex set of tasks and handoffs is set into motion. A swim-lane diagram illustrates the numerous steps and handoffs that go into delivering care to a patient with a malignant melanoma on the SFT platform compared to FTF care, which requires fewer handoffs (Figure).

This process improvement project examined whether handoffs necessitated by SFT care lengthened the timeline of care for biopsy-proven primary cutaneous malignant melanoma. The stakes of delay in care are high. A 2018 study using the National Cancer Database found that a delay of > 30 days from biopsy to definitive excision (the date definitive surgical procedure for the condition is performed) resulted in a measurable increase in melanoma-related mortality. ⁶ This study sought to identify areas where the SFT timeline of care could be shortened.

Methods

This retrospective cohort study was approved by the VAPSHCS Institutional Review Board. The study drew from secondary data obtained from VistA, the VA Corporate Data Warehouse, the Veterans Integrated Service Network (VISN) 20 database, the American Academy of Dermatology Teledermatology Program database, and the VA Computerized Patient Record System.

Patients registered for ≥ 1 year at VAPSHCS with a diagnosis of primary cutaneous malignant melanoma by the Pathology service between January 1, 2006, and December 31, 2013, were included. Patients with metastatic or recurrent melanoma were excluded.

Cases were randomly selected from a melanoma database previously validated and used for another quality improvement project.⁷ There were initially 115 patient cases extracted from this database for both the FTF and SFT groups. Eighty-seven SFT and 107 FTF cases met inclusion criteria. To further analyze these groups, we split the FTF group into 2 subgroups: FTF dermatology (patients whose melanomas were entered into care in a dermatology clinic) and FTF primary care (patients whose melanomas were entered into care in primary care or a nondermatology setting).

The timeline of care was divided into 2 major time intervals: (1) entry into episode of care (EEC; the date a lesion was first documented in the electronic health record) to biopsy; and (2) biopsy to definitive excision. The SFT process was divided into the following intervals: EEC to imaging request (the date a clinician requested imaging); imaging request to imaging completion (the date an imager photographed a patient’s lesion); imaging completion to SFT consultation request (the date the SFT consultation was requested); SFT consultation request to consultation completion (the date an SFT reader completed the consultation request for a patient); and SFT consultation completion to biopsy. Mean and median interval lengths were compared between groups and additional analyses identified steps that may have contributed to delays in care.

To address potential bias based on access to care for rural veterans, SFT and FTF primary care cases were categorized into groups based on their location: (1) EEC and biopsy conducted at the same facility; (2) EEC and biopsy conducted at different facilities within the same health care system (main health care facility and its community-based outpatient clinics); and (3) EEC and biopsy conducted at different health care systems.

Statistics

Means, medians, and SDs were calculated in Excel. The Mann-Whitney U test was used to compare SFT medians to the FTF data and X² test was used to compare proportions for secondary analyses.

Results

The median (mean) interval from EEC to definitive excision was 73 days (85) for SFT and 58 days (73) for FTF (P = .004) (Table). To understand this difference, the distribution of intervals from EEC to biopsy and biopsy to definitive excision were calculated. Only 38% of SFT cases were biopsied within 20 days compared to 65% of FTF cases (P < .001). The difference in time from biopsy to definitive excision distributions were not statistically significant, suggesting that the difference is actually a reflection of the differences seen in the period between EEC and biopsy.

EEC and biopsy occurred at the same facility in 85% and 82% of FTF primary care and SFT cases, respectively. EEC and biopsy occurred at different facilities within the same health care system in 15% and 16% of FTF primary care and SFT cases, respectively. EEC and biopsy occurred at different health care systems in 0% and 2% of FTF primary care and SFT cases, respectively. Geographic bias did not impact results for either group of veterans.

The interval between EEC and biopsy was shorter for FTF dermatology cases than for FTF primary care cases. For FTF dermatology cases, 96% were biopsied within 20 days compared with 34% of FTF primary care cases (P < .001).

To further analyze the difference in the EEC to biopsy interval duration between SFT and FTF primary care the timeline was divided into smaller steps: EEC to imaging completion, imaging completion to SFT consult completion, and SFT consult completion to biopsy. From EEC to SFT consult completion, SFT cases took a median of 6.0 days and a mean of 12.3 days, reflecting the administrative handoffs that must occur in SFT. A total of 82% of FTF primary care cases were entered into care and consultation was requested on the same day, while this was true for only 1% of SFT cases.

Since mortality data were not collected, the frequency of in situ melanomas and invasive melanomas (pathologic stage pT1a or greater) was used as a proxy for comparing outcomes. No significant difference was found in the frequency of in situ vs invasive melanomas in the SFT and FTF dermatology groups; however, there was a much higher frequency of invasive melanomas in the FTF primary care group (P = .007).

Discussion

This study compared the time to treatment for SFT vs FTF and identified important differences. The episode of care for melanomas diagnosed by SFT was statistically significantly longer (15 days) than those diagnosed by FTF. The interval between biopsy and definitive excision was a median of 34 and 38 days, and a mean of 48 and 44 days for SFT and FTF, respectively, which were not statistically significant. The difference in the total duration of the interval between EEC and definitive excision was accounted for by the duration of the interval from EEC to biopsy. When excluding dermatology clinic cases from the FTF group, there was no difference in the interval between EEC and biopsy for SFT and FTF primary care. The handoffs in SFT accounted for a median of 6 days and mean of 12 days, a significant portion of the timeline, and is a target for process improvement. The delay necessitated by handoffs did not significantly affect the distribution of in situ and invasive melanomas in the SFT and FTF dermatology groups. This suggests that SFT may have better outcomes than FTF primary care.

There has been extensive research on the timeline from the patient initially noticing a lesion to the EEC.^8-11 There is also a body of research on the timeline from biopsy to definitive excision. ^6,12-16 However, there has been little research on the timeline between EEC and biopsy, which comprises a large portion of the overall timeline of both SFT care and FTF care. This study analyzed the delays that can occur in this interval. When patients first enter FTF dermatology care, this timeline is quite short because lesions are often biopsied on the same day. When patients enter into care with their primary or nondermatology clinician, there can be significant delays.

Since the stakes are high when it comes to treating melanoma, it is important to minimize the overall timeline. A 6-day median and 12-day mean were established as targets for teledermatology handoffs. Ideally, a lesion should be entered into an episode of care, imaged, and sent for consultation on the same day. To help further understand delays in administrative handoffs, we stratified the SFT cases by VISN 20 sites and spoke with an administrator at a top performing site. Between 2006 and 2013, this site had a dedicated full-time imager as well as a backup imager that ensured images were taken quickly, usually on the same day the lesion was entered into care. Unfortunately, this is not the standard at all VISN 20 sites and certainly contributes to the overall delay in care in SFT

Minimizing the timeline of care is possible, as shown by the Danish health system, which developed a fast-track referral system after recognizing the need to minimize delays between the presentation, diagnosis, and treatment of cutaneous melanomas. In Denmark, a patient who presents to a general practitioner with a suspicious lesion is referred to secondary care for excision biopsy within 6 days. Diagnosis is made within 2 weeks, and, if necessary, definitive excision is offered within 9 days of the diagnosis. This translates into a maximum 20-day EEC to biopsy timeline and maximum 29-day EEC to definitive excision timeline. Although an intervention such as this may be difficult to implement in the United States due to its size and decentralized health care system, it would, however, be more realistic within the VA due to its centralized structure. The Danish system shows that with appropriate resource allocation and strict timeframes for treatment referrals, the timeline can be minimized.¹⁷

Despite the delay in the SFT timeline, this study found no significant difference between the distribution of in situ vs invasive melanomas in FTF dermatology and SFT groups. One possible explanation for this is that SFT increases access to dermatologist care, meaning clinicians may be more willing to consult SFT for less advanced– appearing lesions.

The finding that SFT diagnosed a larger proportion of in situ melanomas than FTF primary care is consistent with the findings of Ferrándiz et al, who reported that the mean Breslow thickness was significantly lower among patients in an SFT group compared to patients in an FTF group consisting of general practitioners. ¹⁸ However, the study population was not randomized and the results may have been impacted by ascertainment bias. Ferrándiz et al hypothesized that clinicians may have a lower threshold for consulting teledermatology, resulting in lower mean Breslow thicknesses.¹⁸ Karavan et al found the opposite results, with a higher mean Breslow thickness in SFT compared to a primary care FTF group.¹⁹ The data presented here suggest that SFT has room for process improvement yet is essentially equivalent to FTF dermatology in terms of outcomes.

Limitations

The majority of patients in this study were aged > 50 years, White, and male. The results may not be representative for other populations. The study was relatively small compared to studies that looked at other aspects of the melanoma care timeline. The study was not powered to ascertain mortality, the most important metric for melanoma.

Conclusions

The episode of care was significantly longer for melanomas diagnosed by SFT than those diagnosed by FTF; however, timelines were not statistically different when FTF lesions entered into care in dermatology were excluded. A median 6-day and mean 12.3-day delay in administrative handoffs occurred at the beginning of the SFT process and is a target for process improvement. Considering the high stakes of melanoma, the SFT timeline could be reduced if EEC, imaging, and SFT consultation all happened in the same day.

Store-and-forward teledermatology (SFT) allows clinical images and information to be sent to a dermatologist for evaluation. In fiscal year (FY) 2018, 117,780 SFT consultations were completed in the Veterans Health Administration. Continued growth is expected since SFT has proven to be an effective method for improving access to face-to-face (FTF) dermatology care.¹ In the same period, the US Department of Veterans Affairs (VA) Puget Sound Health Care System (VAPSHCS) completed 12,563 consultations in a mean 1.1 days from entry into episode of care (EEC), according to data reported by VA Teledermatology Program Administrator Chris Foster.

Obtaining a prompt consultation is reported to be an overwhelming advantage of using SFT.^2-5 Rapid turnaround may appear to make SFT specialist care more accessible to veterans, yet this is an oversimplification. The process of delivering care (rather than consultation) through SFT is more complex than reading the images and reporting the findings. When a skin condition is identified by a primary care clinician and that person decides to request an SFT consultation, a complex set of tasks and handoffs is set into motion. A swim-lane diagram illustrates the numerous steps and handoffs that go into delivering care to a patient with a malignant melanoma on the SFT platform compared to FTF care, which requires fewer handoffs (Figure).

This process improvement project examined whether handoffs necessitated by SFT care lengthened the timeline of care for biopsy-proven primary cutaneous malignant melanoma. The stakes of delay in care are high. A 2018 study using the National Cancer Database found that a delay of > 30 days from biopsy to definitive excision (the date definitive surgical procedure for the condition is performed) resulted in a measurable increase in melanoma-related mortality. ⁶ This study sought to identify areas where the SFT timeline of care could be shortened.

Methods

This retrospective cohort study was approved by the VAPSHCS Institutional Review Board. The study drew from secondary data obtained from VistA, the VA Corporate Data Warehouse, the Veterans Integrated Service Network (VISN) 20 database, the American Academy of Dermatology Teledermatology Program database, and the VA Computerized Patient Record System.

Patients registered for ≥ 1 year at VAPSHCS with a diagnosis of primary cutaneous malignant melanoma by the Pathology service between January 1, 2006, and December 31, 2013, were included. Patients with metastatic or recurrent melanoma were excluded.

Cases were randomly selected from a melanoma database previously validated and used for another quality improvement project.⁷ There were initially 115 patient cases extracted from this database for both the FTF and SFT groups. Eighty-seven SFT and 107 FTF cases met inclusion criteria. To further analyze these groups, we split the FTF group into 2 subgroups: FTF dermatology (patients whose melanomas were entered into care in a dermatology clinic) and FTF primary care (patients whose melanomas were entered into care in primary care or a nondermatology setting).

The timeline of care was divided into 2 major time intervals: (1) entry into episode of care (EEC; the date a lesion was first documented in the electronic health record) to biopsy; and (2) biopsy to definitive excision. The SFT process was divided into the following intervals: EEC to imaging request (the date a clinician requested imaging); imaging request to imaging completion (the date an imager photographed a patient’s lesion); imaging completion to SFT consultation request (the date the SFT consultation was requested); SFT consultation request to consultation completion (the date an SFT reader completed the consultation request for a patient); and SFT consultation completion to biopsy. Mean and median interval lengths were compared between groups and additional analyses identified steps that may have contributed to delays in care.

To address potential bias based on access to care for rural veterans, SFT and FTF primary care cases were categorized into groups based on their location: (1) EEC and biopsy conducted at the same facility; (2) EEC and biopsy conducted at different facilities within the same health care system (main health care facility and its community-based outpatient clinics); and (3) EEC and biopsy conducted at different health care systems.

Statistics

Means, medians, and SDs were calculated in Excel. The Mann-Whitney U test was used to compare SFT medians to the FTF data and X² test was used to compare proportions for secondary analyses.

Results

The median (mean) interval from EEC to definitive excision was 73 days (85) for SFT and 58 days (73) for FTF (P = .004) (Table). To understand this difference, the distribution of intervals from EEC to biopsy and biopsy to definitive excision were calculated. Only 38% of SFT cases were biopsied within 20 days compared to 65% of FTF cases (P < .001). The difference in time from biopsy to definitive excision distributions were not statistically significant, suggesting that the difference is actually a reflection of the differences seen in the period between EEC and biopsy.

EEC and biopsy occurred at the same facility in 85% and 82% of FTF primary care and SFT cases, respectively. EEC and biopsy occurred at different facilities within the same health care system in 15% and 16% of FTF primary care and SFT cases, respectively. EEC and biopsy occurred at different health care systems in 0% and 2% of FTF primary care and SFT cases, respectively. Geographic bias did not impact results for either group of veterans.

The interval between EEC and biopsy was shorter for FTF dermatology cases than for FTF primary care cases. For FTF dermatology cases, 96% were biopsied within 20 days compared with 34% of FTF primary care cases (P < .001).

To further analyze the difference in the EEC to biopsy interval duration between SFT and FTF primary care the timeline was divided into smaller steps: EEC to imaging completion, imaging completion to SFT consult completion, and SFT consult completion to biopsy. From EEC to SFT consult completion, SFT cases took a median of 6.0 days and a mean of 12.3 days, reflecting the administrative handoffs that must occur in SFT. A total of 82% of FTF primary care cases were entered into care and consultation was requested on the same day, while this was true for only 1% of SFT cases.

Since mortality data were not collected, the frequency of in situ melanomas and invasive melanomas (pathologic stage pT1a or greater) was used as a proxy for comparing outcomes. No significant difference was found in the frequency of in situ vs invasive melanomas in the SFT and FTF dermatology groups; however, there was a much higher frequency of invasive melanomas in the FTF primary care group (P = .007).

Discussion

This study compared the time to treatment for SFT vs FTF and identified important differences. The episode of care for melanomas diagnosed by SFT was statistically significantly longer (15 days) than those diagnosed by FTF. The interval between biopsy and definitive excision was a median of 34 and 38 days, and a mean of 48 and 44 days for SFT and FTF, respectively, which were not statistically significant. The difference in the total duration of the interval between EEC and definitive excision was accounted for by the duration of the interval from EEC to biopsy. When excluding dermatology clinic cases from the FTF group, there was no difference in the interval between EEC and biopsy for SFT and FTF primary care. The handoffs in SFT accounted for a median of 6 days and mean of 12 days, a significant portion of the timeline, and is a target for process improvement. The delay necessitated by handoffs did not significantly affect the distribution of in situ and invasive melanomas in the SFT and FTF dermatology groups. This suggests that SFT may have better outcomes than FTF primary care.

There has been extensive research on the timeline from the patient initially noticing a lesion to the EEC.^8-11 There is also a body of research on the timeline from biopsy to definitive excision. ^6,12-16 However, there has been little research on the timeline between EEC and biopsy, which comprises a large portion of the overall timeline of both SFT care and FTF care. This study analyzed the delays that can occur in this interval. When patients first enter FTF dermatology care, this timeline is quite short because lesions are often biopsied on the same day. When patients enter into care with their primary or nondermatology clinician, there can be significant delays.

Since the stakes are high when it comes to treating melanoma, it is important to minimize the overall timeline. A 6-day median and 12-day mean were established as targets for teledermatology handoffs. Ideally, a lesion should be entered into an episode of care, imaged, and sent for consultation on the same day. To help further understand delays in administrative handoffs, we stratified the SFT cases by VISN 20 sites and spoke with an administrator at a top performing site. Between 2006 and 2013, this site had a dedicated full-time imager as well as a backup imager that ensured images were taken quickly, usually on the same day the lesion was entered into care. Unfortunately, this is not the standard at all VISN 20 sites and certainly contributes to the overall delay in care in SFT

Minimizing the timeline of care is possible, as shown by the Danish health system, which developed a fast-track referral system after recognizing the need to minimize delays between the presentation, diagnosis, and treatment of cutaneous melanomas. In Denmark, a patient who presents to a general practitioner with a suspicious lesion is referred to secondary care for excision biopsy within 6 days. Diagnosis is made within 2 weeks, and, if necessary, definitive excision is offered within 9 days of the diagnosis. This translates into a maximum 20-day EEC to biopsy timeline and maximum 29-day EEC to definitive excision timeline. Although an intervention such as this may be difficult to implement in the United States due to its size and decentralized health care system, it would, however, be more realistic within the VA due to its centralized structure. The Danish system shows that with appropriate resource allocation and strict timeframes for treatment referrals, the timeline can be minimized.¹⁷

Despite the delay in the SFT timeline, this study found no significant difference between the distribution of in situ vs invasive melanomas in FTF dermatology and SFT groups. One possible explanation for this is that SFT increases access to dermatologist care, meaning clinicians may be more willing to consult SFT for less advanced– appearing lesions.

The finding that SFT diagnosed a larger proportion of in situ melanomas than FTF primary care is consistent with the findings of Ferrándiz et al, who reported that the mean Breslow thickness was significantly lower among patients in an SFT group compared to patients in an FTF group consisting of general practitioners. ¹⁸ However, the study population was not randomized and the results may have been impacted by ascertainment bias. Ferrándiz et al hypothesized that clinicians may have a lower threshold for consulting teledermatology, resulting in lower mean Breslow thicknesses.¹⁸ Karavan et al found the opposite results, with a higher mean Breslow thickness in SFT compared to a primary care FTF group.¹⁹ The data presented here suggest that SFT has room for process improvement yet is essentially equivalent to FTF dermatology in terms of outcomes.

Limitations

The majority of patients in this study were aged > 50 years, White, and male. The results may not be representative for other populations. The study was relatively small compared to studies that looked at other aspects of the melanoma care timeline. The study was not powered to ascertain mortality, the most important metric for melanoma.

Conclusions

The episode of care was significantly longer for melanomas diagnosed by SFT than those diagnosed by FTF; however, timelines were not statistically different when FTF lesions entered into care in dermatology were excluded. A median 6-day and mean 12.3-day delay in administrative handoffs occurred at the beginning of the SFT process and is a target for process improvement. Considering the high stakes of melanoma, the SFT timeline could be reduced if EEC, imaging, and SFT consultation all happened in the same day.

To the Editor:

Early skin cancer detection improves patient outcomes¹; however, socioeconomic and racial disparities may impact access to dermatologic care.² Although non-Hispanic White individuals have a high incidence of skin cancer, they experience higher melanoma-specific survival rates than non-White patients, who often receive later-stage diagnoses and experience higher mortality.² Furthermore, racial/ ethnic minorities often face longer surgery wait times after diagnosis and have lower socioeconomic status (SES) and less favorable health insurance coverage, contributing to poorer outcomes.^2,3

To examine access to full-body skin examinations (FBSEs) by board-certified dermatologists in Los Angeles (LA) County, California, we analyzed the availability of FBSEs based on racial demographics, income, and insurance type (Medicaid [Medi-Cal] vs private [Blue Cross Blue Shield (BCBS)]). Demographic data by zip code were obtained from the US Census Bureau.⁴ This validated metric highlights socioeconomic disparities and minimizes data gaps^5,6 and was used to assess health care access among different population subgroups. Dermatologists’ contact information was obtained from the Find a Dermatologist page on the American Academy of Dermatology website and the listed phone numbers of their practice were used to contact them. Practices with board-certified dermatologists accepting new patients were included in the study; practices were not included if they had exclusive insurance plans; were pediatric, cosmetic, or research only; or were nonresponsive to calls. From August 2022 to September 2022, each practice was called twice within a 36-hour period—once by a simulated patient with Medi-Cal and once by a simulated patient with BCBS—and were asked about availability for new patient FBSE appointments and accepted insurance types. Data were analyzed using SAS software (SAS Institute Inc.).

Los Angeles County comprises 269 zip codes, of which 82 (30.5%) have dermatology practices. Of 213 total dermatologists in LA County listed on the American Academy of Dermatology website, 193 (90.6%) met preliminary criteria, and 169 (79.3%) were successfully contacted. Almost all (94.6% [160/169]) accepted new patients for FBSEs; of those, 63.1% (101/160) accepted only private insurance, 16.9% (27/160) accepted both private insurance and Medi-Cal, and 16.2% (26/160) did not accept any insurance. Racial predominance for each dermatology practice was analyzed by zip code (Table). Dermatologists included in our study were significantly more concentrated in predominantly non- Hispanic White areas of LA County vs predominantly Hispanic areas (P<.0001). Notably, the average income in predominantly non-Hispanic White zip codes ($114,757.74) was significantly higher than in predominantly Hispanic areas ($58,278.54)(P=.001)(Table).⁴

In LA County, 40.1% (108/269) of zip codes have no racial majority, 28.2% (76/269) are predominantly Hispanic, 27.5% (74/269) are predominantly non-Hispanic White, 2.2% (6/269) are predominantly Black, and 1.9% (5/269) are predominantly Asian.⁴ There are no dermatologists in predominantly Black zip codes, 2 in predominantly Asian zip codes, 14 in predominantly Hispanic zip codes, 38 in zip codes with no racial majority, and 106 in predominantly non-Hispanic White zip codes. There are significantly more dermatologists in predominantly non-Hispanic White zip codes compared to predominantly Hispanic zip codes (P<.0001). In LA County, the average income in predominantly Asian, non-Hispanic White, and Hispanic zip codes was $93,594, $114,757.84, and $58,278.54, respectively, in 2021.4 The average income in predominantly non-Hispanic White zip codes was significantly higher than in predominantly Hispanic zip codes (P=.001). There were no income data available for predominantly Black zip codes or zip codes with no racial majority.

The results from our study revealed potential barriers to FBSEs for racial and ethnic minorities in LA County, which supports previous research on the impact of SES, race, and insurance on access to dermatologic care.^2,3 Predominantly Hispanic zip codes have significantly lower income (P<.0001) and fewer dermatologists (P=.001) compared to zip codes that are predominantly non-Hispanic White, reflecting how lower SES correlates with worse health outcomes and higher melanoma mortality. Conversely, predominantly non-Hispanic White areas with higher income have better access to dermatologists, which may contribute to the improved melanoma survival rates among White patients. Additionally, most dermatologists accept only private insurance, further highlighting the disparity in FBSE access for non-White patients across LA County. While our study focused on FBSE access, our findings may point to a wider barrier to dermatologic care, especially in zip codes with fewer dermatologists. Further studies are needed to determine whether these areas also face barriers to accessing primary care.

Our study was limited by the exclusion of nonphysician providers (eg, nurse practitioners, physician assistants), a small sample size, and lack of available economic data for predominantly Black zip codes.⁴ Additionally, the exclusion of practices with exclusive insurance plans (eg, Kaiser Permanente) limited the generalizability of our findings, as our results did not account for the populations served by these practices. Furthermore, our analysis did not account for variations in practice size or the proportion of care provided to patients with different insurance types, which could impact overall accessibility. Additional studies are needed to explore the impact of these factors on access to general dermatologic care and not just FBSEs.

Racial/ethnic minorities and lower SES populations face major barriers to FBSE access in LA County, such as difficulty finding a dermatologist in their area or one who accepts Medi-Cal. Addressing these disparities is crucial for improving skin cancer outcomes. Further research is needed to develop strategies to eliminate these barriers to dermatologic care, such as increasing access to teledermatology, offering mobile dermatology clinics, and improving insurance coverage.

To the Editor:

Early skin cancer detection improves patient outcomes¹; however, socioeconomic and racial disparities may impact access to dermatologic care.² Although non-Hispanic White individuals have a high incidence of skin cancer, they experience higher melanoma-specific survival rates than non-White patients, who often receive later-stage diagnoses and experience higher mortality.² Furthermore, racial/ ethnic minorities often face longer surgery wait times after diagnosis and have lower socioeconomic status (SES) and less favorable health insurance coverage, contributing to poorer outcomes.^2,3

To examine access to full-body skin examinations (FBSEs) by board-certified dermatologists in Los Angeles (LA) County, California, we analyzed the availability of FBSEs based on racial demographics, income, and insurance type (Medicaid [Medi-Cal] vs private [Blue Cross Blue Shield (BCBS)]). Demographic data by zip code were obtained from the US Census Bureau.⁴ This validated metric highlights socioeconomic disparities and minimizes data gaps^5,6 and was used to assess health care access among different population subgroups. Dermatologists’ contact information was obtained from the Find a Dermatologist page on the American Academy of Dermatology website and the listed phone numbers of their practice were used to contact them. Practices with board-certified dermatologists accepting new patients were included in the study; practices were not included if they had exclusive insurance plans; were pediatric, cosmetic, or research only; or were nonresponsive to calls. From August 2022 to September 2022, each practice was called twice within a 36-hour period—once by a simulated patient with Medi-Cal and once by a simulated patient with BCBS—and were asked about availability for new patient FBSE appointments and accepted insurance types. Data were analyzed using SAS software (SAS Institute Inc.).

Los Angeles County comprises 269 zip codes, of which 82 (30.5%) have dermatology practices. Of 213 total dermatologists in LA County listed on the American Academy of Dermatology website, 193 (90.6%) met preliminary criteria, and 169 (79.3%) were successfully contacted. Almost all (94.6% [160/169]) accepted new patients for FBSEs; of those, 63.1% (101/160) accepted only private insurance, 16.9% (27/160) accepted both private insurance and Medi-Cal, and 16.2% (26/160) did not accept any insurance. Racial predominance for each dermatology practice was analyzed by zip code (Table). Dermatologists included in our study were significantly more concentrated in predominantly non- Hispanic White areas of LA County vs predominantly Hispanic areas (P<.0001). Notably, the average income in predominantly non-Hispanic White zip codes ($114,757.74) was significantly higher than in predominantly Hispanic areas ($58,278.54)(P=.001)(Table).⁴

In LA County, 40.1% (108/269) of zip codes have no racial majority, 28.2% (76/269) are predominantly Hispanic, 27.5% (74/269) are predominantly non-Hispanic White, 2.2% (6/269) are predominantly Black, and 1.9% (5/269) are predominantly Asian.⁴ There are no dermatologists in predominantly Black zip codes, 2 in predominantly Asian zip codes, 14 in predominantly Hispanic zip codes, 38 in zip codes with no racial majority, and 106 in predominantly non-Hispanic White zip codes. There are significantly more dermatologists in predominantly non-Hispanic White zip codes compared to predominantly Hispanic zip codes (P<.0001). In LA County, the average income in predominantly Asian, non-Hispanic White, and Hispanic zip codes was $93,594, $114,757.84, and $58,278.54, respectively, in 2021.4 The average income in predominantly non-Hispanic White zip codes was significantly higher than in predominantly Hispanic zip codes (P=.001). There were no income data available for predominantly Black zip codes or zip codes with no racial majority.

The results from our study revealed potential barriers to FBSEs for racial and ethnic minorities in LA County, which supports previous research on the impact of SES, race, and insurance on access to dermatologic care.^2,3 Predominantly Hispanic zip codes have significantly lower income (P<.0001) and fewer dermatologists (P=.001) compared to zip codes that are predominantly non-Hispanic White, reflecting how lower SES correlates with worse health outcomes and higher melanoma mortality. Conversely, predominantly non-Hispanic White areas with higher income have better access to dermatologists, which may contribute to the improved melanoma survival rates among White patients. Additionally, most dermatologists accept only private insurance, further highlighting the disparity in FBSE access for non-White patients across LA County. While our study focused on FBSE access, our findings may point to a wider barrier to dermatologic care, especially in zip codes with fewer dermatologists. Further studies are needed to determine whether these areas also face barriers to accessing primary care.

Our study was limited by the exclusion of nonphysician providers (eg, nurse practitioners, physician assistants), a small sample size, and lack of available economic data for predominantly Black zip codes.⁴ Additionally, the exclusion of practices with exclusive insurance plans (eg, Kaiser Permanente) limited the generalizability of our findings, as our results did not account for the populations served by these practices. Furthermore, our analysis did not account for variations in practice size or the proportion of care provided to patients with different insurance types, which could impact overall accessibility. Additional studies are needed to explore the impact of these factors on access to general dermatologic care and not just FBSEs.

Racial/ethnic minorities and lower SES populations face major barriers to FBSE access in LA County, such as difficulty finding a dermatologist in their area or one who accepts Medi-Cal. Addressing these disparities is crucial for improving skin cancer outcomes. Further research is needed to develop strategies to eliminate these barriers to dermatologic care, such as increasing access to teledermatology, offering mobile dermatology clinics, and improving insurance coverage.

To the Editor:

Early skin cancer detection improves patient outcomes¹; however, socioeconomic and racial disparities may impact access to dermatologic care.² Although non-Hispanic White individuals have a high incidence of skin cancer, they experience higher melanoma-specific survival rates than non-White patients, who often receive later-stage diagnoses and experience higher mortality.² Furthermore, racial/ ethnic minorities often face longer surgery wait times after diagnosis and have lower socioeconomic status (SES) and less favorable health insurance coverage, contributing to poorer outcomes.^2,3

To examine access to full-body skin examinations (FBSEs) by board-certified dermatologists in Los Angeles (LA) County, California, we analyzed the availability of FBSEs based on racial demographics, income, and insurance type (Medicaid [Medi-Cal] vs private [Blue Cross Blue Shield (BCBS)]). Demographic data by zip code were obtained from the US Census Bureau.⁴ This validated metric highlights socioeconomic disparities and minimizes data gaps^5,6 and was used to assess health care access among different population subgroups. Dermatologists’ contact information was obtained from the Find a Dermatologist page on the American Academy of Dermatology website and the listed phone numbers of their practice were used to contact them. Practices with board-certified dermatologists accepting new patients were included in the study; practices were not included if they had exclusive insurance plans; were pediatric, cosmetic, or research only; or were nonresponsive to calls. From August 2022 to September 2022, each practice was called twice within a 36-hour period—once by a simulated patient with Medi-Cal and once by a simulated patient with BCBS—and were asked about availability for new patient FBSE appointments and accepted insurance types. Data were analyzed using SAS software (SAS Institute Inc.).

Los Angeles County comprises 269 zip codes, of which 82 (30.5%) have dermatology practices. Of 213 total dermatologists in LA County listed on the American Academy of Dermatology website, 193 (90.6%) met preliminary criteria, and 169 (79.3%) were successfully contacted. Almost all (94.6% [160/169]) accepted new patients for FBSEs; of those, 63.1% (101/160) accepted only private insurance, 16.9% (27/160) accepted both private insurance and Medi-Cal, and 16.2% (26/160) did not accept any insurance. Racial predominance for each dermatology practice was analyzed by zip code (Table). Dermatologists included in our study were significantly more concentrated in predominantly non- Hispanic White areas of LA County vs predominantly Hispanic areas (P<.0001). Notably, the average income in predominantly non-Hispanic White zip codes ($114,757.74) was significantly higher than in predominantly Hispanic areas ($58,278.54)(P=.001)(Table).⁴

In LA County, 40.1% (108/269) of zip codes have no racial majority, 28.2% (76/269) are predominantly Hispanic, 27.5% (74/269) are predominantly non-Hispanic White, 2.2% (6/269) are predominantly Black, and 1.9% (5/269) are predominantly Asian.⁴ There are no dermatologists in predominantly Black zip codes, 2 in predominantly Asian zip codes, 14 in predominantly Hispanic zip codes, 38 in zip codes with no racial majority, and 106 in predominantly non-Hispanic White zip codes. There are significantly more dermatologists in predominantly non-Hispanic White zip codes compared to predominantly Hispanic zip codes (P<.0001). In LA County, the average income in predominantly Asian, non-Hispanic White, and Hispanic zip codes was $93,594, $114,757.84, and $58,278.54, respectively, in 2021.4 The average income in predominantly non-Hispanic White zip codes was significantly higher than in predominantly Hispanic zip codes (P=.001). There were no income data available for predominantly Black zip codes or zip codes with no racial majority.

The results from our study revealed potential barriers to FBSEs for racial and ethnic minorities in LA County, which supports previous research on the impact of SES, race, and insurance on access to dermatologic care.^2,3 Predominantly Hispanic zip codes have significantly lower income (P<.0001) and fewer dermatologists (P=.001) compared to zip codes that are predominantly non-Hispanic White, reflecting how lower SES correlates with worse health outcomes and higher melanoma mortality. Conversely, predominantly non-Hispanic White areas with higher income have better access to dermatologists, which may contribute to the improved melanoma survival rates among White patients. Additionally, most dermatologists accept only private insurance, further highlighting the disparity in FBSE access for non-White patients across LA County. While our study focused on FBSE access, our findings may point to a wider barrier to dermatologic care, especially in zip codes with fewer dermatologists. Further studies are needed to determine whether these areas also face barriers to accessing primary care.

Our study was limited by the exclusion of nonphysician providers (eg, nurse practitioners, physician assistants), a small sample size, and lack of available economic data for predominantly Black zip codes.⁴ Additionally, the exclusion of practices with exclusive insurance plans (eg, Kaiser Permanente) limited the generalizability of our findings, as our results did not account for the populations served by these practices. Furthermore, our analysis did not account for variations in practice size or the proportion of care provided to patients with different insurance types, which could impact overall accessibility. Additional studies are needed to explore the impact of these factors on access to general dermatologic care and not just FBSEs.

Racial/ethnic minorities and lower SES populations face major barriers to FBSE access in LA County, such as difficulty finding a dermatologist in their area or one who accepts Medi-Cal. Addressing these disparities is crucial for improving skin cancer outcomes. Further research is needed to develop strategies to eliminate these barriers to dermatologic care, such as increasing access to teledermatology, offering mobile dermatology clinics, and improving insurance coverage.

To the Editor:

The incidence of nonmelanoma skin cancer (NMSC) is rapidly increasing worldwide. Due to its highly curable nature when treated early, accurate diagnosis is the cornerstone to good patient outcomes.¹ Accurate diagnosis of skin cancer and subsequent treatment decisions rely heavily on the congruence between clinical observations and histopathologic assessments. Clinical misdiagnosis of a malignant lesion can lead to delayed and suboptimal treatment, which may contribute to serious complications such as metastasis or even mortality. In this study, data from clinically diagnosed basal cell carcinomas (BCCs) and squamous cell carcinomas (SCCs) were compared to their identified histopathologic subtype classifications. The accuracy of the clinical diagnosis of these NMSCs was assessed by determining the rate of misdiagnosis and the respective positive predictive value (PPV).

A retrospective review of medical records from a private dermatology practice in Lubbock, Texas, was conducted to identify patients diagnosed with NMSC from January 1, 2017, through December 31, 2021. A total of 11,229 NMSCs were diagnosed and treated in 5877 patients. Of the NMSCs diagnosed, 11,145 were identified as keratinocyte carcinomas and were classified as BCCs or SCCs. The accuracy of the clinical diagnoses was determined by comparison to the histologic subtype identified via biopsy of the lesion. Although the use of a dermatoscope during the clinical encounter was not formally recorded, reports from the examining dermatologists indicated it was not used in the majority of cases.

If a lesion was clinically diagnosed as a BCC but was identified as a subtype of SCC on histology (or vice versa), the lesion was considered to be mismatched. The number of mismatched lesions and the mismatch rate for each lesion type/subtype is recorded in the Table. Of the total 11,145 keratinocyte carcinomas included in our study, there was an overall 10.63% mismatch rate, with 1185 of the malignancies having a differing clinical diagnosis (eg, BCC vs SCC) from the histologic findings. The clinical mismatch rate was notably higher for SCC compared to BCC (15.83% vs 7.03%, respectively).

The Table provides a breakdown of the BCC subtypes identified by histology with their computed mismatch rate and PPV. It is worth clarifying that lesions classified as more than one BCC subtype per the histologic findings were diagnosed as mixed BCC; these were further classified as mixed-aggressive BCC (if at least one aggressive BCC subtype was present) and mixed nonaggressive BCC (if no aggressive BCC subtype was present). Overall, BCCs were less likely to be misdiagnosed, with an average PPV of 92.97% compared to 84.17% for SCCs. Basosquamous BCC was the BCC subtype with the highest mismatch rate (25.48%), while sclerosing BCC has the lowest overall mismatch rate (1.33%). The most common malignancy was BCC, with nodular BCC being the most common subtype.

The Table also breaks down the SCC subtypes, reporting the most commonly misdiagnosed of any BCC or SCC subtype to be poorly differentiated SCC (mismatch rate, 38.46%). The lowest mismatch rate of the SCC subtypes was 5.97% for well-differentiated SCC.

There was an overall PPV of 89.37% in clinically evaluated malignancies and their respective histologic subtypes. Basal cell carcinoma had a lower overall mismatch rate of 7.03% compared to 15.83% in SCC. The most common misdiagnosis was attributed to poorly differentiated SCC (mismatch rate, 38.46%), while the least common misdiagnosed malignancy was sclerosing BCC (1.33%). The high mismatch rate of poorly differentiated SCC may be due to its diverging presentation from a typical SCC as a flat lesion with the absence of scaling, keratin, or bleeding, leading to the misdiagnosis of BCC.²

Accurate clinical diagnosis of NMSCs is the basis for further evaluation and treatment that should ensue in a timely manner; however, accurately identifying BCCs vs SCCs solely based on clinical examination can be challenging due to variable manifestations and overlapping features. Basal cell carcinoma commonly presents as a shiny pink/flesh-colored nodule, macule, or patch with surface telangiectasia, sometimes appearing with ulceration or crusting.³ Alternatively, SCC typically appears as a firm, sharply demarcated, red nodule with a thick overlying scale.⁴ Definitive diagnoses can be difficult upon clinical examination since these features can be shared between the 2 subtypes. To aid in these uncertainties, a growing number of clinicians are implementing the use of dermoscopy in their everyday practice.

Dermoscopy is an extremely useful tool in improving the diagnostic accuracy of skin cancers compared to examination with the naked eye, as it provides detailed visualization of specific structures and patterns in skin cancer lesions.⁵ The dermoscopic appearance of BCC is characterized by pearly blue-gray or translucent globules with arborizing vessels, spoke-wheel structures, and leaflike areas.^5,6 Conversely, dermoscopic features of SCC may include a milky-red globule with a scaly, sharply demarcated, crusted lesion with polymorphous vasculature, sometimes resembling a persistent sore or nonhealing wound.^4,5 Though the use of dermoscopy can aid in diagnosis upon initial examination, certain factors such as trauma, ulceration, and previous treatments that distorted the lesion’s architecture may lead to misdiagnosis. Furthermore, the distinct vascular patterns found in BCC and SCC may be mistaken for each other and therefore lead to misdiagnosis upon examination.⁷ Other variables that may complicate diagnosis include the location of the lesion, its size, and the presence of other skin conditions or nearby lesions.

The primary limitation of the current study was the limited scope of the data, as they were derived from patients seen at one private dermatology practice, preventing the generalizability of our findings. However, our results show trends similar to those observed in other studies analyzing the clinical accuracy of skin cancer diagnoses, with higher PPVs for BCC compared to SCC. A study by Ahnlide and Bjellerup⁸ was based in a hospital dermatology department and demonstrated a PPV of 85.5% for BCC compared to 92.97% in our study; for SCC, the PPV was 67.3% compared to 84.17% in our study. In another study by Heal et al,⁹ data were collected from an Australian registry that included records of all histologically confirmed skin cancers from December 1996 to October 1999 from 202 general practitioners and 42 specialists, including 1 dermatologist. The PPVs for BCC and SCC were 72.7% and 49.4%, respectively. Although our results indicated higher PPVs compared to these 2 studies, some of the discrepancies can be accounted for by the differences in clinical setting as well as the lack of expertise of nondermatologist physicians in identifying skin malignancies in the study by Heal et al.⁹

The current study was further limited by the lack of data quantifying the number of lesions clinically suspected to be malignant but found to be histologically benign. It is typical for clinicians to have a low threshold to biopsy a suspicious lesion with atypical features (eg, rapid evolution and growth, bleeding, crusting). Furthermore, the identification of risk factors in the patient’s medical and family history (eg, exposure to radiation, personal or family history of skin cancers) can heavily influence a clinician’s decision to biopsy a lesion with an atypical appearance.¹⁰ Many benign lesions are biopsied to avoid missing a diagnosis of malignancy. Consequently, our results suggest a high degree of clinical misdiagnosis of BCCs and SCCs. Obtaining data on the number of lesions suspected to be BCC or SCC that were found to be histologically benign would be a valuable addition to our study, as it would provide a measurable insight into the sensitivity of clinicians’ decision-making to identify a lesion as suspicious and warranting biopsy.

While clinical diagnosis plays a vital role in identifying suspected NMSCs such as BCC and SCC, its accuracy can be limited even with the use of dermoscopy. Overall, our data have shown a high rate of diagnostic accuracy upon suspicion of malignancy, but the different variables that affect clinical presentation promote histologic diagnosis to prevail as the gold standard.

To the Editor:

The incidence of nonmelanoma skin cancer (NMSC) is rapidly increasing worldwide. Due to its highly curable nature when treated early, accurate diagnosis is the cornerstone to good patient outcomes.¹ Accurate diagnosis of skin cancer and subsequent treatment decisions rely heavily on the congruence between clinical observations and histopathologic assessments. Clinical misdiagnosis of a malignant lesion can lead to delayed and suboptimal treatment, which may contribute to serious complications such as metastasis or even mortality. In this study, data from clinically diagnosed basal cell carcinomas (BCCs) and squamous cell carcinomas (SCCs) were compared to their identified histopathologic subtype classifications. The accuracy of the clinical diagnosis of these NMSCs was assessed by determining the rate of misdiagnosis and the respective positive predictive value (PPV).

A retrospective review of medical records from a private dermatology practice in Lubbock, Texas, was conducted to identify patients diagnosed with NMSC from January 1, 2017, through December 31, 2021. A total of 11,229 NMSCs were diagnosed and treated in 5877 patients. Of the NMSCs diagnosed, 11,145 were identified as keratinocyte carcinomas and were classified as BCCs or SCCs. The accuracy of the clinical diagnoses was determined by comparison to the histologic subtype identified via biopsy of the lesion. Although the use of a dermatoscope during the clinical encounter was not formally recorded, reports from the examining dermatologists indicated it was not used in the majority of cases.

If a lesion was clinically diagnosed as a BCC but was identified as a subtype of SCC on histology (or vice versa), the lesion was considered to be mismatched. The number of mismatched lesions and the mismatch rate for each lesion type/subtype is recorded in the Table. Of the total 11,145 keratinocyte carcinomas included in our study, there was an overall 10.63% mismatch rate, with 1185 of the malignancies having a differing clinical diagnosis (eg, BCC vs SCC) from the histologic findings. The clinical mismatch rate was notably higher for SCC compared to BCC (15.83% vs 7.03%, respectively).

The Table provides a breakdown of the BCC subtypes identified by histology with their computed mismatch rate and PPV. It is worth clarifying that lesions classified as more than one BCC subtype per the histologic findings were diagnosed as mixed BCC; these were further classified as mixed-aggressive BCC (if at least one aggressive BCC subtype was present) and mixed nonaggressive BCC (if no aggressive BCC subtype was present). Overall, BCCs were less likely to be misdiagnosed, with an average PPV of 92.97% compared to 84.17% for SCCs. Basosquamous BCC was the BCC subtype with the highest mismatch rate (25.48%), while sclerosing BCC has the lowest overall mismatch rate (1.33%). The most common malignancy was BCC, with nodular BCC being the most common subtype.

The Table also breaks down the SCC subtypes, reporting the most commonly misdiagnosed of any BCC or SCC subtype to be poorly differentiated SCC (mismatch rate, 38.46%). The lowest mismatch rate of the SCC subtypes was 5.97% for well-differentiated SCC.

There was an overall PPV of 89.37% in clinically evaluated malignancies and their respective histologic subtypes. Basal cell carcinoma had a lower overall mismatch rate of 7.03% compared to 15.83% in SCC. The most common misdiagnosis was attributed to poorly differentiated SCC (mismatch rate, 38.46%), while the least common misdiagnosed malignancy was sclerosing BCC (1.33%). The high mismatch rate of poorly differentiated SCC may be due to its diverging presentation from a typical SCC as a flat lesion with the absence of scaling, keratin, or bleeding, leading to the misdiagnosis of BCC.²

Accurate clinical diagnosis of NMSCs is the basis for further evaluation and treatment that should ensue in a timely manner; however, accurately identifying BCCs vs SCCs solely based on clinical examination can be challenging due to variable manifestations and overlapping features. Basal cell carcinoma commonly presents as a shiny pink/flesh-colored nodule, macule, or patch with surface telangiectasia, sometimes appearing with ulceration or crusting.³ Alternatively, SCC typically appears as a firm, sharply demarcated, red nodule with a thick overlying scale.⁴ Definitive diagnoses can be difficult upon clinical examination since these features can be shared between the 2 subtypes. To aid in these uncertainties, a growing number of clinicians are implementing the use of dermoscopy in their everyday practice.

Dermoscopy is an extremely useful tool in improving the diagnostic accuracy of skin cancers compared to examination with the naked eye, as it provides detailed visualization of specific structures and patterns in skin cancer lesions.⁵ The dermoscopic appearance of BCC is characterized by pearly blue-gray or translucent globules with arborizing vessels, spoke-wheel structures, and leaflike areas.^5,6 Conversely, dermoscopic features of SCC may include a milky-red globule with a scaly, sharply demarcated, crusted lesion with polymorphous vasculature, sometimes resembling a persistent sore or nonhealing wound.^4,5 Though the use of dermoscopy can aid in diagnosis upon initial examination, certain factors such as trauma, ulceration, and previous treatments that distorted the lesion’s architecture may lead to misdiagnosis. Furthermore, the distinct vascular patterns found in BCC and SCC may be mistaken for each other and therefore lead to misdiagnosis upon examination.⁷ Other variables that may complicate diagnosis include the location of the lesion, its size, and the presence of other skin conditions or nearby lesions.

The primary limitation of the current study was the limited scope of the data, as they were derived from patients seen at one private dermatology practice, preventing the generalizability of our findings. However, our results show trends similar to those observed in other studies analyzing the clinical accuracy of skin cancer diagnoses, with higher PPVs for BCC compared to SCC. A study by Ahnlide and Bjellerup⁸ was based in a hospital dermatology department and demonstrated a PPV of 85.5% for BCC compared to 92.97% in our study; for SCC, the PPV was 67.3% compared to 84.17% in our study. In another study by Heal et al,⁹ data were collected from an Australian registry that included records of all histologically confirmed skin cancers from December 1996 to October 1999 from 202 general practitioners and 42 specialists, including 1 dermatologist. The PPVs for BCC and SCC were 72.7% and 49.4%, respectively. Although our results indicated higher PPVs compared to these 2 studies, some of the discrepancies can be accounted for by the differences in clinical setting as well as the lack of expertise of nondermatologist physicians in identifying skin malignancies in the study by Heal et al.⁹

The current study was further limited by the lack of data quantifying the number of lesions clinically suspected to be malignant but found to be histologically benign. It is typical for clinicians to have a low threshold to biopsy a suspicious lesion with atypical features (eg, rapid evolution and growth, bleeding, crusting). Furthermore, the identification of risk factors in the patient’s medical and family history (eg, exposure to radiation, personal or family history of skin cancers) can heavily influence a clinician’s decision to biopsy a lesion with an atypical appearance.¹⁰ Many benign lesions are biopsied to avoid missing a diagnosis of malignancy. Consequently, our results suggest a high degree of clinical misdiagnosis of BCCs and SCCs. Obtaining data on the number of lesions suspected to be BCC or SCC that were found to be histologically benign would be a valuable addition to our study, as it would provide a measurable insight into the sensitivity of clinicians’ decision-making to identify a lesion as suspicious and warranting biopsy.

While clinical diagnosis plays a vital role in identifying suspected NMSCs such as BCC and SCC, its accuracy can be limited even with the use of dermoscopy. Overall, our data have shown a high rate of diagnostic accuracy upon suspicion of malignancy, but the different variables that affect clinical presentation promote histologic diagnosis to prevail as the gold standard.

To the Editor:

The incidence of nonmelanoma skin cancer (NMSC) is rapidly increasing worldwide. Due to its highly curable nature when treated early, accurate diagnosis is the cornerstone to good patient outcomes.¹ Accurate diagnosis of skin cancer and subsequent treatment decisions rely heavily on the congruence between clinical observations and histopathologic assessments. Clinical misdiagnosis of a malignant lesion can lead to delayed and suboptimal treatment, which may contribute to serious complications such as metastasis or even mortality. In this study, data from clinically diagnosed basal cell carcinomas (BCCs) and squamous cell carcinomas (SCCs) were compared to their identified histopathologic subtype classifications. The accuracy of the clinical diagnosis of these NMSCs was assessed by determining the rate of misdiagnosis and the respective positive predictive value (PPV).

A retrospective review of medical records from a private dermatology practice in Lubbock, Texas, was conducted to identify patients diagnosed with NMSC from January 1, 2017, through December 31, 2021. A total of 11,229 NMSCs were diagnosed and treated in 5877 patients. Of the NMSCs diagnosed, 11,145 were identified as keratinocyte carcinomas and were classified as BCCs or SCCs. The accuracy of the clinical diagnoses was determined by comparison to the histologic subtype identified via biopsy of the lesion. Although the use of a dermatoscope during the clinical encounter was not formally recorded, reports from the examining dermatologists indicated it was not used in the majority of cases.

If a lesion was clinically diagnosed as a BCC but was identified as a subtype of SCC on histology (or vice versa), the lesion was considered to be mismatched. The number of mismatched lesions and the mismatch rate for each lesion type/subtype is recorded in the Table. Of the total 11,145 keratinocyte carcinomas included in our study, there was an overall 10.63% mismatch rate, with 1185 of the malignancies having a differing clinical diagnosis (eg, BCC vs SCC) from the histologic findings. The clinical mismatch rate was notably higher for SCC compared to BCC (15.83% vs 7.03%, respectively).

The Table provides a breakdown of the BCC subtypes identified by histology with their computed mismatch rate and PPV. It is worth clarifying that lesions classified as more than one BCC subtype per the histologic findings were diagnosed as mixed BCC; these were further classified as mixed-aggressive BCC (if at least one aggressive BCC subtype was present) and mixed nonaggressive BCC (if no aggressive BCC subtype was present). Overall, BCCs were less likely to be misdiagnosed, with an average PPV of 92.97% compared to 84.17% for SCCs. Basosquamous BCC was the BCC subtype with the highest mismatch rate (25.48%), while sclerosing BCC has the lowest overall mismatch rate (1.33%). The most common malignancy was BCC, with nodular BCC being the most common subtype.

The Table also breaks down the SCC subtypes, reporting the most commonly misdiagnosed of any BCC or SCC subtype to be poorly differentiated SCC (mismatch rate, 38.46%). The lowest mismatch rate of the SCC subtypes was 5.97% for well-differentiated SCC.

There was an overall PPV of 89.37% in clinically evaluated malignancies and their respective histologic subtypes. Basal cell carcinoma had a lower overall mismatch rate of 7.03% compared to 15.83% in SCC. The most common misdiagnosis was attributed to poorly differentiated SCC (mismatch rate, 38.46%), while the least common misdiagnosed malignancy was sclerosing BCC (1.33%). The high mismatch rate of poorly differentiated SCC may be due to its diverging presentation from a typical SCC as a flat lesion with the absence of scaling, keratin, or bleeding, leading to the misdiagnosis of BCC.²

Accurate clinical diagnosis of NMSCs is the basis for further evaluation and treatment that should ensue in a timely manner; however, accurately identifying BCCs vs SCCs solely based on clinical examination can be challenging due to variable manifestations and overlapping features. Basal cell carcinoma commonly presents as a shiny pink/flesh-colored nodule, macule, or patch with surface telangiectasia, sometimes appearing with ulceration or crusting.³ Alternatively, SCC typically appears as a firm, sharply demarcated, red nodule with a thick overlying scale.⁴ Definitive diagnoses can be difficult upon clinical examination since these features can be shared between the 2 subtypes. To aid in these uncertainties, a growing number of clinicians are implementing the use of dermoscopy in their everyday practice.

Dermoscopy is an extremely useful tool in improving the diagnostic accuracy of skin cancers compared to examination with the naked eye, as it provides detailed visualization of specific structures and patterns in skin cancer lesions.⁵ The dermoscopic appearance of BCC is characterized by pearly blue-gray or translucent globules with arborizing vessels, spoke-wheel structures, and leaflike areas.^5,6 Conversely, dermoscopic features of SCC may include a milky-red globule with a scaly, sharply demarcated, crusted lesion with polymorphous vasculature, sometimes resembling a persistent sore or nonhealing wound.^4,5 Though the use of dermoscopy can aid in diagnosis upon initial examination, certain factors such as trauma, ulceration, and previous treatments that distorted the lesion’s architecture may lead to misdiagnosis. Furthermore, the distinct vascular patterns found in BCC and SCC may be mistaken for each other and therefore lead to misdiagnosis upon examination.⁷ Other variables that may complicate diagnosis include the location of the lesion, its size, and the presence of other skin conditions or nearby lesions.

The primary limitation of the current study was the limited scope of the data, as they were derived from patients seen at one private dermatology practice, preventing the generalizability of our findings. However, our results show trends similar to those observed in other studies analyzing the clinical accuracy of skin cancer diagnoses, with higher PPVs for BCC compared to SCC. A study by Ahnlide and Bjellerup⁸ was based in a hospital dermatology department and demonstrated a PPV of 85.5% for BCC compared to 92.97% in our study; for SCC, the PPV was 67.3% compared to 84.17% in our study. In another study by Heal et al,⁹ data were collected from an Australian registry that included records of all histologically confirmed skin cancers from December 1996 to October 1999 from 202 general practitioners and 42 specialists, including 1 dermatologist. The PPVs for BCC and SCC were 72.7% and 49.4%, respectively. Although our results indicated higher PPVs compared to these 2 studies, some of the discrepancies can be accounted for by the differences in clinical setting as well as the lack of expertise of nondermatologist physicians in identifying skin malignancies in the study by Heal et al.⁹

The current study was further limited by the lack of data quantifying the number of lesions clinically suspected to be malignant but found to be histologically benign. It is typical for clinicians to have a low threshold to biopsy a suspicious lesion with atypical features (eg, rapid evolution and growth, bleeding, crusting). Furthermore, the identification of risk factors in the patient’s medical and family history (eg, exposure to radiation, personal or family history of skin cancers) can heavily influence a clinician’s decision to biopsy a lesion with an atypical appearance.¹⁰ Many benign lesions are biopsied to avoid missing a diagnosis of malignancy. Consequently, our results suggest a high degree of clinical misdiagnosis of BCCs and SCCs. Obtaining data on the number of lesions suspected to be BCC or SCC that were found to be histologically benign would be a valuable addition to our study, as it would provide a measurable insight into the sensitivity of clinicians’ decision-making to identify a lesion as suspicious and warranting biopsy.

While clinical diagnosis plays a vital role in identifying suspected NMSCs such as BCC and SCC, its accuracy can be limited even with the use of dermoscopy. Overall, our data have shown a high rate of diagnostic accuracy upon suspicion of malignancy, but the different variables that affect clinical presentation promote histologic diagnosis to prevail as the gold standard.

To the Editor:

Psoriasis is a chronic inflammatory condition that affects individuals in various extracutaneous ways.¹ Prior studies have documented a decrease in exercise intensity among patients with psoriasis²; however, few studies have specifically investigated baseline mobility in this population. Baseline mobility denotes an individual’s fundamental ability to walk or move around without assistance of any kind. Impaired mobility—when baseline mobility is compromised—is an aspect of the wider diversity, equity, and inclusion framework that underscores the significance of recognizing challenges and promoting inclusive measures, both at the point of care and in research.³ study sought to analyze the relationship between psoriasis and baseline mobility among US adults (aged 45 to 80 years) utilizing the latest data from the National Health and Nutrition Examination Survey (NHANES) database for psoriasis.⁴ We used three 2-year cycles of NHANES data to create a 2009-2014 dataset.

The overall NHANES response rate among adults aged 45 to 80 years between 2009 and 2014 was 67.9%. Patients were categorized as having impaired mobility if they responded “yes” to the following question: “Because of a health problem, do you have difficulty walking without using any special equipment?” Psoriasis status was assessed by the following question: “Have you ever been told by a doctor or other health professional that you had psoriasis?” Multivariable logistic regression analyses were performed using Stata/SE 18.0 software (StataCorp LLC) to assess the relationship between psoriasis and impaired mobility. Age, income, education, sex, race, tobacco use, diabetes status, body mass index, and arthritis status were controlled for in our models.

Our analysis initially included 9982 participants; 14 did not respond to questions assessing psoriasis and impaired mobility and were excluded. The prevalence of impaired mobility in patients with psoriasis was 17.1% compared with 10.9% among those without psoriasis (Table 1). There was a significant association between psoriasis and impaired mobility among patients aged 45 to 80 years after adjusting for potential confounding variables (adjusted odds ratio [AOR], 1.54; 95% CI, 1.04- 2.29; P=.032)(Table 2). Analyses of subgroups yielded no statistically significant results.

Our study demonstrated a statistically significant difference in mobility between individuals with psoriasis compared with the general population, which remained significant when controlling for arthritis, obesity, and diabetes (P=.032). This may be the result of several influences. First, the location of the psoriasis may impact mobility. Plantar psoriasis—a manifestation on the soles of the feet—can cause discomfort and pain, which can hinder walking and standing.⁵ Second, a study by Lasselin et al⁶ found that systemic inflammation contributes to mobility impairment through alterations in gait and posture, which suggests that the inflammatory processes inherent in psoriasis could intrinsically modify walking speed and stride, potentially exacerbating mobility difficulties independent of other comorbid conditions. These findings suggest that psoriasis may disproportionately affect individuals with impaired mobility, independent of comorbid arthritis, obesity, and diabetes.

These findings have broad implications for diversity, equity, and inclusion. They should prompt us to consider the practical challenges faced by this patient population and the ways that we can address barriers to care. Offering telehealth appointments, making primary care referrals for impaired mobility workups, and advising patients of direct-to-home delivery of prescriptions are good places to start.

Limitations to our study include the lack of specificity in the survey question, self-reporting bias, and the inability to control for the psoriasis location. Further investigations are warranted in large, representative US adult populations to assess the implications of impaired mobility in patients with psoriasis.

To the Editor:

Psoriasis is a chronic inflammatory condition that affects individuals in various extracutaneous ways.¹ Prior studies have documented a decrease in exercise intensity among patients with psoriasis²; however, few studies have specifically investigated baseline mobility in this population. Baseline mobility denotes an individual’s fundamental ability to walk or move around without assistance of any kind. Impaired mobility—when baseline mobility is compromised—is an aspect of the wider diversity, equity, and inclusion framework that underscores the significance of recognizing challenges and promoting inclusive measures, both at the point of care and in research.³ study sought to analyze the relationship between psoriasis and baseline mobility among US adults (aged 45 to 80 years) utilizing the latest data from the National Health and Nutrition Examination Survey (NHANES) database for psoriasis.⁴ We used three 2-year cycles of NHANES data to create a 2009-2014 dataset.

The overall NHANES response rate among adults aged 45 to 80 years between 2009 and 2014 was 67.9%. Patients were categorized as having impaired mobility if they responded “yes” to the following question: “Because of a health problem, do you have difficulty walking without using any special equipment?” Psoriasis status was assessed by the following question: “Have you ever been told by a doctor or other health professional that you had psoriasis?” Multivariable logistic regression analyses were performed using Stata/SE 18.0 software (StataCorp LLC) to assess the relationship between psoriasis and impaired mobility. Age, income, education, sex, race, tobacco use, diabetes status, body mass index, and arthritis status were controlled for in our models.

Our analysis initially included 9982 participants; 14 did not respond to questions assessing psoriasis and impaired mobility and were excluded. The prevalence of impaired mobility in patients with psoriasis was 17.1% compared with 10.9% among those without psoriasis (Table 1). There was a significant association between psoriasis and impaired mobility among patients aged 45 to 80 years after adjusting for potential confounding variables (adjusted odds ratio [AOR], 1.54; 95% CI, 1.04- 2.29; P=.032)(Table 2). Analyses of subgroups yielded no statistically significant results.

Our study demonstrated a statistically significant difference in mobility between individuals with psoriasis compared with the general population, which remained significant when controlling for arthritis, obesity, and diabetes (P=.032). This may be the result of several influences. First, the location of the psoriasis may impact mobility. Plantar psoriasis—a manifestation on the soles of the feet—can cause discomfort and pain, which can hinder walking and standing.⁵ Second, a study by Lasselin et al⁶ found that systemic inflammation contributes to mobility impairment through alterations in gait and posture, which suggests that the inflammatory processes inherent in psoriasis could intrinsically modify walking speed and stride, potentially exacerbating mobility difficulties independent of other comorbid conditions. These findings suggest that psoriasis may disproportionately affect individuals with impaired mobility, independent of comorbid arthritis, obesity, and diabetes.

These findings have broad implications for diversity, equity, and inclusion. They should prompt us to consider the practical challenges faced by this patient population and the ways that we can address barriers to care. Offering telehealth appointments, making primary care referrals for impaired mobility workups, and advising patients of direct-to-home delivery of prescriptions are good places to start.

Limitations to our study include the lack of specificity in the survey question, self-reporting bias, and the inability to control for the psoriasis location. Further investigations are warranted in large, representative US adult populations to assess the implications of impaired mobility in patients with psoriasis.

To the Editor:

Psoriasis is a chronic inflammatory condition that affects individuals in various extracutaneous ways.¹ Prior studies have documented a decrease in exercise intensity among patients with psoriasis²; however, few studies have specifically investigated baseline mobility in this population. Baseline mobility denotes an individual’s fundamental ability to walk or move around without assistance of any kind. Impaired mobility—when baseline mobility is compromised—is an aspect of the wider diversity, equity, and inclusion framework that underscores the significance of recognizing challenges and promoting inclusive measures, both at the point of care and in research.³ study sought to analyze the relationship between psoriasis and baseline mobility among US adults (aged 45 to 80 years) utilizing the latest data from the National Health and Nutrition Examination Survey (NHANES) database for psoriasis.⁴ We used three 2-year cycles of NHANES data to create a 2009-2014 dataset.

The overall NHANES response rate among adults aged 45 to 80 years between 2009 and 2014 was 67.9%. Patients were categorized as having impaired mobility if they responded “yes” to the following question: “Because of a health problem, do you have difficulty walking without using any special equipment?” Psoriasis status was assessed by the following question: “Have you ever been told by a doctor or other health professional that you had psoriasis?” Multivariable logistic regression analyses were performed using Stata/SE 18.0 software (StataCorp LLC) to assess the relationship between psoriasis and impaired mobility. Age, income, education, sex, race, tobacco use, diabetes status, body mass index, and arthritis status were controlled for in our models.

Our analysis initially included 9982 participants; 14 did not respond to questions assessing psoriasis and impaired mobility and were excluded. The prevalence of impaired mobility in patients with psoriasis was 17.1% compared with 10.9% among those without psoriasis (Table 1). There was a significant association between psoriasis and impaired mobility among patients aged 45 to 80 years after adjusting for potential confounding variables (adjusted odds ratio [AOR], 1.54; 95% CI, 1.04- 2.29; P=.032)(Table 2). Analyses of subgroups yielded no statistically significant results.

Our study demonstrated a statistically significant difference in mobility between individuals with psoriasis compared with the general population, which remained significant when controlling for arthritis, obesity, and diabetes (P=.032). This may be the result of several influences. First, the location of the psoriasis may impact mobility. Plantar psoriasis—a manifestation on the soles of the feet—can cause discomfort and pain, which can hinder walking and standing.⁵ Second, a study by Lasselin et al⁶ found that systemic inflammation contributes to mobility impairment through alterations in gait and posture, which suggests that the inflammatory processes inherent in psoriasis could intrinsically modify walking speed and stride, potentially exacerbating mobility difficulties independent of other comorbid conditions. These findings suggest that psoriasis may disproportionately affect individuals with impaired mobility, independent of comorbid arthritis, obesity, and diabetes.

These findings have broad implications for diversity, equity, and inclusion. They should prompt us to consider the practical challenges faced by this patient population and the ways that we can address barriers to care. Offering telehealth appointments, making primary care referrals for impaired mobility workups, and advising patients of direct-to-home delivery of prescriptions are good places to start.

Limitations to our study include the lack of specificity in the survey question, self-reporting bias, and the inability to control for the psoriasis location. Further investigations are warranted in large, representative US adult populations to assess the implications of impaired mobility in patients with psoriasis.

To the Editor:

Cutaneous medical conditions can have a substantial impact on patients’ functioning and quality of life. Many patients with severe skin disease are eligible to receive disability assistance that can provide them with essential income and health care. Previous research has highlighted disability assessment as one of the most important ways physicians can help mitigate the health consequences of poverty.¹ Dermatologists can play an important role in the disability assessment process by documenting the facts associated with patients’ skin conditions.

Although skin conditions have a relatively high prevalence, they remain underrepresented in disability claims. Between 1997 and 2004, occupational skin diseases accounted for 12% to 17% of nonfatal work-related illnesses; however, during that same period, skin conditions comprised only 0.21% of disability claims in the United States.^2,3 Historically, there has been hesitancy among dermatologists to complete disability paperwork; a 1976 survey of dermatologists cited extensive paperwork, “troublesome patients,” and fee schedule issues as reasons.⁴ The lack of training regarding disability assessment in medical school and residency also has been noted.⁵

To characterize modern attitudes toward disability assessments, we conducted a survey of dermatologists across the United States. Our study was reviewed and declared exempt by the institutional review board of the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center (Torrance, California)(approval #18CR-32242-01). Using convenience sampling, we emailed dermatologists from the Association of Professors of Dermatology and dermatology state societies in all 50 states inviting them to participate in our voluntary and anonymous survey, which was administered using SurveyMonkey. The use of all society mailing lists was approved by the respective owners. The 15-question survey included multiple choice, Likert scale, and free response sections. Summary and descriptive statistics were used to describe respondent demographics and identify any patterns in responses.

For each Likert-based question, participants ranked their degree of agreement with a statement as: 1=strongly disagree, 2=somewhat disagree, 3=neither agree nor disagree/neutral, 4=somewhat agree, and 5=strongly agree. The mean response and standard deviation were reported for each Likert scale prompt. Preplanned 1-sample t testing was used to analyze Likert scale data, in which the mean response for each prompt was compared to a baseline response of 3 (neutral). A P value <.05 was considered statistically significant. Statistical analyses were performed using SPSS Statistics for MacOS, version 27 (IBM).

Seventy-eight dermatologists agreed to participate, and 70 completed the survey, for a response rate of 89.7% (Table 1). The dermatologists we surveyed practiced in a variety of clinical settings, including academic public hospitals (46.2% [36/78]), academic private hospitals (33.3% [26/78]), and private practices (32.1% [25/78]), and 60.3% (47/78) reported providing disability documentation at some point. Most of the respondents (64.3% [45/70]) did not perform assessments in an average month (Table 2). Medical assessment documentation was provided most frequently for workers’ compensation (50.0% [35/70]), private insurance (27.1% [19/70]), and Social Security Disability Insurance (25.7% [18/70]). Dermatologists overwhelmingly reported no formal training for disability assessment in medical school (94.3% [66/70]), residency (97.1% [68/70]), or clinical practice (81.4% [57/70]).

In the Likert scale prompts, respondents agreed that they were uncertain of their role in disability assessment (mean response, 3.6; P<.001). Moreover, they were uncomfortable providing assessments (mean response, 3.5; P<.001) and felt that they did not have sufficient time to perform them (mean response, 3.6; P<.001). Dermatologists disagreed that they received adequate compensation for performing assessments (mean response, 2.2; P<.001) and felt that they did not have enough time to participate in assessments (mean response, 3.6; P<.001). Respondents generally did not feel distrustful of patients seeking disability assessment (mean response, 2.8; P=.043). Dermatologists neither agreed nor disagreed when asked if they thought that physicians can determine disability status (mean response, 3.2; P=.118). The details of the Likert scale responses are described in Table 3. Respondents also were uncertain as to which dermatologic conditions were eligible for disability. When asked to select which conditions from a list of 10 were eligible per the Social Security Administration listing of disability impairments, only 15.4% (12/70) of respondents correctly identified that all the conditions qualified; these included ichthyosis, pemphigus vulgaris, allergic contact dermatitis, hidradenitis suppurativa, systemic lupus erythematosus, chromoblastomycosis, xeroderma pigmentosum, burns, malignant melanoma, and scleroderma.⁶

In the free-response prompts, respondents frequently described extensive paperwork, inadequate time, and lack of reimbursement as barriers to providing documentation. Often, dermatologists found that the forms were not well matched to the skin conditions they were evaluating and rather had a musculoskeletal focus. Multiple individuals commented on the challenge in assessing the percentage of disability and functional/psychosocial impairment in skin conditions. One respondent noted that workers’ compensation forms ask if the patient is “…permanent and stationary…for most conditions this has no meaning in dermatology.” Some felt hesitant to provide documentation because they had insufficient patient history, especially regarding employment, and opted to defer to primary care providers who might be more familiar with the full patient history.

A dermatologist described their perspective as follows:

“…As a specialist I feel that I don’t have a complete look into all the factors that could contribute to a patient[’]s need to go on disability, and I don’t have experience with filling out disability requests. That being said, if a patient[’]s request for disability was due to a skin disease that I know way more about than [a] primary care [physician] would, I would do the disability assessment.”

Another respondent noted the complexity in “establishing causality” for workers’ compensation. Another dermatologist reported,

“The most frequent challenging situation I encounter is being asked to evaluate for maximum medical improvement after patch testing. If the patient is not fully avoiding contact allergens either at home or at work, then I typically document that they are not at [maximum medical improvement]. The reality is that most frequently it is due to exposure to allergens at home so the line between what is a legitimate worker’s comp[ensation] issue and what is a home life choice is blurry.”

Nevertheless, respondents expressed interest in learning more about disability assessment procedures. Summary guides, lectures, and prefilled paperwork were the most popular initiatives that respondents agreed would be beneficial toward becoming educated regarding disability assessment (78.6%, 58.6%, and 58.6%, respectively)(Table 2). One respondent noted that “previous [internal medicine] history help[ed]” them in performing cutaneous disability assessments.

As with any survey, our study did have some inherent limitations. Only a relatively small sample size was willing to complete the survey. There was a predominance of respondents from California (34.6% [27/78]), as well as those practicing for less than 15 years (58.9% [46/78])(Figure). This could limit generalizability to the national population of dermatologists. In addition, there was potential for recall bias and errors in responding given the self-reported nature of the study. Different individuals may interpret the Likert scale options in various ways, which could skew results unintentionally. However, the survey was largely qualitative in nature, making it a legitimate tool for answering our research questions. Moreover, we were able to hear the perspectives of dermatologists across diverse practice settings, with free response prompts to increase the depth of the survey.

Almost 50 years later, our survey echoes common themes from Adams’ 1976 survey.⁴ Inadequate compensation, limited time, and burdensome paperwork all continue to hinder dermatologists’ ability to perform disability assessments. Our participants frequently commented that the current disability forms are not congruent with the nature of skin conditions, making it challenging to accurately document the facts.

Moreover, respondents felt uncertain in their role in disability assessment and occasionally noted distrust of patients or insufficient patient history as barriers to completing assessments. They also were unsure if physicians can grant disability status. This is a common misconception among physicians that leads to discomfort in helping with disability assessment.⁷ The role of physicians in disability assessment is to document the facts of a patient’s illness, not to determine whether they are eligible for benefits. We discovered uncertainty in our respondents’ ability to identify conditions eligible for disability, highlighting an area in need of greater education for physicians.

Despite these obstacles, respondents were interested in learning more about disability assessment and highlighted several practical approaches that could help them better perform this task. As skin specialists, dermatologists are the best-equipped physicians to assess cutaneous conditions and should play a greater role in performing disability assessments, which could be achieved through increased educational initiatives and individual physician motivation.⁷ We call for greater collaboration and reflection on the importance of disability assistance among dermatologists to increase participation in the disability-assessment process.

To the Editor:

Cutaneous medical conditions can have a substantial impact on patients’ functioning and quality of life. Many patients with severe skin disease are eligible to receive disability assistance that can provide them with essential income and health care. Previous research has highlighted disability assessment as one of the most important ways physicians can help mitigate the health consequences of poverty.¹ Dermatologists can play an important role in the disability assessment process by documenting the facts associated with patients’ skin conditions.

Although skin conditions have a relatively high prevalence, they remain underrepresented in disability claims. Between 1997 and 2004, occupational skin diseases accounted for 12% to 17% of nonfatal work-related illnesses; however, during that same period, skin conditions comprised only 0.21% of disability claims in the United States.^2,3 Historically, there has been hesitancy among dermatologists to complete disability paperwork; a 1976 survey of dermatologists cited extensive paperwork, “troublesome patients,” and fee schedule issues as reasons.⁴ The lack of training regarding disability assessment in medical school and residency also has been noted.⁵

To characterize modern attitudes toward disability assessments, we conducted a survey of dermatologists across the United States. Our study was reviewed and declared exempt by the institutional review board of the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center (Torrance, California)(approval #18CR-32242-01). Using convenience sampling, we emailed dermatologists from the Association of Professors of Dermatology and dermatology state societies in all 50 states inviting them to participate in our voluntary and anonymous survey, which was administered using SurveyMonkey. The use of all society mailing lists was approved by the respective owners. The 15-question survey included multiple choice, Likert scale, and free response sections. Summary and descriptive statistics were used to describe respondent demographics and identify any patterns in responses.

For each Likert-based question, participants ranked their degree of agreement with a statement as: 1=strongly disagree, 2=somewhat disagree, 3=neither agree nor disagree/neutral, 4=somewhat agree, and 5=strongly agree. The mean response and standard deviation were reported for each Likert scale prompt. Preplanned 1-sample t testing was used to analyze Likert scale data, in which the mean response for each prompt was compared to a baseline response of 3 (neutral). A P value <.05 was considered statistically significant. Statistical analyses were performed using SPSS Statistics for MacOS, version 27 (IBM).

Seventy-eight dermatologists agreed to participate, and 70 completed the survey, for a response rate of 89.7% (Table 1). The dermatologists we surveyed practiced in a variety of clinical settings, including academic public hospitals (46.2% [36/78]), academic private hospitals (33.3% [26/78]), and private practices (32.1% [25/78]), and 60.3% (47/78) reported providing disability documentation at some point. Most of the respondents (64.3% [45/70]) did not perform assessments in an average month (Table 2). Medical assessment documentation was provided most frequently for workers’ compensation (50.0% [35/70]), private insurance (27.1% [19/70]), and Social Security Disability Insurance (25.7% [18/70]). Dermatologists overwhelmingly reported no formal training for disability assessment in medical school (94.3% [66/70]), residency (97.1% [68/70]), or clinical practice (81.4% [57/70]).

In the Likert scale prompts, respondents agreed that they were uncertain of their role in disability assessment (mean response, 3.6; P<.001). Moreover, they were uncomfortable providing assessments (mean response, 3.5; P<.001) and felt that they did not have sufficient time to perform them (mean response, 3.6; P<.001). Dermatologists disagreed that they received adequate compensation for performing assessments (mean response, 2.2; P<.001) and felt that they did not have enough time to participate in assessments (mean response, 3.6; P<.001). Respondents generally did not feel distrustful of patients seeking disability assessment (mean response, 2.8; P=.043). Dermatologists neither agreed nor disagreed when asked if they thought that physicians can determine disability status (mean response, 3.2; P=.118). The details of the Likert scale responses are described in Table 3. Respondents also were uncertain as to which dermatologic conditions were eligible for disability. When asked to select which conditions from a list of 10 were eligible per the Social Security Administration listing of disability impairments, only 15.4% (12/70) of respondents correctly identified that all the conditions qualified; these included ichthyosis, pemphigus vulgaris, allergic contact dermatitis, hidradenitis suppurativa, systemic lupus erythematosus, chromoblastomycosis, xeroderma pigmentosum, burns, malignant melanoma, and scleroderma.⁶

In the free-response prompts, respondents frequently described extensive paperwork, inadequate time, and lack of reimbursement as barriers to providing documentation. Often, dermatologists found that the forms were not well matched to the skin conditions they were evaluating and rather had a musculoskeletal focus. Multiple individuals commented on the challenge in assessing the percentage of disability and functional/psychosocial impairment in skin conditions. One respondent noted that workers’ compensation forms ask if the patient is “…permanent and stationary…for most conditions this has no meaning in dermatology.” Some felt hesitant to provide documentation because they had insufficient patient history, especially regarding employment, and opted to defer to primary care providers who might be more familiar with the full patient history.

A dermatologist described their perspective as follows:

“…As a specialist I feel that I don’t have a complete look into all the factors that could contribute to a patient[’]s need to go on disability, and I don’t have experience with filling out disability requests. That being said, if a patient[’]s request for disability was due to a skin disease that I know way more about than [a] primary care [physician] would, I would do the disability assessment.”

Another respondent noted the complexity in “establishing causality” for workers’ compensation. Another dermatologist reported,

“The most frequent challenging situation I encounter is being asked to evaluate for maximum medical improvement after patch testing. If the patient is not fully avoiding contact allergens either at home or at work, then I typically document that they are not at [maximum medical improvement]. The reality is that most frequently it is due to exposure to allergens at home so the line between what is a legitimate worker’s comp[ensation] issue and what is a home life choice is blurry.”

Nevertheless, respondents expressed interest in learning more about disability assessment procedures. Summary guides, lectures, and prefilled paperwork were the most popular initiatives that respondents agreed would be beneficial toward becoming educated regarding disability assessment (78.6%, 58.6%, and 58.6%, respectively)(Table 2). One respondent noted that “previous [internal medicine] history help[ed]” them in performing cutaneous disability assessments.

As with any survey, our study did have some inherent limitations. Only a relatively small sample size was willing to complete the survey. There was a predominance of respondents from California (34.6% [27/78]), as well as those practicing for less than 15 years (58.9% [46/78])(Figure). This could limit generalizability to the national population of dermatologists. In addition, there was potential for recall bias and errors in responding given the self-reported nature of the study. Different individuals may interpret the Likert scale options in various ways, which could skew results unintentionally. However, the survey was largely qualitative in nature, making it a legitimate tool for answering our research questions. Moreover, we were able to hear the perspectives of dermatologists across diverse practice settings, with free response prompts to increase the depth of the survey.

Almost 50 years later, our survey echoes common themes from Adams’ 1976 survey.⁴ Inadequate compensation, limited time, and burdensome paperwork all continue to hinder dermatologists’ ability to perform disability assessments. Our participants frequently commented that the current disability forms are not congruent with the nature of skin conditions, making it challenging to accurately document the facts.

Moreover, respondents felt uncertain in their role in disability assessment and occasionally noted distrust of patients or insufficient patient history as barriers to completing assessments. They also were unsure if physicians can grant disability status. This is a common misconception among physicians that leads to discomfort in helping with disability assessment.⁷ The role of physicians in disability assessment is to document the facts of a patient’s illness, not to determine whether they are eligible for benefits. We discovered uncertainty in our respondents’ ability to identify conditions eligible for disability, highlighting an area in need of greater education for physicians.

Despite these obstacles, respondents were interested in learning more about disability assessment and highlighted several practical approaches that could help them better perform this task. As skin specialists, dermatologists are the best-equipped physicians to assess cutaneous conditions and should play a greater role in performing disability assessments, which could be achieved through increased educational initiatives and individual physician motivation.⁷ We call for greater collaboration and reflection on the importance of disability assistance among dermatologists to increase participation in the disability-assessment process.

To the Editor:

Cutaneous medical conditions can have a substantial impact on patients’ functioning and quality of life. Many patients with severe skin disease are eligible to receive disability assistance that can provide them with essential income and health care. Previous research has highlighted disability assessment as one of the most important ways physicians can help mitigate the health consequences of poverty.¹ Dermatologists can play an important role in the disability assessment process by documenting the facts associated with patients’ skin conditions.

Although skin conditions have a relatively high prevalence, they remain underrepresented in disability claims. Between 1997 and 2004, occupational skin diseases accounted for 12% to 17% of nonfatal work-related illnesses; however, during that same period, skin conditions comprised only 0.21% of disability claims in the United States.^2,3 Historically, there has been hesitancy among dermatologists to complete disability paperwork; a 1976 survey of dermatologists cited extensive paperwork, “troublesome patients,” and fee schedule issues as reasons.⁴ The lack of training regarding disability assessment in medical school and residency also has been noted.⁵

To characterize modern attitudes toward disability assessments, we conducted a survey of dermatologists across the United States. Our study was reviewed and declared exempt by the institutional review board of the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center (Torrance, California)(approval #18CR-32242-01). Using convenience sampling, we emailed dermatologists from the Association of Professors of Dermatology and dermatology state societies in all 50 states inviting them to participate in our voluntary and anonymous survey, which was administered using SurveyMonkey. The use of all society mailing lists was approved by the respective owners. The 15-question survey included multiple choice, Likert scale, and free response sections. Summary and descriptive statistics were used to describe respondent demographics and identify any patterns in responses.

For each Likert-based question, participants ranked their degree of agreement with a statement as: 1=strongly disagree, 2=somewhat disagree, 3=neither agree nor disagree/neutral, 4=somewhat agree, and 5=strongly agree. The mean response and standard deviation were reported for each Likert scale prompt. Preplanned 1-sample t testing was used to analyze Likert scale data, in which the mean response for each prompt was compared to a baseline response of 3 (neutral). A P value <.05 was considered statistically significant. Statistical analyses were performed using SPSS Statistics for MacOS, version 27 (IBM).

Seventy-eight dermatologists agreed to participate, and 70 completed the survey, for a response rate of 89.7% (Table 1). The dermatologists we surveyed practiced in a variety of clinical settings, including academic public hospitals (46.2% [36/78]), academic private hospitals (33.3% [26/78]), and private practices (32.1% [25/78]), and 60.3% (47/78) reported providing disability documentation at some point. Most of the respondents (64.3% [45/70]) did not perform assessments in an average month (Table 2). Medical assessment documentation was provided most frequently for workers’ compensation (50.0% [35/70]), private insurance (27.1% [19/70]), and Social Security Disability Insurance (25.7% [18/70]). Dermatologists overwhelmingly reported no formal training for disability assessment in medical school (94.3% [66/70]), residency (97.1% [68/70]), or clinical practice (81.4% [57/70]).

In the Likert scale prompts, respondents agreed that they were uncertain of their role in disability assessment (mean response, 3.6; P<.001). Moreover, they were uncomfortable providing assessments (mean response, 3.5; P<.001) and felt that they did not have sufficient time to perform them (mean response, 3.6; P<.001). Dermatologists disagreed that they received adequate compensation for performing assessments (mean response, 2.2; P<.001) and felt that they did not have enough time to participate in assessments (mean response, 3.6; P<.001). Respondents generally did not feel distrustful of patients seeking disability assessment (mean response, 2.8; P=.043). Dermatologists neither agreed nor disagreed when asked if they thought that physicians can determine disability status (mean response, 3.2; P=.118). The details of the Likert scale responses are described in Table 3. Respondents also were uncertain as to which dermatologic conditions were eligible for disability. When asked to select which conditions from a list of 10 were eligible per the Social Security Administration listing of disability impairments, only 15.4% (12/70) of respondents correctly identified that all the conditions qualified; these included ichthyosis, pemphigus vulgaris, allergic contact dermatitis, hidradenitis suppurativa, systemic lupus erythematosus, chromoblastomycosis, xeroderma pigmentosum, burns, malignant melanoma, and scleroderma.⁶

In the free-response prompts, respondents frequently described extensive paperwork, inadequate time, and lack of reimbursement as barriers to providing documentation. Often, dermatologists found that the forms were not well matched to the skin conditions they were evaluating and rather had a musculoskeletal focus. Multiple individuals commented on the challenge in assessing the percentage of disability and functional/psychosocial impairment in skin conditions. One respondent noted that workers’ compensation forms ask if the patient is “…permanent and stationary…for most conditions this has no meaning in dermatology.” Some felt hesitant to provide documentation because they had insufficient patient history, especially regarding employment, and opted to defer to primary care providers who might be more familiar with the full patient history.

A dermatologist described their perspective as follows:

“…As a specialist I feel that I don’t have a complete look into all the factors that could contribute to a patient[’]s need to go on disability, and I don’t have experience with filling out disability requests. That being said, if a patient[’]s request for disability was due to a skin disease that I know way more about than [a] primary care [physician] would, I would do the disability assessment.”

Another respondent noted the complexity in “establishing causality” for workers’ compensation. Another dermatologist reported,

“The most frequent challenging situation I encounter is being asked to evaluate for maximum medical improvement after patch testing. If the patient is not fully avoiding contact allergens either at home or at work, then I typically document that they are not at [maximum medical improvement]. The reality is that most frequently it is due to exposure to allergens at home so the line between what is a legitimate worker’s comp[ensation] issue and what is a home life choice is blurry.”

Nevertheless, respondents expressed interest in learning more about disability assessment procedures. Summary guides, lectures, and prefilled paperwork were the most popular initiatives that respondents agreed would be beneficial toward becoming educated regarding disability assessment (78.6%, 58.6%, and 58.6%, respectively)(Table 2). One respondent noted that “previous [internal medicine] history help[ed]” them in performing cutaneous disability assessments.

As with any survey, our study did have some inherent limitations. Only a relatively small sample size was willing to complete the survey. There was a predominance of respondents from California (34.6% [27/78]), as well as those practicing for less than 15 years (58.9% [46/78])(Figure). This could limit generalizability to the national population of dermatologists. In addition, there was potential for recall bias and errors in responding given the self-reported nature of the study. Different individuals may interpret the Likert scale options in various ways, which could skew results unintentionally. However, the survey was largely qualitative in nature, making it a legitimate tool for answering our research questions. Moreover, we were able to hear the perspectives of dermatologists across diverse practice settings, with free response prompts to increase the depth of the survey.

Almost 50 years later, our survey echoes common themes from Adams’ 1976 survey.⁴ Inadequate compensation, limited time, and burdensome paperwork all continue to hinder dermatologists’ ability to perform disability assessments. Our participants frequently commented that the current disability forms are not congruent with the nature of skin conditions, making it challenging to accurately document the facts.

Moreover, respondents felt uncertain in their role in disability assessment and occasionally noted distrust of patients or insufficient patient history as barriers to completing assessments. They also were unsure if physicians can grant disability status. This is a common misconception among physicians that leads to discomfort in helping with disability assessment.⁷ The role of physicians in disability assessment is to document the facts of a patient’s illness, not to determine whether they are eligible for benefits. We discovered uncertainty in our respondents’ ability to identify conditions eligible for disability, highlighting an area in need of greater education for physicians.

Despite these obstacles, respondents were interested in learning more about disability assessment and highlighted several practical approaches that could help them better perform this task. As skin specialists, dermatologists are the best-equipped physicians to assess cutaneous conditions and should play a greater role in performing disability assessments, which could be achieved through increased educational initiatives and individual physician motivation.⁷ We call for greater collaboration and reflection on the importance of disability assistance among dermatologists to increase participation in the disability-assessment process.

Humans are inherently prone to errors. The extent and consequences of medical errors were documented in the 2000 publication of To Err is Human: Building a Safer Health System.¹ Published research on medical errors in dermatology has emphasized the heuristic issues involved in diagnosis,^2-6 essentially approaching the “why?” and “how?” of such errors. By contrast, the current study aimed to elucidate the “what?”—what are the dermatologic conditions most prone to diagnostic and/or management errors? One study published in 1987 approached this question by analyzing patterns of errors for dermatologic conditions in patients referred for specialty care by primary care physicians.⁷ The current study aimed to update and expand on the findings of this 1987 report by comparing more recent data on the errors made by providers and patients regarding skin conditions.

Methods

Data were collected prospectively from March 18, 2021, through July 25, 2023. Prospective data were obtained by recording the nature of errors noted for all patients seen by a board-certified dermatologist (R.J.P.) during routine outpatient practice in Norfolk, Virginia. This practice is limited to medical dermatology and accepts patients of any age from any referral source, with or without medical insurance. Retrospective data were obtained by review of electronic medical records for all patients seen by the same board-certified dermatologist from June 5, 2020, through March 12, 2021, who previously had been seen by an outside provider or were self-referred. In this study, the term diagnosis is used to describe providers’ explicit or imputed conclusions as to the nature of a dermatosis, and the term interpretation is used to describe patients' conclusions about their own condition. For this study, the patients’ self-made interpretations of their dermatoses were deemed to be correct when they agreed with those made by the dermatologist using standard clinicopathologic criteria supplemented by rapid bedside diagnostic techniques, as detailed in the 1987 study.⁷

Cases in which diagnostic or therapeutic errors were noted were entered into a spreadsheet that excluded patients’ names or other identifiers. For each noted case of diagnostic or therapeutic error, the following data were entered: patient’s age and sex; the name of the incorrect diagnosis, interpretation, or treatment; and the name of the correct (missed) diagnosis, along with the source of the error (provider or patient). Provider diagnoses were determined from medical records or patient statements or were imputed from the generally accepted indications for prescribed treatments. A provider was deemed to be any practitioner with prescriptive authority. Patients’ interpretations of their conditions were determined by patient statements or were imputed based on the indications for treatments being used. A treatment error was recorded when a diagnosis or interpretation was deemed to be correct, but treatment was deemed to be inappropriate. The same dermatologist (R.J.P) made all determinations as to the nature of the errors and their source.

Diagnostic errors were determined in several situations: (1) if the interpretation made by the patient of their dermatosis differed from the correct diagnosis in the absence of any additional diagnostic documentation, the correct diagnosis was scored as a missed diagnosis and the incorrect interpretation was scored as such; (2) if the provider’s diagnosis in the patient’s medical record differed from the correct diagnosis, both the correct (missed) and incorrect diagnoses were recorded; and (3) if the indication(s) of the medication(s) prescribed by the provider or used by the patient for their condition differed from the correct diagnosis, an imputed diagnosis based on this indication was scored as the incorrect diagnosis and the correct (missed) diagnosis was recorded; for example, an error would be entered into the spreadsheet for a patient using terbinafine cream for what was actually psoriasis. For a medication with multiple active agents, an error would be entered into the spreadsheet only if none of its indications matched the correct diagnosis; for example, if the patient had been prescribed a betamethasone/clotrimazole product, no error would be scored if the correct diagnosis was a steroid-responsive dermatosis, dermatophytosis, candidiasis, or tinea versicolor. For a single medication with multiple indications, no error would be recorded if the correct diagnosis was any of these indications; for example, in a patient who had been prescribed topical ketoconazole, no error would be scored if the correct diagnosis was dermatophytosis, candidiasis, tinea versicolor, or seborrheic dermatitis. Additionally, no error would be recorded if the correct diagnosis was uncertain at the time of initial patient evaluation or during chart review.

Standard spreadsheet functions and the pandas package⁸ from the Python programming language⁹ were used to extract relevant data from the spreadsheet (Tables 1-4).

Results

A total of 446 patient visits (182 males, 264 females) were included in the study, in which a total of 486 errors were found in the combined prospective and retrospective portions of the study. These errors involved 1.4% of all patient visits for the study period—specifically, all in routine practice as well as all patient records retrospectively reviewed. The age of the patients ranged from 4 to 95 years; the mean age was 51.5 years for males and 50.8 years for females.

The study results are outlined in Tables 1 through 4. To minimize the amount of data provided with no appreciable effect on the results, cases in which an incorrect or missed diagnosis/interpretation occurred only once (ie, unique case errors) were excluded from the tables. Tables 1 and 2 indicate the numbers and types of incorrect and missed diagnoses.

In the combined patient and provider cases, there were 434 instances in which provider diagnoses and patient interpretations were incorrect, 320 (73.7%) of which involved infectious disorders. By contrast, of the 413 instances of provider and patient missed diagnoses 289 (70.0%) were inflammatory dermatoses. The pattern was similar for patients’ incorrect interpretations compared to the incorrect diagnoses of the medical providers. Patients incorrectly interpreted their dermatoses as infectious in 79.5% (101/127) of cases. Similarly, providers incorrectly diagnosed their patients’ dermatoses as infectious in 75.4% (211/280) of cases (Table 3). For patients’ missed diagnoses, 70.7% (82/116) involved inflammatory dermatoses. For providers’ missed diagnoses, 63.9% (179/280) involved inflammatory dermatoses (Table 4).

Treatment errors in the context of correct diagnoses were uncommon. Fifteen (3.4%) such cases were noted in the 446 error-containing patient visits. In 4 (26.7%) of the 15 cases, potent topical corticosteroids were used long term on inappropriate cutaneous sites (eg, genital, facial, or intertriginous areas). Another 4 (26.7%) cases involved fungal infections: nystatin used for tinea versicolor in 1 case and for dermatophytosis in another, widespread dermatophytosis treated topically, and use of a nonindicated topical antifungal for onychomycosis. Other examples involved inadequate dosing of systemic corticosteroids for extensive acute contact dermatitis, psoriasis treated with systemic corticosteroids, inadequate dosing of medication for seborrheic dermatitis, and treatment with valacyclovir based solely on serologic testing.

Comment

The results of our study indicate that errors in management of cutaneous disorders are overwhelmingly diagnostic in nature, while treatment errors appear to be unusual when the correct diagnosis is made. Both the current study and the 1987 study indicated a notable tendency of providers to incorrectly diagnose infectious disorders and to miss the diagnosis of inflammatory dermatoses.⁷ The current study extends this finding to include patients’ interpretive errors. 

It is notable that many of the incorrect and missed diagnoses can be confirmed or ruled out by rapid bedside techniques, namely potassium hydroxide (KOH) preparation for dermatophytes, candidiasis, and tinea versicolor; wet preparation for scabies and pediculosis; Tzanck preparation for herpes simplex and herpes zoster; and crush preparation for molluscum contagiosum. Notably, 57.8% (281/486) of cases in which error was noted involved disorders for which the use of one of these bedside diagnostic tests could have correctly established a diagnosis or ruled out an incorrect one; thus in an ideal world in which these tests were performed perfectly in all appropriate cases, more than half of the errors detected in this study could have been avoided. Dermatophytosis was involved in 35.8% (174/486) of the error-containing patient encounters in this study; therefore, if only the KOH preparation is considered, more than one-third of all errors documented in this study could have been avoided. Unfortunately, surveys have suggested that among dermatologists in the United States and some other countries, KOH preparations are used infrequently.^10-12

Certain limitations were inherent to this study. The data were derived from a single dermatology practice by one physician in one geographic region over a short period of time. These factors may limit the generalizability of the results. Although the goal was to identify all errors made for the patients seen, some errors likely were missed due to incomplete patient history or inaccurate medication listings. There is no absolute way to determine if the diagnoses or the treatments deemed correct by the dermatologist were, in fact, correct. For cases in which a patient’s interpretation or a provider’s diagnosis was imputed from the indication(s) associated with the medication(s) being used, one cannot exclude the possibility that a medication was used appropriately for a nonlabeled or nonstandard indication. The designation of treatment errors may be subject to different interpretations by different clinicians. Despite these limitations, it is likely that the results of this study can be extrapolated to reasonably similar dermatology practices. The apparently persistent and consistent tendency of clinicians to incorrectly diagnose infectious dermatoses and to miss inflammatory conditions has implications for teaching of medical dermatology in the academic and clinical settings. In particular, given that dermatophytosis is the diagnosis involved in the highest number of errors, special emphasis should be placed on this infection in clinician education.

Acknowledgement—The authors would like to acknowledge the essential contributions to this study by Urvi Jain (Virginia Beach, Virginia), particularly for analysis and interpretation of data and for suggestions to improve the manuscript.

Humans are inherently prone to errors. The extent and consequences of medical errors were documented in the 2000 publication of To Err is Human: Building a Safer Health System.¹ Published research on medical errors in dermatology has emphasized the heuristic issues involved in diagnosis,^2-6 essentially approaching the “why?” and “how?” of such errors. By contrast, the current study aimed to elucidate the “what?”—what are the dermatologic conditions most prone to diagnostic and/or management errors? One study published in 1987 approached this question by analyzing patterns of errors for dermatologic conditions in patients referred for specialty care by primary care physicians.⁷ The current study aimed to update and expand on the findings of this 1987 report by comparing more recent data on the errors made by providers and patients regarding skin conditions.

Methods

Data were collected prospectively from March 18, 2021, through July 25, 2023. Prospective data were obtained by recording the nature of errors noted for all patients seen by a board-certified dermatologist (R.J.P.) during routine outpatient practice in Norfolk, Virginia. This practice is limited to medical dermatology and accepts patients of any age from any referral source, with or without medical insurance. Retrospective data were obtained by review of electronic medical records for all patients seen by the same board-certified dermatologist from June 5, 2020, through March 12, 2021, who previously had been seen by an outside provider or were self-referred. In this study, the term diagnosis is used to describe providers’ explicit or imputed conclusions as to the nature of a dermatosis, and the term interpretation is used to describe patients' conclusions about their own condition. For this study, the patients’ self-made interpretations of their dermatoses were deemed to be correct when they agreed with those made by the dermatologist using standard clinicopathologic criteria supplemented by rapid bedside diagnostic techniques, as detailed in the 1987 study.⁷

Cases in which diagnostic or therapeutic errors were noted were entered into a spreadsheet that excluded patients’ names or other identifiers. For each noted case of diagnostic or therapeutic error, the following data were entered: patient’s age and sex; the name of the incorrect diagnosis, interpretation, or treatment; and the name of the correct (missed) diagnosis, along with the source of the error (provider or patient). Provider diagnoses were determined from medical records or patient statements or were imputed from the generally accepted indications for prescribed treatments. A provider was deemed to be any practitioner with prescriptive authority. Patients’ interpretations of their conditions were determined by patient statements or were imputed based on the indications for treatments being used. A treatment error was recorded when a diagnosis or interpretation was deemed to be correct, but treatment was deemed to be inappropriate. The same dermatologist (R.J.P) made all determinations as to the nature of the errors and their source.

Diagnostic errors were determined in several situations: (1) if the interpretation made by the patient of their dermatosis differed from the correct diagnosis in the absence of any additional diagnostic documentation, the correct diagnosis was scored as a missed diagnosis and the incorrect interpretation was scored as such; (2) if the provider’s diagnosis in the patient’s medical record differed from the correct diagnosis, both the correct (missed) and incorrect diagnoses were recorded; and (3) if the indication(s) of the medication(s) prescribed by the provider or used by the patient for their condition differed from the correct diagnosis, an imputed diagnosis based on this indication was scored as the incorrect diagnosis and the correct (missed) diagnosis was recorded; for example, an error would be entered into the spreadsheet for a patient using terbinafine cream for what was actually psoriasis. For a medication with multiple active agents, an error would be entered into the spreadsheet only if none of its indications matched the correct diagnosis; for example, if the patient had been prescribed a betamethasone/clotrimazole product, no error would be scored if the correct diagnosis was a steroid-responsive dermatosis, dermatophytosis, candidiasis, or tinea versicolor. For a single medication with multiple indications, no error would be recorded if the correct diagnosis was any of these indications; for example, in a patient who had been prescribed topical ketoconazole, no error would be scored if the correct diagnosis was dermatophytosis, candidiasis, tinea versicolor, or seborrheic dermatitis. Additionally, no error would be recorded if the correct diagnosis was uncertain at the time of initial patient evaluation or during chart review.

Standard spreadsheet functions and the pandas package⁸ from the Python programming language⁹ were used to extract relevant data from the spreadsheet (Tables 1-4).

Results

A total of 446 patient visits (182 males, 264 females) were included in the study, in which a total of 486 errors were found in the combined prospective and retrospective portions of the study. These errors involved 1.4% of all patient visits for the study period—specifically, all in routine practice as well as all patient records retrospectively reviewed. The age of the patients ranged from 4 to 95 years; the mean age was 51.5 years for males and 50.8 years for females.

The study results are outlined in Tables 1 through 4. To minimize the amount of data provided with no appreciable effect on the results, cases in which an incorrect or missed diagnosis/interpretation occurred only once (ie, unique case errors) were excluded from the tables. Tables 1 and 2 indicate the numbers and types of incorrect and missed diagnoses.

In the combined patient and provider cases, there were 434 instances in which provider diagnoses and patient interpretations were incorrect, 320 (73.7%) of which involved infectious disorders. By contrast, of the 413 instances of provider and patient missed diagnoses 289 (70.0%) were inflammatory dermatoses. The pattern was similar for patients’ incorrect interpretations compared to the incorrect diagnoses of the medical providers. Patients incorrectly interpreted their dermatoses as infectious in 79.5% (101/127) of cases. Similarly, providers incorrectly diagnosed their patients’ dermatoses as infectious in 75.4% (211/280) of cases (Table 3). For patients’ missed diagnoses, 70.7% (82/116) involved inflammatory dermatoses. For providers’ missed diagnoses, 63.9% (179/280) involved inflammatory dermatoses (Table 4).

Treatment errors in the context of correct diagnoses were uncommon. Fifteen (3.4%) such cases were noted in the 446 error-containing patient visits. In 4 (26.7%) of the 15 cases, potent topical corticosteroids were used long term on inappropriate cutaneous sites (eg, genital, facial, or intertriginous areas). Another 4 (26.7%) cases involved fungal infections: nystatin used for tinea versicolor in 1 case and for dermatophytosis in another, widespread dermatophytosis treated topically, and use of a nonindicated topical antifungal for onychomycosis. Other examples involved inadequate dosing of systemic corticosteroids for extensive acute contact dermatitis, psoriasis treated with systemic corticosteroids, inadequate dosing of medication for seborrheic dermatitis, and treatment with valacyclovir based solely on serologic testing.

Comment

The results of our study indicate that errors in management of cutaneous disorders are overwhelmingly diagnostic in nature, while treatment errors appear to be unusual when the correct diagnosis is made. Both the current study and the 1987 study indicated a notable tendency of providers to incorrectly diagnose infectious disorders and to miss the diagnosis of inflammatory dermatoses.⁷ The current study extends this finding to include patients’ interpretive errors. 

It is notable that many of the incorrect and missed diagnoses can be confirmed or ruled out by rapid bedside techniques, namely potassium hydroxide (KOH) preparation for dermatophytes, candidiasis, and tinea versicolor; wet preparation for scabies and pediculosis; Tzanck preparation for herpes simplex and herpes zoster; and crush preparation for molluscum contagiosum. Notably, 57.8% (281/486) of cases in which error was noted involved disorders for which the use of one of these bedside diagnostic tests could have correctly established a diagnosis or ruled out an incorrect one; thus in an ideal world in which these tests were performed perfectly in all appropriate cases, more than half of the errors detected in this study could have been avoided. Dermatophytosis was involved in 35.8% (174/486) of the error-containing patient encounters in this study; therefore, if only the KOH preparation is considered, more than one-third of all errors documented in this study could have been avoided. Unfortunately, surveys have suggested that among dermatologists in the United States and some other countries, KOH preparations are used infrequently.^10-12

Certain limitations were inherent to this study. The data were derived from a single dermatology practice by one physician in one geographic region over a short period of time. These factors may limit the generalizability of the results. Although the goal was to identify all errors made for the patients seen, some errors likely were missed due to incomplete patient history or inaccurate medication listings. There is no absolute way to determine if the diagnoses or the treatments deemed correct by the dermatologist were, in fact, correct. For cases in which a patient’s interpretation or a provider’s diagnosis was imputed from the indication(s) associated with the medication(s) being used, one cannot exclude the possibility that a medication was used appropriately for a nonlabeled or nonstandard indication. The designation of treatment errors may be subject to different interpretations by different clinicians. Despite these limitations, it is likely that the results of this study can be extrapolated to reasonably similar dermatology practices. The apparently persistent and consistent tendency of clinicians to incorrectly diagnose infectious dermatoses and to miss inflammatory conditions has implications for teaching of medical dermatology in the academic and clinical settings. In particular, given that dermatophytosis is the diagnosis involved in the highest number of errors, special emphasis should be placed on this infection in clinician education.

Acknowledgement—The authors would like to acknowledge the essential contributions to this study by Urvi Jain (Virginia Beach, Virginia), particularly for analysis and interpretation of data and for suggestions to improve the manuscript.

Humans are inherently prone to errors. The extent and consequences of medical errors were documented in the 2000 publication of To Err is Human: Building a Safer Health System.¹ Published research on medical errors in dermatology has emphasized the heuristic issues involved in diagnosis,^2-6 essentially approaching the “why?” and “how?” of such errors. By contrast, the current study aimed to elucidate the “what?”—what are the dermatologic conditions most prone to diagnostic and/or management errors? One study published in 1987 approached this question by analyzing patterns of errors for dermatologic conditions in patients referred for specialty care by primary care physicians.⁷ The current study aimed to update and expand on the findings of this 1987 report by comparing more recent data on the errors made by providers and patients regarding skin conditions.

Methods

Data were collected prospectively from March 18, 2021, through July 25, 2023. Prospective data were obtained by recording the nature of errors noted for all patients seen by a board-certified dermatologist (R.J.P.) during routine outpatient practice in Norfolk, Virginia. This practice is limited to medical dermatology and accepts patients of any age from any referral source, with or without medical insurance. Retrospective data were obtained by review of electronic medical records for all patients seen by the same board-certified dermatologist from June 5, 2020, through March 12, 2021, who previously had been seen by an outside provider or were self-referred. In this study, the term diagnosis is used to describe providers’ explicit or imputed conclusions as to the nature of a dermatosis, and the term interpretation is used to describe patients' conclusions about their own condition. For this study, the patients’ self-made interpretations of their dermatoses were deemed to be correct when they agreed with those made by the dermatologist using standard clinicopathologic criteria supplemented by rapid bedside diagnostic techniques, as detailed in the 1987 study.⁷

Cases in which diagnostic or therapeutic errors were noted were entered into a spreadsheet that excluded patients’ names or other identifiers. For each noted case of diagnostic or therapeutic error, the following data were entered: patient’s age and sex; the name of the incorrect diagnosis, interpretation, or treatment; and the name of the correct (missed) diagnosis, along with the source of the error (provider or patient). Provider diagnoses were determined from medical records or patient statements or were imputed from the generally accepted indications for prescribed treatments. A provider was deemed to be any practitioner with prescriptive authority. Patients’ interpretations of their conditions were determined by patient statements or were imputed based on the indications for treatments being used. A treatment error was recorded when a diagnosis or interpretation was deemed to be correct, but treatment was deemed to be inappropriate. The same dermatologist (R.J.P) made all determinations as to the nature of the errors and their source.

Diagnostic errors were determined in several situations: (1) if the interpretation made by the patient of their dermatosis differed from the correct diagnosis in the absence of any additional diagnostic documentation, the correct diagnosis was scored as a missed diagnosis and the incorrect interpretation was scored as such; (2) if the provider’s diagnosis in the patient’s medical record differed from the correct diagnosis, both the correct (missed) and incorrect diagnoses were recorded; and (3) if the indication(s) of the medication(s) prescribed by the provider or used by the patient for their condition differed from the correct diagnosis, an imputed diagnosis based on this indication was scored as the incorrect diagnosis and the correct (missed) diagnosis was recorded; for example, an error would be entered into the spreadsheet for a patient using terbinafine cream for what was actually psoriasis. For a medication with multiple active agents, an error would be entered into the spreadsheet only if none of its indications matched the correct diagnosis; for example, if the patient had been prescribed a betamethasone/clotrimazole product, no error would be scored if the correct diagnosis was a steroid-responsive dermatosis, dermatophytosis, candidiasis, or tinea versicolor. For a single medication with multiple indications, no error would be recorded if the correct diagnosis was any of these indications; for example, in a patient who had been prescribed topical ketoconazole, no error would be scored if the correct diagnosis was dermatophytosis, candidiasis, tinea versicolor, or seborrheic dermatitis. Additionally, no error would be recorded if the correct diagnosis was uncertain at the time of initial patient evaluation or during chart review.

Standard spreadsheet functions and the pandas package⁸ from the Python programming language⁹ were used to extract relevant data from the spreadsheet (Tables 1-4).

Results

A total of 446 patient visits (182 males, 264 females) were included in the study, in which a total of 486 errors were found in the combined prospective and retrospective portions of the study. These errors involved 1.4% of all patient visits for the study period—specifically, all in routine practice as well as all patient records retrospectively reviewed. The age of the patients ranged from 4 to 95 years; the mean age was 51.5 years for males and 50.8 years for females.

The study results are outlined in Tables 1 through 4. To minimize the amount of data provided with no appreciable effect on the results, cases in which an incorrect or missed diagnosis/interpretation occurred only once (ie, unique case errors) were excluded from the tables. Tables 1 and 2 indicate the numbers and types of incorrect and missed diagnoses.

In the combined patient and provider cases, there were 434 instances in which provider diagnoses and patient interpretations were incorrect, 320 (73.7%) of which involved infectious disorders. By contrast, of the 413 instances of provider and patient missed diagnoses 289 (70.0%) were inflammatory dermatoses. The pattern was similar for patients’ incorrect interpretations compared to the incorrect diagnoses of the medical providers. Patients incorrectly interpreted their dermatoses as infectious in 79.5% (101/127) of cases. Similarly, providers incorrectly diagnosed their patients’ dermatoses as infectious in 75.4% (211/280) of cases (Table 3). For patients’ missed diagnoses, 70.7% (82/116) involved inflammatory dermatoses. For providers’ missed diagnoses, 63.9% (179/280) involved inflammatory dermatoses (Table 4).

Treatment errors in the context of correct diagnoses were uncommon. Fifteen (3.4%) such cases were noted in the 446 error-containing patient visits. In 4 (26.7%) of the 15 cases, potent topical corticosteroids were used long term on inappropriate cutaneous sites (eg, genital, facial, or intertriginous areas). Another 4 (26.7%) cases involved fungal infections: nystatin used for tinea versicolor in 1 case and for dermatophytosis in another, widespread dermatophytosis treated topically, and use of a nonindicated topical antifungal for onychomycosis. Other examples involved inadequate dosing of systemic corticosteroids for extensive acute contact dermatitis, psoriasis treated with systemic corticosteroids, inadequate dosing of medication for seborrheic dermatitis, and treatment with valacyclovir based solely on serologic testing.

Comment

The results of our study indicate that errors in management of cutaneous disorders are overwhelmingly diagnostic in nature, while treatment errors appear to be unusual when the correct diagnosis is made. Both the current study and the 1987 study indicated a notable tendency of providers to incorrectly diagnose infectious disorders and to miss the diagnosis of inflammatory dermatoses.⁷ The current study extends this finding to include patients’ interpretive errors. 

It is notable that many of the incorrect and missed diagnoses can be confirmed or ruled out by rapid bedside techniques, namely potassium hydroxide (KOH) preparation for dermatophytes, candidiasis, and tinea versicolor; wet preparation for scabies and pediculosis; Tzanck preparation for herpes simplex and herpes zoster; and crush preparation for molluscum contagiosum. Notably, 57.8% (281/486) of cases in which error was noted involved disorders for which the use of one of these bedside diagnostic tests could have correctly established a diagnosis or ruled out an incorrect one; thus in an ideal world in which these tests were performed perfectly in all appropriate cases, more than half of the errors detected in this study could have been avoided. Dermatophytosis was involved in 35.8% (174/486) of the error-containing patient encounters in this study; therefore, if only the KOH preparation is considered, more than one-third of all errors documented in this study could have been avoided. Unfortunately, surveys have suggested that among dermatologists in the United States and some other countries, KOH preparations are used infrequently.^10-12

Certain limitations were inherent to this study. The data were derived from a single dermatology practice by one physician in one geographic region over a short period of time. These factors may limit the generalizability of the results. Although the goal was to identify all errors made for the patients seen, some errors likely were missed due to incomplete patient history or inaccurate medication listings. There is no absolute way to determine if the diagnoses or the treatments deemed correct by the dermatologist were, in fact, correct. For cases in which a patient’s interpretation or a provider’s diagnosis was imputed from the indication(s) associated with the medication(s) being used, one cannot exclude the possibility that a medication was used appropriately for a nonlabeled or nonstandard indication. The designation of treatment errors may be subject to different interpretations by different clinicians. Despite these limitations, it is likely that the results of this study can be extrapolated to reasonably similar dermatology practices. The apparently persistent and consistent tendency of clinicians to incorrectly diagnose infectious dermatoses and to miss inflammatory conditions has implications for teaching of medical dermatology in the academic and clinical settings. In particular, given that dermatophytosis is the diagnosis involved in the highest number of errors, special emphasis should be placed on this infection in clinician education.

Acknowledgement—The authors would like to acknowledge the essential contributions to this study by Urvi Jain (Virginia Beach, Virginia), particularly for analysis and interpretation of data and for suggestions to improve the manuscript.