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Persistent scaling rash
The clinical pattern of a scaly herald patch predating the eruption of multiple scaly macules is the hallmark of pityriasis rosea (PR). This patient’s severe itching is also classic for PR.
PR’s etiology is believed to be a reactivation of infection from human herpes viruses 6 and 7.1 Prodromal viral symptoms of malaise, sore throat, myalgias, and fever are common.2 Along with the prodromal symptoms, there is often a several-centimeter herald patch that occurs on the trunk. It is often confused with eczema or tinea due to its erythema and scale. (Secondary syphilis is also in the differential.) Sometimes PR can be differentiated by the scale pattern being a collarette instead of diffuse. The diagnosis becomes clearer 1 to 2 weeks later with the onset of multiple small scaly macules across the trunk following the Langer’s skin lines. The course is self-limited but takes several weeks to months to resolve.
If severe, PR may be treated with acyclovir 800 mg orally 5 times daily for 5 days; this is the same regimen for treating varicella zoster (shingles).1,2 Estimated recurrence rates are 4% to 24%.1,3
At age 49 years, this woman was older than the average patient with PR, as the usual age range is 10 to 35 years.1 Her physician advised her that the outbreak might recur. She was also given a prescription for oral hydroxyzine 25 mg to be taken at bedtime if the itching was interfering with her sleep. Her physician told her to return for reevaluation if the rash did not resolve in 3 months. She did not return for reevaluation.
Photo and text courtesy of Daniel Stulberg, MD, FAAFP, Professor and Chair, Department of Family and Community Medicine, Western Michigan University Homer Stryker, MD School of Medicine, Kalamazoo.
1. Drago F, Ciccarese G, Parodi A. Commentary on: "pityriasis rosea recurrence is much higher than previously known: a prospective study." Acta Derm Venereol. 2019;99:1053-1054. doi: 10.2340/00015555-3265
2. Villalon-Gomez JM. Pityriasis rosea: diagnosis and treatment. Am Fam Physician. 2018;97:38-44.
3. Yüksel M. Pityriasis rosea recurrence is much higher than previously known: a prospective study. Acta Derm Venereol. 2019;99:664-667. doi: 10.2340/00015555-3169
The clinical pattern of a scaly herald patch predating the eruption of multiple scaly macules is the hallmark of pityriasis rosea (PR). This patient’s severe itching is also classic for PR.
PR’s etiology is believed to be a reactivation of infection from human herpes viruses 6 and 7.1 Prodromal viral symptoms of malaise, sore throat, myalgias, and fever are common.2 Along with the prodromal symptoms, there is often a several-centimeter herald patch that occurs on the trunk. It is often confused with eczema or tinea due to its erythema and scale. (Secondary syphilis is also in the differential.) Sometimes PR can be differentiated by the scale pattern being a collarette instead of diffuse. The diagnosis becomes clearer 1 to 2 weeks later with the onset of multiple small scaly macules across the trunk following the Langer’s skin lines. The course is self-limited but takes several weeks to months to resolve.
If severe, PR may be treated with acyclovir 800 mg orally 5 times daily for 5 days; this is the same regimen for treating varicella zoster (shingles).1,2 Estimated recurrence rates are 4% to 24%.1,3
At age 49 years, this woman was older than the average patient with PR, as the usual age range is 10 to 35 years.1 Her physician advised her that the outbreak might recur. She was also given a prescription for oral hydroxyzine 25 mg to be taken at bedtime if the itching was interfering with her sleep. Her physician told her to return for reevaluation if the rash did not resolve in 3 months. She did not return for reevaluation.
Photo and text courtesy of Daniel Stulberg, MD, FAAFP, Professor and Chair, Department of Family and Community Medicine, Western Michigan University Homer Stryker, MD School of Medicine, Kalamazoo.
The clinical pattern of a scaly herald patch predating the eruption of multiple scaly macules is the hallmark of pityriasis rosea (PR). This patient’s severe itching is also classic for PR.
PR’s etiology is believed to be a reactivation of infection from human herpes viruses 6 and 7.1 Prodromal viral symptoms of malaise, sore throat, myalgias, and fever are common.2 Along with the prodromal symptoms, there is often a several-centimeter herald patch that occurs on the trunk. It is often confused with eczema or tinea due to its erythema and scale. (Secondary syphilis is also in the differential.) Sometimes PR can be differentiated by the scale pattern being a collarette instead of diffuse. The diagnosis becomes clearer 1 to 2 weeks later with the onset of multiple small scaly macules across the trunk following the Langer’s skin lines. The course is self-limited but takes several weeks to months to resolve.
If severe, PR may be treated with acyclovir 800 mg orally 5 times daily for 5 days; this is the same regimen for treating varicella zoster (shingles).1,2 Estimated recurrence rates are 4% to 24%.1,3
At age 49 years, this woman was older than the average patient with PR, as the usual age range is 10 to 35 years.1 Her physician advised her that the outbreak might recur. She was also given a prescription for oral hydroxyzine 25 mg to be taken at bedtime if the itching was interfering with her sleep. Her physician told her to return for reevaluation if the rash did not resolve in 3 months. She did not return for reevaluation.
Photo and text courtesy of Daniel Stulberg, MD, FAAFP, Professor and Chair, Department of Family and Community Medicine, Western Michigan University Homer Stryker, MD School of Medicine, Kalamazoo.
1. Drago F, Ciccarese G, Parodi A. Commentary on: "pityriasis rosea recurrence is much higher than previously known: a prospective study." Acta Derm Venereol. 2019;99:1053-1054. doi: 10.2340/00015555-3265
2. Villalon-Gomez JM. Pityriasis rosea: diagnosis and treatment. Am Fam Physician. 2018;97:38-44.
3. Yüksel M. Pityriasis rosea recurrence is much higher than previously known: a prospective study. Acta Derm Venereol. 2019;99:664-667. doi: 10.2340/00015555-3169
1. Drago F, Ciccarese G, Parodi A. Commentary on: "pityriasis rosea recurrence is much higher than previously known: a prospective study." Acta Derm Venereol. 2019;99:1053-1054. doi: 10.2340/00015555-3265
2. Villalon-Gomez JM. Pityriasis rosea: diagnosis and treatment. Am Fam Physician. 2018;97:38-44.
3. Yüksel M. Pityriasis rosea recurrence is much higher than previously known: a prospective study. Acta Derm Venereol. 2019;99:664-667. doi: 10.2340/00015555-3169
Concomitant med use may explain poor antidepressant response
Investigators studied over 800 patients who were taking antidepressants for major depressive disorder (MDD) and found that close to two-thirds were taking at least one nonpsychiatric medication with potential depressive symptom side effects (PDSS), more than 30% were taking two or more such medications, and 20% at least three such medications.
These medications, which included antihypertensive medications and corticosteroids, among others, were associated with higher odds of moderate-to-severe depressive symptoms, compared with medications without PDSS.
“When evaluating the reasons for inadequate response to treatment for depression, clinicians should consider whether their patient is also receiving a nonpsychiatric medication with a potential for depressive symptom side effects,” study investigator Mark Olfson, MD, MPH, Elizabeth K. Dollard professor of psychiatry, medicine, and law and professor of epidemiology, Columbia University Irving Medical Center, New York, said in an interview.
The study was published online in the Journal of Clinical Psychiatry.
Previous research limited
“In earlier research, we found that people who were taking medications with a potential to cause depressive symptom side effects were at increased risk of depression, especially those adults who were taking more than one of these medications,” said Dr. Olfson.
This finding led Dr. Olfson and his team to “wonder whether the risks of depressive symptoms associated with these medications extended to people who were being actively treated with antidepressants for depression.”
To investigate, they turned to the National Health and Nutrition Examination Survey (NHANES) – a nationally representative cross-sectional survey of the United States general population.
The study was based on the 2013-2014, 2015-2016, and 2017-2018 waves and included 885 adults who reported using antidepressant medications for greater than or equal to 6 weeks for depression and whose depression could be ascertained.
Prescription medications with PDSS were identified through Micromedex, whose accuracy is “established” and primarily based on the U.S. Food and Drug Administration’s labeled side effects.
Nonantidepressant psychiatric medications and medications for Alzheimer’s disease or substance use disorders were not included in the analysis.
Antidepressant-treated MDD was defined as taking an antidepressant for MDD for greater than or equal to 6 weeks. Depressive symptoms were ascertained using the Patient Health Questionnaire-9 (PHQ-9) with a score of less than 5 representing no/minimal depressive symptoms and a score of greater than or equal to 10 indicating moderate/severe symptoms.
Other variables included self-reported sex, age, race/ethnicity, income, education, health insurance, and common chronic medical conditions such as hypertension, arthritis, lung disease, diabetes mellitus, thyroid disease, cancer, heart disease, liver disease, stroke, and congestive heart failure.
Recovery interrupted
Of the patients in the study treated with antidepressants, most were female, greater than or equal to 50 years, non-Hispanic White, and with a college education (70.55, 62.0%, 81.7%, and 69.4%, respectively).
Selective serotonin reuptake inhibitors were used by 67.9% of participants with MDD. Most had been on the same antidepressant medication for a “long time,” the authors report, with 79.2% and 67.8% taking them for greater than 1 year and greater than 2 years, respectively.
Despite the large number of patients on antidepressants, only 43.0% scored in the no/minimal symptoms range, based on the PHQ-9, while 28.4% scored in the moderate/severe range.
Most patients (85%) took at least one medication for medical conditions, with the majority medications with PDSS: 66.7% took at least one medication with PDSS, 37.3% took at least two, 21.6% took at least three, 10.7% took at least four, and 4.9% took at least five.
Almost 75% were using greater than or equal to 1 medication without PDSS, and about 50% were using greater than 1.
The number of medications with PDSS was significantly associated with lower odds of no/minimal depressive symptoms (AOR, 0.75 [95% CI, 0.64-0.87]; P < .001) and higher odds of moderate/severe symptoms (AOR, 1.14 [1.004-1.29]; P = .044).
“The predicted probability of no/minimal symptoms in those taking 5 medications with PDSS was less than half the predicted probability in those taking no medications with PDSS (0.23 vs. 0.52),” the authors report.
Conversely, the predicted probability of moderate/severe symptoms was ~50% higher in individuals taking 5 versus 0 medications with PDSS (0.36 vs. 0.24).
No corresponding associations were found for medications without PDSS.
The results were even stronger when the researchers repeated their adjusted regression analyses to focus on the 10 individual medications most associated with the severity of depressive symptoms. These were omeprazole, gabapentin, meloxicam, tramadol, ranitidine, baclofen, oxycodone, tizanidine, propranolol, and morphine, with an AOR of 0.42 [0.30-0.60] for no/minimal symptoms and 1.68 [1.24-2.27] for moderate/severe symptoms.
“Many widely prescribed medications, from antihypertensives, such as atenolol and metoprolol to corticosteroids, such as dexamethasone and triamcinolone, are associated with depression side effects,” said Dr. Olfson.
“These medications could interfere with recovery from depression. When available, consideration should be given to selecting a substitute with lower risk for depressive symptoms,” he said.
Role in treatment-resistant depression
In a comment, Dima Qato, PharmD, MPH, PhD, Hygeia Centennial chair and associate professor, University of Southern California School of Pharmacy, Los Angeles, said the study “is an important reminder that the use of medications with depressive symptoms side effects is increasingly common and may contribute to delays in responsiveness or worsen depressive symptoms among individuals being treated for depression.”
Dr. Qato, who is also the director of the Program on Medicines and Public Health, USC School of Pharmacy, and was not involved with the study, recommended that clinicians “consider the role of medications with depression side effects when evaluating patients with treatment-resistant depression.”
The study was not supported by any funding agency. Dr. Olfson and coauthors have disclosed no relevant financial relationships. Dr. Qato is a consultant for the Public Citizen Health Research Group.
A version of this article first appeared on Medscape.com.
Investigators studied over 800 patients who were taking antidepressants for major depressive disorder (MDD) and found that close to two-thirds were taking at least one nonpsychiatric medication with potential depressive symptom side effects (PDSS), more than 30% were taking two or more such medications, and 20% at least three such medications.
These medications, which included antihypertensive medications and corticosteroids, among others, were associated with higher odds of moderate-to-severe depressive symptoms, compared with medications without PDSS.
“When evaluating the reasons for inadequate response to treatment for depression, clinicians should consider whether their patient is also receiving a nonpsychiatric medication with a potential for depressive symptom side effects,” study investigator Mark Olfson, MD, MPH, Elizabeth K. Dollard professor of psychiatry, medicine, and law and professor of epidemiology, Columbia University Irving Medical Center, New York, said in an interview.
The study was published online in the Journal of Clinical Psychiatry.
Previous research limited
“In earlier research, we found that people who were taking medications with a potential to cause depressive symptom side effects were at increased risk of depression, especially those adults who were taking more than one of these medications,” said Dr. Olfson.
This finding led Dr. Olfson and his team to “wonder whether the risks of depressive symptoms associated with these medications extended to people who were being actively treated with antidepressants for depression.”
To investigate, they turned to the National Health and Nutrition Examination Survey (NHANES) – a nationally representative cross-sectional survey of the United States general population.
The study was based on the 2013-2014, 2015-2016, and 2017-2018 waves and included 885 adults who reported using antidepressant medications for greater than or equal to 6 weeks for depression and whose depression could be ascertained.
Prescription medications with PDSS were identified through Micromedex, whose accuracy is “established” and primarily based on the U.S. Food and Drug Administration’s labeled side effects.
Nonantidepressant psychiatric medications and medications for Alzheimer’s disease or substance use disorders were not included in the analysis.
Antidepressant-treated MDD was defined as taking an antidepressant for MDD for greater than or equal to 6 weeks. Depressive symptoms were ascertained using the Patient Health Questionnaire-9 (PHQ-9) with a score of less than 5 representing no/minimal depressive symptoms and a score of greater than or equal to 10 indicating moderate/severe symptoms.
Other variables included self-reported sex, age, race/ethnicity, income, education, health insurance, and common chronic medical conditions such as hypertension, arthritis, lung disease, diabetes mellitus, thyroid disease, cancer, heart disease, liver disease, stroke, and congestive heart failure.
Recovery interrupted
Of the patients in the study treated with antidepressants, most were female, greater than or equal to 50 years, non-Hispanic White, and with a college education (70.55, 62.0%, 81.7%, and 69.4%, respectively).
Selective serotonin reuptake inhibitors were used by 67.9% of participants with MDD. Most had been on the same antidepressant medication for a “long time,” the authors report, with 79.2% and 67.8% taking them for greater than 1 year and greater than 2 years, respectively.
Despite the large number of patients on antidepressants, only 43.0% scored in the no/minimal symptoms range, based on the PHQ-9, while 28.4% scored in the moderate/severe range.
Most patients (85%) took at least one medication for medical conditions, with the majority medications with PDSS: 66.7% took at least one medication with PDSS, 37.3% took at least two, 21.6% took at least three, 10.7% took at least four, and 4.9% took at least five.
Almost 75% were using greater than or equal to 1 medication without PDSS, and about 50% were using greater than 1.
The number of medications with PDSS was significantly associated with lower odds of no/minimal depressive symptoms (AOR, 0.75 [95% CI, 0.64-0.87]; P < .001) and higher odds of moderate/severe symptoms (AOR, 1.14 [1.004-1.29]; P = .044).
“The predicted probability of no/minimal symptoms in those taking 5 medications with PDSS was less than half the predicted probability in those taking no medications with PDSS (0.23 vs. 0.52),” the authors report.
Conversely, the predicted probability of moderate/severe symptoms was ~50% higher in individuals taking 5 versus 0 medications with PDSS (0.36 vs. 0.24).
No corresponding associations were found for medications without PDSS.
The results were even stronger when the researchers repeated their adjusted regression analyses to focus on the 10 individual medications most associated with the severity of depressive symptoms. These were omeprazole, gabapentin, meloxicam, tramadol, ranitidine, baclofen, oxycodone, tizanidine, propranolol, and morphine, with an AOR of 0.42 [0.30-0.60] for no/minimal symptoms and 1.68 [1.24-2.27] for moderate/severe symptoms.
“Many widely prescribed medications, from antihypertensives, such as atenolol and metoprolol to corticosteroids, such as dexamethasone and triamcinolone, are associated with depression side effects,” said Dr. Olfson.
“These medications could interfere with recovery from depression. When available, consideration should be given to selecting a substitute with lower risk for depressive symptoms,” he said.
Role in treatment-resistant depression
In a comment, Dima Qato, PharmD, MPH, PhD, Hygeia Centennial chair and associate professor, University of Southern California School of Pharmacy, Los Angeles, said the study “is an important reminder that the use of medications with depressive symptoms side effects is increasingly common and may contribute to delays in responsiveness or worsen depressive symptoms among individuals being treated for depression.”
Dr. Qato, who is also the director of the Program on Medicines and Public Health, USC School of Pharmacy, and was not involved with the study, recommended that clinicians “consider the role of medications with depression side effects when evaluating patients with treatment-resistant depression.”
The study was not supported by any funding agency. Dr. Olfson and coauthors have disclosed no relevant financial relationships. Dr. Qato is a consultant for the Public Citizen Health Research Group.
A version of this article first appeared on Medscape.com.
Investigators studied over 800 patients who were taking antidepressants for major depressive disorder (MDD) and found that close to two-thirds were taking at least one nonpsychiatric medication with potential depressive symptom side effects (PDSS), more than 30% were taking two or more such medications, and 20% at least three such medications.
These medications, which included antihypertensive medications and corticosteroids, among others, were associated with higher odds of moderate-to-severe depressive symptoms, compared with medications without PDSS.
“When evaluating the reasons for inadequate response to treatment for depression, clinicians should consider whether their patient is also receiving a nonpsychiatric medication with a potential for depressive symptom side effects,” study investigator Mark Olfson, MD, MPH, Elizabeth K. Dollard professor of psychiatry, medicine, and law and professor of epidemiology, Columbia University Irving Medical Center, New York, said in an interview.
The study was published online in the Journal of Clinical Psychiatry.
Previous research limited
“In earlier research, we found that people who were taking medications with a potential to cause depressive symptom side effects were at increased risk of depression, especially those adults who were taking more than one of these medications,” said Dr. Olfson.
This finding led Dr. Olfson and his team to “wonder whether the risks of depressive symptoms associated with these medications extended to people who were being actively treated with antidepressants for depression.”
To investigate, they turned to the National Health and Nutrition Examination Survey (NHANES) – a nationally representative cross-sectional survey of the United States general population.
The study was based on the 2013-2014, 2015-2016, and 2017-2018 waves and included 885 adults who reported using antidepressant medications for greater than or equal to 6 weeks for depression and whose depression could be ascertained.
Prescription medications with PDSS were identified through Micromedex, whose accuracy is “established” and primarily based on the U.S. Food and Drug Administration’s labeled side effects.
Nonantidepressant psychiatric medications and medications for Alzheimer’s disease or substance use disorders were not included in the analysis.
Antidepressant-treated MDD was defined as taking an antidepressant for MDD for greater than or equal to 6 weeks. Depressive symptoms were ascertained using the Patient Health Questionnaire-9 (PHQ-9) with a score of less than 5 representing no/minimal depressive symptoms and a score of greater than or equal to 10 indicating moderate/severe symptoms.
Other variables included self-reported sex, age, race/ethnicity, income, education, health insurance, and common chronic medical conditions such as hypertension, arthritis, lung disease, diabetes mellitus, thyroid disease, cancer, heart disease, liver disease, stroke, and congestive heart failure.
Recovery interrupted
Of the patients in the study treated with antidepressants, most were female, greater than or equal to 50 years, non-Hispanic White, and with a college education (70.55, 62.0%, 81.7%, and 69.4%, respectively).
Selective serotonin reuptake inhibitors were used by 67.9% of participants with MDD. Most had been on the same antidepressant medication for a “long time,” the authors report, with 79.2% and 67.8% taking them for greater than 1 year and greater than 2 years, respectively.
Despite the large number of patients on antidepressants, only 43.0% scored in the no/minimal symptoms range, based on the PHQ-9, while 28.4% scored in the moderate/severe range.
Most patients (85%) took at least one medication for medical conditions, with the majority medications with PDSS: 66.7% took at least one medication with PDSS, 37.3% took at least two, 21.6% took at least three, 10.7% took at least four, and 4.9% took at least five.
Almost 75% were using greater than or equal to 1 medication without PDSS, and about 50% were using greater than 1.
The number of medications with PDSS was significantly associated with lower odds of no/minimal depressive symptoms (AOR, 0.75 [95% CI, 0.64-0.87]; P < .001) and higher odds of moderate/severe symptoms (AOR, 1.14 [1.004-1.29]; P = .044).
“The predicted probability of no/minimal symptoms in those taking 5 medications with PDSS was less than half the predicted probability in those taking no medications with PDSS (0.23 vs. 0.52),” the authors report.
Conversely, the predicted probability of moderate/severe symptoms was ~50% higher in individuals taking 5 versus 0 medications with PDSS (0.36 vs. 0.24).
No corresponding associations were found for medications without PDSS.
The results were even stronger when the researchers repeated their adjusted regression analyses to focus on the 10 individual medications most associated with the severity of depressive symptoms. These were omeprazole, gabapentin, meloxicam, tramadol, ranitidine, baclofen, oxycodone, tizanidine, propranolol, and morphine, with an AOR of 0.42 [0.30-0.60] for no/minimal symptoms and 1.68 [1.24-2.27] for moderate/severe symptoms.
“Many widely prescribed medications, from antihypertensives, such as atenolol and metoprolol to corticosteroids, such as dexamethasone and triamcinolone, are associated with depression side effects,” said Dr. Olfson.
“These medications could interfere with recovery from depression. When available, consideration should be given to selecting a substitute with lower risk for depressive symptoms,” he said.
Role in treatment-resistant depression
In a comment, Dima Qato, PharmD, MPH, PhD, Hygeia Centennial chair and associate professor, University of Southern California School of Pharmacy, Los Angeles, said the study “is an important reminder that the use of medications with depressive symptoms side effects is increasingly common and may contribute to delays in responsiveness or worsen depressive symptoms among individuals being treated for depression.”
Dr. Qato, who is also the director of the Program on Medicines and Public Health, USC School of Pharmacy, and was not involved with the study, recommended that clinicians “consider the role of medications with depression side effects when evaluating patients with treatment-resistant depression.”
The study was not supported by any funding agency. Dr. Olfson and coauthors have disclosed no relevant financial relationships. Dr. Qato is a consultant for the Public Citizen Health Research Group.
A version of this article first appeared on Medscape.com.
FROM THE JOURNAL OF CLINICAL PSYCHIATRY
Novel agent promising for major depression: Phase 3 data
TOPLINE
Patients who received zuranolone 50 mg/d demonstrated significantly greater improvement in depressive symptoms than those who received placebo, with a rapid onset of effect.
METHODOLOGY
The Food and Drug Administration has accepted filing of a new drug application for zuranolone, a neuroactive steroid that targets g-aminobutyric acid type A receptors (GABAAR), for the treatment of major depressive disorder (MDD) and postpartum depression.
The study included 543 mostly White female patients with MDD. The mean age of the patients was 40 years. Participants were randomly assigned to receive oral zuranolone 50 mg or placebo once daily for 14 days.
About 30% of patients were taking an antidepressant.
The primary endpoint was change in Hamilton Depression Rating Scale (HAM-D) score at day 15.
TAKEAWAY
The zuranolone group showed significantly greater improvement in depressive symptoms at 15 days compared with the placebo group (least square mean [LSM] change on HAM-D, –14.1, vs. –12.3; P = .01; Cohen’s d = 0.23).
Improvements were observed on day 3, the earliest assessment, and were sustained at all subsequent visits during the treatment and follow-up period (through day 42).
Results favored zuranolone regardless of the use of antidepressant therapies.
Patients with anxiety who received the active drug experienced improvement in anxiety symptoms compared to the patients who received placebo.
The drug was well tolerated, and there were no new safety findings. The most common treatment-emergent adverse events were somnolence and headache. There was no weight gain, sexual dysfunction, withdrawal symptoms, or increased suicidal ideation or behavior.
IN PRACTICE
The study adds to evidence suggesting zuranolone is a promising novel therapy for treating MDD, the authors noted.
STUDY DETAILS
The study was conducted by Anita H. Clayton, MD, department of psychiatry and neurobehavioral sciences, University of Virginia, Charlottesville, and colleagues. It was published online May 3 in The American Journal of Psychiatry.
LIMITATIONS
The study was short term, and the patient population was severely depressed at study entry, which may limit application to those with mild or moderate symptoms. There was a robust placebo response, possibly partly due to the COVID-19 pandemic, when there was an increase in depressive symptoms in the U.S. population, and so frequent in-person visits may have led to an improvement in symptoms even if the patient was receiving placebo.
DISCLOSURES
The study was funded by Sage Therapeutics and Biogen.
A version of this article first appeared on Medscape.com.
TOPLINE
Patients who received zuranolone 50 mg/d demonstrated significantly greater improvement in depressive symptoms than those who received placebo, with a rapid onset of effect.
METHODOLOGY
The Food and Drug Administration has accepted filing of a new drug application for zuranolone, a neuroactive steroid that targets g-aminobutyric acid type A receptors (GABAAR), for the treatment of major depressive disorder (MDD) and postpartum depression.
The study included 543 mostly White female patients with MDD. The mean age of the patients was 40 years. Participants were randomly assigned to receive oral zuranolone 50 mg or placebo once daily for 14 days.
About 30% of patients were taking an antidepressant.
The primary endpoint was change in Hamilton Depression Rating Scale (HAM-D) score at day 15.
TAKEAWAY
The zuranolone group showed significantly greater improvement in depressive symptoms at 15 days compared with the placebo group (least square mean [LSM] change on HAM-D, –14.1, vs. –12.3; P = .01; Cohen’s d = 0.23).
Improvements were observed on day 3, the earliest assessment, and were sustained at all subsequent visits during the treatment and follow-up period (through day 42).
Results favored zuranolone regardless of the use of antidepressant therapies.
Patients with anxiety who received the active drug experienced improvement in anxiety symptoms compared to the patients who received placebo.
The drug was well tolerated, and there were no new safety findings. The most common treatment-emergent adverse events were somnolence and headache. There was no weight gain, sexual dysfunction, withdrawal symptoms, or increased suicidal ideation or behavior.
IN PRACTICE
The study adds to evidence suggesting zuranolone is a promising novel therapy for treating MDD, the authors noted.
STUDY DETAILS
The study was conducted by Anita H. Clayton, MD, department of psychiatry and neurobehavioral sciences, University of Virginia, Charlottesville, and colleagues. It was published online May 3 in The American Journal of Psychiatry.
LIMITATIONS
The study was short term, and the patient population was severely depressed at study entry, which may limit application to those with mild or moderate symptoms. There was a robust placebo response, possibly partly due to the COVID-19 pandemic, when there was an increase in depressive symptoms in the U.S. population, and so frequent in-person visits may have led to an improvement in symptoms even if the patient was receiving placebo.
DISCLOSURES
The study was funded by Sage Therapeutics and Biogen.
A version of this article first appeared on Medscape.com.
TOPLINE
Patients who received zuranolone 50 mg/d demonstrated significantly greater improvement in depressive symptoms than those who received placebo, with a rapid onset of effect.
METHODOLOGY
The Food and Drug Administration has accepted filing of a new drug application for zuranolone, a neuroactive steroid that targets g-aminobutyric acid type A receptors (GABAAR), for the treatment of major depressive disorder (MDD) and postpartum depression.
The study included 543 mostly White female patients with MDD. The mean age of the patients was 40 years. Participants were randomly assigned to receive oral zuranolone 50 mg or placebo once daily for 14 days.
About 30% of patients were taking an antidepressant.
The primary endpoint was change in Hamilton Depression Rating Scale (HAM-D) score at day 15.
TAKEAWAY
The zuranolone group showed significantly greater improvement in depressive symptoms at 15 days compared with the placebo group (least square mean [LSM] change on HAM-D, –14.1, vs. –12.3; P = .01; Cohen’s d = 0.23).
Improvements were observed on day 3, the earliest assessment, and were sustained at all subsequent visits during the treatment and follow-up period (through day 42).
Results favored zuranolone regardless of the use of antidepressant therapies.
Patients with anxiety who received the active drug experienced improvement in anxiety symptoms compared to the patients who received placebo.
The drug was well tolerated, and there were no new safety findings. The most common treatment-emergent adverse events were somnolence and headache. There was no weight gain, sexual dysfunction, withdrawal symptoms, or increased suicidal ideation or behavior.
IN PRACTICE
The study adds to evidence suggesting zuranolone is a promising novel therapy for treating MDD, the authors noted.
STUDY DETAILS
The study was conducted by Anita H. Clayton, MD, department of psychiatry and neurobehavioral sciences, University of Virginia, Charlottesville, and colleagues. It was published online May 3 in The American Journal of Psychiatry.
LIMITATIONS
The study was short term, and the patient population was severely depressed at study entry, which may limit application to those with mild or moderate symptoms. There was a robust placebo response, possibly partly due to the COVID-19 pandemic, when there was an increase in depressive symptoms in the U.S. population, and so frequent in-person visits may have led to an improvement in symptoms even if the patient was receiving placebo.
DISCLOSURES
The study was funded by Sage Therapeutics and Biogen.
A version of this article first appeared on Medscape.com.
Strategies for complete B-cell depletion evolve for patients with lupus nephritis
SEOUL, SOUTH KOREA – B cell–depleting therapies in patients with lupus nephritis have a higher likelihood of complete response if B cells are almost completely depleted, and strategies for achieving more complete B-cell depletion continue to be tested, according to evidence presented by Richard A. Furie, MD, at an international congress on systemic lupus erythematosus (SLE).
“If you go back about 20 years ago or so, when we designed the LUNAR and EXPLORER trials, we were scared to death of rituximab [Rituxan and biosimilars], about what would happen when you deplete B cells,” said Dr. Furie, chief of the division of rheumatology at Northwell Health in New York.
The LUNAR trial, which compared rituximab with placebo in patients with lupus nephritis, did not show a statistically significant difference in renal outcomes at 1 year. However, a post hoc analysis done several years later told a different story. It looked at patients who achieved complete peripheral depletion of B cells, defined as zero cells per microliter in peripheral blood. “You can see about a fourfold increase in complete response rates in those who were complete B-cell depleters at 1 year,” Dr. Furie told the conference.
It therefore raises the question of how to achieve greater B-cell depletion rates in patients. Dr. Furie said one strategy might be to first mobilize memory B cells and neutralize B cell–activating factor using belimumab (Benlysta), and then treat with rituximab to eliminate B cells. This strategy of sequential belimumab-rituximab treatment has been taken in several clinical trials.
More potent B-cell depletion with obinutuzumab
Another approach is to choose more potent B cell–depleting therapies, such as obinutuzumab (Gazyva), which is an anti-CD20 monoclonal antibody that was approved in 2013 for the treatment of chronic lymphocytic leukemia.
The NOBILITY trial compared obinutuzumab with placebo in 125 patients with lupus nephritis who were on background treatment with mycophenolate and corticosteroids. At 1 year, significantly more patients achieved B-cell thresholds either below 5 cells per microliter or even zero cells per microliter than had been seen previously with rituximab.
That also translated into clinical benefit, Dr. Furie said. By week 76, half the patients who had sustained depletion of B cells below 0.4 cells per microliter had a complete response, compared with 35% of those who still had detectable B cells and 18% of the placebo group. Treatment with obinutuzumab did not show any link to higher rates of serious adverse events, serious infections, or deaths.
“I think we’re all pretty much convinced more is better, without introducing safety issues,” Dr. Furie said in an interview.
Joan Merrill, MD, professor of medicine at the University of Oklahoma Health Sciences Center, Oklahoma City, said the data did suggest that renal outcomes were better with more complete depletion, but raised the question of whether this might increase the risk of infections or infectious severity.
Dr. Furie noted that complete response not only required improvement in proteinuria, complement levels, and anti–double-stranded DNA antibodies, but also in serum creatinine, “because maintenance of eGFR [estimated glomerular filtration rate] is the name of the game with lupus nephritis.”
However, he also pointed out that there may be a ceiling for response rates in patients with lupus nephritis when using stricter endpoints for serum creatinine. The NOBILITY trial required patients to achieve a serum creatinine that did not increase by more than 15% from baseline. But when researchers did an analysis that instead only required patients to achieve a reduction in proteinuria and maintain normal creatinine, the complete response rate in complete B-cell depleters increased to 72%, compared with 50% in partial depleters and 37% in the placebo group.
Newer strategies for greater B-cell depletion
A third strategy for achieving greater B-cell depletion is bispecific T-cell engagers, or BiTEs. “I called it a ‘frenemy,’ where it’s taking the activated T cell and introducing it to the B cell, and it can kill it via direct T-cell killing,” Dr. Furie said in an interview. Mosunetuzumab (Lunsumio) is one example, and is currently in a phase 1 clinical trial of patients with SLE.
And the fourth strategy, which has proved so successful in lymphoma, is chimeric antigen receptor T-cell therapy (CAR T). Dr. Furie cited the recent publication of data from a CAR T clinical trial in five patients with refractory SLE. He said the data were impressive but the question for this treatment approach will be which patients are most likely to benefit and whether CAR T will experience the same ceiling effect because of pre-existing kidney damage.
“We won’t be seeing 100% response rates,” he said. “What we’ll be seeing, as a maximum, might be about 70%.” The big question for B-cell depletion in lupus was therefore how best to achieve it. “Is the future a potent monoclonal antibody, or is it in fact CAR T?”
Dr. Merrill said the analyses from B-cell depletion trials, showing greater response rates among more complete depleters, highlighted the importance of a personalized approach to treating lupus.
“One size fits all is never optimal in any disease, but it will prove a nonstarter in lupus, where we ought to be trying to find the optimal treatment regimen for each patient guided by biomarkers,” she said in an interview.
Dr. Furie reported having financial relationships with Genentech/Roche, which manufactures obinutuzumab and rituximab, as well as GlaxoSmithKline, Kezar Life Sciences, Kyverna Therapeutics, and Takeda. Dr. Merrill reported consulting for and receiving research support from a range of pharmaceutical companies including Genentech/Roche, GlaxoSmithKline, Pfizer, Janssen, Bristol-Myers Squibb, AbbVie, and AstraZeneca.
SEOUL, SOUTH KOREA – B cell–depleting therapies in patients with lupus nephritis have a higher likelihood of complete response if B cells are almost completely depleted, and strategies for achieving more complete B-cell depletion continue to be tested, according to evidence presented by Richard A. Furie, MD, at an international congress on systemic lupus erythematosus (SLE).
“If you go back about 20 years ago or so, when we designed the LUNAR and EXPLORER trials, we were scared to death of rituximab [Rituxan and biosimilars], about what would happen when you deplete B cells,” said Dr. Furie, chief of the division of rheumatology at Northwell Health in New York.
The LUNAR trial, which compared rituximab with placebo in patients with lupus nephritis, did not show a statistically significant difference in renal outcomes at 1 year. However, a post hoc analysis done several years later told a different story. It looked at patients who achieved complete peripheral depletion of B cells, defined as zero cells per microliter in peripheral blood. “You can see about a fourfold increase in complete response rates in those who were complete B-cell depleters at 1 year,” Dr. Furie told the conference.
It therefore raises the question of how to achieve greater B-cell depletion rates in patients. Dr. Furie said one strategy might be to first mobilize memory B cells and neutralize B cell–activating factor using belimumab (Benlysta), and then treat with rituximab to eliminate B cells. This strategy of sequential belimumab-rituximab treatment has been taken in several clinical trials.
More potent B-cell depletion with obinutuzumab
Another approach is to choose more potent B cell–depleting therapies, such as obinutuzumab (Gazyva), which is an anti-CD20 monoclonal antibody that was approved in 2013 for the treatment of chronic lymphocytic leukemia.
The NOBILITY trial compared obinutuzumab with placebo in 125 patients with lupus nephritis who were on background treatment with mycophenolate and corticosteroids. At 1 year, significantly more patients achieved B-cell thresholds either below 5 cells per microliter or even zero cells per microliter than had been seen previously with rituximab.
That also translated into clinical benefit, Dr. Furie said. By week 76, half the patients who had sustained depletion of B cells below 0.4 cells per microliter had a complete response, compared with 35% of those who still had detectable B cells and 18% of the placebo group. Treatment with obinutuzumab did not show any link to higher rates of serious adverse events, serious infections, or deaths.
“I think we’re all pretty much convinced more is better, without introducing safety issues,” Dr. Furie said in an interview.
Joan Merrill, MD, professor of medicine at the University of Oklahoma Health Sciences Center, Oklahoma City, said the data did suggest that renal outcomes were better with more complete depletion, but raised the question of whether this might increase the risk of infections or infectious severity.
Dr. Furie noted that complete response not only required improvement in proteinuria, complement levels, and anti–double-stranded DNA antibodies, but also in serum creatinine, “because maintenance of eGFR [estimated glomerular filtration rate] is the name of the game with lupus nephritis.”
However, he also pointed out that there may be a ceiling for response rates in patients with lupus nephritis when using stricter endpoints for serum creatinine. The NOBILITY trial required patients to achieve a serum creatinine that did not increase by more than 15% from baseline. But when researchers did an analysis that instead only required patients to achieve a reduction in proteinuria and maintain normal creatinine, the complete response rate in complete B-cell depleters increased to 72%, compared with 50% in partial depleters and 37% in the placebo group.
Newer strategies for greater B-cell depletion
A third strategy for achieving greater B-cell depletion is bispecific T-cell engagers, or BiTEs. “I called it a ‘frenemy,’ where it’s taking the activated T cell and introducing it to the B cell, and it can kill it via direct T-cell killing,” Dr. Furie said in an interview. Mosunetuzumab (Lunsumio) is one example, and is currently in a phase 1 clinical trial of patients with SLE.
And the fourth strategy, which has proved so successful in lymphoma, is chimeric antigen receptor T-cell therapy (CAR T). Dr. Furie cited the recent publication of data from a CAR T clinical trial in five patients with refractory SLE. He said the data were impressive but the question for this treatment approach will be which patients are most likely to benefit and whether CAR T will experience the same ceiling effect because of pre-existing kidney damage.
“We won’t be seeing 100% response rates,” he said. “What we’ll be seeing, as a maximum, might be about 70%.” The big question for B-cell depletion in lupus was therefore how best to achieve it. “Is the future a potent monoclonal antibody, or is it in fact CAR T?”
Dr. Merrill said the analyses from B-cell depletion trials, showing greater response rates among more complete depleters, highlighted the importance of a personalized approach to treating lupus.
“One size fits all is never optimal in any disease, but it will prove a nonstarter in lupus, where we ought to be trying to find the optimal treatment regimen for each patient guided by biomarkers,” she said in an interview.
Dr. Furie reported having financial relationships with Genentech/Roche, which manufactures obinutuzumab and rituximab, as well as GlaxoSmithKline, Kezar Life Sciences, Kyverna Therapeutics, and Takeda. Dr. Merrill reported consulting for and receiving research support from a range of pharmaceutical companies including Genentech/Roche, GlaxoSmithKline, Pfizer, Janssen, Bristol-Myers Squibb, AbbVie, and AstraZeneca.
SEOUL, SOUTH KOREA – B cell–depleting therapies in patients with lupus nephritis have a higher likelihood of complete response if B cells are almost completely depleted, and strategies for achieving more complete B-cell depletion continue to be tested, according to evidence presented by Richard A. Furie, MD, at an international congress on systemic lupus erythematosus (SLE).
“If you go back about 20 years ago or so, when we designed the LUNAR and EXPLORER trials, we were scared to death of rituximab [Rituxan and biosimilars], about what would happen when you deplete B cells,” said Dr. Furie, chief of the division of rheumatology at Northwell Health in New York.
The LUNAR trial, which compared rituximab with placebo in patients with lupus nephritis, did not show a statistically significant difference in renal outcomes at 1 year. However, a post hoc analysis done several years later told a different story. It looked at patients who achieved complete peripheral depletion of B cells, defined as zero cells per microliter in peripheral blood. “You can see about a fourfold increase in complete response rates in those who were complete B-cell depleters at 1 year,” Dr. Furie told the conference.
It therefore raises the question of how to achieve greater B-cell depletion rates in patients. Dr. Furie said one strategy might be to first mobilize memory B cells and neutralize B cell–activating factor using belimumab (Benlysta), and then treat with rituximab to eliminate B cells. This strategy of sequential belimumab-rituximab treatment has been taken in several clinical trials.
More potent B-cell depletion with obinutuzumab
Another approach is to choose more potent B cell–depleting therapies, such as obinutuzumab (Gazyva), which is an anti-CD20 monoclonal antibody that was approved in 2013 for the treatment of chronic lymphocytic leukemia.
The NOBILITY trial compared obinutuzumab with placebo in 125 patients with lupus nephritis who were on background treatment with mycophenolate and corticosteroids. At 1 year, significantly more patients achieved B-cell thresholds either below 5 cells per microliter or even zero cells per microliter than had been seen previously with rituximab.
That also translated into clinical benefit, Dr. Furie said. By week 76, half the patients who had sustained depletion of B cells below 0.4 cells per microliter had a complete response, compared with 35% of those who still had detectable B cells and 18% of the placebo group. Treatment with obinutuzumab did not show any link to higher rates of serious adverse events, serious infections, or deaths.
“I think we’re all pretty much convinced more is better, without introducing safety issues,” Dr. Furie said in an interview.
Joan Merrill, MD, professor of medicine at the University of Oklahoma Health Sciences Center, Oklahoma City, said the data did suggest that renal outcomes were better with more complete depletion, but raised the question of whether this might increase the risk of infections or infectious severity.
Dr. Furie noted that complete response not only required improvement in proteinuria, complement levels, and anti–double-stranded DNA antibodies, but also in serum creatinine, “because maintenance of eGFR [estimated glomerular filtration rate] is the name of the game with lupus nephritis.”
However, he also pointed out that there may be a ceiling for response rates in patients with lupus nephritis when using stricter endpoints for serum creatinine. The NOBILITY trial required patients to achieve a serum creatinine that did not increase by more than 15% from baseline. But when researchers did an analysis that instead only required patients to achieve a reduction in proteinuria and maintain normal creatinine, the complete response rate in complete B-cell depleters increased to 72%, compared with 50% in partial depleters and 37% in the placebo group.
Newer strategies for greater B-cell depletion
A third strategy for achieving greater B-cell depletion is bispecific T-cell engagers, or BiTEs. “I called it a ‘frenemy,’ where it’s taking the activated T cell and introducing it to the B cell, and it can kill it via direct T-cell killing,” Dr. Furie said in an interview. Mosunetuzumab (Lunsumio) is one example, and is currently in a phase 1 clinical trial of patients with SLE.
And the fourth strategy, which has proved so successful in lymphoma, is chimeric antigen receptor T-cell therapy (CAR T). Dr. Furie cited the recent publication of data from a CAR T clinical trial in five patients with refractory SLE. He said the data were impressive but the question for this treatment approach will be which patients are most likely to benefit and whether CAR T will experience the same ceiling effect because of pre-existing kidney damage.
“We won’t be seeing 100% response rates,” he said. “What we’ll be seeing, as a maximum, might be about 70%.” The big question for B-cell depletion in lupus was therefore how best to achieve it. “Is the future a potent monoclonal antibody, or is it in fact CAR T?”
Dr. Merrill said the analyses from B-cell depletion trials, showing greater response rates among more complete depleters, highlighted the importance of a personalized approach to treating lupus.
“One size fits all is never optimal in any disease, but it will prove a nonstarter in lupus, where we ought to be trying to find the optimal treatment regimen for each patient guided by biomarkers,” she said in an interview.
Dr. Furie reported having financial relationships with Genentech/Roche, which manufactures obinutuzumab and rituximab, as well as GlaxoSmithKline, Kezar Life Sciences, Kyverna Therapeutics, and Takeda. Dr. Merrill reported consulting for and receiving research support from a range of pharmaceutical companies including Genentech/Roche, GlaxoSmithKline, Pfizer, Janssen, Bristol-Myers Squibb, AbbVie, and AstraZeneca.
AT LUPUS 2023
In TNBC, repeated biopsies may reveal emergent HER2-low expression
Triple-negative breast cancer (TNBC) is characterized by the absence of hormonal receptors and human epidermal growth factor receptor 2 (HER2) expression.
were found to be ineffective in patients with TNBC and known HER2-zero status.These HER2-low results were of great clinical significance for this patient population, said Yael Bar, MD, PhD, during her presentation of the research, at the annual meeting of the American Society of Clinical Oncology (ASCO).
Previously, the DESTINY-Breast04 trial demonstrated that the antibody-drug conjugate (ADC) trastuzumab deruxtecan (T-DXd) improved progression-free survival (PFS) and overall survival (OS) for patients with HER2-low metastatic breast cancer. “As a result [of the DESTINY-Breast04 findings], T-DXd is now approved for HER2-low but not HER2-zero triple-negative metastatic breast cancer."
“While HER2-low is detected in about 30%-50% of patients with triple-negative breast cancer, several studies have shown that HER2 status is heterogeneous and also dynamic over time, said Dr. Bar, who is an international research fellow in the breast cancer group at Mass General Cancer Center, Boston.
In the new study, Dr. Bar and her co-authors retrospectively identified 512 TNBC patients from 2000 to 2022 from an institutional database. They included core, surgical, or metastatic biopsies. Participants had a mean age of 52 years, with 54% over age 50. They were 83% White, 7% African American, 5% Asian, 3% Hispanic, and 2% other. Stage II was most common at diagnosis at 48%, followed by stage 1 (28%), stage 3 (14%), and stage IV (8%).
Most patients had undergone one (38%) or two (45%) biopsies, while 9% underwent three biopsies, 6% underwent four biopsies, and 2% underwent five or more.
Among all 512 patients in the study, 60% had a HER2-low result on their first biopsy. As of the second biopsy, 73% had at least one HER2-low result, with 13% of the first HER2-low results occurring at the second biopsy. As of the third biopsy, 81% had a HER2-low result, with 9% occurring for the first time. At the fourth biopsy, 86% had a positive result, with 8% occurring for the first time. All patients with five or more biopsies had at least one HER2-low result and none were first-time results.
At the second biopsy, a HER2-low result was detected for 32% of patients for the first time. At the third biopsy, a new HER2-low result was detected in 33%, and at the fourth biopsy, a new HER-2 result was detected in 38%.
The researchers matched early and metastatic biopsies in 71 patients, and 44% had changed status: 68% of those with a status change went HER2-low to HER2-zero, 26% from HER2-zero to HER2-low, and 6% from HER2-low to HER2-positive. Among 50 patients with matched metastatic biopsies, 33% had a change in status, with 63% going from HER2-zero to HER2-low, 31% from HER2-low to HER2-zero, and 6% from HER2-low to HER2-positive.
“We showed here that repeat biopsies can identify new HER2-low results for patients who were previously ineligible for T-DXd; and therefore, we think that a repeat biopsy could be considered if feasible and safe. Also, if a repeat biopsy is performed for any reason, but mainly upon metastatic recurrence, receptors should be retested,” said Dr. Bar.
After Dr. Bar’s presentation, Barbara Pistilli, MD served as a discussant. She noted the increased HER2-low results over successive biopsies. “However, here the question is, are these results related to the changes in the analytical methods over the past 20 years or the changes in the guidelines in terms of definition of HER2 status, or are they more related to a true evolution of HER2 status with the evolution of the disease?” she said during her presentation. Dr. Pistilli is chair of the breast disease committee at Gustave Roussy in Villejuif, France.
She also said that HER2 expression can vary even between different parts of the same tumor and called for alternative methods to following HER2 expression. “I don’t think that we can follow our patients with multiple biopsies over the disease evolution, so we have to find other tools, such as target-positive [circulating tumor cells], or antibody-radiolabeled PET scan in order to better follow the intermetastasis target heterogeneity over time, and finally define what is the optimal ADC sequential strategy for each patient,” said Dr. Pistilli.
Comoderator Michael Danso, MD, also weighed in when asked for comment.
“It was an important trial to show that serial biopsies potentially allow more patients to receive trastuzumab deruxtecan,” said Dr. Danso, who is the research director at Virginia Oncology Associates, Norfolk. However, he pointed out the concerns of a statistician who had spoken up during the question-and-answer session who said that the positive results could simply be the consequence of repeated testing. “If you do a test often enough, statistically you’re going to get a difference in outcome. That was an important point made. Also, if you’re going to get 100% of patients who are eventually going to [develop HER2-low status], the question is, can you just treat everybody with trastuzumab deruxtecan and not do these sequential biopsies? Obviously that is subject to cost; it’s subject to toxicity as well, so you probably want documentation that there is a HER2-low result,” said Dr. Danso.
Dr. Bar has no relevant financial disclosures. Dr. Pistilli has consulted for or advised AstraZeneca, Daiichi Sankyo/UCB Japan, Myriad Genetics, Novartis, PIERRE FABRE, and Puma Biotechnology. She has received research funding through her institution from AstraZeneca, Daiichi Sankyo, Gilead Sciences, Merus, Pfizer, and Puma Biotechnology. She has received travel or accommodation expenses from AstraZeneca, Daiichi Sankyo Europe, MSD Oncology, Novartis, Pfizer, and Pierre Fabre. Dr. Danso has received honoraria from Amgen and has consulted or advised Immunomedics, Novartis, Pfizer, and Seagen.
*This story was updated on 6/13/2023.
Triple-negative breast cancer (TNBC) is characterized by the absence of hormonal receptors and human epidermal growth factor receptor 2 (HER2) expression.
were found to be ineffective in patients with TNBC and known HER2-zero status.These HER2-low results were of great clinical significance for this patient population, said Yael Bar, MD, PhD, during her presentation of the research, at the annual meeting of the American Society of Clinical Oncology (ASCO).
Previously, the DESTINY-Breast04 trial demonstrated that the antibody-drug conjugate (ADC) trastuzumab deruxtecan (T-DXd) improved progression-free survival (PFS) and overall survival (OS) for patients with HER2-low metastatic breast cancer. “As a result [of the DESTINY-Breast04 findings], T-DXd is now approved for HER2-low but not HER2-zero triple-negative metastatic breast cancer."
“While HER2-low is detected in about 30%-50% of patients with triple-negative breast cancer, several studies have shown that HER2 status is heterogeneous and also dynamic over time, said Dr. Bar, who is an international research fellow in the breast cancer group at Mass General Cancer Center, Boston.
In the new study, Dr. Bar and her co-authors retrospectively identified 512 TNBC patients from 2000 to 2022 from an institutional database. They included core, surgical, or metastatic biopsies. Participants had a mean age of 52 years, with 54% over age 50. They were 83% White, 7% African American, 5% Asian, 3% Hispanic, and 2% other. Stage II was most common at diagnosis at 48%, followed by stage 1 (28%), stage 3 (14%), and stage IV (8%).
Most patients had undergone one (38%) or two (45%) biopsies, while 9% underwent three biopsies, 6% underwent four biopsies, and 2% underwent five or more.
Among all 512 patients in the study, 60% had a HER2-low result on their first biopsy. As of the second biopsy, 73% had at least one HER2-low result, with 13% of the first HER2-low results occurring at the second biopsy. As of the third biopsy, 81% had a HER2-low result, with 9% occurring for the first time. At the fourth biopsy, 86% had a positive result, with 8% occurring for the first time. All patients with five or more biopsies had at least one HER2-low result and none were first-time results.
At the second biopsy, a HER2-low result was detected for 32% of patients for the first time. At the third biopsy, a new HER2-low result was detected in 33%, and at the fourth biopsy, a new HER-2 result was detected in 38%.
The researchers matched early and metastatic biopsies in 71 patients, and 44% had changed status: 68% of those with a status change went HER2-low to HER2-zero, 26% from HER2-zero to HER2-low, and 6% from HER2-low to HER2-positive. Among 50 patients with matched metastatic biopsies, 33% had a change in status, with 63% going from HER2-zero to HER2-low, 31% from HER2-low to HER2-zero, and 6% from HER2-low to HER2-positive.
“We showed here that repeat biopsies can identify new HER2-low results for patients who were previously ineligible for T-DXd; and therefore, we think that a repeat biopsy could be considered if feasible and safe. Also, if a repeat biopsy is performed for any reason, but mainly upon metastatic recurrence, receptors should be retested,” said Dr. Bar.
After Dr. Bar’s presentation, Barbara Pistilli, MD served as a discussant. She noted the increased HER2-low results over successive biopsies. “However, here the question is, are these results related to the changes in the analytical methods over the past 20 years or the changes in the guidelines in terms of definition of HER2 status, or are they more related to a true evolution of HER2 status with the evolution of the disease?” she said during her presentation. Dr. Pistilli is chair of the breast disease committee at Gustave Roussy in Villejuif, France.
She also said that HER2 expression can vary even between different parts of the same tumor and called for alternative methods to following HER2 expression. “I don’t think that we can follow our patients with multiple biopsies over the disease evolution, so we have to find other tools, such as target-positive [circulating tumor cells], or antibody-radiolabeled PET scan in order to better follow the intermetastasis target heterogeneity over time, and finally define what is the optimal ADC sequential strategy for each patient,” said Dr. Pistilli.
Comoderator Michael Danso, MD, also weighed in when asked for comment.
“It was an important trial to show that serial biopsies potentially allow more patients to receive trastuzumab deruxtecan,” said Dr. Danso, who is the research director at Virginia Oncology Associates, Norfolk. However, he pointed out the concerns of a statistician who had spoken up during the question-and-answer session who said that the positive results could simply be the consequence of repeated testing. “If you do a test often enough, statistically you’re going to get a difference in outcome. That was an important point made. Also, if you’re going to get 100% of patients who are eventually going to [develop HER2-low status], the question is, can you just treat everybody with trastuzumab deruxtecan and not do these sequential biopsies? Obviously that is subject to cost; it’s subject to toxicity as well, so you probably want documentation that there is a HER2-low result,” said Dr. Danso.
Dr. Bar has no relevant financial disclosures. Dr. Pistilli has consulted for or advised AstraZeneca, Daiichi Sankyo/UCB Japan, Myriad Genetics, Novartis, PIERRE FABRE, and Puma Biotechnology. She has received research funding through her institution from AstraZeneca, Daiichi Sankyo, Gilead Sciences, Merus, Pfizer, and Puma Biotechnology. She has received travel or accommodation expenses from AstraZeneca, Daiichi Sankyo Europe, MSD Oncology, Novartis, Pfizer, and Pierre Fabre. Dr. Danso has received honoraria from Amgen and has consulted or advised Immunomedics, Novartis, Pfizer, and Seagen.
*This story was updated on 6/13/2023.
Triple-negative breast cancer (TNBC) is characterized by the absence of hormonal receptors and human epidermal growth factor receptor 2 (HER2) expression.
were found to be ineffective in patients with TNBC and known HER2-zero status.These HER2-low results were of great clinical significance for this patient population, said Yael Bar, MD, PhD, during her presentation of the research, at the annual meeting of the American Society of Clinical Oncology (ASCO).
Previously, the DESTINY-Breast04 trial demonstrated that the antibody-drug conjugate (ADC) trastuzumab deruxtecan (T-DXd) improved progression-free survival (PFS) and overall survival (OS) for patients with HER2-low metastatic breast cancer. “As a result [of the DESTINY-Breast04 findings], T-DXd is now approved for HER2-low but not HER2-zero triple-negative metastatic breast cancer."
“While HER2-low is detected in about 30%-50% of patients with triple-negative breast cancer, several studies have shown that HER2 status is heterogeneous and also dynamic over time, said Dr. Bar, who is an international research fellow in the breast cancer group at Mass General Cancer Center, Boston.
In the new study, Dr. Bar and her co-authors retrospectively identified 512 TNBC patients from 2000 to 2022 from an institutional database. They included core, surgical, or metastatic biopsies. Participants had a mean age of 52 years, with 54% over age 50. They were 83% White, 7% African American, 5% Asian, 3% Hispanic, and 2% other. Stage II was most common at diagnosis at 48%, followed by stage 1 (28%), stage 3 (14%), and stage IV (8%).
Most patients had undergone one (38%) or two (45%) biopsies, while 9% underwent three biopsies, 6% underwent four biopsies, and 2% underwent five or more.
Among all 512 patients in the study, 60% had a HER2-low result on their first biopsy. As of the second biopsy, 73% had at least one HER2-low result, with 13% of the first HER2-low results occurring at the second biopsy. As of the third biopsy, 81% had a HER2-low result, with 9% occurring for the first time. At the fourth biopsy, 86% had a positive result, with 8% occurring for the first time. All patients with five or more biopsies had at least one HER2-low result and none were first-time results.
At the second biopsy, a HER2-low result was detected for 32% of patients for the first time. At the third biopsy, a new HER2-low result was detected in 33%, and at the fourth biopsy, a new HER-2 result was detected in 38%.
The researchers matched early and metastatic biopsies in 71 patients, and 44% had changed status: 68% of those with a status change went HER2-low to HER2-zero, 26% from HER2-zero to HER2-low, and 6% from HER2-low to HER2-positive. Among 50 patients with matched metastatic biopsies, 33% had a change in status, with 63% going from HER2-zero to HER2-low, 31% from HER2-low to HER2-zero, and 6% from HER2-low to HER2-positive.
“We showed here that repeat biopsies can identify new HER2-low results for patients who were previously ineligible for T-DXd; and therefore, we think that a repeat biopsy could be considered if feasible and safe. Also, if a repeat biopsy is performed for any reason, but mainly upon metastatic recurrence, receptors should be retested,” said Dr. Bar.
After Dr. Bar’s presentation, Barbara Pistilli, MD served as a discussant. She noted the increased HER2-low results over successive biopsies. “However, here the question is, are these results related to the changes in the analytical methods over the past 20 years or the changes in the guidelines in terms of definition of HER2 status, or are they more related to a true evolution of HER2 status with the evolution of the disease?” she said during her presentation. Dr. Pistilli is chair of the breast disease committee at Gustave Roussy in Villejuif, France.
She also said that HER2 expression can vary even between different parts of the same tumor and called for alternative methods to following HER2 expression. “I don’t think that we can follow our patients with multiple biopsies over the disease evolution, so we have to find other tools, such as target-positive [circulating tumor cells], or antibody-radiolabeled PET scan in order to better follow the intermetastasis target heterogeneity over time, and finally define what is the optimal ADC sequential strategy for each patient,” said Dr. Pistilli.
Comoderator Michael Danso, MD, also weighed in when asked for comment.
“It was an important trial to show that serial biopsies potentially allow more patients to receive trastuzumab deruxtecan,” said Dr. Danso, who is the research director at Virginia Oncology Associates, Norfolk. However, he pointed out the concerns of a statistician who had spoken up during the question-and-answer session who said that the positive results could simply be the consequence of repeated testing. “If you do a test often enough, statistically you’re going to get a difference in outcome. That was an important point made. Also, if you’re going to get 100% of patients who are eventually going to [develop HER2-low status], the question is, can you just treat everybody with trastuzumab deruxtecan and not do these sequential biopsies? Obviously that is subject to cost; it’s subject to toxicity as well, so you probably want documentation that there is a HER2-low result,” said Dr. Danso.
Dr. Bar has no relevant financial disclosures. Dr. Pistilli has consulted for or advised AstraZeneca, Daiichi Sankyo/UCB Japan, Myriad Genetics, Novartis, PIERRE FABRE, and Puma Biotechnology. She has received research funding through her institution from AstraZeneca, Daiichi Sankyo, Gilead Sciences, Merus, Pfizer, and Puma Biotechnology. She has received travel or accommodation expenses from AstraZeneca, Daiichi Sankyo Europe, MSD Oncology, Novartis, Pfizer, and Pierre Fabre. Dr. Danso has received honoraria from Amgen and has consulted or advised Immunomedics, Novartis, Pfizer, and Seagen.
*This story was updated on 6/13/2023.
FROM ASCO 2023
Lobar vs. sublobar resection in stage 1 lung cancer
Thoracic Oncology & Chest Imaging Network
Pleural Disease Section
Lobectomy with intrathoracic nodal dissection remains the standard of care for early stage (tumor size ≤ 3.0 cm) peripheral non–small cell lung cancer (NSCLC). This practice is primarily influenced by data from the mid-1990s associating limited resection (segmentectomy or wedge resection) with increased recurrence rate and mortality compared with lobectomy (Ginsberg et al. Ann Thorac Surg. 1995;60:615). Recent advances in video and robot-assisted thoracic surgery, as well as the implementation of lung cancer screening, improvement in minimally invasive diagnostic modalities, and neoadjuvant therapies have driven the medical community to revisit the role of sublobar lung resection.
Two newly published large randomized control multicenter multinational trials (Saji et al. Lancet. 2022;399:1670 and Altorki et al. N Engl J Med. 2023;388:489) have challenged our well-established practices. They compared overall and disease-free survival sublobar to lobar resection of early stage NSCLC (tumor size ≤ 2.0 cm and negative intraoperative nodal disease) and demonstrated noninferiority of sublobar resection with respect to overall survival and disease-free survival. While the sublobar resection in the Saji et al. trial consisted strictly of segmentectomy, the majority of sublobar resections in the Altorki et al. trial were wedge resections. Interestingly, both trials chose lower cut-offs for tumor size (≤ 2.0 cm) compared with the Ginsberg trial (≤ 3.0 cm), which could arguably have accounted for this difference in outcomes.
Christopher Yurosko, DO – Section Fellow-in-Training
Melissa Rosas, MD – Section Member-at-Large
Labib Debiane, MD - Section Member-at-Large
Thoracic Oncology & Chest Imaging Network
Pleural Disease Section
Lobectomy with intrathoracic nodal dissection remains the standard of care for early stage (tumor size ≤ 3.0 cm) peripheral non–small cell lung cancer (NSCLC). This practice is primarily influenced by data from the mid-1990s associating limited resection (segmentectomy or wedge resection) with increased recurrence rate and mortality compared with lobectomy (Ginsberg et al. Ann Thorac Surg. 1995;60:615). Recent advances in video and robot-assisted thoracic surgery, as well as the implementation of lung cancer screening, improvement in minimally invasive diagnostic modalities, and neoadjuvant therapies have driven the medical community to revisit the role of sublobar lung resection.
Two newly published large randomized control multicenter multinational trials (Saji et al. Lancet. 2022;399:1670 and Altorki et al. N Engl J Med. 2023;388:489) have challenged our well-established practices. They compared overall and disease-free survival sublobar to lobar resection of early stage NSCLC (tumor size ≤ 2.0 cm and negative intraoperative nodal disease) and demonstrated noninferiority of sublobar resection with respect to overall survival and disease-free survival. While the sublobar resection in the Saji et al. trial consisted strictly of segmentectomy, the majority of sublobar resections in the Altorki et al. trial were wedge resections. Interestingly, both trials chose lower cut-offs for tumor size (≤ 2.0 cm) compared with the Ginsberg trial (≤ 3.0 cm), which could arguably have accounted for this difference in outcomes.
Christopher Yurosko, DO – Section Fellow-in-Training
Melissa Rosas, MD – Section Member-at-Large
Labib Debiane, MD - Section Member-at-Large
Thoracic Oncology & Chest Imaging Network
Pleural Disease Section
Lobectomy with intrathoracic nodal dissection remains the standard of care for early stage (tumor size ≤ 3.0 cm) peripheral non–small cell lung cancer (NSCLC). This practice is primarily influenced by data from the mid-1990s associating limited resection (segmentectomy or wedge resection) with increased recurrence rate and mortality compared with lobectomy (Ginsberg et al. Ann Thorac Surg. 1995;60:615). Recent advances in video and robot-assisted thoracic surgery, as well as the implementation of lung cancer screening, improvement in minimally invasive diagnostic modalities, and neoadjuvant therapies have driven the medical community to revisit the role of sublobar lung resection.
Two newly published large randomized control multicenter multinational trials (Saji et al. Lancet. 2022;399:1670 and Altorki et al. N Engl J Med. 2023;388:489) have challenged our well-established practices. They compared overall and disease-free survival sublobar to lobar resection of early stage NSCLC (tumor size ≤ 2.0 cm and negative intraoperative nodal disease) and demonstrated noninferiority of sublobar resection with respect to overall survival and disease-free survival. While the sublobar resection in the Saji et al. trial consisted strictly of segmentectomy, the majority of sublobar resections in the Altorki et al. trial were wedge resections. Interestingly, both trials chose lower cut-offs for tumor size (≤ 2.0 cm) compared with the Ginsberg trial (≤ 3.0 cm), which could arguably have accounted for this difference in outcomes.
Christopher Yurosko, DO – Section Fellow-in-Training
Melissa Rosas, MD – Section Member-at-Large
Labib Debiane, MD - Section Member-at-Large
Applications of ChatGPT and Large Language Models in Medicine and Health Care: Benefits and Pitfalls
The development of [artificial intelligence] is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other.
Bill Gates 1
As the world emerges from the pandemic and the health care system faces new challenges, technology has become an increasingly important tool for health care professionals (HCPs). One such technology is the large language model (LLM), which has the potential to revolutionize the health care industry. ChatGPT, a popular LLM developed by OpenAI, has gained particular attention in the medical community for its ability to pass the United States Medical Licensing Exam.2 This article will explore the benefits and potential pitfalls of using LLMs like ChatGPT in medicine and health care.
Benefits
HCP burnout is a serious issue that can lead to lower productivity, increased medical errors, and decreased patient satisfaction.3 LLMs can alleviate some administrative burdens on HCPs, allowing them to focus on patient care. By assisting with billing, coding, insurance claims, and organizing schedules, LLMs like ChatGPT can free up time for HCPs to focus on what they do best: providing quality patient care.4 ChatGPT also can assist with diagnoses by providing accurate and reliable information based on a vast amount of clinical data. By learning the relationships between different medical conditions, symptoms, and treatment options, ChatGPT can provide an appropriate differential diagnosis (Figure 1).
Imaging medical specialists like radiologists, pathologists, dermatologists, and others can benefit from combining computer vision diagnostics with ChatGPT report creation abilities to streamline the diagnostic workflow and improve diagnostic accuracy (Figure 2).
Although using ChatGPT and other LLMs in mental health care has potential benefits, it is essential to note that they are not a substitute for human interaction and personalized care. While ChatGPT can remember information from previous conversations, it cannot provide the same level of personalized, high-quality care that a professional therapist or HCP can. However, by augmenting the work of HCPs, ChatGPT and other LLMs have the potential to make mental health care more accessible and efficient. In addition to providing effective screening in underserved areas, ChatGPT technology may improve the competence of physician assistants and nurse practitioners in delivering mental health care. With the increased incidence of mental health problems in veterans, the pertinence of a ChatGPT-like feature will only increase with time.9
ChatGPT can also be integrated into health care organizations’ websites and mobile apps, providing patients instant access to medical information, self-care advice, symptom checkers, scheduling appointments, and arranging transportation. These features can reduce the burden on health care staff and help patients stay informed and motivated to take an active role in their health. Additionally, health care organizations can use ChatGPT to engage patients by providing reminders for medication renewals and assistance with self-care.4,6,10,11
The potential of artificial intelligence (AI) in the field of medical education and research is immense. According to a study by Gilson and colleagues, ChatGPT has shown promising results as a medical education tool.12 ChatGPT can simulate clinical scenarios, provide real-time feedback, and improve diagnostic skills. It also offers new interactive and personalized learning opportunities for medical students and HCPs.13 ChatGPT can help researchers by streamlining the process of data analysis. It can also administer surveys or questionnaires, facilitate data collection on preferences and experiences, and help in writing scientific publications.14 Nevertheless, to fully unlock the potential of these AI models, additional models that perform checks for factual accuracy, plagiarism, and copyright infringement must be developed.15,16
AI Bill of Rights
In order to protect the American public, the White House Office of Science and Technology Policy (OSTP) has released a blueprint for an AI Bill of Rights that emphasizes 5 principles to protect the public from the harmful effects of AI models, including safe and effective systems; algorithmic discrimination protection; data privacy; notice and explanation; and human alternatives, considerations, and fallback (Figure 3).17
One of the biggest challenges with LLMs like ChatGPT is the prevalence of inaccurate information or so-called hallucinations.16 These inaccuracies stem from the inability of LLMs to distinguish between real and fake information. To prevent hallucinations, researchers have proposed several methods, including training models on more diverse data, using adversarial training methods, and human-in-the-loop approaches.21 In addition, medicine-specific models like GatorTron, medPaLM, and Almanac were developed, increasing the accuracy of factual results.22-24 Unfortunately, only the GatorTron model is available to the public through the NVIDIA developers’ program.25
Despite these shortcomings, the future of LLMs in health care is promising. Although these models will not replace HCPs, they can help reduce the unnecessary burden on them, prevent burnout, and enable HCPs and patients spend more time together. Establishing an official hospital AI oversight governing body that would promote best practices could ensure the trustworthy implementation of these new technologies.26
Conclusions
The use of ChatGPT and other LLMs in health care has the potential to revolutionize the industry. By assisting HCPs with administrative tasks, improving the accuracy and reliability of diagnoses, and engaging patients, ChatGPT can help health care organizations provide better care to their patients. While LLMs are not a substitute for human interaction and personalized care, they can augment the work of HCPs, making health care more accessible and efficient. As the health care industry continues to evolve, it will be exciting to see how ChatGPT and other LLMs are used to improve patient outcomes and quality of care. In addition, AI technologies like ChatGPT offer enormous potential in medical education and research. To ensure that the benefits outweigh the risks, developing trustworthy AI health care products and establishing oversight governing bodies to ensure their implementation is essential. By doing so, we can help HCPs focus on what matters most, providing high-quality care to patients.
Acknowledgments
This material is the result of work supported by resources and the use of facilities at the James A. Haley Veterans’ Hospital.
1. Bill Gates. The age of AI has begun. March 21, 2023. Accessed May 10, 2023. https://www.gatesnotes.com/the-age-of-ai-has-begun
2. Kung TH, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2(2):e0000198. Published 2023 Feb 9. doi:10.1371/journal.pdig.0000198
3. Shanafelt TD, West CP, Sinsky C, et al. Changes in burnout and satisfaction with work-life integration in physicians and the general US working population between 2011 and 2020. Mayo Clin Proc. 2022;97(3):491-506. doi:10.1016/j.mayocp.2021.11.021
4. Goodman RS, Patrinely JR Jr, Osterman T, Wheless L, Johnson DB. On the cusp: considering the impact of artificial intelligence language models in healthcare. Med. 2023;4(3):139-140. doi:10.1016/j.medj.2023.02.008
5. Will ChatGPT transform healthcare? Nat Med. 2023;29(3):505-506. doi:10.1038/s41591-023-02289-5
6. Hopkins AM, Logan JM, Kichenadasse G, Sorich MJ. Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift. JNCI Cancer Spectr. 2023;7(2):pkad010. doi:10.1093/jncics/pkad010
7. Babar Z, van Laarhoven T, Zanzotto FM, Marchiori E. Evaluating diagnostic content of AI-generated radiology reports of chest X-rays. Artif Intell Med. 2021;116:102075. doi:10.1016/j.artmed.2021.102075
8. Lecler A, Duron L, Soyer P. Revolutionizing radiology with GPT-based models: current applications, future possibilities and limitations of ChatGPT. Diagn Interv Imaging. 2023;S2211-5684(23)00027-X. doi:10.1016/j.diii.2023.02.003
9. Germain JM. Is ChatGPT smart enough to practice mental health therapy? March 23, 2023. Accessed May 11, 2023. https://www.technewsworld.com/story/is-chatgpt-smart-enough-to-practice-mental-health-therapy-178064.html
10. Cascella M, Montomoli J, Bellini V, Bignami E. Evaluating the feasibility of ChatGPT in healthcare: an analysis of multiple clinical and research scenarios. J Med Syst. 2023;47(1):33. Published 2023 Mar 4. doi:10.1007/s10916-023-01925-4
11. Jungwirth D, Haluza D. Artificial intelligence and public health: an exploratory study. Int J Environ Res Public Health. 2023;20(5):4541. Published 2023 Mar 3. doi:10.3390/ijerph20054541
12. Gilson A, Safranek CW, Huang T, et al. How does ChatGPT perform on the United States Medical Licensing Examination? The implications of large language models for medical education and knowledge assessment. JMIR Med Educ. 2023;9:e45312. Published 2023 Feb 8. doi:10.2196/45312
13. Eysenbach G. The role of ChatGPT, generative language models, and artificial intelligence in medical education: a conversation with ChatGPT and a call for papers. JMIR Med Educ. 2023;9:e46885. Published 2023 Mar 6. doi:10.2196/46885
14. Macdonald C, Adeloye D, Sheikh A, Rudan I. Can ChatGPT draft a research article? An example of population-level vaccine effectiveness analysis. J Glob Health. 2023;13:01003. Published 2023 Feb 17. doi:10.7189/jogh.13.01003
15. Masters K. Ethical use of artificial intelligence in health professions education: AMEE Guide No.158. Med Teach. 2023;1-11. doi:10.1080/0142159X.2023.2186203
16. Smith CS. Hallucinations could blunt ChatGPT’s success. IEEE Spectrum. March 13, 2023. Accessed May 11, 2023. https://spectrum.ieee.org/ai-hallucination
17. Executive Office of the President, Office of Science and Technology Policy. Blueprint for an AI Bill of Rights. Accessed May 11, 2023. https://www.whitehouse.gov/ostp/ai-bill-of-rights
18. Executive office of the President. Executive Order 13960: promoting the use of trustworthy artificial intelligence in the federal government. Fed Regist. 2020;89(236):78939-78943.
19. US Department of Commerce, National institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). Published January 2023. doi:10.6028/NIST.AI.100-1
20. Microsoft. Azure Cognitive Search—Cloud Search Service. Accessed May 11, 2023. https://azure.microsoft.com/en-us/products/search
21. Aiyappa R, An J, Kwak H, Ahn YY. Can we trust the evaluation on ChatGPT? March 22, 2023. Accessed May 11, 2023. https://arxiv.org/abs/2303.12767v1
22. Yang X, Chen A, Pournejatian N, et al. GatorTron: a large clinical language model to unlock patient information from unstructured electronic health records. Updated December 16, 2022. Accessed May 11, 2023. https://arxiv.org/abs/2203.03540v3
23. Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. December 26, 2022. Accessed May 11, 2023. https://arxiv.org/abs/2212.13138v1
24. Zakka C, Chaurasia A, Shad R, Hiesinger W. Almanac: knowledge-grounded language models for clinical medicine. March 1, 2023. Accessed May 11, 2023. https://arxiv.org/abs/2303.01229v1
25. NVIDIA. GatorTron-OG. Accessed May 11, 2023. https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og
26. Borkowski AA, Jakey CE, Thomas LB, Viswanadhan N, Mastorides SM. Establishing a hospital artificial intelligence committee to improve patient care. Fed Pract. 2022;39(8):334-336. doi:10.12788/fp.0299
The development of [artificial intelligence] is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other.
Bill Gates 1
As the world emerges from the pandemic and the health care system faces new challenges, technology has become an increasingly important tool for health care professionals (HCPs). One such technology is the large language model (LLM), which has the potential to revolutionize the health care industry. ChatGPT, a popular LLM developed by OpenAI, has gained particular attention in the medical community for its ability to pass the United States Medical Licensing Exam.2 This article will explore the benefits and potential pitfalls of using LLMs like ChatGPT in medicine and health care.
Benefits
HCP burnout is a serious issue that can lead to lower productivity, increased medical errors, and decreased patient satisfaction.3 LLMs can alleviate some administrative burdens on HCPs, allowing them to focus on patient care. By assisting with billing, coding, insurance claims, and organizing schedules, LLMs like ChatGPT can free up time for HCPs to focus on what they do best: providing quality patient care.4 ChatGPT also can assist with diagnoses by providing accurate and reliable information based on a vast amount of clinical data. By learning the relationships between different medical conditions, symptoms, and treatment options, ChatGPT can provide an appropriate differential diagnosis (Figure 1).
Imaging medical specialists like radiologists, pathologists, dermatologists, and others can benefit from combining computer vision diagnostics with ChatGPT report creation abilities to streamline the diagnostic workflow and improve diagnostic accuracy (Figure 2).
Although using ChatGPT and other LLMs in mental health care has potential benefits, it is essential to note that they are not a substitute for human interaction and personalized care. While ChatGPT can remember information from previous conversations, it cannot provide the same level of personalized, high-quality care that a professional therapist or HCP can. However, by augmenting the work of HCPs, ChatGPT and other LLMs have the potential to make mental health care more accessible and efficient. In addition to providing effective screening in underserved areas, ChatGPT technology may improve the competence of physician assistants and nurse practitioners in delivering mental health care. With the increased incidence of mental health problems in veterans, the pertinence of a ChatGPT-like feature will only increase with time.9
ChatGPT can also be integrated into health care organizations’ websites and mobile apps, providing patients instant access to medical information, self-care advice, symptom checkers, scheduling appointments, and arranging transportation. These features can reduce the burden on health care staff and help patients stay informed and motivated to take an active role in their health. Additionally, health care organizations can use ChatGPT to engage patients by providing reminders for medication renewals and assistance with self-care.4,6,10,11
The potential of artificial intelligence (AI) in the field of medical education and research is immense. According to a study by Gilson and colleagues, ChatGPT has shown promising results as a medical education tool.12 ChatGPT can simulate clinical scenarios, provide real-time feedback, and improve diagnostic skills. It also offers new interactive and personalized learning opportunities for medical students and HCPs.13 ChatGPT can help researchers by streamlining the process of data analysis. It can also administer surveys or questionnaires, facilitate data collection on preferences and experiences, and help in writing scientific publications.14 Nevertheless, to fully unlock the potential of these AI models, additional models that perform checks for factual accuracy, plagiarism, and copyright infringement must be developed.15,16
AI Bill of Rights
In order to protect the American public, the White House Office of Science and Technology Policy (OSTP) has released a blueprint for an AI Bill of Rights that emphasizes 5 principles to protect the public from the harmful effects of AI models, including safe and effective systems; algorithmic discrimination protection; data privacy; notice and explanation; and human alternatives, considerations, and fallback (Figure 3).17
One of the biggest challenges with LLMs like ChatGPT is the prevalence of inaccurate information or so-called hallucinations.16 These inaccuracies stem from the inability of LLMs to distinguish between real and fake information. To prevent hallucinations, researchers have proposed several methods, including training models on more diverse data, using adversarial training methods, and human-in-the-loop approaches.21 In addition, medicine-specific models like GatorTron, medPaLM, and Almanac were developed, increasing the accuracy of factual results.22-24 Unfortunately, only the GatorTron model is available to the public through the NVIDIA developers’ program.25
Despite these shortcomings, the future of LLMs in health care is promising. Although these models will not replace HCPs, they can help reduce the unnecessary burden on them, prevent burnout, and enable HCPs and patients spend more time together. Establishing an official hospital AI oversight governing body that would promote best practices could ensure the trustworthy implementation of these new technologies.26
Conclusions
The use of ChatGPT and other LLMs in health care has the potential to revolutionize the industry. By assisting HCPs with administrative tasks, improving the accuracy and reliability of diagnoses, and engaging patients, ChatGPT can help health care organizations provide better care to their patients. While LLMs are not a substitute for human interaction and personalized care, they can augment the work of HCPs, making health care more accessible and efficient. As the health care industry continues to evolve, it will be exciting to see how ChatGPT and other LLMs are used to improve patient outcomes and quality of care. In addition, AI technologies like ChatGPT offer enormous potential in medical education and research. To ensure that the benefits outweigh the risks, developing trustworthy AI health care products and establishing oversight governing bodies to ensure their implementation is essential. By doing so, we can help HCPs focus on what matters most, providing high-quality care to patients.
Acknowledgments
This material is the result of work supported by resources and the use of facilities at the James A. Haley Veterans’ Hospital.
The development of [artificial intelligence] is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other.
Bill Gates 1
As the world emerges from the pandemic and the health care system faces new challenges, technology has become an increasingly important tool for health care professionals (HCPs). One such technology is the large language model (LLM), which has the potential to revolutionize the health care industry. ChatGPT, a popular LLM developed by OpenAI, has gained particular attention in the medical community for its ability to pass the United States Medical Licensing Exam.2 This article will explore the benefits and potential pitfalls of using LLMs like ChatGPT in medicine and health care.
Benefits
HCP burnout is a serious issue that can lead to lower productivity, increased medical errors, and decreased patient satisfaction.3 LLMs can alleviate some administrative burdens on HCPs, allowing them to focus on patient care. By assisting with billing, coding, insurance claims, and organizing schedules, LLMs like ChatGPT can free up time for HCPs to focus on what they do best: providing quality patient care.4 ChatGPT also can assist with diagnoses by providing accurate and reliable information based on a vast amount of clinical data. By learning the relationships between different medical conditions, symptoms, and treatment options, ChatGPT can provide an appropriate differential diagnosis (Figure 1).
Imaging medical specialists like radiologists, pathologists, dermatologists, and others can benefit from combining computer vision diagnostics with ChatGPT report creation abilities to streamline the diagnostic workflow and improve diagnostic accuracy (Figure 2).
Although using ChatGPT and other LLMs in mental health care has potential benefits, it is essential to note that they are not a substitute for human interaction and personalized care. While ChatGPT can remember information from previous conversations, it cannot provide the same level of personalized, high-quality care that a professional therapist or HCP can. However, by augmenting the work of HCPs, ChatGPT and other LLMs have the potential to make mental health care more accessible and efficient. In addition to providing effective screening in underserved areas, ChatGPT technology may improve the competence of physician assistants and nurse practitioners in delivering mental health care. With the increased incidence of mental health problems in veterans, the pertinence of a ChatGPT-like feature will only increase with time.9
ChatGPT can also be integrated into health care organizations’ websites and mobile apps, providing patients instant access to medical information, self-care advice, symptom checkers, scheduling appointments, and arranging transportation. These features can reduce the burden on health care staff and help patients stay informed and motivated to take an active role in their health. Additionally, health care organizations can use ChatGPT to engage patients by providing reminders for medication renewals and assistance with self-care.4,6,10,11
The potential of artificial intelligence (AI) in the field of medical education and research is immense. According to a study by Gilson and colleagues, ChatGPT has shown promising results as a medical education tool.12 ChatGPT can simulate clinical scenarios, provide real-time feedback, and improve diagnostic skills. It also offers new interactive and personalized learning opportunities for medical students and HCPs.13 ChatGPT can help researchers by streamlining the process of data analysis. It can also administer surveys or questionnaires, facilitate data collection on preferences and experiences, and help in writing scientific publications.14 Nevertheless, to fully unlock the potential of these AI models, additional models that perform checks for factual accuracy, plagiarism, and copyright infringement must be developed.15,16
AI Bill of Rights
In order to protect the American public, the White House Office of Science and Technology Policy (OSTP) has released a blueprint for an AI Bill of Rights that emphasizes 5 principles to protect the public from the harmful effects of AI models, including safe and effective systems; algorithmic discrimination protection; data privacy; notice and explanation; and human alternatives, considerations, and fallback (Figure 3).17
One of the biggest challenges with LLMs like ChatGPT is the prevalence of inaccurate information or so-called hallucinations.16 These inaccuracies stem from the inability of LLMs to distinguish between real and fake information. To prevent hallucinations, researchers have proposed several methods, including training models on more diverse data, using adversarial training methods, and human-in-the-loop approaches.21 In addition, medicine-specific models like GatorTron, medPaLM, and Almanac were developed, increasing the accuracy of factual results.22-24 Unfortunately, only the GatorTron model is available to the public through the NVIDIA developers’ program.25
Despite these shortcomings, the future of LLMs in health care is promising. Although these models will not replace HCPs, they can help reduce the unnecessary burden on them, prevent burnout, and enable HCPs and patients spend more time together. Establishing an official hospital AI oversight governing body that would promote best practices could ensure the trustworthy implementation of these new technologies.26
Conclusions
The use of ChatGPT and other LLMs in health care has the potential to revolutionize the industry. By assisting HCPs with administrative tasks, improving the accuracy and reliability of diagnoses, and engaging patients, ChatGPT can help health care organizations provide better care to their patients. While LLMs are not a substitute for human interaction and personalized care, they can augment the work of HCPs, making health care more accessible and efficient. As the health care industry continues to evolve, it will be exciting to see how ChatGPT and other LLMs are used to improve patient outcomes and quality of care. In addition, AI technologies like ChatGPT offer enormous potential in medical education and research. To ensure that the benefits outweigh the risks, developing trustworthy AI health care products and establishing oversight governing bodies to ensure their implementation is essential. By doing so, we can help HCPs focus on what matters most, providing high-quality care to patients.
Acknowledgments
This material is the result of work supported by resources and the use of facilities at the James A. Haley Veterans’ Hospital.
1. Bill Gates. The age of AI has begun. March 21, 2023. Accessed May 10, 2023. https://www.gatesnotes.com/the-age-of-ai-has-begun
2. Kung TH, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2(2):e0000198. Published 2023 Feb 9. doi:10.1371/journal.pdig.0000198
3. Shanafelt TD, West CP, Sinsky C, et al. Changes in burnout and satisfaction with work-life integration in physicians and the general US working population between 2011 and 2020. Mayo Clin Proc. 2022;97(3):491-506. doi:10.1016/j.mayocp.2021.11.021
4. Goodman RS, Patrinely JR Jr, Osterman T, Wheless L, Johnson DB. On the cusp: considering the impact of artificial intelligence language models in healthcare. Med. 2023;4(3):139-140. doi:10.1016/j.medj.2023.02.008
5. Will ChatGPT transform healthcare? Nat Med. 2023;29(3):505-506. doi:10.1038/s41591-023-02289-5
6. Hopkins AM, Logan JM, Kichenadasse G, Sorich MJ. Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift. JNCI Cancer Spectr. 2023;7(2):pkad010. doi:10.1093/jncics/pkad010
7. Babar Z, van Laarhoven T, Zanzotto FM, Marchiori E. Evaluating diagnostic content of AI-generated radiology reports of chest X-rays. Artif Intell Med. 2021;116:102075. doi:10.1016/j.artmed.2021.102075
8. Lecler A, Duron L, Soyer P. Revolutionizing radiology with GPT-based models: current applications, future possibilities and limitations of ChatGPT. Diagn Interv Imaging. 2023;S2211-5684(23)00027-X. doi:10.1016/j.diii.2023.02.003
9. Germain JM. Is ChatGPT smart enough to practice mental health therapy? March 23, 2023. Accessed May 11, 2023. https://www.technewsworld.com/story/is-chatgpt-smart-enough-to-practice-mental-health-therapy-178064.html
10. Cascella M, Montomoli J, Bellini V, Bignami E. Evaluating the feasibility of ChatGPT in healthcare: an analysis of multiple clinical and research scenarios. J Med Syst. 2023;47(1):33. Published 2023 Mar 4. doi:10.1007/s10916-023-01925-4
11. Jungwirth D, Haluza D. Artificial intelligence and public health: an exploratory study. Int J Environ Res Public Health. 2023;20(5):4541. Published 2023 Mar 3. doi:10.3390/ijerph20054541
12. Gilson A, Safranek CW, Huang T, et al. How does ChatGPT perform on the United States Medical Licensing Examination? The implications of large language models for medical education and knowledge assessment. JMIR Med Educ. 2023;9:e45312. Published 2023 Feb 8. doi:10.2196/45312
13. Eysenbach G. The role of ChatGPT, generative language models, and artificial intelligence in medical education: a conversation with ChatGPT and a call for papers. JMIR Med Educ. 2023;9:e46885. Published 2023 Mar 6. doi:10.2196/46885
14. Macdonald C, Adeloye D, Sheikh A, Rudan I. Can ChatGPT draft a research article? An example of population-level vaccine effectiveness analysis. J Glob Health. 2023;13:01003. Published 2023 Feb 17. doi:10.7189/jogh.13.01003
15. Masters K. Ethical use of artificial intelligence in health professions education: AMEE Guide No.158. Med Teach. 2023;1-11. doi:10.1080/0142159X.2023.2186203
16. Smith CS. Hallucinations could blunt ChatGPT’s success. IEEE Spectrum. March 13, 2023. Accessed May 11, 2023. https://spectrum.ieee.org/ai-hallucination
17. Executive Office of the President, Office of Science and Technology Policy. Blueprint for an AI Bill of Rights. Accessed May 11, 2023. https://www.whitehouse.gov/ostp/ai-bill-of-rights
18. Executive office of the President. Executive Order 13960: promoting the use of trustworthy artificial intelligence in the federal government. Fed Regist. 2020;89(236):78939-78943.
19. US Department of Commerce, National institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). Published January 2023. doi:10.6028/NIST.AI.100-1
20. Microsoft. Azure Cognitive Search—Cloud Search Service. Accessed May 11, 2023. https://azure.microsoft.com/en-us/products/search
21. Aiyappa R, An J, Kwak H, Ahn YY. Can we trust the evaluation on ChatGPT? March 22, 2023. Accessed May 11, 2023. https://arxiv.org/abs/2303.12767v1
22. Yang X, Chen A, Pournejatian N, et al. GatorTron: a large clinical language model to unlock patient information from unstructured electronic health records. Updated December 16, 2022. Accessed May 11, 2023. https://arxiv.org/abs/2203.03540v3
23. Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. December 26, 2022. Accessed May 11, 2023. https://arxiv.org/abs/2212.13138v1
24. Zakka C, Chaurasia A, Shad R, Hiesinger W. Almanac: knowledge-grounded language models for clinical medicine. March 1, 2023. Accessed May 11, 2023. https://arxiv.org/abs/2303.01229v1
25. NVIDIA. GatorTron-OG. Accessed May 11, 2023. https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og
26. Borkowski AA, Jakey CE, Thomas LB, Viswanadhan N, Mastorides SM. Establishing a hospital artificial intelligence committee to improve patient care. Fed Pract. 2022;39(8):334-336. doi:10.12788/fp.0299
1. Bill Gates. The age of AI has begun. March 21, 2023. Accessed May 10, 2023. https://www.gatesnotes.com/the-age-of-ai-has-begun
2. Kung TH, Cheatham M, Medenilla A, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2(2):e0000198. Published 2023 Feb 9. doi:10.1371/journal.pdig.0000198
3. Shanafelt TD, West CP, Sinsky C, et al. Changes in burnout and satisfaction with work-life integration in physicians and the general US working population between 2011 and 2020. Mayo Clin Proc. 2022;97(3):491-506. doi:10.1016/j.mayocp.2021.11.021
4. Goodman RS, Patrinely JR Jr, Osterman T, Wheless L, Johnson DB. On the cusp: considering the impact of artificial intelligence language models in healthcare. Med. 2023;4(3):139-140. doi:10.1016/j.medj.2023.02.008
5. Will ChatGPT transform healthcare? Nat Med. 2023;29(3):505-506. doi:10.1038/s41591-023-02289-5
6. Hopkins AM, Logan JM, Kichenadasse G, Sorich MJ. Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift. JNCI Cancer Spectr. 2023;7(2):pkad010. doi:10.1093/jncics/pkad010
7. Babar Z, van Laarhoven T, Zanzotto FM, Marchiori E. Evaluating diagnostic content of AI-generated radiology reports of chest X-rays. Artif Intell Med. 2021;116:102075. doi:10.1016/j.artmed.2021.102075
8. Lecler A, Duron L, Soyer P. Revolutionizing radiology with GPT-based models: current applications, future possibilities and limitations of ChatGPT. Diagn Interv Imaging. 2023;S2211-5684(23)00027-X. doi:10.1016/j.diii.2023.02.003
9. Germain JM. Is ChatGPT smart enough to practice mental health therapy? March 23, 2023. Accessed May 11, 2023. https://www.technewsworld.com/story/is-chatgpt-smart-enough-to-practice-mental-health-therapy-178064.html
10. Cascella M, Montomoli J, Bellini V, Bignami E. Evaluating the feasibility of ChatGPT in healthcare: an analysis of multiple clinical and research scenarios. J Med Syst. 2023;47(1):33. Published 2023 Mar 4. doi:10.1007/s10916-023-01925-4
11. Jungwirth D, Haluza D. Artificial intelligence and public health: an exploratory study. Int J Environ Res Public Health. 2023;20(5):4541. Published 2023 Mar 3. doi:10.3390/ijerph20054541
12. Gilson A, Safranek CW, Huang T, et al. How does ChatGPT perform on the United States Medical Licensing Examination? The implications of large language models for medical education and knowledge assessment. JMIR Med Educ. 2023;9:e45312. Published 2023 Feb 8. doi:10.2196/45312
13. Eysenbach G. The role of ChatGPT, generative language models, and artificial intelligence in medical education: a conversation with ChatGPT and a call for papers. JMIR Med Educ. 2023;9:e46885. Published 2023 Mar 6. doi:10.2196/46885
14. Macdonald C, Adeloye D, Sheikh A, Rudan I. Can ChatGPT draft a research article? An example of population-level vaccine effectiveness analysis. J Glob Health. 2023;13:01003. Published 2023 Feb 17. doi:10.7189/jogh.13.01003
15. Masters K. Ethical use of artificial intelligence in health professions education: AMEE Guide No.158. Med Teach. 2023;1-11. doi:10.1080/0142159X.2023.2186203
16. Smith CS. Hallucinations could blunt ChatGPT’s success. IEEE Spectrum. March 13, 2023. Accessed May 11, 2023. https://spectrum.ieee.org/ai-hallucination
17. Executive Office of the President, Office of Science and Technology Policy. Blueprint for an AI Bill of Rights. Accessed May 11, 2023. https://www.whitehouse.gov/ostp/ai-bill-of-rights
18. Executive office of the President. Executive Order 13960: promoting the use of trustworthy artificial intelligence in the federal government. Fed Regist. 2020;89(236):78939-78943.
19. US Department of Commerce, National institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). Published January 2023. doi:10.6028/NIST.AI.100-1
20. Microsoft. Azure Cognitive Search—Cloud Search Service. Accessed May 11, 2023. https://azure.microsoft.com/en-us/products/search
21. Aiyappa R, An J, Kwak H, Ahn YY. Can we trust the evaluation on ChatGPT? March 22, 2023. Accessed May 11, 2023. https://arxiv.org/abs/2303.12767v1
22. Yang X, Chen A, Pournejatian N, et al. GatorTron: a large clinical language model to unlock patient information from unstructured electronic health records. Updated December 16, 2022. Accessed May 11, 2023. https://arxiv.org/abs/2203.03540v3
23. Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. December 26, 2022. Accessed May 11, 2023. https://arxiv.org/abs/2212.13138v1
24. Zakka C, Chaurasia A, Shad R, Hiesinger W. Almanac: knowledge-grounded language models for clinical medicine. March 1, 2023. Accessed May 11, 2023. https://arxiv.org/abs/2303.01229v1
25. NVIDIA. GatorTron-OG. Accessed May 11, 2023. https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og
26. Borkowski AA, Jakey CE, Thomas LB, Viswanadhan N, Mastorides SM. Establishing a hospital artificial intelligence committee to improve patient care. Fed Pract. 2022;39(8):334-336. doi:10.12788/fp.0299
WOW! You spend that much time on the EHR?
Unlike many of you, maybe even most of you, I can recall when my office records were handwritten, some would say scribbled, on pieces of paper. They were decipherable by a select few. Some veteran assistants never mastered the skill. Pages were sometimes lavishly illustrated with drawings of body parts, often because I couldn’t remember or spell the correct anatomic term. When I needed to send a referring letter to another provider I typed it myself because dictating never quite suited my personality.
When I joined a small primary care group, the computer-savvy lead physician and a programmer developed our own homegrown EHR. It relied on scanning documents, as so many of us still generated handwritten notes. Even the most vociferous Luddites among us loved the system from day 2.
However, for a variety of reasons, some defensible some just plain bad, our beloved system needed to be replaced after 7 years. We then invested in an off-the-shelf EHR system that promised more capabilities. We were told there would be a learning curve but the plateau would come quickly and we would enjoy our new electronic assistant.
You’ve lived the rest of the story. The learning curve was steep and long and the plateau was a time gobbler. I was probably the most efficient provider in the group, and after 6 months I was leaving the office an hour later than I had been and was seeing the same number of patients. Most of my coworkers were staying and/or working on the computer at home for an extra 2 hours. This change could be easily documented by speaking with our spouses and children. I understand from my colleagues who have stayed in the business that over the ensuing decade and a half since my first experience with the EHR, its insatiable appetite for a clinician’s time has not abated.
The authors of a recent article in Annals of Family Medicine offer up some advice on how this tragic situation might be brought under control. First, the investigators point out that the phenomenon of after-hours EHR work, sometimes referred to as WOW (work outside of work), has not gone unnoticed by health system administrators and vendors who develop and sell the EHRs. However, analyzing the voluminous data necessary is not any easy task and for the most part has resulted in metrics that cannot be easily applied over a variety of practice scenarios. Many health care organizations, even large ones, have simply given up and rely on the WOW data and recommendations provided by the vendors, obviously lending the situation a faint odor of conflict of interest.
The bottom line is that . It would seem to me just asking the spouses and significant others of the clinicians would be sufficient. But, authors of the paper have more specific recommendations. First, they suggest that time working on the computer outside of scheduled time with patients should be separated from any other calculation of EHR usage. They encourage vendors and time-management researchers to develop standardized and validated methods for measuring active EHR use. And, finally they recommend that all EHR work done outside of time scheduled with patients be attributed to WOW. They feel that clearly labeling it work outside of work offers health care organizations a better chance of developing policies that will address the scourge of burnout.
This, unfortunately, is another tragic example of how clinicians have lost control of our work environments. The fact that 20 years have passed and there is still no standardized method for determining how much time we spend on the computer is more evidence we need to raise our voices.
Dr. Wilkoff practiced primary care pediatrics in Brunswick, Maine, for nearly 40 years. He has authored several books on behavioral pediatrics, including “How to Say No to Your Toddler.” Other than a Littman stethoscope he accepted as a first-year medical student in 1966, Dr. Wilkoff reports having nothing to disclose. Email him at [email protected].
Unlike many of you, maybe even most of you, I can recall when my office records were handwritten, some would say scribbled, on pieces of paper. They were decipherable by a select few. Some veteran assistants never mastered the skill. Pages were sometimes lavishly illustrated with drawings of body parts, often because I couldn’t remember or spell the correct anatomic term. When I needed to send a referring letter to another provider I typed it myself because dictating never quite suited my personality.
When I joined a small primary care group, the computer-savvy lead physician and a programmer developed our own homegrown EHR. It relied on scanning documents, as so many of us still generated handwritten notes. Even the most vociferous Luddites among us loved the system from day 2.
However, for a variety of reasons, some defensible some just plain bad, our beloved system needed to be replaced after 7 years. We then invested in an off-the-shelf EHR system that promised more capabilities. We were told there would be a learning curve but the plateau would come quickly and we would enjoy our new electronic assistant.
You’ve lived the rest of the story. The learning curve was steep and long and the plateau was a time gobbler. I was probably the most efficient provider in the group, and after 6 months I was leaving the office an hour later than I had been and was seeing the same number of patients. Most of my coworkers were staying and/or working on the computer at home for an extra 2 hours. This change could be easily documented by speaking with our spouses and children. I understand from my colleagues who have stayed in the business that over the ensuing decade and a half since my first experience with the EHR, its insatiable appetite for a clinician’s time has not abated.
The authors of a recent article in Annals of Family Medicine offer up some advice on how this tragic situation might be brought under control. First, the investigators point out that the phenomenon of after-hours EHR work, sometimes referred to as WOW (work outside of work), has not gone unnoticed by health system administrators and vendors who develop and sell the EHRs. However, analyzing the voluminous data necessary is not any easy task and for the most part has resulted in metrics that cannot be easily applied over a variety of practice scenarios. Many health care organizations, even large ones, have simply given up and rely on the WOW data and recommendations provided by the vendors, obviously lending the situation a faint odor of conflict of interest.
The bottom line is that . It would seem to me just asking the spouses and significant others of the clinicians would be sufficient. But, authors of the paper have more specific recommendations. First, they suggest that time working on the computer outside of scheduled time with patients should be separated from any other calculation of EHR usage. They encourage vendors and time-management researchers to develop standardized and validated methods for measuring active EHR use. And, finally they recommend that all EHR work done outside of time scheduled with patients be attributed to WOW. They feel that clearly labeling it work outside of work offers health care organizations a better chance of developing policies that will address the scourge of burnout.
This, unfortunately, is another tragic example of how clinicians have lost control of our work environments. The fact that 20 years have passed and there is still no standardized method for determining how much time we spend on the computer is more evidence we need to raise our voices.
Dr. Wilkoff practiced primary care pediatrics in Brunswick, Maine, for nearly 40 years. He has authored several books on behavioral pediatrics, including “How to Say No to Your Toddler.” Other than a Littman stethoscope he accepted as a first-year medical student in 1966, Dr. Wilkoff reports having nothing to disclose. Email him at [email protected].
Unlike many of you, maybe even most of you, I can recall when my office records were handwritten, some would say scribbled, on pieces of paper. They were decipherable by a select few. Some veteran assistants never mastered the skill. Pages were sometimes lavishly illustrated with drawings of body parts, often because I couldn’t remember or spell the correct anatomic term. When I needed to send a referring letter to another provider I typed it myself because dictating never quite suited my personality.
When I joined a small primary care group, the computer-savvy lead physician and a programmer developed our own homegrown EHR. It relied on scanning documents, as so many of us still generated handwritten notes. Even the most vociferous Luddites among us loved the system from day 2.
However, for a variety of reasons, some defensible some just plain bad, our beloved system needed to be replaced after 7 years. We then invested in an off-the-shelf EHR system that promised more capabilities. We were told there would be a learning curve but the plateau would come quickly and we would enjoy our new electronic assistant.
You’ve lived the rest of the story. The learning curve was steep and long and the plateau was a time gobbler. I was probably the most efficient provider in the group, and after 6 months I was leaving the office an hour later than I had been and was seeing the same number of patients. Most of my coworkers were staying and/or working on the computer at home for an extra 2 hours. This change could be easily documented by speaking with our spouses and children. I understand from my colleagues who have stayed in the business that over the ensuing decade and a half since my first experience with the EHR, its insatiable appetite for a clinician’s time has not abated.
The authors of a recent article in Annals of Family Medicine offer up some advice on how this tragic situation might be brought under control. First, the investigators point out that the phenomenon of after-hours EHR work, sometimes referred to as WOW (work outside of work), has not gone unnoticed by health system administrators and vendors who develop and sell the EHRs. However, analyzing the voluminous data necessary is not any easy task and for the most part has resulted in metrics that cannot be easily applied over a variety of practice scenarios. Many health care organizations, even large ones, have simply given up and rely on the WOW data and recommendations provided by the vendors, obviously lending the situation a faint odor of conflict of interest.
The bottom line is that . It would seem to me just asking the spouses and significant others of the clinicians would be sufficient. But, authors of the paper have more specific recommendations. First, they suggest that time working on the computer outside of scheduled time with patients should be separated from any other calculation of EHR usage. They encourage vendors and time-management researchers to develop standardized and validated methods for measuring active EHR use. And, finally they recommend that all EHR work done outside of time scheduled with patients be attributed to WOW. They feel that clearly labeling it work outside of work offers health care organizations a better chance of developing policies that will address the scourge of burnout.
This, unfortunately, is another tragic example of how clinicians have lost control of our work environments. The fact that 20 years have passed and there is still no standardized method for determining how much time we spend on the computer is more evidence we need to raise our voices.
Dr. Wilkoff practiced primary care pediatrics in Brunswick, Maine, for nearly 40 years. He has authored several books on behavioral pediatrics, including “How to Say No to Your Toddler.” Other than a Littman stethoscope he accepted as a first-year medical student in 1966, Dr. Wilkoff reports having nothing to disclose. Email him at [email protected].