Women's Health

Cardiovascular disease (CVD) is the leading cause of death among women in the United States.¹ Most CVD is due to the buildup of plaque (ie, cholesterol, proteins, calcium, and inflammatory cells) in artery walls.² The plaque may lead to atherosclerotic cardiovascular disease (ASCVD), which includes coronary heart disease, cerebrovascular disease, peripheral artery disease, and aortic atherosclerotic disease.^2,3 Control and reduction of ASCVD risk factors, including high cholesterol levels, elevated blood pressure, insulin resistance, smoking, and a sedentary lifestyle, can contribute to a reduction in ASCVD morbidity and mortality.² People with type 2 diabetes mellitus (T2DM) have an increased prevalence of lipid abnormalities, contributing to their high risk of ASCVD.^4,5

The prescribing of statins (3-hydroxy-3-methyl-glutaryl-coenzmye A reductase inhibitors) is the cornerstone of lipid-lowering therapy and cardiovascular risk reduction for primary and secondary prevention of ASCVD.⁶ The American Diabetes Association (ADA) and American College of Cardiology/American Heart Association (ACC/AHA) recommend moderate- to high-intensity statins for primary prevention in patients with T2DM and high-intensity statins for secondary prevention in those with or without diabetes when not contraindicated.^4,5,7 Despite eligibility according to guideline recommendations, research predominantly shows that women are less likely to receive statin therapy; however, this trend is improving. ^[6,8-11] To explain the sex differences in statin use, Nanna et al found that there is a combination of women being offered statin therapy less frequently, declining therapy more frequently, and discontinuing treatment more frequently.¹¹ One possibility for discontinuing treatment could be statin-associated muscle symptoms (SAMS), which occur in about 10% of patients.¹² The incidence of adverse effects (AEs) may be related to the way statins are metabolized.

Pharmacogenomic testing is free for veterans through the US Department of Veterans Affairs (VA) PHASER program, which offers information and recommendations for a panel of 11 gene variants. The panel includes genes related to common medication classes such as anticoagulants, antiplatelets, proton pump inhibitors, nonsteroidal anti-inflammatory drugs, opioids, antidepressants, and statins. The VA PHASER panel includes the solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene, which is predominantly expressed in the liver and facilitates the hepatic uptake of most statins.^13,14 A reduced function of SLCO1B1 can lead to higher statin levels, resulting in increased concentrations that may potentially cause SAMS.^13,14 Some alleles associated with reduced function include SLCO1B1*5, *15, *23, *31, and *46 to *49, whereas others are associated with increased function, such as SLCO1B1 *14 and *20 (Appendix).¹⁵ Supporting evidence shows the SLCO1B1*5 nucleotide polymorphism increases plasma levels of simvastatin and atorvastatin, affecting effectiveness or toxicity. ¹³ Females tend to have a lower body weight and higher percentage of body fat compared with males, which might lead to higher concentrations of lipophilic drugs, including atorvastatin and simvastatin, which may be exacerbated by decreased function of SLCO1B1*5.¹⁵ With pharmacogenomic testing, therapeutic recommendations can be made to improve the overall safety and efficacy of statins, thus improving adherence using a patient-specific approach.^14,15

Methods

Carl Vinson VA Medical Center (CVVAMC) serves about 42,000 veterans in Central and South Georgia, of which about 15% are female. Of the female veterans enrolled in care, 63% identify as Black, 27% White, and 1.5% as Asian, American Indian/Alaska Native, or Native Hawaiian/Other Pacific Islander. The 2020 Veterans Chartbook report showed that female veterans and minority racial and ethnic groups had worse access to health care and higher mortality rates than their male and non-Hispanic White counterparts.¹⁶

The Primary Care Equity Dashboard (PCED) was developed to engage the VA health care workforce in the process of identifying and addressing inequities in local patient populations.¹⁷ Using electronic quality measure data, the PCED provides Veterans Integrated Service Network-level and facility-level performance on several metrics.¹⁸ The PCED had not been previously used at the CVVAMC, and few publications or quality improvement projects regarding its use have been reported by the VA Office of Health Equity. PCED helped identify disparities when comparing female to male patients in the prescribing of statin therapy for patients with CVD and statin therapy for patients with T2DM.

VA PHASER pharmacogenomic analyses provided an opportunity to expand this quality improvement project. Sanford Health and the VA collaborated on the PHASER program to offer free genetic testing for veterans. The program launched in 2019 and expanded to various VA sites, including CVVAMC in March 2023. This program has been extended to December 31, 2025.

The primary objective of this quality improvement project was to increase statin prescribing among female veterans with T2DM and/or CVD to reduce cardiovascular risk. Secondary outcomes included increased pharmacogenomic testing and the assessment of pharmacogenomic results related to statin therapy. This project was approved by the CVVAMC Pharmacy and Therapeutics Committee. The PCED was used to identify female veterans with T2DM and/or CVD without an active prescription for a statin between July and October 2023. A review of Computerized Patient Record System patient charts was completed to screen for prespecified inclusion and exclusion criteria. Veterans were included if they were assigned female at birth, were enrolled in care at CVVAMC, and had a diagnosis of T2DM or CVD (history of myocardial infarction, coronary bypass graft, percutaneous coronary intervention, or other revascularization in any setting).

Veterans were excluded if they were currently pregnant, trying to conceive, breastfeeding, had a T1DM diagnosis, had previously documented hypersensitivity to a statin, active liver failure or decompensated cirrhosis, previously documented statin-associated rhabdomyolysis or autoimmune myopathy, an active prescription for a proprotein convertase subtilisin/kexin type 9 inhibitor, or previously documented statin intolerance (defined as the inability to tolerate ≥ 3 statins, with ≥ 1 prescribed at low intensity or alternate-day dosing). The female veterans were compared to 2 comparators: the facility's male veterans and the VA national average, identified via the PCED.

Once a veteran was screened, they were telephoned between October 2023 and February 2024 and provided education on statin use and pharmacogenomic testing using a standardized note template. An order was placed for participants who provided verbal consent for pharmacogenomic testing. Those who agreed to statin initiation were referred to a clinical pharmacist practitioner (CPP) who contacted them at a later date to prescribe a statin following the recommendations of the 2019 ACC/AHA and 2023 ADA guidelines and pharmacogenomic testing, if applicable.^4,5,7 Appropriate monitoring and follow-up occurred at the discretion of each CPP. Data collection included: age, race, diagnoses (T2DM, CVD, or both), baseline lipid panel (total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein), hepatic function, name and dose of statin, reasons for declining statin therapy, and pharmacogenomic testing results related to SLCO1B1.

Results

At baseline in July 2023, 77.8% of female veterans with T2DM were prescribed a statin, which exceeded the national VA average (77.0%), but was below the rate for male veterans (78.7%) in the facility comparator group.17 Additionally, 82.2% of females with CVD were prescribed a statin, which was below the national VA average of 86.0% and the 84.9% of male veterans in the facility comparator group.17 The PCED identified 189 female veterans from July 2023 to October 2023 who may benefit from statin therapy. Thirty-three females met the exclusion criteria. Of the 156 included veterans, 129 (82.7%) were successfully contacted and 27 (17.3%) could not be reached by telephone after 3 attempts (Figure 1). The 129 female veterans contacted had a mean age of 59 years and the majority were Black (82.9%) (Table 1).

Primary Outcomes

Of the 129 contacted veterans, 31 (24.0%) had a non-VA statin prescription, 13 (10.1%) had an active VA statin prescription, and 85 (65.9%) did not have a statin prescription, despite being eligible. Statin adherence was confirmed with participants, and the medication list was updated accordingly.

Of the 85 veterans with no active statin therapy, 37 (43.5%) accepted a new statin prescription and 48 (56.5%) declined. There were various reasons provided for declining statin therapy: 17 participants (35.4%) declined due to concern for AEs (Table 2).

From July 2023 to March 2024, the percentage of female veterans with active statin therapy with T2DM increased from 77.8% to 79.0%. For those with active statin therapy with CVD, usage increased from 82.2% to 90.2%, which exceeded the national VA average and facility male comparator group (Figures 2 and 3).¹⁷

Secondary Outcomes

Seventy-one of 129 veterans (55.0%) gave verbal consent, and 47 (66.2%) completed the pharmacogenomic testing; 58 (45.0%) declined. Five veterans (10.6%) had a known SLCO1B1 allele variant present. One veteran required a change in statin therapy based on the results (eAppendix).

Discussion

This project aimed to increase statin prescribing among female veterans with T2DM and/or CVD to reduce cardiovascular risk and increase pharmacogenomic testing using the PCED and care managed by CPPs. The results of this quality improvement project illustrated that both metrics have improved at CVVAMC as a result of the intervention. The results in both metrics now exceed the PCED national VA average, and the CVD metric also exceeds that of the facility male comparator group. While there was only a 1.2% increase from July 2023 to March 2024 for patients with T2DM, there was an 8.0% increase for patients with CVD. Despite standardized education on statin use, more veterans declined therapy than accepted it, mostly due to concern for AEs. Recording the reasons for declining statin therapy offered valuable insight that can be used in additional discussions with veterans and clinicians.

Pharmacogenomics gives clinicians the unique opportunity to take a proactive approach to better predict drug responses, potentially allowing for less trial and error with medications, fewer AEs, greater trust in the clinician, and improved medication adherence. The CPPs incorporated pharmacogenomic testing into their practice, which led to identifying 5 SLCO1B1 gene abnormalities. The PCED served as a powerful tool for advancing equity-focused quality improvement initiatives on a local level and was crucial in prioritizing the detection of veterans potentially receiving suboptimal care.

Limitations

The nature of “cold calls” made it challenging to establish contact for inclusion in this study. An alternative to increase engagement could have been scheduled phone or face-to-face visits. While the use of the PCED was crucial, data did not account for statins listed in the non-VA medication list. All 31 patients with statins prescribed outside the VA had a start date added to provide the most accurate representation of the data moving forward.

Another limitation in this project was its small sample size and population. CVVAMC serves about 6200 female veterans, with roughly 63% identifying as Black. The preponderance of Black individuals (83%) in this project is typical for the female patient population at CVVAMC but may not reflect the demographics of other populations. Other limitations to this project consisted of scheduling conflicts. Appointments for laboratory draws at community-based outpatient clinics were subject to availability, which resulted in some delay in completion of pharmacogenomic testing.

Conclusions

CPPs can help reduce inequity in health care delivery. Increased incorporation of the PCED into regular practice within the VA is recommended to continue addressing sex disparities in statin use, diabetes control, blood pressure management, cancer screenings, and vaccination needs. CVVAMC plans to expand its use through another quality improvement project focused on reducing sex disparities in blood pressure management. Improving educational resources made available to veterans on the importance of statin therapy and potential to mitigate AEs through use of the VA PHASER program also would be helpful. This project successfully improved CVVAMC metrics for female veterans appropriately prescribed statin therapy and increased access to pharmacogenomic testing. Most importantly, it helped close the sex-based gap in CVD risk reduction care.

Cardiovascular disease (CVD) is the leading cause of death among women in the United States.¹ Most CVD is due to the buildup of plaque (ie, cholesterol, proteins, calcium, and inflammatory cells) in artery walls.² The plaque may lead to atherosclerotic cardiovascular disease (ASCVD), which includes coronary heart disease, cerebrovascular disease, peripheral artery disease, and aortic atherosclerotic disease.^2,3 Control and reduction of ASCVD risk factors, including high cholesterol levels, elevated blood pressure, insulin resistance, smoking, and a sedentary lifestyle, can contribute to a reduction in ASCVD morbidity and mortality.² People with type 2 diabetes mellitus (T2DM) have an increased prevalence of lipid abnormalities, contributing to their high risk of ASCVD.^4,5

The prescribing of statins (3-hydroxy-3-methyl-glutaryl-coenzmye A reductase inhibitors) is the cornerstone of lipid-lowering therapy and cardiovascular risk reduction for primary and secondary prevention of ASCVD.⁶ The American Diabetes Association (ADA) and American College of Cardiology/American Heart Association (ACC/AHA) recommend moderate- to high-intensity statins for primary prevention in patients with T2DM and high-intensity statins for secondary prevention in those with or without diabetes when not contraindicated.^4,5,7 Despite eligibility according to guideline recommendations, research predominantly shows that women are less likely to receive statin therapy; however, this trend is improving. ^[6,8-11] To explain the sex differences in statin use, Nanna et al found that there is a combination of women being offered statin therapy less frequently, declining therapy more frequently, and discontinuing treatment more frequently.¹¹ One possibility for discontinuing treatment could be statin-associated muscle symptoms (SAMS), which occur in about 10% of patients.¹² The incidence of adverse effects (AEs) may be related to the way statins are metabolized.

Pharmacogenomic testing is free for veterans through the US Department of Veterans Affairs (VA) PHASER program, which offers information and recommendations for a panel of 11 gene variants. The panel includes genes related to common medication classes such as anticoagulants, antiplatelets, proton pump inhibitors, nonsteroidal anti-inflammatory drugs, opioids, antidepressants, and statins. The VA PHASER panel includes the solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene, which is predominantly expressed in the liver and facilitates the hepatic uptake of most statins.^13,14 A reduced function of SLCO1B1 can lead to higher statin levels, resulting in increased concentrations that may potentially cause SAMS.^13,14 Some alleles associated with reduced function include SLCO1B1*5, *15, *23, *31, and *46 to *49, whereas others are associated with increased function, such as SLCO1B1 *14 and *20 (Appendix).¹⁵ Supporting evidence shows the SLCO1B1*5 nucleotide polymorphism increases plasma levels of simvastatin and atorvastatin, affecting effectiveness or toxicity. ¹³ Females tend to have a lower body weight and higher percentage of body fat compared with males, which might lead to higher concentrations of lipophilic drugs, including atorvastatin and simvastatin, which may be exacerbated by decreased function of SLCO1B1*5.¹⁵ With pharmacogenomic testing, therapeutic recommendations can be made to improve the overall safety and efficacy of statins, thus improving adherence using a patient-specific approach.^14,15

Methods

Carl Vinson VA Medical Center (CVVAMC) serves about 42,000 veterans in Central and South Georgia, of which about 15% are female. Of the female veterans enrolled in care, 63% identify as Black, 27% White, and 1.5% as Asian, American Indian/Alaska Native, or Native Hawaiian/Other Pacific Islander. The 2020 Veterans Chartbook report showed that female veterans and minority racial and ethnic groups had worse access to health care and higher mortality rates than their male and non-Hispanic White counterparts.¹⁶

The Primary Care Equity Dashboard (PCED) was developed to engage the VA health care workforce in the process of identifying and addressing inequities in local patient populations.¹⁷ Using electronic quality measure data, the PCED provides Veterans Integrated Service Network-level and facility-level performance on several metrics.¹⁸ The PCED had not been previously used at the CVVAMC, and few publications or quality improvement projects regarding its use have been reported by the VA Office of Health Equity. PCED helped identify disparities when comparing female to male patients in the prescribing of statin therapy for patients with CVD and statin therapy for patients with T2DM.

VA PHASER pharmacogenomic analyses provided an opportunity to expand this quality improvement project. Sanford Health and the VA collaborated on the PHASER program to offer free genetic testing for veterans. The program launched in 2019 and expanded to various VA sites, including CVVAMC in March 2023. This program has been extended to December 31, 2025.

The primary objective of this quality improvement project was to increase statin prescribing among female veterans with T2DM and/or CVD to reduce cardiovascular risk. Secondary outcomes included increased pharmacogenomic testing and the assessment of pharmacogenomic results related to statin therapy. This project was approved by the CVVAMC Pharmacy and Therapeutics Committee. The PCED was used to identify female veterans with T2DM and/or CVD without an active prescription for a statin between July and October 2023. A review of Computerized Patient Record System patient charts was completed to screen for prespecified inclusion and exclusion criteria. Veterans were included if they were assigned female at birth, were enrolled in care at CVVAMC, and had a diagnosis of T2DM or CVD (history of myocardial infarction, coronary bypass graft, percutaneous coronary intervention, or other revascularization in any setting).

Veterans were excluded if they were currently pregnant, trying to conceive, breastfeeding, had a T1DM diagnosis, had previously documented hypersensitivity to a statin, active liver failure or decompensated cirrhosis, previously documented statin-associated rhabdomyolysis or autoimmune myopathy, an active prescription for a proprotein convertase subtilisin/kexin type 9 inhibitor, or previously documented statin intolerance (defined as the inability to tolerate ≥ 3 statins, with ≥ 1 prescribed at low intensity or alternate-day dosing). The female veterans were compared to 2 comparators: the facility's male veterans and the VA national average, identified via the PCED.

Once a veteran was screened, they were telephoned between October 2023 and February 2024 and provided education on statin use and pharmacogenomic testing using a standardized note template. An order was placed for participants who provided verbal consent for pharmacogenomic testing. Those who agreed to statin initiation were referred to a clinical pharmacist practitioner (CPP) who contacted them at a later date to prescribe a statin following the recommendations of the 2019 ACC/AHA and 2023 ADA guidelines and pharmacogenomic testing, if applicable.^4,5,7 Appropriate monitoring and follow-up occurred at the discretion of each CPP. Data collection included: age, race, diagnoses (T2DM, CVD, or both), baseline lipid panel (total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein), hepatic function, name and dose of statin, reasons for declining statin therapy, and pharmacogenomic testing results related to SLCO1B1.

Results

At baseline in July 2023, 77.8% of female veterans with T2DM were prescribed a statin, which exceeded the national VA average (77.0%), but was below the rate for male veterans (78.7%) in the facility comparator group.17 Additionally, 82.2% of females with CVD were prescribed a statin, which was below the national VA average of 86.0% and the 84.9% of male veterans in the facility comparator group.17 The PCED identified 189 female veterans from July 2023 to October 2023 who may benefit from statin therapy. Thirty-three females met the exclusion criteria. Of the 156 included veterans, 129 (82.7%) were successfully contacted and 27 (17.3%) could not be reached by telephone after 3 attempts (Figure 1). The 129 female veterans contacted had a mean age of 59 years and the majority were Black (82.9%) (Table 1).

Primary Outcomes

Of the 129 contacted veterans, 31 (24.0%) had a non-VA statin prescription, 13 (10.1%) had an active VA statin prescription, and 85 (65.9%) did not have a statin prescription, despite being eligible. Statin adherence was confirmed with participants, and the medication list was updated accordingly.

Of the 85 veterans with no active statin therapy, 37 (43.5%) accepted a new statin prescription and 48 (56.5%) declined. There were various reasons provided for declining statin therapy: 17 participants (35.4%) declined due to concern for AEs (Table 2).

From July 2023 to March 2024, the percentage of female veterans with active statin therapy with T2DM increased from 77.8% to 79.0%. For those with active statin therapy with CVD, usage increased from 82.2% to 90.2%, which exceeded the national VA average and facility male comparator group (Figures 2 and 3).¹⁷

Secondary Outcomes

Seventy-one of 129 veterans (55.0%) gave verbal consent, and 47 (66.2%) completed the pharmacogenomic testing; 58 (45.0%) declined. Five veterans (10.6%) had a known SLCO1B1 allele variant present. One veteran required a change in statin therapy based on the results (eAppendix).

Discussion

This project aimed to increase statin prescribing among female veterans with T2DM and/or CVD to reduce cardiovascular risk and increase pharmacogenomic testing using the PCED and care managed by CPPs. The results of this quality improvement project illustrated that both metrics have improved at CVVAMC as a result of the intervention. The results in both metrics now exceed the PCED national VA average, and the CVD metric also exceeds that of the facility male comparator group. While there was only a 1.2% increase from July 2023 to March 2024 for patients with T2DM, there was an 8.0% increase for patients with CVD. Despite standardized education on statin use, more veterans declined therapy than accepted it, mostly due to concern for AEs. Recording the reasons for declining statin therapy offered valuable insight that can be used in additional discussions with veterans and clinicians.

Pharmacogenomics gives clinicians the unique opportunity to take a proactive approach to better predict drug responses, potentially allowing for less trial and error with medications, fewer AEs, greater trust in the clinician, and improved medication adherence. The CPPs incorporated pharmacogenomic testing into their practice, which led to identifying 5 SLCO1B1 gene abnormalities. The PCED served as a powerful tool for advancing equity-focused quality improvement initiatives on a local level and was crucial in prioritizing the detection of veterans potentially receiving suboptimal care.

Limitations

The nature of “cold calls” made it challenging to establish contact for inclusion in this study. An alternative to increase engagement could have been scheduled phone or face-to-face visits. While the use of the PCED was crucial, data did not account for statins listed in the non-VA medication list. All 31 patients with statins prescribed outside the VA had a start date added to provide the most accurate representation of the data moving forward.

Another limitation in this project was its small sample size and population. CVVAMC serves about 6200 female veterans, with roughly 63% identifying as Black. The preponderance of Black individuals (83%) in this project is typical for the female patient population at CVVAMC but may not reflect the demographics of other populations. Other limitations to this project consisted of scheduling conflicts. Appointments for laboratory draws at community-based outpatient clinics were subject to availability, which resulted in some delay in completion of pharmacogenomic testing.

Conclusions

CPPs can help reduce inequity in health care delivery. Increased incorporation of the PCED into regular practice within the VA is recommended to continue addressing sex disparities in statin use, diabetes control, blood pressure management, cancer screenings, and vaccination needs. CVVAMC plans to expand its use through another quality improvement project focused on reducing sex disparities in blood pressure management. Improving educational resources made available to veterans on the importance of statin therapy and potential to mitigate AEs through use of the VA PHASER program also would be helpful. This project successfully improved CVVAMC metrics for female veterans appropriately prescribed statin therapy and increased access to pharmacogenomic testing. Most importantly, it helped close the sex-based gap in CVD risk reduction care.

Cardiovascular disease (CVD) is the leading cause of death among women in the United States.¹ Most CVD is due to the buildup of plaque (ie, cholesterol, proteins, calcium, and inflammatory cells) in artery walls.² The plaque may lead to atherosclerotic cardiovascular disease (ASCVD), which includes coronary heart disease, cerebrovascular disease, peripheral artery disease, and aortic atherosclerotic disease.^2,3 Control and reduction of ASCVD risk factors, including high cholesterol levels, elevated blood pressure, insulin resistance, smoking, and a sedentary lifestyle, can contribute to a reduction in ASCVD morbidity and mortality.² People with type 2 diabetes mellitus (T2DM) have an increased prevalence of lipid abnormalities, contributing to their high risk of ASCVD.^4,5

The prescribing of statins (3-hydroxy-3-methyl-glutaryl-coenzmye A reductase inhibitors) is the cornerstone of lipid-lowering therapy and cardiovascular risk reduction for primary and secondary prevention of ASCVD.⁶ The American Diabetes Association (ADA) and American College of Cardiology/American Heart Association (ACC/AHA) recommend moderate- to high-intensity statins for primary prevention in patients with T2DM and high-intensity statins for secondary prevention in those with or without diabetes when not contraindicated.^4,5,7 Despite eligibility according to guideline recommendations, research predominantly shows that women are less likely to receive statin therapy; however, this trend is improving. ^[6,8-11] To explain the sex differences in statin use, Nanna et al found that there is a combination of women being offered statin therapy less frequently, declining therapy more frequently, and discontinuing treatment more frequently.¹¹ One possibility for discontinuing treatment could be statin-associated muscle symptoms (SAMS), which occur in about 10% of patients.¹² The incidence of adverse effects (AEs) may be related to the way statins are metabolized.

Pharmacogenomic testing is free for veterans through the US Department of Veterans Affairs (VA) PHASER program, which offers information and recommendations for a panel of 11 gene variants. The panel includes genes related to common medication classes such as anticoagulants, antiplatelets, proton pump inhibitors, nonsteroidal anti-inflammatory drugs, opioids, antidepressants, and statins. The VA PHASER panel includes the solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene, which is predominantly expressed in the liver and facilitates the hepatic uptake of most statins.^13,14 A reduced function of SLCO1B1 can lead to higher statin levels, resulting in increased concentrations that may potentially cause SAMS.^13,14 Some alleles associated with reduced function include SLCO1B1*5, *15, *23, *31, and *46 to *49, whereas others are associated with increased function, such as SLCO1B1 *14 and *20 (Appendix).¹⁵ Supporting evidence shows the SLCO1B1*5 nucleotide polymorphism increases plasma levels of simvastatin and atorvastatin, affecting effectiveness or toxicity. ¹³ Females tend to have a lower body weight and higher percentage of body fat compared with males, which might lead to higher concentrations of lipophilic drugs, including atorvastatin and simvastatin, which may be exacerbated by decreased function of SLCO1B1*5.¹⁵ With pharmacogenomic testing, therapeutic recommendations can be made to improve the overall safety and efficacy of statins, thus improving adherence using a patient-specific approach.^14,15

Methods

Carl Vinson VA Medical Center (CVVAMC) serves about 42,000 veterans in Central and South Georgia, of which about 15% are female. Of the female veterans enrolled in care, 63% identify as Black, 27% White, and 1.5% as Asian, American Indian/Alaska Native, or Native Hawaiian/Other Pacific Islander. The 2020 Veterans Chartbook report showed that female veterans and minority racial and ethnic groups had worse access to health care and higher mortality rates than their male and non-Hispanic White counterparts.¹⁶

The Primary Care Equity Dashboard (PCED) was developed to engage the VA health care workforce in the process of identifying and addressing inequities in local patient populations.¹⁷ Using electronic quality measure data, the PCED provides Veterans Integrated Service Network-level and facility-level performance on several metrics.¹⁸ The PCED had not been previously used at the CVVAMC, and few publications or quality improvement projects regarding its use have been reported by the VA Office of Health Equity. PCED helped identify disparities when comparing female to male patients in the prescribing of statin therapy for patients with CVD and statin therapy for patients with T2DM.

VA PHASER pharmacogenomic analyses provided an opportunity to expand this quality improvement project. Sanford Health and the VA collaborated on the PHASER program to offer free genetic testing for veterans. The program launched in 2019 and expanded to various VA sites, including CVVAMC in March 2023. This program has been extended to December 31, 2025.

The primary objective of this quality improvement project was to increase statin prescribing among female veterans with T2DM and/or CVD to reduce cardiovascular risk. Secondary outcomes included increased pharmacogenomic testing and the assessment of pharmacogenomic results related to statin therapy. This project was approved by the CVVAMC Pharmacy and Therapeutics Committee. The PCED was used to identify female veterans with T2DM and/or CVD without an active prescription for a statin between July and October 2023. A review of Computerized Patient Record System patient charts was completed to screen for prespecified inclusion and exclusion criteria. Veterans were included if they were assigned female at birth, were enrolled in care at CVVAMC, and had a diagnosis of T2DM or CVD (history of myocardial infarction, coronary bypass graft, percutaneous coronary intervention, or other revascularization in any setting).

Veterans were excluded if they were currently pregnant, trying to conceive, breastfeeding, had a T1DM diagnosis, had previously documented hypersensitivity to a statin, active liver failure or decompensated cirrhosis, previously documented statin-associated rhabdomyolysis or autoimmune myopathy, an active prescription for a proprotein convertase subtilisin/kexin type 9 inhibitor, or previously documented statin intolerance (defined as the inability to tolerate ≥ 3 statins, with ≥ 1 prescribed at low intensity or alternate-day dosing). The female veterans were compared to 2 comparators: the facility's male veterans and the VA national average, identified via the PCED.

Once a veteran was screened, they were telephoned between October 2023 and February 2024 and provided education on statin use and pharmacogenomic testing using a standardized note template. An order was placed for participants who provided verbal consent for pharmacogenomic testing. Those who agreed to statin initiation were referred to a clinical pharmacist practitioner (CPP) who contacted them at a later date to prescribe a statin following the recommendations of the 2019 ACC/AHA and 2023 ADA guidelines and pharmacogenomic testing, if applicable.^4,5,7 Appropriate monitoring and follow-up occurred at the discretion of each CPP. Data collection included: age, race, diagnoses (T2DM, CVD, or both), baseline lipid panel (total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein), hepatic function, name and dose of statin, reasons for declining statin therapy, and pharmacogenomic testing results related to SLCO1B1.

Results

At baseline in July 2023, 77.8% of female veterans with T2DM were prescribed a statin, which exceeded the national VA average (77.0%), but was below the rate for male veterans (78.7%) in the facility comparator group.17 Additionally, 82.2% of females with CVD were prescribed a statin, which was below the national VA average of 86.0% and the 84.9% of male veterans in the facility comparator group.17 The PCED identified 189 female veterans from July 2023 to October 2023 who may benefit from statin therapy. Thirty-three females met the exclusion criteria. Of the 156 included veterans, 129 (82.7%) were successfully contacted and 27 (17.3%) could not be reached by telephone after 3 attempts (Figure 1). The 129 female veterans contacted had a mean age of 59 years and the majority were Black (82.9%) (Table 1).

Primary Outcomes

Of the 129 contacted veterans, 31 (24.0%) had a non-VA statin prescription, 13 (10.1%) had an active VA statin prescription, and 85 (65.9%) did not have a statin prescription, despite being eligible. Statin adherence was confirmed with participants, and the medication list was updated accordingly.

Of the 85 veterans with no active statin therapy, 37 (43.5%) accepted a new statin prescription and 48 (56.5%) declined. There were various reasons provided for declining statin therapy: 17 participants (35.4%) declined due to concern for AEs (Table 2).

From July 2023 to March 2024, the percentage of female veterans with active statin therapy with T2DM increased from 77.8% to 79.0%. For those with active statin therapy with CVD, usage increased from 82.2% to 90.2%, which exceeded the national VA average and facility male comparator group (Figures 2 and 3).¹⁷

Secondary Outcomes

Seventy-one of 129 veterans (55.0%) gave verbal consent, and 47 (66.2%) completed the pharmacogenomic testing; 58 (45.0%) declined. Five veterans (10.6%) had a known SLCO1B1 allele variant present. One veteran required a change in statin therapy based on the results (eAppendix).

Discussion

This project aimed to increase statin prescribing among female veterans with T2DM and/or CVD to reduce cardiovascular risk and increase pharmacogenomic testing using the PCED and care managed by CPPs. The results of this quality improvement project illustrated that both metrics have improved at CVVAMC as a result of the intervention. The results in both metrics now exceed the PCED national VA average, and the CVD metric also exceeds that of the facility male comparator group. While there was only a 1.2% increase from July 2023 to March 2024 for patients with T2DM, there was an 8.0% increase for patients with CVD. Despite standardized education on statin use, more veterans declined therapy than accepted it, mostly due to concern for AEs. Recording the reasons for declining statin therapy offered valuable insight that can be used in additional discussions with veterans and clinicians.

Pharmacogenomics gives clinicians the unique opportunity to take a proactive approach to better predict drug responses, potentially allowing for less trial and error with medications, fewer AEs, greater trust in the clinician, and improved medication adherence. The CPPs incorporated pharmacogenomic testing into their practice, which led to identifying 5 SLCO1B1 gene abnormalities. The PCED served as a powerful tool for advancing equity-focused quality improvement initiatives on a local level and was crucial in prioritizing the detection of veterans potentially receiving suboptimal care.

Limitations

The nature of “cold calls” made it challenging to establish contact for inclusion in this study. An alternative to increase engagement could have been scheduled phone or face-to-face visits. While the use of the PCED was crucial, data did not account for statins listed in the non-VA medication list. All 31 patients with statins prescribed outside the VA had a start date added to provide the most accurate representation of the data moving forward.

Another limitation in this project was its small sample size and population. CVVAMC serves about 6200 female veterans, with roughly 63% identifying as Black. The preponderance of Black individuals (83%) in this project is typical for the female patient population at CVVAMC but may not reflect the demographics of other populations. Other limitations to this project consisted of scheduling conflicts. Appointments for laboratory draws at community-based outpatient clinics were subject to availability, which resulted in some delay in completion of pharmacogenomic testing.

Conclusions

CPPs can help reduce inequity in health care delivery. Increased incorporation of the PCED into regular practice within the VA is recommended to continue addressing sex disparities in statin use, diabetes control, blood pressure management, cancer screenings, and vaccination needs. CVVAMC plans to expand its use through another quality improvement project focused on reducing sex disparities in blood pressure management. Improving educational resources made available to veterans on the importance of statin therapy and potential to mitigate AEs through use of the VA PHASER program also would be helpful. This project successfully improved CVVAMC metrics for female veterans appropriately prescribed statin therapy and increased access to pharmacogenomic testing. Most importantly, it helped close the sex-based gap in CVD risk reduction care.

In this Practical AI column, we’ve explored everything from large language models to the nuances of trial matching, but one of the most immediate and impactful applications of AI is unfolding right now in breast imaging. For oncologists, this isn’t an abstract future — with new screening guidelines, dense-breast mandates, and a shrinking radiology workforce, it’s the imaging reports and patient questions landing in your clinic today.

Here is what oncologists need to know, and how to put it to work for their patients.

 

Why AI in Mammography Matters

More than 200 million women undergo breast cancer screening each year. In the US alone, 10% of the 40 million women screened annually require additional diagnostic imaging, and 4%–5% of these women are eventually diagnosed with breast cancer.

Two major shifts are redefining breast cancer screening in the US: The US Preventive Services Task Force (USPSTF) now recommends biennial screening from age 40 to 74 years, and notifying patients of breast density is a federal requirement as of September 10, 2024. That means more mammograms, more patient questions, and more downstream oncology decisions. Patients will increasingly ask about “dense” breast results and what to do next. Add a national radiologist shortage into the mix, and the pressure on timely callbacks, biopsies, and treatment planning will only grow.

 

Can AI Help Without Compromising Care?

The short answer is yes. With AI, we may be able to transform these rate-limiting steps into opportunities for earlier detection, decentralized screening, and smarter triage and save hundreds of thousands of women from an unnecessary diagnostic procedure, if implemented deliberately.

Don’t Confuse Today’s AI With Yesterday’s CAD 

Think of older computer-aided detection (CAD) like a 1990s chemotherapy drug: It sometimes helped, but it came with significant toxicity and rarely delivered consistent survival benefits. Today’s deep-learning AI is closer to targeted therapy — trained on millions of “trial participants” (mammograms), more precise, and applied in specific contexts where it adds value. If you once dismissed CAD as noise, it’s time to revisit what AI can now offer.

The role of AI is broader than drawing boxes. It provides second readings, worklist triage, risk prediction, density assessment, and decision support. FDA has cleared several AI tools for both 2D and digital breast tomosynthesis (DBT), which include iCAD ProFound (DBT), ScreenPoint Transpara (2D/DBT), and Lunit INSIGHT DBT. 

Some of the strongest evidence for AI in mammography is as a second reader during screening. Large trials show that AI plus one radiologist can match reading from two radiologists, cutting workload by about 40%. For example, the MASAI randomized trial showed that AI-supported screening achieved similar cancer detection but cut human screen-reading workload about 44% vs standard double reading (39,996 vs 40,024 participants). The primary interval cancer outcomes are maturing, but the safety analysis is reassuring.

Reducing second reads and arbitration time are important for clinicians because it frees capacity for callbacks and diagnostic workups. This will be especially key given that screening now starts at age 40. That will mean about 21 to 22 million more women are newly eligible, translating to about 10 to 11 million additional mammograms each year under biennial screening.

Another important area where AI can make its mark in mammography is triage and time to diagnosis. The results from a randomized implementation study showed that AI-prioritized worklists accelerated time to additional imaging and biopsy diagnosis without harming efficiency for others — exactly the kind of outcome patients feel.

Multiple studies have demonstrated improved diagnostic performance and shorter reading times when AI supports DBT interpretation, which is important because DBT can otherwise be time intensive.

We are also seeing rapid advancement in risk-based screening, moving beyond a single dense vs not dense approach. Deep-learning risk models, such as Mirai, predict 1- to 5-year breast cancer risk directly from the mammogram, and these tools are now being assessed prospectively to guide supplemental MRI. Cost-effectiveness modeling supports risk-stratified intervals vs one-size-fits-all schedules.

Finally, automated density tools, such as Transpara Density and Volpara, offer objective, reproducible volumetric measures that map to the Breast Imaging-Reporting and Data System, which is useful for Mammography Quality Standards Act-required reporting and as inputs to risk calculators.

While early evidence suggests AI may help surface future or interval cancers earlier, including more invasive tumors, the definitive impacts on interval cancer rates and mortality require longitudinal follow-up, which is now in progress.

 

Pitfalls to Watch For

Bias is real. Studies show false-positive differences by race, age, and density. AI can even infer racial identity from images, potentially amplifying disparities. Performance can also shift by vendor, demographics, and prevalence.

A Radiology study of 4855 DBT exams showed that an algorithm produced more false-positive case scores in Black patients and older patients (aged 71-80 years) patients and in women with extremely dense breasts. This can happen because AI can infer proxies for race directly from images, even when humans cannot, and this can propagate disparities if not addressed. External validations and reviews emphasize that performance can shift with device manufacturer, demographics, and prevalence, which is why all tools need to undergo local validation and calibration. 

Here’s a pragmatic adoption checklist before going live with an AI tool.

• Confirm FDA clearance: Verify the name and version of the algorithm, imaging modes (2D vs DBT), and operating points. Confirm 510(k) numbers.
• Local validation: Test on your patient mix and vendor stack (Hologic, GE, Siemens, Fuji). Compare this to your baseline recall rate, positive predictive value of recall (PPV1), cancer detection rate, and reading time. Commit to recalibration if drift occurs.
• Equity plan: Monitor false-positive and negative false-rates by age, race/ethnicity, and density; document corrective actions if disparities emerge. (This isn’t optional.)
• Workflow clarity: Is AI a second reader, an additional reader, or a triage tool? Who arbitrates discordance? What’s the escalation path for high-risk or interval cancer-like patterns?
• Regulatory strategy: Confirm whether the vendor has (or will file) a Predetermined Change Control Plan so models can be updated safely without repeated submissions. Also confirm how you’ll be notified about performance-relevant changes.
• Data governance: Audit logs of AI outputs, retention, protected health information handling, and the patient communication policy for AI-assisted reads.

After going live, set up a quarterly dashboard. It should include cancer detection rate per 1000 patients, recall rate, PPV1, interval cancer rate (as it matures), reading time, and turnaround time to diagnostic imaging or biopsy — all stratified by age, race/ethnicity, and density.

Here, I dissect what this discussion means through the lens of Moravec’s paradox (machines excel at what clinicians find hard, and vice versa) and offer a possible playbook for putting these tools to work.

 

What to Tell Patients

When speaking with patients, emphasize that a radiologist still reads their mammogram. AI helps with consistency and efficiency; it doesn’t replace human oversight. Patients with dense breasts should still expect a standard notice; discussion of individualized risk factors, such as family history, genetics, and prior biopsies; and consideration of supplemental imaging if risk warrants. But it’s also important to tell these patients that while dense breasts are common, they do not automatically mean high cancer risk.

As for screening schedules, remind patients that screening is at least biennial from 40 to 74 years of age per the USPSTF guidelines; however, specialty groups may recommend starting on an annual schedule at 40.

 

What You Can Implement Now

There are multiple practical use cases you can introduce now. One is to use AI as a second reader or an additional reader safety net to preserve detection while reducing human workload. This helps your breast center absorb screening expansion to age 40 without diluting quality. Another is to turn on AI triage to shorten the time to callback and biopsy for the few who need it most — patients notice and appreciate faster answers. You can also begin adopting automated density plus risk models to move beyond “dense/not dense.” For selected patients, AI-informed risk can justify MRI or tailored intervals. 

Here’s a quick cheat sheet (for your next leadership or tumor-board meeting).

 

Do:

• Use AI as a second or additional reader or triage tool, not as a black box.
• Track cancer detection rate, recall, PPV1, interval cancers, and reading time, stratified by age, race, and breast density.
• Pair automated density with AI risk to personalize screening and supplemental imaging.
• Enroll patients in future clinical trials, such as PRISM, the first large-scale randomized controlled trial of AI for screening mammography. This US-based, $16 million, seven-site study is funded by the Patient-Centered Outcomes Research Institute.

Don’t:

• Assume “AI = CAD.” The 2015 CAD story is over; modern deep learning systems are different and require different oversight.
• Go live without a local validation and equity plan or without clarity on software updates.
• Forget to remind patients that screening starts at age 40, and dense breast notifications are now universal. Use the visit to discuss risk, supplemental imaging, and why a human still directs their care.

The Bottom Line

AI won’t replace radiologists or read mammograms for us — just as PET scans didn’t replace oncologists and stethoscopes didn’t make cardiologists obsolete. What it will do is catch what the tired human eye might miss, shave days off anxious waiting, and turn breast density into data instead of doubt. For oncologists, that means staging sooner, enrolling smarter, and spending more time talking with patients instead of chasing callbacks.

In short, AI may not take the picture, but it helps us frame the story, making it sharper, faster, and with fewer blind spots. By pairing this powerful technology with rigorous, equity-focused local validation and transparent governance under the FDA’s emerging Predetermined Change Control Plan framework, we can realize the tangible benefits of practical AI for our patients without widening disparities. 

Now, during Breast Cancer Awareness Month, how about we add on AI to that pink ribbon — how cool would that be?

Thoughts? Drop me a line at Arturo.AI.MedTech@gmail.com. Let’s keep the conversation — and pink ribbons — going.

Arturo Loaiza-Bonilla, MD, MSEd, is the co-founder and chief medical AI officer at Massive Bio, a company connecting patients to clinical trials using artificial intelligence. His research and professional interests focus on precision medicine, clinical trial design, digital health, entrepreneurship, and patient advocacy. Dr Loaiza-Bonilla serves as Systemwide Chief of Hematology and Oncology at St. Luke’s University Health Network, where he maintains a connection to patient care by attending to patients 2 days a week.

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

In this Practical AI column, we’ve explored everything from large language models to the nuances of trial matching, but one of the most immediate and impactful applications of AI is unfolding right now in breast imaging. For oncologists, this isn’t an abstract future — with new screening guidelines, dense-breast mandates, and a shrinking radiology workforce, it’s the imaging reports and patient questions landing in your clinic today.

Here is what oncologists need to know, and how to put it to work for their patients.

 

Why AI in Mammography Matters

More than 200 million women undergo breast cancer screening each year. In the US alone, 10% of the 40 million women screened annually require additional diagnostic imaging, and 4%–5% of these women are eventually diagnosed with breast cancer.

Two major shifts are redefining breast cancer screening in the US: The US Preventive Services Task Force (USPSTF) now recommends biennial screening from age 40 to 74 years, and notifying patients of breast density is a federal requirement as of September 10, 2024. That means more mammograms, more patient questions, and more downstream oncology decisions. Patients will increasingly ask about “dense” breast results and what to do next. Add a national radiologist shortage into the mix, and the pressure on timely callbacks, biopsies, and treatment planning will only grow.

 

Can AI Help Without Compromising Care?

The short answer is yes. With AI, we may be able to transform these rate-limiting steps into opportunities for earlier detection, decentralized screening, and smarter triage and save hundreds of thousands of women from an unnecessary diagnostic procedure, if implemented deliberately.

Don’t Confuse Today’s AI With Yesterday’s CAD 

Think of older computer-aided detection (CAD) like a 1990s chemotherapy drug: It sometimes helped, but it came with significant toxicity and rarely delivered consistent survival benefits. Today’s deep-learning AI is closer to targeted therapy — trained on millions of “trial participants” (mammograms), more precise, and applied in specific contexts where it adds value. If you once dismissed CAD as noise, it’s time to revisit what AI can now offer.

The role of AI is broader than drawing boxes. It provides second readings, worklist triage, risk prediction, density assessment, and decision support. FDA has cleared several AI tools for both 2D and digital breast tomosynthesis (DBT), which include iCAD ProFound (DBT), ScreenPoint Transpara (2D/DBT), and Lunit INSIGHT DBT. 

Some of the strongest evidence for AI in mammography is as a second reader during screening. Large trials show that AI plus one radiologist can match reading from two radiologists, cutting workload by about 40%. For example, the MASAI randomized trial showed that AI-supported screening achieved similar cancer detection but cut human screen-reading workload about 44% vs standard double reading (39,996 vs 40,024 participants). The primary interval cancer outcomes are maturing, but the safety analysis is reassuring.

Reducing second reads and arbitration time are important for clinicians because it frees capacity for callbacks and diagnostic workups. This will be especially key given that screening now starts at age 40. That will mean about 21 to 22 million more women are newly eligible, translating to about 10 to 11 million additional mammograms each year under biennial screening.

Another important area where AI can make its mark in mammography is triage and time to diagnosis. The results from a randomized implementation study showed that AI-prioritized worklists accelerated time to additional imaging and biopsy diagnosis without harming efficiency for others — exactly the kind of outcome patients feel.

Multiple studies have demonstrated improved diagnostic performance and shorter reading times when AI supports DBT interpretation, which is important because DBT can otherwise be time intensive.

We are also seeing rapid advancement in risk-based screening, moving beyond a single dense vs not dense approach. Deep-learning risk models, such as Mirai, predict 1- to 5-year breast cancer risk directly from the mammogram, and these tools are now being assessed prospectively to guide supplemental MRI. Cost-effectiveness modeling supports risk-stratified intervals vs one-size-fits-all schedules.

Finally, automated density tools, such as Transpara Density and Volpara, offer objective, reproducible volumetric measures that map to the Breast Imaging-Reporting and Data System, which is useful for Mammography Quality Standards Act-required reporting and as inputs to risk calculators.

While early evidence suggests AI may help surface future or interval cancers earlier, including more invasive tumors, the definitive impacts on interval cancer rates and mortality require longitudinal follow-up, which is now in progress.

 

Pitfalls to Watch For

Bias is real. Studies show false-positive differences by race, age, and density. AI can even infer racial identity from images, potentially amplifying disparities. Performance can also shift by vendor, demographics, and prevalence.

A Radiology study of 4855 DBT exams showed that an algorithm produced more false-positive case scores in Black patients and older patients (aged 71-80 years) patients and in women with extremely dense breasts. This can happen because AI can infer proxies for race directly from images, even when humans cannot, and this can propagate disparities if not addressed. External validations and reviews emphasize that performance can shift with device manufacturer, demographics, and prevalence, which is why all tools need to undergo local validation and calibration. 

Here’s a pragmatic adoption checklist before going live with an AI tool.

• Confirm FDA clearance: Verify the name and version of the algorithm, imaging modes (2D vs DBT), and operating points. Confirm 510(k) numbers.
• Local validation: Test on your patient mix and vendor stack (Hologic, GE, Siemens, Fuji). Compare this to your baseline recall rate, positive predictive value of recall (PPV1), cancer detection rate, and reading time. Commit to recalibration if drift occurs.
• Equity plan: Monitor false-positive and negative false-rates by age, race/ethnicity, and density; document corrective actions if disparities emerge. (This isn’t optional.)
• Workflow clarity: Is AI a second reader, an additional reader, or a triage tool? Who arbitrates discordance? What’s the escalation path for high-risk or interval cancer-like patterns?
• Regulatory strategy: Confirm whether the vendor has (or will file) a Predetermined Change Control Plan so models can be updated safely without repeated submissions. Also confirm how you’ll be notified about performance-relevant changes.
• Data governance: Audit logs of AI outputs, retention, protected health information handling, and the patient communication policy for AI-assisted reads.

After going live, set up a quarterly dashboard. It should include cancer detection rate per 1000 patients, recall rate, PPV1, interval cancer rate (as it matures), reading time, and turnaround time to diagnostic imaging or biopsy — all stratified by age, race/ethnicity, and density.

Here, I dissect what this discussion means through the lens of Moravec’s paradox (machines excel at what clinicians find hard, and vice versa) and offer a possible playbook for putting these tools to work.

 

What to Tell Patients

When speaking with patients, emphasize that a radiologist still reads their mammogram. AI helps with consistency and efficiency; it doesn’t replace human oversight. Patients with dense breasts should still expect a standard notice; discussion of individualized risk factors, such as family history, genetics, and prior biopsies; and consideration of supplemental imaging if risk warrants. But it’s also important to tell these patients that while dense breasts are common, they do not automatically mean high cancer risk.

As for screening schedules, remind patients that screening is at least biennial from 40 to 74 years of age per the USPSTF guidelines; however, specialty groups may recommend starting on an annual schedule at 40.

 

What You Can Implement Now

There are multiple practical use cases you can introduce now. One is to use AI as a second reader or an additional reader safety net to preserve detection while reducing human workload. This helps your breast center absorb screening expansion to age 40 without diluting quality. Another is to turn on AI triage to shorten the time to callback and biopsy for the few who need it most — patients notice and appreciate faster answers. You can also begin adopting automated density plus risk models to move beyond “dense/not dense.” For selected patients, AI-informed risk can justify MRI or tailored intervals. 

Here’s a quick cheat sheet (for your next leadership or tumor-board meeting).

 

Do:

• Use AI as a second or additional reader or triage tool, not as a black box.
• Track cancer detection rate, recall, PPV1, interval cancers, and reading time, stratified by age, race, and breast density.
• Pair automated density with AI risk to personalize screening and supplemental imaging.
• Enroll patients in future clinical trials, such as PRISM, the first large-scale randomized controlled trial of AI for screening mammography. This US-based, $16 million, seven-site study is funded by the Patient-Centered Outcomes Research Institute.

Don’t:

• Assume “AI = CAD.” The 2015 CAD story is over; modern deep learning systems are different and require different oversight.
• Go live without a local validation and equity plan or without clarity on software updates.
• Forget to remind patients that screening starts at age 40, and dense breast notifications are now universal. Use the visit to discuss risk, supplemental imaging, and why a human still directs their care.

The Bottom Line

AI won’t replace radiologists or read mammograms for us — just as PET scans didn’t replace oncologists and stethoscopes didn’t make cardiologists obsolete. What it will do is catch what the tired human eye might miss, shave days off anxious waiting, and turn breast density into data instead of doubt. For oncologists, that means staging sooner, enrolling smarter, and spending more time talking with patients instead of chasing callbacks.

In short, AI may not take the picture, but it helps us frame the story, making it sharper, faster, and with fewer blind spots. By pairing this powerful technology with rigorous, equity-focused local validation and transparent governance under the FDA’s emerging Predetermined Change Control Plan framework, we can realize the tangible benefits of practical AI for our patients without widening disparities. 

Now, during Breast Cancer Awareness Month, how about we add on AI to that pink ribbon — how cool would that be?

Thoughts? Drop me a line at Arturo.AI.MedTech@gmail.com. Let’s keep the conversation — and pink ribbons — going.

Arturo Loaiza-Bonilla, MD, MSEd, is the co-founder and chief medical AI officer at Massive Bio, a company connecting patients to clinical trials using artificial intelligence. His research and professional interests focus on precision medicine, clinical trial design, digital health, entrepreneurship, and patient advocacy. Dr Loaiza-Bonilla serves as Systemwide Chief of Hematology and Oncology at St. Luke’s University Health Network, where he maintains a connection to patient care by attending to patients 2 days a week.

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

In this Practical AI column, we’ve explored everything from large language models to the nuances of trial matching, but one of the most immediate and impactful applications of AI is unfolding right now in breast imaging. For oncologists, this isn’t an abstract future — with new screening guidelines, dense-breast mandates, and a shrinking radiology workforce, it’s the imaging reports and patient questions landing in your clinic today.

Here is what oncologists need to know, and how to put it to work for their patients.

 

Why AI in Mammography Matters

More than 200 million women undergo breast cancer screening each year. In the US alone, 10% of the 40 million women screened annually require additional diagnostic imaging, and 4%–5% of these women are eventually diagnosed with breast cancer.

Two major shifts are redefining breast cancer screening in the US: The US Preventive Services Task Force (USPSTF) now recommends biennial screening from age 40 to 74 years, and notifying patients of breast density is a federal requirement as of September 10, 2024. That means more mammograms, more patient questions, and more downstream oncology decisions. Patients will increasingly ask about “dense” breast results and what to do next. Add a national radiologist shortage into the mix, and the pressure on timely callbacks, biopsies, and treatment planning will only grow.

 

Can AI Help Without Compromising Care?

The short answer is yes. With AI, we may be able to transform these rate-limiting steps into opportunities for earlier detection, decentralized screening, and smarter triage and save hundreds of thousands of women from an unnecessary diagnostic procedure, if implemented deliberately.

Don’t Confuse Today’s AI With Yesterday’s CAD 

Think of older computer-aided detection (CAD) like a 1990s chemotherapy drug: It sometimes helped, but it came with significant toxicity and rarely delivered consistent survival benefits. Today’s deep-learning AI is closer to targeted therapy — trained on millions of “trial participants” (mammograms), more precise, and applied in specific contexts where it adds value. If you once dismissed CAD as noise, it’s time to revisit what AI can now offer.

The role of AI is broader than drawing boxes. It provides second readings, worklist triage, risk prediction, density assessment, and decision support. FDA has cleared several AI tools for both 2D and digital breast tomosynthesis (DBT), which include iCAD ProFound (DBT), ScreenPoint Transpara (2D/DBT), and Lunit INSIGHT DBT. 

Some of the strongest evidence for AI in mammography is as a second reader during screening. Large trials show that AI plus one radiologist can match reading from two radiologists, cutting workload by about 40%. For example, the MASAI randomized trial showed that AI-supported screening achieved similar cancer detection but cut human screen-reading workload about 44% vs standard double reading (39,996 vs 40,024 participants). The primary interval cancer outcomes are maturing, but the safety analysis is reassuring.

Reducing second reads and arbitration time are important for clinicians because it frees capacity for callbacks and diagnostic workups. This will be especially key given that screening now starts at age 40. That will mean about 21 to 22 million more women are newly eligible, translating to about 10 to 11 million additional mammograms each year under biennial screening.

Another important area where AI can make its mark in mammography is triage and time to diagnosis. The results from a randomized implementation study showed that AI-prioritized worklists accelerated time to additional imaging and biopsy diagnosis without harming efficiency for others — exactly the kind of outcome patients feel.

Multiple studies have demonstrated improved diagnostic performance and shorter reading times when AI supports DBT interpretation, which is important because DBT can otherwise be time intensive.

We are also seeing rapid advancement in risk-based screening, moving beyond a single dense vs not dense approach. Deep-learning risk models, such as Mirai, predict 1- to 5-year breast cancer risk directly from the mammogram, and these tools are now being assessed prospectively to guide supplemental MRI. Cost-effectiveness modeling supports risk-stratified intervals vs one-size-fits-all schedules.

Finally, automated density tools, such as Transpara Density and Volpara, offer objective, reproducible volumetric measures that map to the Breast Imaging-Reporting and Data System, which is useful for Mammography Quality Standards Act-required reporting and as inputs to risk calculators.

While early evidence suggests AI may help surface future or interval cancers earlier, including more invasive tumors, the definitive impacts on interval cancer rates and mortality require longitudinal follow-up, which is now in progress.

 

Pitfalls to Watch For

Bias is real. Studies show false-positive differences by race, age, and density. AI can even infer racial identity from images, potentially amplifying disparities. Performance can also shift by vendor, demographics, and prevalence.

A Radiology study of 4855 DBT exams showed that an algorithm produced more false-positive case scores in Black patients and older patients (aged 71-80 years) patients and in women with extremely dense breasts. This can happen because AI can infer proxies for race directly from images, even when humans cannot, and this can propagate disparities if not addressed. External validations and reviews emphasize that performance can shift with device manufacturer, demographics, and prevalence, which is why all tools need to undergo local validation and calibration. 

Here’s a pragmatic adoption checklist before going live with an AI tool.

• Confirm FDA clearance: Verify the name and version of the algorithm, imaging modes (2D vs DBT), and operating points. Confirm 510(k) numbers.
• Local validation: Test on your patient mix and vendor stack (Hologic, GE, Siemens, Fuji). Compare this to your baseline recall rate, positive predictive value of recall (PPV1), cancer detection rate, and reading time. Commit to recalibration if drift occurs.
• Equity plan: Monitor false-positive and negative false-rates by age, race/ethnicity, and density; document corrective actions if disparities emerge. (This isn’t optional.)
• Workflow clarity: Is AI a second reader, an additional reader, or a triage tool? Who arbitrates discordance? What’s the escalation path for high-risk or interval cancer-like patterns?
• Regulatory strategy: Confirm whether the vendor has (or will file) a Predetermined Change Control Plan so models can be updated safely without repeated submissions. Also confirm how you’ll be notified about performance-relevant changes.
• Data governance: Audit logs of AI outputs, retention, protected health information handling, and the patient communication policy for AI-assisted reads.

After going live, set up a quarterly dashboard. It should include cancer detection rate per 1000 patients, recall rate, PPV1, interval cancer rate (as it matures), reading time, and turnaround time to diagnostic imaging or biopsy — all stratified by age, race/ethnicity, and density.

Here, I dissect what this discussion means through the lens of Moravec’s paradox (machines excel at what clinicians find hard, and vice versa) and offer a possible playbook for putting these tools to work.

 

What to Tell Patients

When speaking with patients, emphasize that a radiologist still reads their mammogram. AI helps with consistency and efficiency; it doesn’t replace human oversight. Patients with dense breasts should still expect a standard notice; discussion of individualized risk factors, such as family history, genetics, and prior biopsies; and consideration of supplemental imaging if risk warrants. But it’s also important to tell these patients that while dense breasts are common, they do not automatically mean high cancer risk.

As for screening schedules, remind patients that screening is at least biennial from 40 to 74 years of age per the USPSTF guidelines; however, specialty groups may recommend starting on an annual schedule at 40.

 

What You Can Implement Now

There are multiple practical use cases you can introduce now. One is to use AI as a second reader or an additional reader safety net to preserve detection while reducing human workload. This helps your breast center absorb screening expansion to age 40 without diluting quality. Another is to turn on AI triage to shorten the time to callback and biopsy for the few who need it most — patients notice and appreciate faster answers. You can also begin adopting automated density plus risk models to move beyond “dense/not dense.” For selected patients, AI-informed risk can justify MRI or tailored intervals. 

Here’s a quick cheat sheet (for your next leadership or tumor-board meeting).

 

Do:

• Use AI as a second or additional reader or triage tool, not as a black box.
• Track cancer detection rate, recall, PPV1, interval cancers, and reading time, stratified by age, race, and breast density.
• Pair automated density with AI risk to personalize screening and supplemental imaging.
• Enroll patients in future clinical trials, such as PRISM, the first large-scale randomized controlled trial of AI for screening mammography. This US-based, $16 million, seven-site study is funded by the Patient-Centered Outcomes Research Institute.

Don’t:

• Assume “AI = CAD.” The 2015 CAD story is over; modern deep learning systems are different and require different oversight.
• Go live without a local validation and equity plan or without clarity on software updates.
• Forget to remind patients that screening starts at age 40, and dense breast notifications are now universal. Use the visit to discuss risk, supplemental imaging, and why a human still directs their care.

The Bottom Line

AI won’t replace radiologists or read mammograms for us — just as PET scans didn’t replace oncologists and stethoscopes didn’t make cardiologists obsolete. What it will do is catch what the tired human eye might miss, shave days off anxious waiting, and turn breast density into data instead of doubt. For oncologists, that means staging sooner, enrolling smarter, and spending more time talking with patients instead of chasing callbacks.

In short, AI may not take the picture, but it helps us frame the story, making it sharper, faster, and with fewer blind spots. By pairing this powerful technology with rigorous, equity-focused local validation and transparent governance under the FDA’s emerging Predetermined Change Control Plan framework, we can realize the tangible benefits of practical AI for our patients without widening disparities. 

Now, during Breast Cancer Awareness Month, how about we add on AI to that pink ribbon — how cool would that be?

Thoughts? Drop me a line at Arturo.AI.MedTech@gmail.com. Let’s keep the conversation — and pink ribbons — going.

Arturo Loaiza-Bonilla, MD, MSEd, is the co-founder and chief medical AI officer at Massive Bio, a company connecting patients to clinical trials using artificial intelligence. His research and professional interests focus on precision medicine, clinical trial design, digital health, entrepreneurship, and patient advocacy. Dr Loaiza-Bonilla serves as Systemwide Chief of Hematology and Oncology at St. Luke’s University Health Network, where he maintains a connection to patient care by attending to patients 2 days a week.

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

Medroxyprogesterone acetate is an injectable medication indicated for contraception and management of endometriosis-associated pain in females of reproductive age.¹ Medroxyprogesterone inhibits gonadotropin secretion, which prevents follicular maturation and ovulation. This leads to endometrial thinning and a contraceptive effect. Adverse drug reactions (ADRs), such as weight gain, menstrual bleeding irregularities, and bone loss appear to be dose- and time-related. Two formulations of medroxyprogesterone acetate are available: 150 mg depot medroxyprogesterone acetate intramuscular (DMPA-IM) and 104 mg DMPA subcutaneous (DMPA-SC).² Originally, medroxyprogesterone acetate injections required administration by a health care worker. While the current labeling for DMPA-SC still indicates a requirement for administration by a health care worker, data show that the medication can be safe and effective when self-administered.³

Self-Administered Contraception

The 2019 World Health Organization (WHO) guideline on self-care interventions recommends making self-administered injectable contraception available to individuals of reproductive age.³ The WHO recommendation is based on evidence from the Depo Self-Administration Study, which included 401 patients randomized 1:1 to receive self-administered or clinic-administered DMPA-SC. This study concluded that self-administration improved continuation of contraception.⁴

The North Florida/South Georgia Veterans Health System (NFSGVHS) is the largest US Department of Veterans Affairs (VA) health care system, serving > 22,000 female veterans. All primary care practitioners (PCP) have been trained in women’s health (WH). 

The WH patient-aligned care team (PACT) clinical pharmacy practitioner (CPP) proposed using DMPA-SC for outpatient self-administration to increase access, improve patient satisfaction, and reduce burden on patients and nurses for administration appointments. The Pharmacy and Therapeutics Committee (P&T), WH Medical Director, and Chief of Gynecology approved the proposal. DMPA-SC was added to the ordering menu with order sets. The order set included instructions that outlined the 12-week dosing interval, instructions to contact the prescriber if the injection was > 2 weeks overdue (aligning with dosing recommendations for administration every 12 to 14 weeks), and an optional order for a home pregnancy test if necessary. These instructions were designed to ensure proper self-administration of the medication and timely follow-up care. 

The gynecology and PACT health care practitioners (HCPs), including physicians, pharmacists, nurses, and medical assistants, received DMPA-SC education, which consisted of a review of medication, ADRs, contraindications, and administration. An NFSGVHS procedure was developed to ensure patients received self-administration education. DMPA-SC prescriptions were mailed to patients with scheduled nursing appointments. The patient would then bring DMPA-SC to the nursing appointment where they received administration instruction and completed the first injection under nurse supervision to ensure appropriate technique. Patients were offered supplementary educational documents and a calendar to keep track of injection days. The patients were responsible for ordering refills and administering subsequent injections at home. Once all stakeholders received education and order sets were in place, prescribers and nurses could begin offering the option for initiation of self-administered DMPA-SC to patients. All conversions or new prescriptions were initiated by prescribers as a part of usual care.

Medication Use Evaluation

A medication use evaluation was conducted about 1 year after the rollout to assess use, adherence, and impact of DMPA-SC for patient-self administration as a new contraceptive option for NFSGVHS patients.

A retrospective chart review was conducted for patients dispensed DMPA-SC from June 1, 2022, to July 1, 2023. Baseline body mass index (BMI), recorded prior to initiation of DMPA-SC, was compared with the most recent BMI on record at the completion of the study to evaluate weight change. Nursing visit attendance for the first injection was also assessed. Adherence was evaluated by reviewing the date of the initial DMPA-SC prescription, the date of the patient's first nursing visit, and subsequent refill patterns. A 2-week margin of error was established to account for the flexibility within the recommended dosing interval and delays in postal service delivery.

Forty patients were initiated on DMPA-SC for patient self-administration. The mean age of patients was 37.2 years. All 40 patients were female. Twenty-two patients (55%) identified as Black, 17 (43%) as White, and 1 (3%) as Asian. The majority (90%) of patients were non-Hispanic. The mean baseline BMI was 30 and BMI after DMPA-SC initiation was 30.4.

Twenty-eight (70%) patients had a nursing appointment, adhering to the NFSGVHS protocol. Five patients (13%) discontinued use and switched to DMPA-IM administered by an HCP and 4 (10%) discontinued use following an ADR (hives, mood changes, bruising, and menometrorrhagia). Of the 31 patients who continued therapy, 25 (81%) were refilling appropriately (Table). 

Six patients with unidentified reasons for nonadherence were contacted to determine if there were unmet contraceptive needs. This subgroup included patients with an active prescription for DMPA-SC that did not meet refill expectations. Nonadherence was mostly due to forgetfulness, however 1 patient was unable to refill her DMPA-SC in a timely manner due to an outside hospital admission and another was unreachable. These conversations were documented in the electronic health record (EHR) and all patients requesting follow-up, reinitiation of therapy, or alternative regimens, the appropriate parties were notified to coordinate care.

Discussion

The uptake in DMPA-SC prescribing suggests prescribers and patients have embraced self-administration as an option for contraception. Most patients were appropriately scheduled for nursing appointments to reinforce education and ensure appropriate self-injection technique, as outlined in the NFSGVHS procedure.

The need to improve adherence to NFSGVHS procedure was identified because not all patients had scheduled nursing appointments. This is concerning because some patients may have started self-injecting DMPA-SC without proper education, which could lead to improper injection technique and diminished effectiveness. Nursing appointments ensure appropriate self-injection techniques and reinforce the importance of refilling every 12 weeks for proper effectiveness. Nonadherence to contraceptive therapy may result in unintended pregnancy, although no pregnancies were reported by patients in this study. Pharmacist involvement in DMPA-SC initiation and follow-up monitoring may help ensure adherence to local procedure for initiation and improve patient adherence. 

There is limited evidence comparing weight gain related to DMPA-SC vs DMPA-IM. However, in a small, 2-year, randomized study, weight changes were considered comparable for both cohorts with a mean increase of 3.5 kg in the DMPA-IM group vs 3.4 kg in the DMPA-SC group.⁵ While our analysis did not formally evaluate weight changes, BMI data were collected to evaluate for evidence of weight change. The duration of therapy varied per patient and may not have been long enough to see comparable weight changes. 

Strengths of this project include the use of the PACT multidisciplinary approach in primary care including physicians, pharmacists, and nurses. The NFSGVHS EHR is comprehensive, and data including appointments and pharmacy refill information was readily available for collection and evaluation. Limitations included inconsistent documentation in the patient’s EHR which made collection of some data difficult.

Cost Estimates

NFSGVHS had 231 patients prescribed DMPA-IM at the time of DMPA-SC rollout and 40 patients initiated DMPA-SC therapy in the first year. There are possible cost savings associated with the use of DMPA-SC compared to DMPA-IM. Although DMPA-IM costs about $120 annually and DMPA-SC costs about $252 annually, this does not account for indirect costs such as supplies, overhead cost, nursing visits, and patient travel.⁶ Additionally, allowing patients to self-administer the DMPA-SC injection at home provides nurses time to care for other patients.

Moving forward, the PACT and gynecology teams will receive instruction on the importance of adhering to NFSGVHS procedures to ensure new patients prescribed DMPA-SC receive education and present for nursing appointments to ensure appropriate self-injection.

DMPA has historically been administered in the clinic setting by an HCP; therefore, the prescriber was available to assess adherence to therapy based on patient’s attendance to scheduled clinic appointments. Some prescribers may feel apprehensive about shifting the onus of medication adherence to the patient when prescribing DMPA-SC. However, this model is comparable to any other prescription form of birth control, such as combined hormonal contraceptive pills, where the prescriber expects the patient to take the medication as prescribed and refill their prescriptions in a timely manner to avoid gaps in therapy. The findings of this project suggest the majority of patients who were prescribed self-administered DMPA-SC for contraception were adherent to therapy. The utility of self-administration of DMPA-SC for other labeled or off-label indications was not evaluated; however, it is possible that patients who are motivated to self-administer the medication (regardless of indication) would also demonstrate similar adherence rates.

Conclusions

The majority of patients who started DMPA-SC tolerated the medication well and continued to refill therapy within the recommended time period. Patient self-administration of DMPA-SC can enhance access by removing barriers to administration, increase patient autonomy and contraceptive continuation rates. Overall, the increase in DMPA-SC prescriptions suggests that patients and HCPs support the option for DMPA-SC self-administration at NFSGVHS.

Medroxyprogesterone acetate is an injectable medication indicated for contraception and management of endometriosis-associated pain in females of reproductive age.¹ Medroxyprogesterone inhibits gonadotropin secretion, which prevents follicular maturation and ovulation. This leads to endometrial thinning and a contraceptive effect. Adverse drug reactions (ADRs), such as weight gain, menstrual bleeding irregularities, and bone loss appear to be dose- and time-related. Two formulations of medroxyprogesterone acetate are available: 150 mg depot medroxyprogesterone acetate intramuscular (DMPA-IM) and 104 mg DMPA subcutaneous (DMPA-SC).² Originally, medroxyprogesterone acetate injections required administration by a health care worker. While the current labeling for DMPA-SC still indicates a requirement for administration by a health care worker, data show that the medication can be safe and effective when self-administered.³

Self-Administered Contraception

The 2019 World Health Organization (WHO) guideline on self-care interventions recommends making self-administered injectable contraception available to individuals of reproductive age.³ The WHO recommendation is based on evidence from the Depo Self-Administration Study, which included 401 patients randomized 1:1 to receive self-administered or clinic-administered DMPA-SC. This study concluded that self-administration improved continuation of contraception.⁴

The North Florida/South Georgia Veterans Health System (NFSGVHS) is the largest US Department of Veterans Affairs (VA) health care system, serving > 22,000 female veterans. All primary care practitioners (PCP) have been trained in women’s health (WH). 

The WH patient-aligned care team (PACT) clinical pharmacy practitioner (CPP) proposed using DMPA-SC for outpatient self-administration to increase access, improve patient satisfaction, and reduce burden on patients and nurses for administration appointments. The Pharmacy and Therapeutics Committee (P&T), WH Medical Director, and Chief of Gynecology approved the proposal. DMPA-SC was added to the ordering menu with order sets. The order set included instructions that outlined the 12-week dosing interval, instructions to contact the prescriber if the injection was > 2 weeks overdue (aligning with dosing recommendations for administration every 12 to 14 weeks), and an optional order for a home pregnancy test if necessary. These instructions were designed to ensure proper self-administration of the medication and timely follow-up care. 

The gynecology and PACT health care practitioners (HCPs), including physicians, pharmacists, nurses, and medical assistants, received DMPA-SC education, which consisted of a review of medication, ADRs, contraindications, and administration. An NFSGVHS procedure was developed to ensure patients received self-administration education. DMPA-SC prescriptions were mailed to patients with scheduled nursing appointments. The patient would then bring DMPA-SC to the nursing appointment where they received administration instruction and completed the first injection under nurse supervision to ensure appropriate technique. Patients were offered supplementary educational documents and a calendar to keep track of injection days. The patients were responsible for ordering refills and administering subsequent injections at home. Once all stakeholders received education and order sets were in place, prescribers and nurses could begin offering the option for initiation of self-administered DMPA-SC to patients. All conversions or new prescriptions were initiated by prescribers as a part of usual care.

Medication Use Evaluation

A medication use evaluation was conducted about 1 year after the rollout to assess use, adherence, and impact of DMPA-SC for patient-self administration as a new contraceptive option for NFSGVHS patients.

A retrospective chart review was conducted for patients dispensed DMPA-SC from June 1, 2022, to July 1, 2023. Baseline body mass index (BMI), recorded prior to initiation of DMPA-SC, was compared with the most recent BMI on record at the completion of the study to evaluate weight change. Nursing visit attendance for the first injection was also assessed. Adherence was evaluated by reviewing the date of the initial DMPA-SC prescription, the date of the patient's first nursing visit, and subsequent refill patterns. A 2-week margin of error was established to account for the flexibility within the recommended dosing interval and delays in postal service delivery.

Forty patients were initiated on DMPA-SC for patient self-administration. The mean age of patients was 37.2 years. All 40 patients were female. Twenty-two patients (55%) identified as Black, 17 (43%) as White, and 1 (3%) as Asian. The majority (90%) of patients were non-Hispanic. The mean baseline BMI was 30 and BMI after DMPA-SC initiation was 30.4.

Twenty-eight (70%) patients had a nursing appointment, adhering to the NFSGVHS protocol. Five patients (13%) discontinued use and switched to DMPA-IM administered by an HCP and 4 (10%) discontinued use following an ADR (hives, mood changes, bruising, and menometrorrhagia). Of the 31 patients who continued therapy, 25 (81%) were refilling appropriately (Table). 

Six patients with unidentified reasons for nonadherence were contacted to determine if there were unmet contraceptive needs. This subgroup included patients with an active prescription for DMPA-SC that did not meet refill expectations. Nonadherence was mostly due to forgetfulness, however 1 patient was unable to refill her DMPA-SC in a timely manner due to an outside hospital admission and another was unreachable. These conversations were documented in the electronic health record (EHR) and all patients requesting follow-up, reinitiation of therapy, or alternative regimens, the appropriate parties were notified to coordinate care.

Discussion

The uptake in DMPA-SC prescribing suggests prescribers and patients have embraced self-administration as an option for contraception. Most patients were appropriately scheduled for nursing appointments to reinforce education and ensure appropriate self-injection technique, as outlined in the NFSGVHS procedure.

The need to improve adherence to NFSGVHS procedure was identified because not all patients had scheduled nursing appointments. This is concerning because some patients may have started self-injecting DMPA-SC without proper education, which could lead to improper injection technique and diminished effectiveness. Nursing appointments ensure appropriate self-injection techniques and reinforce the importance of refilling every 12 weeks for proper effectiveness. Nonadherence to contraceptive therapy may result in unintended pregnancy, although no pregnancies were reported by patients in this study. Pharmacist involvement in DMPA-SC initiation and follow-up monitoring may help ensure adherence to local procedure for initiation and improve patient adherence. 

There is limited evidence comparing weight gain related to DMPA-SC vs DMPA-IM. However, in a small, 2-year, randomized study, weight changes were considered comparable for both cohorts with a mean increase of 3.5 kg in the DMPA-IM group vs 3.4 kg in the DMPA-SC group.⁵ While our analysis did not formally evaluate weight changes, BMI data were collected to evaluate for evidence of weight change. The duration of therapy varied per patient and may not have been long enough to see comparable weight changes. 

Strengths of this project include the use of the PACT multidisciplinary approach in primary care including physicians, pharmacists, and nurses. The NFSGVHS EHR is comprehensive, and data including appointments and pharmacy refill information was readily available for collection and evaluation. Limitations included inconsistent documentation in the patient’s EHR which made collection of some data difficult.

Cost Estimates

NFSGVHS had 231 patients prescribed DMPA-IM at the time of DMPA-SC rollout and 40 patients initiated DMPA-SC therapy in the first year. There are possible cost savings associated with the use of DMPA-SC compared to DMPA-IM. Although DMPA-IM costs about $120 annually and DMPA-SC costs about $252 annually, this does not account for indirect costs such as supplies, overhead cost, nursing visits, and patient travel.⁶ Additionally, allowing patients to self-administer the DMPA-SC injection at home provides nurses time to care for other patients.

Moving forward, the PACT and gynecology teams will receive instruction on the importance of adhering to NFSGVHS procedures to ensure new patients prescribed DMPA-SC receive education and present for nursing appointments to ensure appropriate self-injection.

DMPA has historically been administered in the clinic setting by an HCP; therefore, the prescriber was available to assess adherence to therapy based on patient’s attendance to scheduled clinic appointments. Some prescribers may feel apprehensive about shifting the onus of medication adherence to the patient when prescribing DMPA-SC. However, this model is comparable to any other prescription form of birth control, such as combined hormonal contraceptive pills, where the prescriber expects the patient to take the medication as prescribed and refill their prescriptions in a timely manner to avoid gaps in therapy. The findings of this project suggest the majority of patients who were prescribed self-administered DMPA-SC for contraception were adherent to therapy. The utility of self-administration of DMPA-SC for other labeled or off-label indications was not evaluated; however, it is possible that patients who are motivated to self-administer the medication (regardless of indication) would also demonstrate similar adherence rates.

Conclusions

The majority of patients who started DMPA-SC tolerated the medication well and continued to refill therapy within the recommended time period. Patient self-administration of DMPA-SC can enhance access by removing barriers to administration, increase patient autonomy and contraceptive continuation rates. Overall, the increase in DMPA-SC prescriptions suggests that patients and HCPs support the option for DMPA-SC self-administration at NFSGVHS.

Medroxyprogesterone acetate is an injectable medication indicated for contraception and management of endometriosis-associated pain in females of reproductive age.¹ Medroxyprogesterone inhibits gonadotropin secretion, which prevents follicular maturation and ovulation. This leads to endometrial thinning and a contraceptive effect. Adverse drug reactions (ADRs), such as weight gain, menstrual bleeding irregularities, and bone loss appear to be dose- and time-related. Two formulations of medroxyprogesterone acetate are available: 150 mg depot medroxyprogesterone acetate intramuscular (DMPA-IM) and 104 mg DMPA subcutaneous (DMPA-SC).² Originally, medroxyprogesterone acetate injections required administration by a health care worker. While the current labeling for DMPA-SC still indicates a requirement for administration by a health care worker, data show that the medication can be safe and effective when self-administered.³

Self-Administered Contraception

The 2019 World Health Organization (WHO) guideline on self-care interventions recommends making self-administered injectable contraception available to individuals of reproductive age.³ The WHO recommendation is based on evidence from the Depo Self-Administration Study, which included 401 patients randomized 1:1 to receive self-administered or clinic-administered DMPA-SC. This study concluded that self-administration improved continuation of contraception.⁴

The North Florida/South Georgia Veterans Health System (NFSGVHS) is the largest US Department of Veterans Affairs (VA) health care system, serving > 22,000 female veterans. All primary care practitioners (PCP) have been trained in women’s health (WH). 

The WH patient-aligned care team (PACT) clinical pharmacy practitioner (CPP) proposed using DMPA-SC for outpatient self-administration to increase access, improve patient satisfaction, and reduce burden on patients and nurses for administration appointments. The Pharmacy and Therapeutics Committee (P&T), WH Medical Director, and Chief of Gynecology approved the proposal. DMPA-SC was added to the ordering menu with order sets. The order set included instructions that outlined the 12-week dosing interval, instructions to contact the prescriber if the injection was > 2 weeks overdue (aligning with dosing recommendations for administration every 12 to 14 weeks), and an optional order for a home pregnancy test if necessary. These instructions were designed to ensure proper self-administration of the medication and timely follow-up care. 

The gynecology and PACT health care practitioners (HCPs), including physicians, pharmacists, nurses, and medical assistants, received DMPA-SC education, which consisted of a review of medication, ADRs, contraindications, and administration. An NFSGVHS procedure was developed to ensure patients received self-administration education. DMPA-SC prescriptions were mailed to patients with scheduled nursing appointments. The patient would then bring DMPA-SC to the nursing appointment where they received administration instruction and completed the first injection under nurse supervision to ensure appropriate technique. Patients were offered supplementary educational documents and a calendar to keep track of injection days. The patients were responsible for ordering refills and administering subsequent injections at home. Once all stakeholders received education and order sets were in place, prescribers and nurses could begin offering the option for initiation of self-administered DMPA-SC to patients. All conversions or new prescriptions were initiated by prescribers as a part of usual care.

Medication Use Evaluation

A medication use evaluation was conducted about 1 year after the rollout to assess use, adherence, and impact of DMPA-SC for patient-self administration as a new contraceptive option for NFSGVHS patients.

A retrospective chart review was conducted for patients dispensed DMPA-SC from June 1, 2022, to July 1, 2023. Baseline body mass index (BMI), recorded prior to initiation of DMPA-SC, was compared with the most recent BMI on record at the completion of the study to evaluate weight change. Nursing visit attendance for the first injection was also assessed. Adherence was evaluated by reviewing the date of the initial DMPA-SC prescription, the date of the patient's first nursing visit, and subsequent refill patterns. A 2-week margin of error was established to account for the flexibility within the recommended dosing interval and delays in postal service delivery.

Forty patients were initiated on DMPA-SC for patient self-administration. The mean age of patients was 37.2 years. All 40 patients were female. Twenty-two patients (55%) identified as Black, 17 (43%) as White, and 1 (3%) as Asian. The majority (90%) of patients were non-Hispanic. The mean baseline BMI was 30 and BMI after DMPA-SC initiation was 30.4.

Twenty-eight (70%) patients had a nursing appointment, adhering to the NFSGVHS protocol. Five patients (13%) discontinued use and switched to DMPA-IM administered by an HCP and 4 (10%) discontinued use following an ADR (hives, mood changes, bruising, and menometrorrhagia). Of the 31 patients who continued therapy, 25 (81%) were refilling appropriately (Table). 

Six patients with unidentified reasons for nonadherence were contacted to determine if there were unmet contraceptive needs. This subgroup included patients with an active prescription for DMPA-SC that did not meet refill expectations. Nonadherence was mostly due to forgetfulness, however 1 patient was unable to refill her DMPA-SC in a timely manner due to an outside hospital admission and another was unreachable. These conversations were documented in the electronic health record (EHR) and all patients requesting follow-up, reinitiation of therapy, or alternative regimens, the appropriate parties were notified to coordinate care.

Discussion

The uptake in DMPA-SC prescribing suggests prescribers and patients have embraced self-administration as an option for contraception. Most patients were appropriately scheduled for nursing appointments to reinforce education and ensure appropriate self-injection technique, as outlined in the NFSGVHS procedure.

The need to improve adherence to NFSGVHS procedure was identified because not all patients had scheduled nursing appointments. This is concerning because some patients may have started self-injecting DMPA-SC without proper education, which could lead to improper injection technique and diminished effectiveness. Nursing appointments ensure appropriate self-injection techniques and reinforce the importance of refilling every 12 weeks for proper effectiveness. Nonadherence to contraceptive therapy may result in unintended pregnancy, although no pregnancies were reported by patients in this study. Pharmacist involvement in DMPA-SC initiation and follow-up monitoring may help ensure adherence to local procedure for initiation and improve patient adherence. 

There is limited evidence comparing weight gain related to DMPA-SC vs DMPA-IM. However, in a small, 2-year, randomized study, weight changes were considered comparable for both cohorts with a mean increase of 3.5 kg in the DMPA-IM group vs 3.4 kg in the DMPA-SC group.⁵ While our analysis did not formally evaluate weight changes, BMI data were collected to evaluate for evidence of weight change. The duration of therapy varied per patient and may not have been long enough to see comparable weight changes. 

Strengths of this project include the use of the PACT multidisciplinary approach in primary care including physicians, pharmacists, and nurses. The NFSGVHS EHR is comprehensive, and data including appointments and pharmacy refill information was readily available for collection and evaluation. Limitations included inconsistent documentation in the patient’s EHR which made collection of some data difficult.

Cost Estimates

NFSGVHS had 231 patients prescribed DMPA-IM at the time of DMPA-SC rollout and 40 patients initiated DMPA-SC therapy in the first year. There are possible cost savings associated with the use of DMPA-SC compared to DMPA-IM. Although DMPA-IM costs about $120 annually and DMPA-SC costs about $252 annually, this does not account for indirect costs such as supplies, overhead cost, nursing visits, and patient travel.⁶ Additionally, allowing patients to self-administer the DMPA-SC injection at home provides nurses time to care for other patients.

Moving forward, the PACT and gynecology teams will receive instruction on the importance of adhering to NFSGVHS procedures to ensure new patients prescribed DMPA-SC receive education and present for nursing appointments to ensure appropriate self-injection.

DMPA has historically been administered in the clinic setting by an HCP; therefore, the prescriber was available to assess adherence to therapy based on patient’s attendance to scheduled clinic appointments. Some prescribers may feel apprehensive about shifting the onus of medication adherence to the patient when prescribing DMPA-SC. However, this model is comparable to any other prescription form of birth control, such as combined hormonal contraceptive pills, where the prescriber expects the patient to take the medication as prescribed and refill their prescriptions in a timely manner to avoid gaps in therapy. The findings of this project suggest the majority of patients who were prescribed self-administered DMPA-SC for contraception were adherent to therapy. The utility of self-administration of DMPA-SC for other labeled or off-label indications was not evaluated; however, it is possible that patients who are motivated to self-administer the medication (regardless of indication) would also demonstrate similar adherence rates.

Conclusions

The majority of patients who started DMPA-SC tolerated the medication well and continued to refill therapy within the recommended time period. Patient self-administration of DMPA-SC can enhance access by removing barriers to administration, increase patient autonomy and contraceptive continuation rates. Overall, the increase in DMPA-SC prescriptions suggests that patients and HCPs support the option for DMPA-SC self-administration at NFSGVHS.

Click here to view more from Federal Health Care Data Trends 2025.

Click here to view more from Federal Health Care Data Trends 2025.

Click here to view more from Federal Health Care Data Trends 2025.

TOPLINE: A national survey of 8996 females reveals comparable mammography screening rates between those who identify as veterans (57.9%) and nonveterans (55.2%).

METHODOLOGY:

• Researchers analyzed data from the 2019 National Health Interview Survey, a cross-sectional national survey tracking health information.

• Female respondents aged 40 to 74 years without history of breast cancer were included in the analysis.

• Analysis evaluated the association between screening and veteran status through logistic regression, adjusting for potential confounders.

• Survey procedures accounted for complex sampling design to obtain valid estimates for the civilian, noninstitutionalized US population.

TAKEAWAY:

• Analysis included 8996 female survey respondents, including 169 veterans (1.9%) and 320 (3.2%) reported having military health coverage.

• Mammography screening rates within the last year were comparable between veterans (57.9%) and nonveterans (55.2%).

• Veteran status showed no significant association with differences in mammography screening percentages (P = .96).

• Among insured participants, military health insurance demonstrated no significant association with mammography screening percentages (P = .13).

• The authors suggest that radiology practices should design proactive outreach strategies to address the needs of the growing number of female veterans who may face increased breast cancer risk due to military environmental exposures.

IN PRACTICE: “Although the results from our study demonstrate comparable mammography screening percentages, veterans may face additional risk factors for breast cancer due to occupational,” the authors argue.

SOURCE: This summary is based on a preprint published online in the Journal of the American College of Radiology: Milton A, Miles R, Gettle LM, Van Geertruyden P, Narayan AK. Utilization of Mammography Screening in Female Veterans: Cross-Sectional Survey Results from the National Health Interview Survey. J Am Coll Radiol. Published online April 24, 2025. doi:10.1016/j.jacr.2025.04.017

LIMITATIONS: The study relied on self-reported adherence data, which could overestimate screening percentages. Data collection occurred prior to updated United States Preventive Services Task Force guidelines recommending routine mammography screening for women starting at age 40 years every 2 years. The relatively small number of female veteran respondents limited the precision of population estimates. Additionally, the data were collected before the COVID-19 pandemic, which has been associated with reduced mammographic screening, particularly in medically underserved populations.

DISCLOSURES: Anand Narayan disclosed receiving financial support from Susan G. Komen Breast Cancer Foundation and National Academy of Medicine. The study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The remaining authors reported no potential conflicts of interest. Additional disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

TOPLINE: A national survey of 8996 females reveals comparable mammography screening rates between those who identify as veterans (57.9%) and nonveterans (55.2%).

METHODOLOGY:

• Researchers analyzed data from the 2019 National Health Interview Survey, a cross-sectional national survey tracking health information.

• Female respondents aged 40 to 74 years without history of breast cancer were included in the analysis.

• Analysis evaluated the association between screening and veteran status through logistic regression, adjusting for potential confounders.

• Survey procedures accounted for complex sampling design to obtain valid estimates for the civilian, noninstitutionalized US population.

TAKEAWAY:

• Analysis included 8996 female survey respondents, including 169 veterans (1.9%) and 320 (3.2%) reported having military health coverage.

• Mammography screening rates within the last year were comparable between veterans (57.9%) and nonveterans (55.2%).

• Veteran status showed no significant association with differences in mammography screening percentages (P = .96).

• Among insured participants, military health insurance demonstrated no significant association with mammography screening percentages (P = .13).

• The authors suggest that radiology practices should design proactive outreach strategies to address the needs of the growing number of female veterans who may face increased breast cancer risk due to military environmental exposures.

IN PRACTICE: “Although the results from our study demonstrate comparable mammography screening percentages, veterans may face additional risk factors for breast cancer due to occupational,” the authors argue.

SOURCE: This summary is based on a preprint published online in the Journal of the American College of Radiology: Milton A, Miles R, Gettle LM, Van Geertruyden P, Narayan AK. Utilization of Mammography Screening in Female Veterans: Cross-Sectional Survey Results from the National Health Interview Survey. J Am Coll Radiol. Published online April 24, 2025. doi:10.1016/j.jacr.2025.04.017

LIMITATIONS: The study relied on self-reported adherence data, which could overestimate screening percentages. Data collection occurred prior to updated United States Preventive Services Task Force guidelines recommending routine mammography screening for women starting at age 40 years every 2 years. The relatively small number of female veteran respondents limited the precision of population estimates. Additionally, the data were collected before the COVID-19 pandemic, which has been associated with reduced mammographic screening, particularly in medically underserved populations.

DISCLOSURES: Anand Narayan disclosed receiving financial support from Susan G. Komen Breast Cancer Foundation and National Academy of Medicine. The study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The remaining authors reported no potential conflicts of interest. Additional disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

TOPLINE: A national survey of 8996 females reveals comparable mammography screening rates between those who identify as veterans (57.9%) and nonveterans (55.2%).

METHODOLOGY:

• Researchers analyzed data from the 2019 National Health Interview Survey, a cross-sectional national survey tracking health information.

• Female respondents aged 40 to 74 years without history of breast cancer were included in the analysis.

• Analysis evaluated the association between screening and veteran status through logistic regression, adjusting for potential confounders.

• Survey procedures accounted for complex sampling design to obtain valid estimates for the civilian, noninstitutionalized US population.

TAKEAWAY:

• Analysis included 8996 female survey respondents, including 169 veterans (1.9%) and 320 (3.2%) reported having military health coverage.

• Mammography screening rates within the last year were comparable between veterans (57.9%) and nonveterans (55.2%).

• Veteran status showed no significant association with differences in mammography screening percentages (P = .96).

• Among insured participants, military health insurance demonstrated no significant association with mammography screening percentages (P = .13).

• The authors suggest that radiology practices should design proactive outreach strategies to address the needs of the growing number of female veterans who may face increased breast cancer risk due to military environmental exposures.

IN PRACTICE: “Although the results from our study demonstrate comparable mammography screening percentages, veterans may face additional risk factors for breast cancer due to occupational,” the authors argue.

SOURCE: This summary is based on a preprint published online in the Journal of the American College of Radiology: Milton A, Miles R, Gettle LM, Van Geertruyden P, Narayan AK. Utilization of Mammography Screening in Female Veterans: Cross-Sectional Survey Results from the National Health Interview Survey. J Am Coll Radiol. Published online April 24, 2025. doi:10.1016/j.jacr.2025.04.017

LIMITATIONS: The study relied on self-reported adherence data, which could overestimate screening percentages. Data collection occurred prior to updated United States Preventive Services Task Force guidelines recommending routine mammography screening for women starting at age 40 years every 2 years. The relatively small number of female veteran respondents limited the precision of population estimates. Additionally, the data were collected before the COVID-19 pandemic, which has been associated with reduced mammographic screening, particularly in medically underserved populations.

DISCLOSURES: Anand Narayan disclosed receiving financial support from Susan G. Komen Breast Cancer Foundation and National Academy of Medicine. The study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The remaining authors reported no potential conflicts of interest. Additional disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

The number of sexual assaults in the US military dropped for the first time in a decade, according an annual report from the Pentagon, while benefits claims for assault survivors are on the rise. 

     Records show that 29,000 active-duty members reported being sexually assaulted in 2023, or 7000 fewer than in 2021. A confidential survey also found the number of service members who experienced some type of unwanted sexual contact dropped nearly 20%, leaving the Pentagon “cautiously optimistic“ its investments in preventing sexual assault and building a healthy climate are having an impact.

     Despite these investments, issues persist. An Army anesthesiologist recently pleaded guilty to 41 charges of sexual misconduct involving 21 victims at Madigan Army Medical Center at Joint Base Lewis-McChord in Washington. The alleged incidents occurred between 2019 and 2022 and involved the doctor unnecessarily focusing on the genital area of patients during what he described as routine examinations. Maj. Michael Stockin faces nearly 14 years in prison, should the judge accept the plea agreement Stockin and his attorneys made with government prosecutors.

     Additionally, a report from the Watson Institute of International and Public Affairs at Brown University indicated that 24% of active-duty women and 1.9% of active-duty men experienced sexual assault from 2001 to 2021.During post-9/11 wars, “the prioritization of force readiness above all else allowed the problem of sexual assault to fester, papering over internal violence and gender inequalities within military institutions,” the report said. There was also a slight uptick in reports of military sexual assaults in 2020, when troops were largely on lockdown as a result of the COVID-19 pandemic.

     Efforts to address sexual assault in the military have increased in the past 10 years to the tune of 10 Department of Defense Inspector General engagements, 60 Government Accountability Office recommendations, > 200 government panel and task force recommendations, > 150 Congressional provisions, and > 50 Secretary of Defense initiatives. Additionally, the 2022 National Defense Authorization gave authority in sexual assault cases to independent prosecutors rather than commanders. Other reforms have included incorporating trauma-informed practices in the claims process.

     Meanwhile, the US Department of Veterans Affairs (VA) has also been attempting to convince more sexual assault survivors to file claims for benefits. Assistant Deputy Under Secretary for Field Operations Kenesha Britton said in December that the VA has held 3500 events in the past 14 months focused on benefits for victims of military sexual assault and harassment. It appears to be working, as the VA received 57,400 claims for military sexual trauma in fiscal year 2024 (an 18% increase from 2023), and approved > 63% of them, compared to 40% more than a decade ago. Prior to Oct. 1, VA staffers processed > 11,000 cases in a single day twice. Since that date, they have processed that amount on 9 separate occasions.

     “We recognize the remarkable courage it takes for survivors of military sexual trauma to seek the benefits and support they’ve earned,” Britton said. “Our mission is driven by a commitment to ensure survivors are met with care, dignity and sensitivity throughout the claims process.”

     The increase in trust is a byproduct of the outreach campaigns, VA Under Secretary for Benefits Josh Jacobs said: “[M]ore veterans are coming in to apply for benefits and I think that has to do with building trust because we are actively trying to reach veterans telling them we want to connect them with their earned benefits.”

The number of sexual assaults in the US military dropped for the first time in a decade, according an annual report from the Pentagon, while benefits claims for assault survivors are on the rise. 

     Records show that 29,000 active-duty members reported being sexually assaulted in 2023, or 7000 fewer than in 2021. A confidential survey also found the number of service members who experienced some type of unwanted sexual contact dropped nearly 20%, leaving the Pentagon “cautiously optimistic“ its investments in preventing sexual assault and building a healthy climate are having an impact.

     Despite these investments, issues persist. An Army anesthesiologist recently pleaded guilty to 41 charges of sexual misconduct involving 21 victims at Madigan Army Medical Center at Joint Base Lewis-McChord in Washington. The alleged incidents occurred between 2019 and 2022 and involved the doctor unnecessarily focusing on the genital area of patients during what he described as routine examinations. Maj. Michael Stockin faces nearly 14 years in prison, should the judge accept the plea agreement Stockin and his attorneys made with government prosecutors.

     Additionally, a report from the Watson Institute of International and Public Affairs at Brown University indicated that 24% of active-duty women and 1.9% of active-duty men experienced sexual assault from 2001 to 2021.During post-9/11 wars, “the prioritization of force readiness above all else allowed the problem of sexual assault to fester, papering over internal violence and gender inequalities within military institutions,” the report said. There was also a slight uptick in reports of military sexual assaults in 2020, when troops were largely on lockdown as a result of the COVID-19 pandemic.

     Efforts to address sexual assault in the military have increased in the past 10 years to the tune of 10 Department of Defense Inspector General engagements, 60 Government Accountability Office recommendations, > 200 government panel and task force recommendations, > 150 Congressional provisions, and > 50 Secretary of Defense initiatives. Additionally, the 2022 National Defense Authorization gave authority in sexual assault cases to independent prosecutors rather than commanders. Other reforms have included incorporating trauma-informed practices in the claims process.

     Meanwhile, the US Department of Veterans Affairs (VA) has also been attempting to convince more sexual assault survivors to file claims for benefits. Assistant Deputy Under Secretary for Field Operations Kenesha Britton said in December that the VA has held 3500 events in the past 14 months focused on benefits for victims of military sexual assault and harassment. It appears to be working, as the VA received 57,400 claims for military sexual trauma in fiscal year 2024 (an 18% increase from 2023), and approved > 63% of them, compared to 40% more than a decade ago. Prior to Oct. 1, VA staffers processed > 11,000 cases in a single day twice. Since that date, they have processed that amount on 9 separate occasions.

     “We recognize the remarkable courage it takes for survivors of military sexual trauma to seek the benefits and support they’ve earned,” Britton said. “Our mission is driven by a commitment to ensure survivors are met with care, dignity and sensitivity throughout the claims process.”

     The increase in trust is a byproduct of the outreach campaigns, VA Under Secretary for Benefits Josh Jacobs said: “[M]ore veterans are coming in to apply for benefits and I think that has to do with building trust because we are actively trying to reach veterans telling them we want to connect them with their earned benefits.”

The number of sexual assaults in the US military dropped for the first time in a decade, according an annual report from the Pentagon, while benefits claims for assault survivors are on the rise. 

     Records show that 29,000 active-duty members reported being sexually assaulted in 2023, or 7000 fewer than in 2021. A confidential survey also found the number of service members who experienced some type of unwanted sexual contact dropped nearly 20%, leaving the Pentagon “cautiously optimistic“ its investments in preventing sexual assault and building a healthy climate are having an impact.

     Despite these investments, issues persist. An Army anesthesiologist recently pleaded guilty to 41 charges of sexual misconduct involving 21 victims at Madigan Army Medical Center at Joint Base Lewis-McChord in Washington. The alleged incidents occurred between 2019 and 2022 and involved the doctor unnecessarily focusing on the genital area of patients during what he described as routine examinations. Maj. Michael Stockin faces nearly 14 years in prison, should the judge accept the plea agreement Stockin and his attorneys made with government prosecutors.

     Additionally, a report from the Watson Institute of International and Public Affairs at Brown University indicated that 24% of active-duty women and 1.9% of active-duty men experienced sexual assault from 2001 to 2021.During post-9/11 wars, “the prioritization of force readiness above all else allowed the problem of sexual assault to fester, papering over internal violence and gender inequalities within military institutions,” the report said. There was also a slight uptick in reports of military sexual assaults in 2020, when troops were largely on lockdown as a result of the COVID-19 pandemic.

     Efforts to address sexual assault in the military have increased in the past 10 years to the tune of 10 Department of Defense Inspector General engagements, 60 Government Accountability Office recommendations, > 200 government panel and task force recommendations, > 150 Congressional provisions, and > 50 Secretary of Defense initiatives. Additionally, the 2022 National Defense Authorization gave authority in sexual assault cases to independent prosecutors rather than commanders. Other reforms have included incorporating trauma-informed practices in the claims process.

     Meanwhile, the US Department of Veterans Affairs (VA) has also been attempting to convince more sexual assault survivors to file claims for benefits. Assistant Deputy Under Secretary for Field Operations Kenesha Britton said in December that the VA has held 3500 events in the past 14 months focused on benefits for victims of military sexual assault and harassment. It appears to be working, as the VA received 57,400 claims for military sexual trauma in fiscal year 2024 (an 18% increase from 2023), and approved > 63% of them, compared to 40% more than a decade ago. Prior to Oct. 1, VA staffers processed > 11,000 cases in a single day twice. Since that date, they have processed that amount on 9 separate occasions.

     “We recognize the remarkable courage it takes for survivors of military sexual trauma to seek the benefits and support they’ve earned,” Britton said. “Our mission is driven by a commitment to ensure survivors are met with care, dignity and sensitivity throughout the claims process.”

     The increase in trust is a byproduct of the outreach campaigns, VA Under Secretary for Benefits Josh Jacobs said: “[M]ore veterans are coming in to apply for benefits and I think that has to do with building trust because we are actively trying to reach veterans telling them we want to connect them with their earned benefits.”

New data link higher county-level radon exposure to gestational diabetes (GD) in women who haven’t previously given birth, emphasizing the need to consider environmental risks in maternal and fetal healthcare.

Yijia Zhang, PhD, with the Department of Obstetrics and Gynecology, Vagelos College of Physicians and Surgeons at Columbia University Irving Medical Center in New York, and colleagues found in a study of 9107 nulliparous pregnant women that those living in US counties with higher radon levels (2 picocuries [pCi]/L) had higher odds of developing GD than those in counties with lower (< 1 pCi/L) radon levels (odds ratio [OR], 1.37; 95% CI, 1.02-1.84.) The researchers used three radon categories, and the middle level was 1 to < 2 pCi/L.

Findings were published online on January 10 in JAMA Network Open. The researchers used data from The Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b), a multicenter, prospective cohort study that examines factors associated with pregnancy-related outcomes.

“To our knowledge, this is the first study to examine the association between radon exposure and the risk of GD,” the authors wrote.

The researchers also found higher odds of GD in women who had ever smoked who lived in counties with a higher (2 pCi/L) radon level (OR, 2.09; 95% CI, 1.41-3.11) and women living in counties with both higher radon and fine particulate matter air pollutants (PM2.5) levels (OR, 1.93; 95% CI, 1.31-2.83), though no statistically significant interactions were observed. 

 

GD Affects 10% of Pregnancies

GD affects about 10% of pregnancies every year in the United States, according to the Centers for Disease Control and Prevention, and can affect women and offspring long term as it raises mothers’ risk of type 2 diabetes and cardiovascular disease and raises the risk for childhood obesity. Radon exposure’s link with lung cancer risk has been well established, but its link to other health risks is uncertain, the authors note.

The authors said their findings are hypothesis-generating and said, “It is vital to conduct studies that incorporate individual-level indoor radon exposure data,” to get closer to understanding the underlying mechanisms.

 

Individual-Level Exposure Measures Needed

They note that the average radon level in a county might not reflect an individual’s exposure and individual-level residential factors involved with radon exposure, such as household mitigation, and whether a dwelling has a basement, for instance, “are crucial for enhancing the precision of exposure assessment.”

In an invited commentary, Alberto Ruano-Ravina, PhD, and Lucía Martín-Gisbert, MSc, both with the Department of Preventive Medicine and Public Health at the University of Santiago de Compostela in Galicia, Spain, also urged that individual-level studies be conducted to further investigate radon’s link to health risks, noting that “[r]adon is possibly the most prevalent indoor carcinogen to which human beings are exposed.”

“There is no reason for not having these studies once we have some evidence of an association from ecological studies,” they wrote. They point out that reliable radon assessments are easy and inexpensive.

“The potential association of radon exposure with gestational diabetes or any other disease should be better analyzed using exclusively radon-prone areas. An observance of a dose-response effect may be indicative of a causal relationship, and it could be easily evidenced in radon-prone areas should such a relationship exist,” the commenters wrote.

Such areas have low, medium, high, and extremely high concentration levels, the commenters wrote. Zhang’s team, they point out, had to use only three exposure levels because the number of residents in high-exposure areas (exceeding 3 pCi/L) was too small.

“It is time now to move forward and really understand the full implications of radon exposure for health,” they concluded.

One coauthor reported serving on the board of directors for Merck for Mothers and as a board member for March for Moms outside the submitted work. One coauthor reported grants from the National Heart, Lung, and Blood Institute and the National Institutes of Health (NIH) during the conduct of the study. Four coauthors reported grants from the NIH during the conduct of the study. One coauthor reported grants from the NIH during the conduct of the study and being a cofounder of Naima Health and receiving personal fees from Organon outside the submitted work. Both commenters reported no relevant financial disclosures.

A version of this article appeared on Medscape.com.

New data link higher county-level radon exposure to gestational diabetes (GD) in women who haven’t previously given birth, emphasizing the need to consider environmental risks in maternal and fetal healthcare.

Yijia Zhang, PhD, with the Department of Obstetrics and Gynecology, Vagelos College of Physicians and Surgeons at Columbia University Irving Medical Center in New York, and colleagues found in a study of 9107 nulliparous pregnant women that those living in US counties with higher radon levels (2 picocuries [pCi]/L) had higher odds of developing GD than those in counties with lower (< 1 pCi/L) radon levels (odds ratio [OR], 1.37; 95% CI, 1.02-1.84.) The researchers used three radon categories, and the middle level was 1 to < 2 pCi/L.

Findings were published online on January 10 in JAMA Network Open. The researchers used data from The Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b), a multicenter, prospective cohort study that examines factors associated with pregnancy-related outcomes.

“To our knowledge, this is the first study to examine the association between radon exposure and the risk of GD,” the authors wrote.

The researchers also found higher odds of GD in women who had ever smoked who lived in counties with a higher (2 pCi/L) radon level (OR, 2.09; 95% CI, 1.41-3.11) and women living in counties with both higher radon and fine particulate matter air pollutants (PM2.5) levels (OR, 1.93; 95% CI, 1.31-2.83), though no statistically significant interactions were observed. 

 

GD Affects 10% of Pregnancies

GD affects about 10% of pregnancies every year in the United States, according to the Centers for Disease Control and Prevention, and can affect women and offspring long term as it raises mothers’ risk of type 2 diabetes and cardiovascular disease and raises the risk for childhood obesity. Radon exposure’s link with lung cancer risk has been well established, but its link to other health risks is uncertain, the authors note.

The authors said their findings are hypothesis-generating and said, “It is vital to conduct studies that incorporate individual-level indoor radon exposure data,” to get closer to understanding the underlying mechanisms.

 

Individual-Level Exposure Measures Needed

They note that the average radon level in a county might not reflect an individual’s exposure and individual-level residential factors involved with radon exposure, such as household mitigation, and whether a dwelling has a basement, for instance, “are crucial for enhancing the precision of exposure assessment.”

In an invited commentary, Alberto Ruano-Ravina, PhD, and Lucía Martín-Gisbert, MSc, both with the Department of Preventive Medicine and Public Health at the University of Santiago de Compostela in Galicia, Spain, also urged that individual-level studies be conducted to further investigate radon’s link to health risks, noting that “[r]adon is possibly the most prevalent indoor carcinogen to which human beings are exposed.”

“There is no reason for not having these studies once we have some evidence of an association from ecological studies,” they wrote. They point out that reliable radon assessments are easy and inexpensive.

“The potential association of radon exposure with gestational diabetes or any other disease should be better analyzed using exclusively radon-prone areas. An observance of a dose-response effect may be indicative of a causal relationship, and it could be easily evidenced in radon-prone areas should such a relationship exist,” the commenters wrote.

Such areas have low, medium, high, and extremely high concentration levels, the commenters wrote. Zhang’s team, they point out, had to use only three exposure levels because the number of residents in high-exposure areas (exceeding 3 pCi/L) was too small.

“It is time now to move forward and really understand the full implications of radon exposure for health,” they concluded.

One coauthor reported serving on the board of directors for Merck for Mothers and as a board member for March for Moms outside the submitted work. One coauthor reported grants from the National Heart, Lung, and Blood Institute and the National Institutes of Health (NIH) during the conduct of the study. Four coauthors reported grants from the NIH during the conduct of the study. One coauthor reported grants from the NIH during the conduct of the study and being a cofounder of Naima Health and receiving personal fees from Organon outside the submitted work. Both commenters reported no relevant financial disclosures.

A version of this article appeared on Medscape.com.

New data link higher county-level radon exposure to gestational diabetes (GD) in women who haven’t previously given birth, emphasizing the need to consider environmental risks in maternal and fetal healthcare.

Yijia Zhang, PhD, with the Department of Obstetrics and Gynecology, Vagelos College of Physicians and Surgeons at Columbia University Irving Medical Center in New York, and colleagues found in a study of 9107 nulliparous pregnant women that those living in US counties with higher radon levels (2 picocuries [pCi]/L) had higher odds of developing GD than those in counties with lower (< 1 pCi/L) radon levels (odds ratio [OR], 1.37; 95% CI, 1.02-1.84.) The researchers used three radon categories, and the middle level was 1 to < 2 pCi/L.

Findings were published online on January 10 in JAMA Network Open. The researchers used data from The Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b), a multicenter, prospective cohort study that examines factors associated with pregnancy-related outcomes.

“To our knowledge, this is the first study to examine the association between radon exposure and the risk of GD,” the authors wrote.

The researchers also found higher odds of GD in women who had ever smoked who lived in counties with a higher (2 pCi/L) radon level (OR, 2.09; 95% CI, 1.41-3.11) and women living in counties with both higher radon and fine particulate matter air pollutants (PM2.5) levels (OR, 1.93; 95% CI, 1.31-2.83), though no statistically significant interactions were observed. 

 

GD Affects 10% of Pregnancies

GD affects about 10% of pregnancies every year in the United States, according to the Centers for Disease Control and Prevention, and can affect women and offspring long term as it raises mothers’ risk of type 2 diabetes and cardiovascular disease and raises the risk for childhood obesity. Radon exposure’s link with lung cancer risk has been well established, but its link to other health risks is uncertain, the authors note.

The authors said their findings are hypothesis-generating and said, “It is vital to conduct studies that incorporate individual-level indoor radon exposure data,” to get closer to understanding the underlying mechanisms.

 

Individual-Level Exposure Measures Needed

They note that the average radon level in a county might not reflect an individual’s exposure and individual-level residential factors involved with radon exposure, such as household mitigation, and whether a dwelling has a basement, for instance, “are crucial for enhancing the precision of exposure assessment.”

In an invited commentary, Alberto Ruano-Ravina, PhD, and Lucía Martín-Gisbert, MSc, both with the Department of Preventive Medicine and Public Health at the University of Santiago de Compostela in Galicia, Spain, also urged that individual-level studies be conducted to further investigate radon’s link to health risks, noting that “[r]adon is possibly the most prevalent indoor carcinogen to which human beings are exposed.”

“There is no reason for not having these studies once we have some evidence of an association from ecological studies,” they wrote. They point out that reliable radon assessments are easy and inexpensive.

“The potential association of radon exposure with gestational diabetes or any other disease should be better analyzed using exclusively radon-prone areas. An observance of a dose-response effect may be indicative of a causal relationship, and it could be easily evidenced in radon-prone areas should such a relationship exist,” the commenters wrote.

Such areas have low, medium, high, and extremely high concentration levels, the commenters wrote. Zhang’s team, they point out, had to use only three exposure levels because the number of residents in high-exposure areas (exceeding 3 pCi/L) was too small.

“It is time now to move forward and really understand the full implications of radon exposure for health,” they concluded.

One coauthor reported serving on the board of directors for Merck for Mothers and as a board member for March for Moms outside the submitted work. One coauthor reported grants from the National Heart, Lung, and Blood Institute and the National Institutes of Health (NIH) during the conduct of the study. Four coauthors reported grants from the NIH during the conduct of the study. One coauthor reported grants from the NIH during the conduct of the study and being a cofounder of Naima Health and receiving personal fees from Organon outside the submitted work. Both commenters reported no relevant financial disclosures.

A version of this article appeared on Medscape.com.