Neglected tropical diseases (NTDs) are a group of 20 diseases that typically are chronic and cause long-term disability, which negatively impacts work productivity, child survival, and school performance and attendance with adverse effect on future earnings.¹ Data from the 2013 Global Burden of Disease study revealed that half of the world’s NTDs occur in poor populations living in wealthy countries.² Neglected tropical diseases with skin manifestations include parasitic infections (eg, American trypanosomiasis, African trypanosomiasis, dracunculiasis, echinococcosis, foodborne trematodiases, leishmaniasis, lymphatic filariasis, onchocerciasis, scabies and other ectoparasites, schistosomiasis, soil-transmitted helminths, taeniasis/cysticercosis), bacterial infections (eg, Buruli ulcer, leprosy, yaws), fungal infections (eg, mycetoma, chromoblastomycosis, deep mycoses), and viral infections (eg, dengue, chikungunya). Rabies and snakebite envenomization involve the skin through inoculation. Within the larger group of NTDs, the World Health Organization has identified “skin NTDs” as a subgroup of NTDs that present primarily with changes in the skin.³ In the absence of early diagnosis and treatment of these diseases, chronic and lifelong disfigurement, disability, stigma, and socioeconomic losses ensue.

The Department of Health of the Government of Western Australia stated:

Stigma is a mark of disgrace that sets a person apart from others. When a person is labeled by their illness they are no longer seen as an individual but as part of a stereotyped group. Negative attitudes and beliefs toward this group create prejudice which leads to negative actions and discrimination.⁴

Stigma associated with skin NTDs exemplifies how skin diseases can have enduring impact on individuals.⁵ For example, scarring from inactive cutaneous leishmaniasis carries heavy psychosocial burden. Young women reported that facial scarring from cutaneous leishmaniasis led to marriage rejections.⁶ Some even reported extreme suicidal ideations.⁷ Recently, major depressive disorder associated with scarring from inactive cutaneous leishmaniasis has been recognized as a notable contributor to disease burden from cutaneous leishmaniasis.⁸

Lymphatic filariasis is a major cause of leg and scrotal lymphedema worldwide. Even when the condition is treated, lymphedema often persists due to chronic irreversible lymphatic damage. A systematic review of 18 stigma studies in lymphatic filariasis found common themes related to the deleterious consequences of stigma on social relationships; work and education opportunities; health outcomes from reduced treatment-seeking behavior; and mental health, including anxiety, depression, and suicidal tendencies.⁹ In one subdistrict in India, implementation of a community-based lymphedema management program that consisted of teaching hygiene and limb care for more than 20,000 lymphedema patients and performing community outreach activities (eg, street plays, radio programs, informational brochures) to teach people about lymphatic filariasis and lymphedema care was associated with community members being accepting of patients and an improvement in their understanding of disease etiology.¹⁰

Skin involvement from onchocerciasis infection (onchocercal skin disease) is another condition associated with notable stigma.⁹ Through the African Programme for Onchocerciasis Control, annual mass drug administration of ivermectin in onchocerciasis-endemic communities has reduced the rate of onchocercal skin disease in these communities. In looking at perception of stigma in onchocercal skin diseases before community-directed ivermectin therapy and 7 to 10 years after, avoidance of people with onchocercal skin disease decreased from 32.7% to 4.3%. There also was an improvement in relationships between healthy people and those with onchocercal skin disease.¹¹

One of the most stigmatizing conditions is leprosy, often referred to as Hansen disease to give credit to the person who discovered that leprosy was caused by Mycobacterium leprae and not from sin, being cursed, or genetic inheritance. Even with this knowledge, stigma persists that can lead to family abandonment and social isolation, which further impacts afflicted individuals’ willingness to seek care, thus leading to disease progression. More recently, there has been research looking at interventions to reduce the stigma that individuals afflicted with leprosy face. In a study from Indonesia where individuals with leprosy were randomized to counseling, socioeconomic development, or contact between community members and affected people, all interventions were associated with a reduction in stigma.¹² A rights-based counseling module integrated individual, family, and group forms of counseling and consisted of 5 sessions that focused on medical knowledge of leprosy and rights of individuals with leprosy, along with elements of cognitive behavioral therapy. Socioeconomic development involved opportunities for business training, creation of community groups through which microfinance services were administered, and other assistance to improve livelihood. Informed by evidence from the field of human immunodeficiency virus and mental health that contact with affected people reduces negative attitudes and behavior among those participating in the intervention, contact between community members and persons affected by leprosy occurred through dialogue and interaction at events held in schools, village halls, and mosques. Furthermore, early detection and subsequent early treatment of leprosy can prevent individuals from the disability and disfigurement that we commonly associate with the disease, which often is not the message that afflicted individuals and their communities are hearing and seeing. Targeting media portrayal, the New Face of Leprosy project seeks to shift the messaging around leprosy to one of hope and positivity by promoting positive images—not presenting severe disfigurement as the representative image of leprosy—and strong messaging that the disease is curable.¹³

Although steps are being taken to address the psychosocial burden of skin NTDs, there is still much work to be done. From the public health lens that largely governs the policies and approaches toward addressing NTDs, the focus often is on interrupting and eliminating disease transmission. Morbidity management, including reduction in stigma and functional impairment, is not always the priority. It is in this space that dermatologists are uniquely positioned to advocate for management approaches that address the morbidity associated with skin NTDs. We have an intimate understanding of how impactful skin diseases can be, even if they are not commonly fatal. Globally, skin diseases are the fourth leading cause of nonfatal disease burden,¹⁴ yet dermatology lacks effective evidence-based interventions for reducing stigma in our patients with visible chronic diseases.¹⁵

Every day, we see firsthand how skin diseases affect not only our patients but also their families, friends, and caregivers. Although we may not see skin NTDs on a regular basis in our clinics, we can understand almost intuitively how devastating skin NTDs could be on individuals, families, and communities. For patients with skin NTDs, receiving medical therapy is only one component of treatment. In addition to optimizing early diagnosis and treatment, interventions taken to educate families and communities affected by skin NTDs are vitally important. Stigma reduction is possible, as we have seen from the aforementioned interventions used in communities with lymphatic filariasis, onchocerciasis, and leprosy. We call upon our fellow dermatologists to take interest in creating, evaluating, and promoting interventions that address stigma in skin NTDs; it is critical in achieving and maintaining health and well-being for our patients.

Neglected tropical diseases (NTDs) are a group of 20 diseases that typically are chronic and cause long-term disability, which negatively impacts work productivity, child survival, and school performance and attendance with adverse effect on future earnings.¹ Data from the 2013 Global Burden of Disease study revealed that half of the world’s NTDs occur in poor populations living in wealthy countries.² Neglected tropical diseases with skin manifestations include parasitic infections (eg, American trypanosomiasis, African trypanosomiasis, dracunculiasis, echinococcosis, foodborne trematodiases, leishmaniasis, lymphatic filariasis, onchocerciasis, scabies and other ectoparasites, schistosomiasis, soil-transmitted helminths, taeniasis/cysticercosis), bacterial infections (eg, Buruli ulcer, leprosy, yaws), fungal infections (eg, mycetoma, chromoblastomycosis, deep mycoses), and viral infections (eg, dengue, chikungunya). Rabies and snakebite envenomization involve the skin through inoculation. Within the larger group of NTDs, the World Health Organization has identified “skin NTDs” as a subgroup of NTDs that present primarily with changes in the skin.³ In the absence of early diagnosis and treatment of these diseases, chronic and lifelong disfigurement, disability, stigma, and socioeconomic losses ensue.

The Department of Health of the Government of Western Australia stated:

Stigma is a mark of disgrace that sets a person apart from others. When a person is labeled by their illness they are no longer seen as an individual but as part of a stereotyped group. Negative attitudes and beliefs toward this group create prejudice which leads to negative actions and discrimination.⁴

Stigma associated with skin NTDs exemplifies how skin diseases can have enduring impact on individuals.⁵ For example, scarring from inactive cutaneous leishmaniasis carries heavy psychosocial burden. Young women reported that facial scarring from cutaneous leishmaniasis led to marriage rejections.⁶ Some even reported extreme suicidal ideations.⁷ Recently, major depressive disorder associated with scarring from inactive cutaneous leishmaniasis has been recognized as a notable contributor to disease burden from cutaneous leishmaniasis.⁸

Lymphatic filariasis is a major cause of leg and scrotal lymphedema worldwide. Even when the condition is treated, lymphedema often persists due to chronic irreversible lymphatic damage. A systematic review of 18 stigma studies in lymphatic filariasis found common themes related to the deleterious consequences of stigma on social relationships; work and education opportunities; health outcomes from reduced treatment-seeking behavior; and mental health, including anxiety, depression, and suicidal tendencies.⁹ In one subdistrict in India, implementation of a community-based lymphedema management program that consisted of teaching hygiene and limb care for more than 20,000 lymphedema patients and performing community outreach activities (eg, street plays, radio programs, informational brochures) to teach people about lymphatic filariasis and lymphedema care was associated with community members being accepting of patients and an improvement in their understanding of disease etiology.¹⁰

Skin involvement from onchocerciasis infection (onchocercal skin disease) is another condition associated with notable stigma.⁹ Through the African Programme for Onchocerciasis Control, annual mass drug administration of ivermectin in onchocerciasis-endemic communities has reduced the rate of onchocercal skin disease in these communities. In looking at perception of stigma in onchocercal skin diseases before community-directed ivermectin therapy and 7 to 10 years after, avoidance of people with onchocercal skin disease decreased from 32.7% to 4.3%. There also was an improvement in relationships between healthy people and those with onchocercal skin disease.¹¹

One of the most stigmatizing conditions is leprosy, often referred to as Hansen disease to give credit to the person who discovered that leprosy was caused by Mycobacterium leprae and not from sin, being cursed, or genetic inheritance. Even with this knowledge, stigma persists that can lead to family abandonment and social isolation, which further impacts afflicted individuals’ willingness to seek care, thus leading to disease progression. More recently, there has been research looking at interventions to reduce the stigma that individuals afflicted with leprosy face. In a study from Indonesia where individuals with leprosy were randomized to counseling, socioeconomic development, or contact between community members and affected people, all interventions were associated with a reduction in stigma.¹² A rights-based counseling module integrated individual, family, and group forms of counseling and consisted of 5 sessions that focused on medical knowledge of leprosy and rights of individuals with leprosy, along with elements of cognitive behavioral therapy. Socioeconomic development involved opportunities for business training, creation of community groups through which microfinance services were administered, and other assistance to improve livelihood. Informed by evidence from the field of human immunodeficiency virus and mental health that contact with affected people reduces negative attitudes and behavior among those participating in the intervention, contact between community members and persons affected by leprosy occurred through dialogue and interaction at events held in schools, village halls, and mosques. Furthermore, early detection and subsequent early treatment of leprosy can prevent individuals from the disability and disfigurement that we commonly associate with the disease, which often is not the message that afflicted individuals and their communities are hearing and seeing. Targeting media portrayal, the New Face of Leprosy project seeks to shift the messaging around leprosy to one of hope and positivity by promoting positive images—not presenting severe disfigurement as the representative image of leprosy—and strong messaging that the disease is curable.¹³

Although steps are being taken to address the psychosocial burden of skin NTDs, there is still much work to be done. From the public health lens that largely governs the policies and approaches toward addressing NTDs, the focus often is on interrupting and eliminating disease transmission. Morbidity management, including reduction in stigma and functional impairment, is not always the priority. It is in this space that dermatologists are uniquely positioned to advocate for management approaches that address the morbidity associated with skin NTDs. We have an intimate understanding of how impactful skin diseases can be, even if they are not commonly fatal. Globally, skin diseases are the fourth leading cause of nonfatal disease burden,¹⁴ yet dermatology lacks effective evidence-based interventions for reducing stigma in our patients with visible chronic diseases.¹⁵

Every day, we see firsthand how skin diseases affect not only our patients but also their families, friends, and caregivers. Although we may not see skin NTDs on a regular basis in our clinics, we can understand almost intuitively how devastating skin NTDs could be on individuals, families, and communities. For patients with skin NTDs, receiving medical therapy is only one component of treatment. In addition to optimizing early diagnosis and treatment, interventions taken to educate families and communities affected by skin NTDs are vitally important. Stigma reduction is possible, as we have seen from the aforementioned interventions used in communities with lymphatic filariasis, onchocerciasis, and leprosy. We call upon our fellow dermatologists to take interest in creating, evaluating, and promoting interventions that address stigma in skin NTDs; it is critical in achieving and maintaining health and well-being for our patients.

Neglected tropical diseases (NTDs) are a group of 20 diseases that typically are chronic and cause long-term disability, which negatively impacts work productivity, child survival, and school performance and attendance with adverse effect on future earnings.¹ Data from the 2013 Global Burden of Disease study revealed that half of the world’s NTDs occur in poor populations living in wealthy countries.² Neglected tropical diseases with skin manifestations include parasitic infections (eg, American trypanosomiasis, African trypanosomiasis, dracunculiasis, echinococcosis, foodborne trematodiases, leishmaniasis, lymphatic filariasis, onchocerciasis, scabies and other ectoparasites, schistosomiasis, soil-transmitted helminths, taeniasis/cysticercosis), bacterial infections (eg, Buruli ulcer, leprosy, yaws), fungal infections (eg, mycetoma, chromoblastomycosis, deep mycoses), and viral infections (eg, dengue, chikungunya). Rabies and snakebite envenomization involve the skin through inoculation. Within the larger group of NTDs, the World Health Organization has identified “skin NTDs” as a subgroup of NTDs that present primarily with changes in the skin.³ In the absence of early diagnosis and treatment of these diseases, chronic and lifelong disfigurement, disability, stigma, and socioeconomic losses ensue.

The Department of Health of the Government of Western Australia stated:

Stigma is a mark of disgrace that sets a person apart from others. When a person is labeled by their illness they are no longer seen as an individual but as part of a stereotyped group. Negative attitudes and beliefs toward this group create prejudice which leads to negative actions and discrimination.⁴

Stigma associated with skin NTDs exemplifies how skin diseases can have enduring impact on individuals.⁵ For example, scarring from inactive cutaneous leishmaniasis carries heavy psychosocial burden. Young women reported that facial scarring from cutaneous leishmaniasis led to marriage rejections.⁶ Some even reported extreme suicidal ideations.⁷ Recently, major depressive disorder associated with scarring from inactive cutaneous leishmaniasis has been recognized as a notable contributor to disease burden from cutaneous leishmaniasis.⁸

Lymphatic filariasis is a major cause of leg and scrotal lymphedema worldwide. Even when the condition is treated, lymphedema often persists due to chronic irreversible lymphatic damage. A systematic review of 18 stigma studies in lymphatic filariasis found common themes related to the deleterious consequences of stigma on social relationships; work and education opportunities; health outcomes from reduced treatment-seeking behavior; and mental health, including anxiety, depression, and suicidal tendencies.⁹ In one subdistrict in India, implementation of a community-based lymphedema management program that consisted of teaching hygiene and limb care for more than 20,000 lymphedema patients and performing community outreach activities (eg, street plays, radio programs, informational brochures) to teach people about lymphatic filariasis and lymphedema care was associated with community members being accepting of patients and an improvement in their understanding of disease etiology.¹⁰

Skin involvement from onchocerciasis infection (onchocercal skin disease) is another condition associated with notable stigma.⁹ Through the African Programme for Onchocerciasis Control, annual mass drug administration of ivermectin in onchocerciasis-endemic communities has reduced the rate of onchocercal skin disease in these communities. In looking at perception of stigma in onchocercal skin diseases before community-directed ivermectin therapy and 7 to 10 years after, avoidance of people with onchocercal skin disease decreased from 32.7% to 4.3%. There also was an improvement in relationships between healthy people and those with onchocercal skin disease.¹¹

One of the most stigmatizing conditions is leprosy, often referred to as Hansen disease to give credit to the person who discovered that leprosy was caused by Mycobacterium leprae and not from sin, being cursed, or genetic inheritance. Even with this knowledge, stigma persists that can lead to family abandonment and social isolation, which further impacts afflicted individuals’ willingness to seek care, thus leading to disease progression. More recently, there has been research looking at interventions to reduce the stigma that individuals afflicted with leprosy face. In a study from Indonesia where individuals with leprosy were randomized to counseling, socioeconomic development, or contact between community members and affected people, all interventions were associated with a reduction in stigma.¹² A rights-based counseling module integrated individual, family, and group forms of counseling and consisted of 5 sessions that focused on medical knowledge of leprosy and rights of individuals with leprosy, along with elements of cognitive behavioral therapy. Socioeconomic development involved opportunities for business training, creation of community groups through which microfinance services were administered, and other assistance to improve livelihood. Informed by evidence from the field of human immunodeficiency virus and mental health that contact with affected people reduces negative attitudes and behavior among those participating in the intervention, contact between community members and persons affected by leprosy occurred through dialogue and interaction at events held in schools, village halls, and mosques. Furthermore, early detection and subsequent early treatment of leprosy can prevent individuals from the disability and disfigurement that we commonly associate with the disease, which often is not the message that afflicted individuals and their communities are hearing and seeing. Targeting media portrayal, the New Face of Leprosy project seeks to shift the messaging around leprosy to one of hope and positivity by promoting positive images—not presenting severe disfigurement as the representative image of leprosy—and strong messaging that the disease is curable.¹³

Although steps are being taken to address the psychosocial burden of skin NTDs, there is still much work to be done. From the public health lens that largely governs the policies and approaches toward addressing NTDs, the focus often is on interrupting and eliminating disease transmission. Morbidity management, including reduction in stigma and functional impairment, is not always the priority. It is in this space that dermatologists are uniquely positioned to advocate for management approaches that address the morbidity associated with skin NTDs. We have an intimate understanding of how impactful skin diseases can be, even if they are not commonly fatal. Globally, skin diseases are the fourth leading cause of nonfatal disease burden,¹⁴ yet dermatology lacks effective evidence-based interventions for reducing stigma in our patients with visible chronic diseases.¹⁵

Every day, we see firsthand how skin diseases affect not only our patients but also their families, friends, and caregivers. Although we may not see skin NTDs on a regular basis in our clinics, we can understand almost intuitively how devastating skin NTDs could be on individuals, families, and communities. For patients with skin NTDs, receiving medical therapy is only one component of treatment. In addition to optimizing early diagnosis and treatment, interventions taken to educate families and communities affected by skin NTDs are vitally important. Stigma reduction is possible, as we have seen from the aforementioned interventions used in communities with lymphatic filariasis, onchocerciasis, and leprosy. We call upon our fellow dermatologists to take interest in creating, evaluating, and promoting interventions that address stigma in skin NTDs; it is critical in achieving and maintaining health and well-being for our patients.

Nearly 2.7 million veterans who rely on the Veterans Health Administration (VHA) for their health care live in rural communities.¹ Of these, more than half are aged ≥ 65 years. Rural veterans have greater rates of service-related disability and chronic medical conditions than do their urban counterparts.^1,2 Yet because of their rural location, they face unique challenges, including long travel times and distances to health care services, lack of public transportation options, and limited availability of specialized medical and social support services.

Compounding these geographic barriers is a more general lack of workforce infrastructure and a dearth of clinical health care providers (HCPs) skilled in geriatric medicine. The demand for geriatricians is projected to outpace supply and result in a national shortage of nearly 27 000 geriatricians by 2025.³ Moreover, the overwhelming majority (90%) of HCPs identifying as geriatric specialists reside in urban areas.⁴ This creates tremendous pressure on the health care system to provide remote care for older veterans contending with complex conditions, and ultimately these veterans may not receive the specialized care they need.

Telehealth modalities bridge these gaps by bringing health care to veterans in rural communities. They may also hold promise for strengthening community care in rural areas through workforce development and dissemination of educational resources. The VHA has been recognized as a leader in the field of telehealth since it began offering telehealth services to veterans in 1977^5-8 and served more than 677 000 Veterans via telehealth in fiscal year (FY) 2015.⁹ The VHA currently employs multiple modes of telehealth to increase veterans’ access to health care, including: (1) synchronous technology like clinical video telehealth (CVT), which provides live encounters between HCPs and patients using videoconferencing software; and (2) asynchronous technology, such as store-and-forward communication that offers remote transmission and clinical interpretation of veteran health data. The VHA has also strengthened its broad telehealth infrastructure by staffing VHA clinical sites with telehealth clinical technicians and providing telehealth hardware throughout.

The Department of Veterans Affairs (VA) Office of Geriatrics and Extended Care (GEC) and Office of Rural Health (ORH) established the Geriatric Research Education and Clinical Centers (GRECC) Connect project in 2014 to leverage the existing telehealth technologies at the VA to meet the health care needs of older veterans. GRECC Connect builds on the VHA network of geriatrics expertise in GRECCs by providing telehealth-based consultative support for rural primary care provider (PCP) teams, older veterans, and their families. This program profile describes this project’s mission, structure, and activities.

Program Overview

GRECC Connect leverages the clinical expertise and administrative infrastructure of participating GRECCs in order to reach clinicians and veterans in primarily rural communities.¹⁰ GRECCs are VA centers of excellence focused on aging and comprise a large network of interdisciplinary geriatrics expertise. All GRECCs have strong affiliations with local universities and are located in urban VA medical centers (VAMCs). GRECC Connect is based on a hub-and-spoke model in which urban GRECC hub sites are connected to community-based outpatient clinic (CBOC) and VAMC spokes that primarily serve veterans in other communities. CBOCs are stand-alone clinics that are geographically separate from a related VA medical center and provide outpatient primary care, mental health care services, and some specialty care services such as cardiology or neurology. They range in size from small, mainly telehealth clinics with 1 technician to large clinics with several specialty providers. Each GRECC hub site partners with an average of 6 CBOCs (range 3-16), each of which is an average distance of 92.8 miles from the related VA medical center (range 20-406 miles).

GRECC Connect was established under the umbrella of the VA Geriatric Scholars Program, which since 2008 integrates geriatrics into rural primary care practices through tailored education for continuing professional development.¹¹ Through intensive courses in geriatrics and quality improvement methods and through participation in local quality improvement projects benefiting older veterans, the Geriatric Scholars Program trains rural PCPs so that they can more effectively and independently diagnose and manage common geriatric syndromes.¹² The network of clinician scholars developed by the Geriatric Scholars Program, all rural frontline clinicians at VA clinics, has given the GRECC Connect project a well-prepared, geriatrics-trained workforce to act as project champions at rural CBOCs and VAMCs. The GRECC Connect project’s goals are to enhance access to geriatric specialty care among older veterans with complex medical problems, geriatric syndromes, and increased risk for institutionalization, and to provide geriatrics-focused educational support to rural HCP teams.

Geriatric Provider Consultations

The first overarching goal of the GRECC Connect project is to improve access to geriatrics specialty care by facilitating linkages between GRECC hub sites and the CBOCs and VAMCs that primarily serve veterans in rural communities. GRECC hub sites offer consultative support from geriatrics specialty team members (eg, geriatricians, nurse practitioners, pharmacists, gero- or neuropsychologists, registered nurses [RNs], and social workers) to rural PCP in their catchment area. This support is offered through a variety of telehealth modalities readily available in the VA (Table 1). These include CVT, in which a veteran located at a rural CBOC is seen using videoconferencing software by a geriatrics specialty provider who is located at a GRECC hub site. At some GRECC hub sites, CVT has also been used to conduct group visits between a GRECC provider at the hub site and several veterans who participate from a rural CBOC. Electronic consultations, or e-consults, involve a rural provider entering a clinical question in the VA Computerized Patient Record System. The question is then triaged, and a geriatrics provider at a GRECC responds, based on review of that veteran’s chart. At some GRECC hub sites, the e-consults are more extensive and may include telephone contact with the veteran or their caregiver.

Consultations between GRECC-based teams and rural PCPs may cover any aspect of geriatrics care, ranging from broad concerns to subspecialty areas of geriatric medicine. For instance, general geriatrics consultation may address polypharmacy, during either care transitions or ongoing care. Consultation may also reflect the specific focus area of a particular GRECC, such as cognitive assessment (eg, Pittsburgh GRECC), management of osteoporosis to address falls (eg, Durham GRECC, Miami GRECC), and continence care (eg, Birmingham/Atlanta GRECC).¹³ Most consultations are initiated by a remote HCP who is seeking geriatrics expertise from the GRECC team.

Some GRECC hub sites, however, employ case finding strategies, or detailed chart reviews, in order to identify older veterans who may benefit from geriatrics consultation. For veterans identified through those mechanisms, the GRECC clinicians suggest that the rural HCP either request or allow an e-consult or evaluation via CVT for those veterans. The geriatric consultations may help identify additional care needs for older veterans and lead to recommendations, orders, or remote provision of a variety of other actions, including VA or non-VA services (eg, home-based primary care, home nursing service, respite service, social support services such as Meals on Wheels); neuropsychological testing; physical or occupational therapy; audiology or optometry referral; falls and fracture risk assessment and interventions to reduce falls (eg, home safety evaluation, physical therapy); osteoporosis risk assessments (eg, densitometry, recommendations for pharmacologic therapy) to reduce the risk of injury or nontraumatic fractures from falls; palliative care for incontinence and hospice; and counseling on geriatric issues such as dementia caregiving, advanced directives, and driving cessation.

More recently, the Miami GRECC has begun evaluating rural veterans at risk for hypoglycemia, providing patient education and counseling about hypoglycemia, and making recommendations to the veterans’ primary care teams.¹⁴ Consultations may also lead to the appropriate use or discontinuation of medications, related to polypharmacy. GRECC-based teams, for example, have helped rural HCPs modify medication doses, start appropriate medications for dementia and depression, and identify and stop potentially inappropriate medications (eg, those that increase fall risk or that have significant anticholinergic properties).¹⁵

GRECC Connect Geriatric Case Conference Series

The second overarching goal of the GRECC Connect project is to provide geriatrics-focused educational support to equip PCPs to better serve their aging veteran patients. This is achieved through twice-monthly, case-based conferences supported by the VA Employee Education System (EES) and delivered through a webinar interface. Case conferences are targeted to members of the health care team who may provide care for rural older adults, including physicians, nurse practitioners, physician assistants, RNs, psychologists, social workers, physical and occupational therapists, and pharmacists. The format of these sessions includes a clinical case presentation, a didactic portion to enhance knowledge of participants, and an open question/answer period. The conferences focus on discussions of challenging clinical cases, addressing common problems (eg, driving concerns), and the assessment/management of geriatric syndromes (eg, cognitive decline, falls, polypharmacy). These conferences aim to improve the knowledge and skills of rural clinical teams in taking care of older veterans and to disseminate best practices in geriatric medicine, using case discussions to highlight practical applications of practices to clinical care. Recent GRECC Connect geriatric case conferences are listed in Table 2 and are recorded and archived to ensure that busy clinicians may access these trainings at the time of their choosing. These materials are catalogued and archived on the EES server.

Early Experience

GRECC Connect tracks on an annual basis the number of unique veterans served, number of participating GRECC hub sites and CBOCs, mileage from veteran homes to teleconsultation sites, and number of clinicians and staff engaged in GRECC Connect education programs.¹⁶ Since its inception in 2014, the GRECC Connect project has provided direct clinical support to more than 4000 unique veterans (eFigure), of whom half were seen for a cognition-related issue. Consultations were made on behalf of 1,622 veterans in FY 2018, of whom 60% were from rural or highly rural communities and 56.8% were served by CVT visits. The number of GRECC hub sites has increased from 4 in FY 2014 to 12 (of 20 total GRECCs) in FY 2018. The locations of current GRECC hub sites can be found on the Geriatric Scholars website: www.gerischolars.org. Through this expansion, GRECC Connect provides geriatric consultative and educational support to > 70 rural VA clinics in 10 of the 18 Veterans Integrated Service Networks (VISNs).

To assess the reduction in commute times from teleconsultation, we calculated the difference between the mileage from veteran homes to teleconsultation sites (ie, rural clinics) and the mileage from veteran homes to VAMCs where geriatric teams are located. We estimate that the 1622 veterans served in FY 2018 saved a total of 179 121 miles in travel through GRECC Connect. Veterans traveled 106 fewer miles and on average saved $58 in out-of-pocket savings (based on US General Services Administration 2018 standard mileage reimbursement rate of $0.545 per mile). However, many of the veterans have reported anecdotally that the reduction in mileage traveled was less important than the elimination of stress involved in urban navigating, driving, and parking.

More difficult to measure, GRECC Connect seeks to enhance veteran safety by reducing driving distances for older veterans whose driving abilities may be influenced by many age-related health conditions (eg, visual changes, cognitive impairment). For these and other reasons, surveyed veterans overwhelmingly reported that they would be likely to recommend teleconsultation services to other veterans, and that they preferred telemedicine consultation over traveling long distances for in-person clinical consultations.¹⁶

Since its inception in 2014, GRECC Connect has provided case-based education to a total of 2335 unique clinicians and staff. Participants have included physicians, nurse practitioners, RNs, social workers, and pharmacists. This distribution reflects the interdisciplinary nature of geriatric care. A plurality of participants (39%) were RNs. Surveyed participants in the GRECC Connect geriatrics case conference series report high overall satisfaction with the learning activity, acquisition of new knowledge and skills, and intention to apply new knowledge and skills to improve job performance.¹⁰ In addition, participants agreed that the online training platform was effective for learning and that they would recommend the education series to other HCPs.^10,16

Discussion

During its rapid 4-year scale up, GRECC Connect has established a national network and enhanced relationships between GRECC-based clinical teams and rural provider teams. In doing so, the program has begun to improve rural veterans’ access to geriatric specialty care. By providing continuing education to members of the interprofessional health care team, GRECC Connect develops rural providers’ clinical competency and promotes geriatrics skills and expertise. These activities are synergistic: Clinical support enables rural HCPs to become better at managing their own patients, while formal educational activities highlight the availability of specialized consultation available through GRECC Connect. Through ongoing creation of handbooks, workflows, and data analytic strategies, GRECC Connect aims to disseminate this model to additional GRECCs as well as other GEC programs to promote “anywhere to anywhere” VA health care.¹⁷

Barriers and Facilitators

GRECC Connect has had notable implementation challenges while new consultation relationships have been forged in order to provide geriatric expertise to rural areas where it is not otherwise available. Many GRECCs had already established connections with rural CBOCs. Among GRECCs that had previously established consultative relationships with rural clinics, the use of telehealth modalities to provide geriatric clinical resources has been a natural extension of these partnerships. GRECCs that lacked these connections, however, often had to obtain buy-in from multiple stakeholders, including rural HCPs and teams, administrative leads, and local telehealth coordinators, and they required VISN- and facility-level leadership to encourage and sustain rural team participation.

Depending on the distance of the GRECC hub-site to the CBOC, efforts to establish and sustain partnerships may require multiple contacts over time (eg, via face-to-face meetings, one-on-one outreach) and large-scale advertising of consultative services. Continuous engagement with CBOC-based teams also involves development of case finding strategies (eg, hospital discharge information, diagnoses, clinical criteria) to better identify veterans who may benefit from GRECC Connect consultation. Owing to the heterogeneity of technological resources, space, scheduling capacity, and staffing at CBOCs, GRECC sites continue to have variable engagement with their CBOC partners.

The inclusion of GRECC Connect within the Geriatric Scholars Program helps ensure that clinician scholars can serve as project champions at their respective rural sites. Rural HCPs with full-time clinical duties initially had difficulty carving out time to participate in GRECC Connect’s case-based conferences. However, the webinar platform has improved and sustained provider participation, and enduring recordings of the presentations allow clinicians to participate in the conferences at their convenience. Finally, the project experienced delays in taking certain administrative steps and hiring staff needed to support the establishment of telehealth modalities—even within a single health care system like the VA, each medical center and regional system has unique policies that complicate how telehealth modalities can be set up.

Conclusion and Future Directions

The GRECC Connect project aims to establish and support meaningful partnerships between urban geriatric specialists and rural HCPs to facilitate veterans’ increased access to geriatric specialty care. VA ORH has recognized it as a Rural Promising Practice, and GRECC Connect is currently being disseminated through an enterprise-wide initiative. Early evidence demonstrates that over 4 years, the expansion of GRECC Connect has helped meet critical aims of improving provider confidence and skills in geriatric management, and of increasing direct service provision. We have also used nationwide education platforms (eg, VA EES) to deliver geriatrics-focused education to health care teams.

Older rural veterans and their caregivers may benefit from this program through decreased travel-associated burden and report high satisfaction with these programs. Through a recently established collaboration with the GEC Data Analysis Center, we will use national data to refine our ability to identify at-risk, older rural veterans and to better evaluate their service needs and the GRECC Connect clinical impact. Because the VA is rapidly expanding use of telehealth and other virtual and digital methods to increase access to care, continued investments in telehealth are central to the VA 5-year strategic plan.¹⁸ In this spirit, GRECC Connect will continue to expand its program offerings and to leverage telehealth technologies to meet the needs of older veterans.

Acknowledgments

The authors wish to acknowledge Lisa Tenover, MD, PhD, (Palo Alto GRECC) for her contributions to this manuscript; the VA Rural Health Resource Center–Western Region; and GRECC Connect team members for their tireless work to ensure this project’s success. The GRECC Teams include Atlanta/Birmingham (Julia [Annette] Tedford, RN; Marquitta Cox, LMSW; Lisa Welch, LMSW; Mark Phillips; Lanie Walters, PharmD; Kroshona Tabb, PhD; Robert Langford, and Jason [Thomas] Sanders, HT, TCT); Bronx/NY Harbor (Ab Brody, RN; PhD, GNP-BC; Nick Koufacos, LMSW; and Shatice Jones); Canandaigua (Gary Kochersberger, MD; Suzanne Gillespie, MD; Gary Warner, PhD; Christie Hylwa, RPh CCP; Sharon Fell, LMSW; and Dorian Savino, MPA); Durham (Mamata Yanamadala, MBBS; Christy Knight, LCSW, MSW; and Julie Vognsen); Eastern Colorado (Larry Bourg, MD; Skotti Church, MD; Morgan Elmore, DO; Stephanie Hartz, LCSW; Carolyn Horney, MD; Steven Huart, AuD; Kathryn Nearing, PhD; Elizabeth O’Brien, PharmD; Laurence Robbins, MD; Robert Schwartz, MD; Karen Shea, MD; and Joleen Sussman, PhD); Little Rock (Prasad Padala, MD; and Tanya Taylor, RN); Madison (Ryan Bartkus, MD; Timothy Howell, MD; Lindsay Clark, PhD; Lauren Welch, PharmD, BCGP; Ellen Wanninger, MSW, CAPSW; Stacie Monson, RN, BSN; and Teresa Swader, MSW, LCSW); Miami (Carlos Gomez Orozo); New England (Malissa Kraft, PsyD); Palo Alto (Terri Huh, PhD, ABPP; Philip Choe, DO; Dawna Dougherty, LCSW; Ashley Scales, MPH); Pittsburgh (Stacey Shaffer, MD; Carol Dolbee, CRNP; Nancy Kovell, LCSW; Paul Bulgarelli, DO; Lauren Jost, PsyD; and Marcia Homer, RN-BC); and San Antonio (Becky Powers, MD; Che Kelly, RN, BSN; Cynthia Stewart, LCSW; Rebecca Rottman-Sagebiel, PharmD, BCPS, CGP; Melody Moris; Daniel MacCarthy; and Chen-pin Wang, PhD).

Nearly 2.7 million veterans who rely on the Veterans Health Administration (VHA) for their health care live in rural communities.¹ Of these, more than half are aged ≥ 65 years. Rural veterans have greater rates of service-related disability and chronic medical conditions than do their urban counterparts.^1,2 Yet because of their rural location, they face unique challenges, including long travel times and distances to health care services, lack of public transportation options, and limited availability of specialized medical and social support services.

Compounding these geographic barriers is a more general lack of workforce infrastructure and a dearth of clinical health care providers (HCPs) skilled in geriatric medicine. The demand for geriatricians is projected to outpace supply and result in a national shortage of nearly 27 000 geriatricians by 2025.³ Moreover, the overwhelming majority (90%) of HCPs identifying as geriatric specialists reside in urban areas.⁴ This creates tremendous pressure on the health care system to provide remote care for older veterans contending with complex conditions, and ultimately these veterans may not receive the specialized care they need.

Telehealth modalities bridge these gaps by bringing health care to veterans in rural communities. They may also hold promise for strengthening community care in rural areas through workforce development and dissemination of educational resources. The VHA has been recognized as a leader in the field of telehealth since it began offering telehealth services to veterans in 1977^5-8 and served more than 677 000 Veterans via telehealth in fiscal year (FY) 2015.⁹ The VHA currently employs multiple modes of telehealth to increase veterans’ access to health care, including: (1) synchronous technology like clinical video telehealth (CVT), which provides live encounters between HCPs and patients using videoconferencing software; and (2) asynchronous technology, such as store-and-forward communication that offers remote transmission and clinical interpretation of veteran health data. The VHA has also strengthened its broad telehealth infrastructure by staffing VHA clinical sites with telehealth clinical technicians and providing telehealth hardware throughout.

The Department of Veterans Affairs (VA) Office of Geriatrics and Extended Care (GEC) and Office of Rural Health (ORH) established the Geriatric Research Education and Clinical Centers (GRECC) Connect project in 2014 to leverage the existing telehealth technologies at the VA to meet the health care needs of older veterans. GRECC Connect builds on the VHA network of geriatrics expertise in GRECCs by providing telehealth-based consultative support for rural primary care provider (PCP) teams, older veterans, and their families. This program profile describes this project’s mission, structure, and activities.

Program Overview

GRECC Connect leverages the clinical expertise and administrative infrastructure of participating GRECCs in order to reach clinicians and veterans in primarily rural communities.¹⁰ GRECCs are VA centers of excellence focused on aging and comprise a large network of interdisciplinary geriatrics expertise. All GRECCs have strong affiliations with local universities and are located in urban VA medical centers (VAMCs). GRECC Connect is based on a hub-and-spoke model in which urban GRECC hub sites are connected to community-based outpatient clinic (CBOC) and VAMC spokes that primarily serve veterans in other communities. CBOCs are stand-alone clinics that are geographically separate from a related VA medical center and provide outpatient primary care, mental health care services, and some specialty care services such as cardiology or neurology. They range in size from small, mainly telehealth clinics with 1 technician to large clinics with several specialty providers. Each GRECC hub site partners with an average of 6 CBOCs (range 3-16), each of which is an average distance of 92.8 miles from the related VA medical center (range 20-406 miles).

GRECC Connect was established under the umbrella of the VA Geriatric Scholars Program, which since 2008 integrates geriatrics into rural primary care practices through tailored education for continuing professional development.¹¹ Through intensive courses in geriatrics and quality improvement methods and through participation in local quality improvement projects benefiting older veterans, the Geriatric Scholars Program trains rural PCPs so that they can more effectively and independently diagnose and manage common geriatric syndromes.¹² The network of clinician scholars developed by the Geriatric Scholars Program, all rural frontline clinicians at VA clinics, has given the GRECC Connect project a well-prepared, geriatrics-trained workforce to act as project champions at rural CBOCs and VAMCs. The GRECC Connect project’s goals are to enhance access to geriatric specialty care among older veterans with complex medical problems, geriatric syndromes, and increased risk for institutionalization, and to provide geriatrics-focused educational support to rural HCP teams.

Geriatric Provider Consultations

The first overarching goal of the GRECC Connect project is to improve access to geriatrics specialty care by facilitating linkages between GRECC hub sites and the CBOCs and VAMCs that primarily serve veterans in rural communities. GRECC hub sites offer consultative support from geriatrics specialty team members (eg, geriatricians, nurse practitioners, pharmacists, gero- or neuropsychologists, registered nurses [RNs], and social workers) to rural PCP in their catchment area. This support is offered through a variety of telehealth modalities readily available in the VA (Table 1). These include CVT, in which a veteran located at a rural CBOC is seen using videoconferencing software by a geriatrics specialty provider who is located at a GRECC hub site. At some GRECC hub sites, CVT has also been used to conduct group visits between a GRECC provider at the hub site and several veterans who participate from a rural CBOC. Electronic consultations, or e-consults, involve a rural provider entering a clinical question in the VA Computerized Patient Record System. The question is then triaged, and a geriatrics provider at a GRECC responds, based on review of that veteran’s chart. At some GRECC hub sites, the e-consults are more extensive and may include telephone contact with the veteran or their caregiver.

Consultations between GRECC-based teams and rural PCPs may cover any aspect of geriatrics care, ranging from broad concerns to subspecialty areas of geriatric medicine. For instance, general geriatrics consultation may address polypharmacy, during either care transitions or ongoing care. Consultation may also reflect the specific focus area of a particular GRECC, such as cognitive assessment (eg, Pittsburgh GRECC), management of osteoporosis to address falls (eg, Durham GRECC, Miami GRECC), and continence care (eg, Birmingham/Atlanta GRECC).¹³ Most consultations are initiated by a remote HCP who is seeking geriatrics expertise from the GRECC team.

Some GRECC hub sites, however, employ case finding strategies, or detailed chart reviews, in order to identify older veterans who may benefit from geriatrics consultation. For veterans identified through those mechanisms, the GRECC clinicians suggest that the rural HCP either request or allow an e-consult or evaluation via CVT for those veterans. The geriatric consultations may help identify additional care needs for older veterans and lead to recommendations, orders, or remote provision of a variety of other actions, including VA or non-VA services (eg, home-based primary care, home nursing service, respite service, social support services such as Meals on Wheels); neuropsychological testing; physical or occupational therapy; audiology or optometry referral; falls and fracture risk assessment and interventions to reduce falls (eg, home safety evaluation, physical therapy); osteoporosis risk assessments (eg, densitometry, recommendations for pharmacologic therapy) to reduce the risk of injury or nontraumatic fractures from falls; palliative care for incontinence and hospice; and counseling on geriatric issues such as dementia caregiving, advanced directives, and driving cessation.

More recently, the Miami GRECC has begun evaluating rural veterans at risk for hypoglycemia, providing patient education and counseling about hypoglycemia, and making recommendations to the veterans’ primary care teams.¹⁴ Consultations may also lead to the appropriate use or discontinuation of medications, related to polypharmacy. GRECC-based teams, for example, have helped rural HCPs modify medication doses, start appropriate medications for dementia and depression, and identify and stop potentially inappropriate medications (eg, those that increase fall risk or that have significant anticholinergic properties).¹⁵

GRECC Connect Geriatric Case Conference Series

The second overarching goal of the GRECC Connect project is to provide geriatrics-focused educational support to equip PCPs to better serve their aging veteran patients. This is achieved through twice-monthly, case-based conferences supported by the VA Employee Education System (EES) and delivered through a webinar interface. Case conferences are targeted to members of the health care team who may provide care for rural older adults, including physicians, nurse practitioners, physician assistants, RNs, psychologists, social workers, physical and occupational therapists, and pharmacists. The format of these sessions includes a clinical case presentation, a didactic portion to enhance knowledge of participants, and an open question/answer period. The conferences focus on discussions of challenging clinical cases, addressing common problems (eg, driving concerns), and the assessment/management of geriatric syndromes (eg, cognitive decline, falls, polypharmacy). These conferences aim to improve the knowledge and skills of rural clinical teams in taking care of older veterans and to disseminate best practices in geriatric medicine, using case discussions to highlight practical applications of practices to clinical care. Recent GRECC Connect geriatric case conferences are listed in Table 2 and are recorded and archived to ensure that busy clinicians may access these trainings at the time of their choosing. These materials are catalogued and archived on the EES server.

Early Experience

GRECC Connect tracks on an annual basis the number of unique veterans served, number of participating GRECC hub sites and CBOCs, mileage from veteran homes to teleconsultation sites, and number of clinicians and staff engaged in GRECC Connect education programs.¹⁶ Since its inception in 2014, the GRECC Connect project has provided direct clinical support to more than 4000 unique veterans (eFigure), of whom half were seen for a cognition-related issue. Consultations were made on behalf of 1,622 veterans in FY 2018, of whom 60% were from rural or highly rural communities and 56.8% were served by CVT visits. The number of GRECC hub sites has increased from 4 in FY 2014 to 12 (of 20 total GRECCs) in FY 2018. The locations of current GRECC hub sites can be found on the Geriatric Scholars website: www.gerischolars.org. Through this expansion, GRECC Connect provides geriatric consultative and educational support to > 70 rural VA clinics in 10 of the 18 Veterans Integrated Service Networks (VISNs).

To assess the reduction in commute times from teleconsultation, we calculated the difference between the mileage from veteran homes to teleconsultation sites (ie, rural clinics) and the mileage from veteran homes to VAMCs where geriatric teams are located. We estimate that the 1622 veterans served in FY 2018 saved a total of 179 121 miles in travel through GRECC Connect. Veterans traveled 106 fewer miles and on average saved $58 in out-of-pocket savings (based on US General Services Administration 2018 standard mileage reimbursement rate of $0.545 per mile). However, many of the veterans have reported anecdotally that the reduction in mileage traveled was less important than the elimination of stress involved in urban navigating, driving, and parking.

More difficult to measure, GRECC Connect seeks to enhance veteran safety by reducing driving distances for older veterans whose driving abilities may be influenced by many age-related health conditions (eg, visual changes, cognitive impairment). For these and other reasons, surveyed veterans overwhelmingly reported that they would be likely to recommend teleconsultation services to other veterans, and that they preferred telemedicine consultation over traveling long distances for in-person clinical consultations.¹⁶

Since its inception in 2014, GRECC Connect has provided case-based education to a total of 2335 unique clinicians and staff. Participants have included physicians, nurse practitioners, RNs, social workers, and pharmacists. This distribution reflects the interdisciplinary nature of geriatric care. A plurality of participants (39%) were RNs. Surveyed participants in the GRECC Connect geriatrics case conference series report high overall satisfaction with the learning activity, acquisition of new knowledge and skills, and intention to apply new knowledge and skills to improve job performance.¹⁰ In addition, participants agreed that the online training platform was effective for learning and that they would recommend the education series to other HCPs.^10,16

Discussion

During its rapid 4-year scale up, GRECC Connect has established a national network and enhanced relationships between GRECC-based clinical teams and rural provider teams. In doing so, the program has begun to improve rural veterans’ access to geriatric specialty care. By providing continuing education to members of the interprofessional health care team, GRECC Connect develops rural providers’ clinical competency and promotes geriatrics skills and expertise. These activities are synergistic: Clinical support enables rural HCPs to become better at managing their own patients, while formal educational activities highlight the availability of specialized consultation available through GRECC Connect. Through ongoing creation of handbooks, workflows, and data analytic strategies, GRECC Connect aims to disseminate this model to additional GRECCs as well as other GEC programs to promote “anywhere to anywhere” VA health care.¹⁷

Barriers and Facilitators

GRECC Connect has had notable implementation challenges while new consultation relationships have been forged in order to provide geriatric expertise to rural areas where it is not otherwise available. Many GRECCs had already established connections with rural CBOCs. Among GRECCs that had previously established consultative relationships with rural clinics, the use of telehealth modalities to provide geriatric clinical resources has been a natural extension of these partnerships. GRECCs that lacked these connections, however, often had to obtain buy-in from multiple stakeholders, including rural HCPs and teams, administrative leads, and local telehealth coordinators, and they required VISN- and facility-level leadership to encourage and sustain rural team participation.

Depending on the distance of the GRECC hub-site to the CBOC, efforts to establish and sustain partnerships may require multiple contacts over time (eg, via face-to-face meetings, one-on-one outreach) and large-scale advertising of consultative services. Continuous engagement with CBOC-based teams also involves development of case finding strategies (eg, hospital discharge information, diagnoses, clinical criteria) to better identify veterans who may benefit from GRECC Connect consultation. Owing to the heterogeneity of technological resources, space, scheduling capacity, and staffing at CBOCs, GRECC sites continue to have variable engagement with their CBOC partners.

The inclusion of GRECC Connect within the Geriatric Scholars Program helps ensure that clinician scholars can serve as project champions at their respective rural sites. Rural HCPs with full-time clinical duties initially had difficulty carving out time to participate in GRECC Connect’s case-based conferences. However, the webinar platform has improved and sustained provider participation, and enduring recordings of the presentations allow clinicians to participate in the conferences at their convenience. Finally, the project experienced delays in taking certain administrative steps and hiring staff needed to support the establishment of telehealth modalities—even within a single health care system like the VA, each medical center and regional system has unique policies that complicate how telehealth modalities can be set up.

Conclusion and Future Directions

The GRECC Connect project aims to establish and support meaningful partnerships between urban geriatric specialists and rural HCPs to facilitate veterans’ increased access to geriatric specialty care. VA ORH has recognized it as a Rural Promising Practice, and GRECC Connect is currently being disseminated through an enterprise-wide initiative. Early evidence demonstrates that over 4 years, the expansion of GRECC Connect has helped meet critical aims of improving provider confidence and skills in geriatric management, and of increasing direct service provision. We have also used nationwide education platforms (eg, VA EES) to deliver geriatrics-focused education to health care teams.

Older rural veterans and their caregivers may benefit from this program through decreased travel-associated burden and report high satisfaction with these programs. Through a recently established collaboration with the GEC Data Analysis Center, we will use national data to refine our ability to identify at-risk, older rural veterans and to better evaluate their service needs and the GRECC Connect clinical impact. Because the VA is rapidly expanding use of telehealth and other virtual and digital methods to increase access to care, continued investments in telehealth are central to the VA 5-year strategic plan.¹⁸ In this spirit, GRECC Connect will continue to expand its program offerings and to leverage telehealth technologies to meet the needs of older veterans.

Acknowledgments

The authors wish to acknowledge Lisa Tenover, MD, PhD, (Palo Alto GRECC) for her contributions to this manuscript; the VA Rural Health Resource Center–Western Region; and GRECC Connect team members for their tireless work to ensure this project’s success. The GRECC Teams include Atlanta/Birmingham (Julia [Annette] Tedford, RN; Marquitta Cox, LMSW; Lisa Welch, LMSW; Mark Phillips; Lanie Walters, PharmD; Kroshona Tabb, PhD; Robert Langford, and Jason [Thomas] Sanders, HT, TCT); Bronx/NY Harbor (Ab Brody, RN; PhD, GNP-BC; Nick Koufacos, LMSW; and Shatice Jones); Canandaigua (Gary Kochersberger, MD; Suzanne Gillespie, MD; Gary Warner, PhD; Christie Hylwa, RPh CCP; Sharon Fell, LMSW; and Dorian Savino, MPA); Durham (Mamata Yanamadala, MBBS; Christy Knight, LCSW, MSW; and Julie Vognsen); Eastern Colorado (Larry Bourg, MD; Skotti Church, MD; Morgan Elmore, DO; Stephanie Hartz, LCSW; Carolyn Horney, MD; Steven Huart, AuD; Kathryn Nearing, PhD; Elizabeth O’Brien, PharmD; Laurence Robbins, MD; Robert Schwartz, MD; Karen Shea, MD; and Joleen Sussman, PhD); Little Rock (Prasad Padala, MD; and Tanya Taylor, RN); Madison (Ryan Bartkus, MD; Timothy Howell, MD; Lindsay Clark, PhD; Lauren Welch, PharmD, BCGP; Ellen Wanninger, MSW, CAPSW; Stacie Monson, RN, BSN; and Teresa Swader, MSW, LCSW); Miami (Carlos Gomez Orozo); New England (Malissa Kraft, PsyD); Palo Alto (Terri Huh, PhD, ABPP; Philip Choe, DO; Dawna Dougherty, LCSW; Ashley Scales, MPH); Pittsburgh (Stacey Shaffer, MD; Carol Dolbee, CRNP; Nancy Kovell, LCSW; Paul Bulgarelli, DO; Lauren Jost, PsyD; and Marcia Homer, RN-BC); and San Antonio (Becky Powers, MD; Che Kelly, RN, BSN; Cynthia Stewart, LCSW; Rebecca Rottman-Sagebiel, PharmD, BCPS, CGP; Melody Moris; Daniel MacCarthy; and Chen-pin Wang, PhD).

Nearly 2.7 million veterans who rely on the Veterans Health Administration (VHA) for their health care live in rural communities.¹ Of these, more than half are aged ≥ 65 years. Rural veterans have greater rates of service-related disability and chronic medical conditions than do their urban counterparts.^1,2 Yet because of their rural location, they face unique challenges, including long travel times and distances to health care services, lack of public transportation options, and limited availability of specialized medical and social support services.

Compounding these geographic barriers is a more general lack of workforce infrastructure and a dearth of clinical health care providers (HCPs) skilled in geriatric medicine. The demand for geriatricians is projected to outpace supply and result in a national shortage of nearly 27 000 geriatricians by 2025.³ Moreover, the overwhelming majority (90%) of HCPs identifying as geriatric specialists reside in urban areas.⁴ This creates tremendous pressure on the health care system to provide remote care for older veterans contending with complex conditions, and ultimately these veterans may not receive the specialized care they need.

Telehealth modalities bridge these gaps by bringing health care to veterans in rural communities. They may also hold promise for strengthening community care in rural areas through workforce development and dissemination of educational resources. The VHA has been recognized as a leader in the field of telehealth since it began offering telehealth services to veterans in 1977^5-8 and served more than 677 000 Veterans via telehealth in fiscal year (FY) 2015.⁹ The VHA currently employs multiple modes of telehealth to increase veterans’ access to health care, including: (1) synchronous technology like clinical video telehealth (CVT), which provides live encounters between HCPs and patients using videoconferencing software; and (2) asynchronous technology, such as store-and-forward communication that offers remote transmission and clinical interpretation of veteran health data. The VHA has also strengthened its broad telehealth infrastructure by staffing VHA clinical sites with telehealth clinical technicians and providing telehealth hardware throughout.

The Department of Veterans Affairs (VA) Office of Geriatrics and Extended Care (GEC) and Office of Rural Health (ORH) established the Geriatric Research Education and Clinical Centers (GRECC) Connect project in 2014 to leverage the existing telehealth technologies at the VA to meet the health care needs of older veterans. GRECC Connect builds on the VHA network of geriatrics expertise in GRECCs by providing telehealth-based consultative support for rural primary care provider (PCP) teams, older veterans, and their families. This program profile describes this project’s mission, structure, and activities.

Program Overview

GRECC Connect leverages the clinical expertise and administrative infrastructure of participating GRECCs in order to reach clinicians and veterans in primarily rural communities.¹⁰ GRECCs are VA centers of excellence focused on aging and comprise a large network of interdisciplinary geriatrics expertise. All GRECCs have strong affiliations with local universities and are located in urban VA medical centers (VAMCs). GRECC Connect is based on a hub-and-spoke model in which urban GRECC hub sites are connected to community-based outpatient clinic (CBOC) and VAMC spokes that primarily serve veterans in other communities. CBOCs are stand-alone clinics that are geographically separate from a related VA medical center and provide outpatient primary care, mental health care services, and some specialty care services such as cardiology or neurology. They range in size from small, mainly telehealth clinics with 1 technician to large clinics with several specialty providers. Each GRECC hub site partners with an average of 6 CBOCs (range 3-16), each of which is an average distance of 92.8 miles from the related VA medical center (range 20-406 miles).

GRECC Connect was established under the umbrella of the VA Geriatric Scholars Program, which since 2008 integrates geriatrics into rural primary care practices through tailored education for continuing professional development.¹¹ Through intensive courses in geriatrics and quality improvement methods and through participation in local quality improvement projects benefiting older veterans, the Geriatric Scholars Program trains rural PCPs so that they can more effectively and independently diagnose and manage common geriatric syndromes.¹² The network of clinician scholars developed by the Geriatric Scholars Program, all rural frontline clinicians at VA clinics, has given the GRECC Connect project a well-prepared, geriatrics-trained workforce to act as project champions at rural CBOCs and VAMCs. The GRECC Connect project’s goals are to enhance access to geriatric specialty care among older veterans with complex medical problems, geriatric syndromes, and increased risk for institutionalization, and to provide geriatrics-focused educational support to rural HCP teams.

Geriatric Provider Consultations

The first overarching goal of the GRECC Connect project is to improve access to geriatrics specialty care by facilitating linkages between GRECC hub sites and the CBOCs and VAMCs that primarily serve veterans in rural communities. GRECC hub sites offer consultative support from geriatrics specialty team members (eg, geriatricians, nurse practitioners, pharmacists, gero- or neuropsychologists, registered nurses [RNs], and social workers) to rural PCP in their catchment area. This support is offered through a variety of telehealth modalities readily available in the VA (Table 1). These include CVT, in which a veteran located at a rural CBOC is seen using videoconferencing software by a geriatrics specialty provider who is located at a GRECC hub site. At some GRECC hub sites, CVT has also been used to conduct group visits between a GRECC provider at the hub site and several veterans who participate from a rural CBOC. Electronic consultations, or e-consults, involve a rural provider entering a clinical question in the VA Computerized Patient Record System. The question is then triaged, and a geriatrics provider at a GRECC responds, based on review of that veteran’s chart. At some GRECC hub sites, the e-consults are more extensive and may include telephone contact with the veteran or their caregiver.

Consultations between GRECC-based teams and rural PCPs may cover any aspect of geriatrics care, ranging from broad concerns to subspecialty areas of geriatric medicine. For instance, general geriatrics consultation may address polypharmacy, during either care transitions or ongoing care. Consultation may also reflect the specific focus area of a particular GRECC, such as cognitive assessment (eg, Pittsburgh GRECC), management of osteoporosis to address falls (eg, Durham GRECC, Miami GRECC), and continence care (eg, Birmingham/Atlanta GRECC).¹³ Most consultations are initiated by a remote HCP who is seeking geriatrics expertise from the GRECC team.

Some GRECC hub sites, however, employ case finding strategies, or detailed chart reviews, in order to identify older veterans who may benefit from geriatrics consultation. For veterans identified through those mechanisms, the GRECC clinicians suggest that the rural HCP either request or allow an e-consult or evaluation via CVT for those veterans. The geriatric consultations may help identify additional care needs for older veterans and lead to recommendations, orders, or remote provision of a variety of other actions, including VA or non-VA services (eg, home-based primary care, home nursing service, respite service, social support services such as Meals on Wheels); neuropsychological testing; physical or occupational therapy; audiology or optometry referral; falls and fracture risk assessment and interventions to reduce falls (eg, home safety evaluation, physical therapy); osteoporosis risk assessments (eg, densitometry, recommendations for pharmacologic therapy) to reduce the risk of injury or nontraumatic fractures from falls; palliative care for incontinence and hospice; and counseling on geriatric issues such as dementia caregiving, advanced directives, and driving cessation.

More recently, the Miami GRECC has begun evaluating rural veterans at risk for hypoglycemia, providing patient education and counseling about hypoglycemia, and making recommendations to the veterans’ primary care teams.¹⁴ Consultations may also lead to the appropriate use or discontinuation of medications, related to polypharmacy. GRECC-based teams, for example, have helped rural HCPs modify medication doses, start appropriate medications for dementia and depression, and identify and stop potentially inappropriate medications (eg, those that increase fall risk or that have significant anticholinergic properties).¹⁵

GRECC Connect Geriatric Case Conference Series

The second overarching goal of the GRECC Connect project is to provide geriatrics-focused educational support to equip PCPs to better serve their aging veteran patients. This is achieved through twice-monthly, case-based conferences supported by the VA Employee Education System (EES) and delivered through a webinar interface. Case conferences are targeted to members of the health care team who may provide care for rural older adults, including physicians, nurse practitioners, physician assistants, RNs, psychologists, social workers, physical and occupational therapists, and pharmacists. The format of these sessions includes a clinical case presentation, a didactic portion to enhance knowledge of participants, and an open question/answer period. The conferences focus on discussions of challenging clinical cases, addressing common problems (eg, driving concerns), and the assessment/management of geriatric syndromes (eg, cognitive decline, falls, polypharmacy). These conferences aim to improve the knowledge and skills of rural clinical teams in taking care of older veterans and to disseminate best practices in geriatric medicine, using case discussions to highlight practical applications of practices to clinical care. Recent GRECC Connect geriatric case conferences are listed in Table 2 and are recorded and archived to ensure that busy clinicians may access these trainings at the time of their choosing. These materials are catalogued and archived on the EES server.

Early Experience

GRECC Connect tracks on an annual basis the number of unique veterans served, number of participating GRECC hub sites and CBOCs, mileage from veteran homes to teleconsultation sites, and number of clinicians and staff engaged in GRECC Connect education programs.¹⁶ Since its inception in 2014, the GRECC Connect project has provided direct clinical support to more than 4000 unique veterans (eFigure), of whom half were seen for a cognition-related issue. Consultations were made on behalf of 1,622 veterans in FY 2018, of whom 60% were from rural or highly rural communities and 56.8% were served by CVT visits. The number of GRECC hub sites has increased from 4 in FY 2014 to 12 (of 20 total GRECCs) in FY 2018. The locations of current GRECC hub sites can be found on the Geriatric Scholars website: www.gerischolars.org. Through this expansion, GRECC Connect provides geriatric consultative and educational support to > 70 rural VA clinics in 10 of the 18 Veterans Integrated Service Networks (VISNs).

To assess the reduction in commute times from teleconsultation, we calculated the difference between the mileage from veteran homes to teleconsultation sites (ie, rural clinics) and the mileage from veteran homes to VAMCs where geriatric teams are located. We estimate that the 1622 veterans served in FY 2018 saved a total of 179 121 miles in travel through GRECC Connect. Veterans traveled 106 fewer miles and on average saved $58 in out-of-pocket savings (based on US General Services Administration 2018 standard mileage reimbursement rate of $0.545 per mile). However, many of the veterans have reported anecdotally that the reduction in mileage traveled was less important than the elimination of stress involved in urban navigating, driving, and parking.

More difficult to measure, GRECC Connect seeks to enhance veteran safety by reducing driving distances for older veterans whose driving abilities may be influenced by many age-related health conditions (eg, visual changes, cognitive impairment). For these and other reasons, surveyed veterans overwhelmingly reported that they would be likely to recommend teleconsultation services to other veterans, and that they preferred telemedicine consultation over traveling long distances for in-person clinical consultations.¹⁶

Since its inception in 2014, GRECC Connect has provided case-based education to a total of 2335 unique clinicians and staff. Participants have included physicians, nurse practitioners, RNs, social workers, and pharmacists. This distribution reflects the interdisciplinary nature of geriatric care. A plurality of participants (39%) were RNs. Surveyed participants in the GRECC Connect geriatrics case conference series report high overall satisfaction with the learning activity, acquisition of new knowledge and skills, and intention to apply new knowledge and skills to improve job performance.¹⁰ In addition, participants agreed that the online training platform was effective for learning and that they would recommend the education series to other HCPs.^10,16

Discussion

During its rapid 4-year scale up, GRECC Connect has established a national network and enhanced relationships between GRECC-based clinical teams and rural provider teams. In doing so, the program has begun to improve rural veterans’ access to geriatric specialty care. By providing continuing education to members of the interprofessional health care team, GRECC Connect develops rural providers’ clinical competency and promotes geriatrics skills and expertise. These activities are synergistic: Clinical support enables rural HCPs to become better at managing their own patients, while formal educational activities highlight the availability of specialized consultation available through GRECC Connect. Through ongoing creation of handbooks, workflows, and data analytic strategies, GRECC Connect aims to disseminate this model to additional GRECCs as well as other GEC programs to promote “anywhere to anywhere” VA health care.¹⁷

Barriers and Facilitators

GRECC Connect has had notable implementation challenges while new consultation relationships have been forged in order to provide geriatric expertise to rural areas where it is not otherwise available. Many GRECCs had already established connections with rural CBOCs. Among GRECCs that had previously established consultative relationships with rural clinics, the use of telehealth modalities to provide geriatric clinical resources has been a natural extension of these partnerships. GRECCs that lacked these connections, however, often had to obtain buy-in from multiple stakeholders, including rural HCPs and teams, administrative leads, and local telehealth coordinators, and they required VISN- and facility-level leadership to encourage and sustain rural team participation.

Depending on the distance of the GRECC hub-site to the CBOC, efforts to establish and sustain partnerships may require multiple contacts over time (eg, via face-to-face meetings, one-on-one outreach) and large-scale advertising of consultative services. Continuous engagement with CBOC-based teams also involves development of case finding strategies (eg, hospital discharge information, diagnoses, clinical criteria) to better identify veterans who may benefit from GRECC Connect consultation. Owing to the heterogeneity of technological resources, space, scheduling capacity, and staffing at CBOCs, GRECC sites continue to have variable engagement with their CBOC partners.

The inclusion of GRECC Connect within the Geriatric Scholars Program helps ensure that clinician scholars can serve as project champions at their respective rural sites. Rural HCPs with full-time clinical duties initially had difficulty carving out time to participate in GRECC Connect’s case-based conferences. However, the webinar platform has improved and sustained provider participation, and enduring recordings of the presentations allow clinicians to participate in the conferences at their convenience. Finally, the project experienced delays in taking certain administrative steps and hiring staff needed to support the establishment of telehealth modalities—even within a single health care system like the VA, each medical center and regional system has unique policies that complicate how telehealth modalities can be set up.

Conclusion and Future Directions

The GRECC Connect project aims to establish and support meaningful partnerships between urban geriatric specialists and rural HCPs to facilitate veterans’ increased access to geriatric specialty care. VA ORH has recognized it as a Rural Promising Practice, and GRECC Connect is currently being disseminated through an enterprise-wide initiative. Early evidence demonstrates that over 4 years, the expansion of GRECC Connect has helped meet critical aims of improving provider confidence and skills in geriatric management, and of increasing direct service provision. We have also used nationwide education platforms (eg, VA EES) to deliver geriatrics-focused education to health care teams.

Older rural veterans and their caregivers may benefit from this program through decreased travel-associated burden and report high satisfaction with these programs. Through a recently established collaboration with the GEC Data Analysis Center, we will use national data to refine our ability to identify at-risk, older rural veterans and to better evaluate their service needs and the GRECC Connect clinical impact. Because the VA is rapidly expanding use of telehealth and other virtual and digital methods to increase access to care, continued investments in telehealth are central to the VA 5-year strategic plan.¹⁸ In this spirit, GRECC Connect will continue to expand its program offerings and to leverage telehealth technologies to meet the needs of older veterans.

Acknowledgments

The authors wish to acknowledge Lisa Tenover, MD, PhD, (Palo Alto GRECC) for her contributions to this manuscript; the VA Rural Health Resource Center–Western Region; and GRECC Connect team members for their tireless work to ensure this project’s success. The GRECC Teams include Atlanta/Birmingham (Julia [Annette] Tedford, RN; Marquitta Cox, LMSW; Lisa Welch, LMSW; Mark Phillips; Lanie Walters, PharmD; Kroshona Tabb, PhD; Robert Langford, and Jason [Thomas] Sanders, HT, TCT); Bronx/NY Harbor (Ab Brody, RN; PhD, GNP-BC; Nick Koufacos, LMSW; and Shatice Jones); Canandaigua (Gary Kochersberger, MD; Suzanne Gillespie, MD; Gary Warner, PhD; Christie Hylwa, RPh CCP; Sharon Fell, LMSW; and Dorian Savino, MPA); Durham (Mamata Yanamadala, MBBS; Christy Knight, LCSW, MSW; and Julie Vognsen); Eastern Colorado (Larry Bourg, MD; Skotti Church, MD; Morgan Elmore, DO; Stephanie Hartz, LCSW; Carolyn Horney, MD; Steven Huart, AuD; Kathryn Nearing, PhD; Elizabeth O’Brien, PharmD; Laurence Robbins, MD; Robert Schwartz, MD; Karen Shea, MD; and Joleen Sussman, PhD); Little Rock (Prasad Padala, MD; and Tanya Taylor, RN); Madison (Ryan Bartkus, MD; Timothy Howell, MD; Lindsay Clark, PhD; Lauren Welch, PharmD, BCGP; Ellen Wanninger, MSW, CAPSW; Stacie Monson, RN, BSN; and Teresa Swader, MSW, LCSW); Miami (Carlos Gomez Orozo); New England (Malissa Kraft, PsyD); Palo Alto (Terri Huh, PhD, ABPP; Philip Choe, DO; Dawna Dougherty, LCSW; Ashley Scales, MPH); Pittsburgh (Stacey Shaffer, MD; Carol Dolbee, CRNP; Nancy Kovell, LCSW; Paul Bulgarelli, DO; Lauren Jost, PsyD; and Marcia Homer, RN-BC); and San Antonio (Becky Powers, MD; Che Kelly, RN, BSN; Cynthia Stewart, LCSW; Rebecca Rottman-Sagebiel, PharmD, BCPS, CGP; Melody Moris; Daniel MacCarthy; and Chen-pin Wang, PhD).

Asthma attacks account for 1.8 million emergency department (ED) visits each year in the US and for 10 deaths daily.¹ Management of asthma attacks includes administration of inhaled ß₂ adrenergic agonists, inhaled anticholinergic agents, IV magnesium sulfate, and corticosteroids.² Status asthmaticus is an intense acute exacerbation of asthma that does not respond to repeated treatments of bronchodilators and corticosteroids.³ It is a medical emergency requiring immediate recognition and treatment. The decision to intubate a patient with status asthmaticus is a clinical decision based on work of breathing, respiratory acidosis, and failure to respond to medical interventions.

In refractory cases of status asthmaticus, intubation and mechanical ventilation are undertaken to provide oxygenation and ventilation until the bronchospasm resolves. However, mechanical ventilation is associated with significant risks, including high end-inspiratory pressures, barotrauma, and volutrauma.⁴ Rescue therapies include muscle relaxation, infusion of ketamine (central acting nonopioid analgesic with bronchodilatory properties), heliox, and general anesthesia.^2,4 We report a case of a patient with life-threatening asthma and status asthmaticus treated with sevoflurane general anesthesia.

Case Presentation

A 55-year-old woman whose medical history was notable for asthma, psoriasis, hypothyroidism, tobacco, and alcohol abuse, and posttraumatic stress disorder (PTSD) presented to the ED. The patient had rarely sought medical attention and had no prior ED visits or hospitalizations in the electronic health record. Her home regimen included an albuterol inhaler used as needed. Her family reported that they had found her in distress in bed in a tripod position, unable to speak and struggling to breath.

Emergency medical services found the patient cyanotic, apneic, and pulseless. She received cardiopulmonary resuscitation for 30 seconds and 1-mg IV epinephrine, and spontaneous circulation returned. The patient arrived in the ED with an oral airway in place receiving bag valve mask ventilation. The patient expelled the oral airway. She was unable to speak due to dyspnea, exhibited persistent cyanosis, fatigue due to work of breathing, and failed to respond to nebulized albuterol/ipratropium bromide, IV methylprednisolone, and magnesium sulfate. The patient met criteria for acute severe asthma, or status asthmaticus. Thus, the patient received rapid sequence induction with rocuronium and ketamine and was intubated.

According to her family, the patient had no previous intensive care unit (ICU) admissions or prior intubations. Her only asthma medication was an albuterol inhaler as needed. The patient worked as a supervisor at a window blind manufacturing company. She lived alone, smoked 2 packs of cigarettes a day for more than 30 years, had no pets, drank unknown quantities of beer, wine, and hard liquor daily, and had smoked marijuana for several years.

The patient’s physical examination was notable for diffuse expiratory wheezes. Laboratory analysis revealed white blood cell count of 13.7 k/mcL, sodium 140 mmol/L, potassium 4.9 mmol/L, chloride 105 mmol/L, CO₂ 17 mmol/L, creatinine 0.98 mg/dL, troponin 0.03 ng/mL, lactate 7.2 mmol/L. Her chest X-ray showed hyperinflation but no focal opacities, pneumothorax, or pulmonary edema. Her endotracheal tube was in good position (Figure 1). A computed tomography pulmonary angiogram showed no pulmonary embolus or emphysema. There were atelectatic changes in the dependent portion of the right lower lobe, central bronchial wall thickening, and no stigmata of air trapping (Figure 2). An echocardiogram revealed a left ventricular ejection fraction of 45%, normal right ventricle and right ventricular size and function with an estimated right ventricular systolic pressure of 40 mm Hg.

The patient was admitted to the ICU and started on continuous infusion cisatracurium for paralysis and deep sedation to improve ventilatory synchrony and decrease auto positive end-expiratory pressure (PEEP). Mechanical ventilation was initiated with volume-cycled assist control ventilation, 6 mL/kg/ideal body weight (IBW) at 5-cm H₂O PEEP, and 1 minute ventilation of 10 liters. The patient had severe air trapping and high airway pressures. The dynamic PEEP was 22-cm H₂O (normal PEEP of 5-cm H₂O), peak airway pressure (PAP) 41-cm H₂O, and plateau pressure 31-cm H₂O. In addition, the arterial blood gas (ABG) showed severe hypercapnic respiratory acidosis without significant hypoxemia with pH 7.15, PaCO₂ 90 mm Hg, and PaO₂ 150 mm Hg.

Pressure controlled ventilation was attempted unsuccessfully due to high airway resistance. Ultimately, the patient was set on volume control with low tidal volume, 6 mL/kg/IBW, high flow 90 L/min, PEEP 0 cm of H₂O, and a low respiratory rate of 10 to achieve an inspiratory to expiratory (I:E) ratio of 1:7. Managing the ventilator to avoid dynamic hyperinflation and auto-PEEP, she remained relatively stable and improved.

By day 4 the patient’s ventilator was set to volume assist control with respiratory rate of 16, tidal volume, 6 mL/kg/IBW, PEEP 5-cm H₂O with auto PEEP of 3-cm H₂O, and fraction of inspired ABG O₂ (FiO₂) 0.35 with PAP of 46-cm H₂O and plateau pressure of 17-cm H₂O. The ABG was pH 7.32, PaCO₂ 65 mm Hg, and PaO₂ 74 mm Hg. However, on hospital day 5, she developed worsening PAP 60 to 77-cm H₂O, plateau pressures 17-cm H₂O, and a dynamic PEEP 16-cm H₂O and was unresponsive to ventilator maneuvers to lower airway pressures and improve ventilation.

The patient had been receiving continuous albuterol and ipratropium nebulizer treatments. Ketamine infusion was considered fraught with potential for a dissociative reaction due to the patient’s significant PTSD. The patient’s family requested avoidance of ketamine infusion since the patient was paralyzed and psychiatric effects could not be monitored. Heliox 80/20 mixture was considered; however, it is incompatible with the ventilator that was being used since it could not account for the density of the helium gas flow in the tidal volumes. Extracorporeal membrane oxygenation (ECMO) was not available at our facility, and the patient was not a candidate for the regional ECMO center.

On hospital day 8, the patient developed worsening respiratory acidosis. The patient’s PAP increased to > 77-cm H₂O, and her ABG revealed pH 7.22, PaCO₂ 90 mm Hg, and PaO₂ 77 mm Hg with FiO₂ 0.4. A chest X-ray demonstrated a new left lower lobe infiltrate. Fiber optic bronchoscopy was notable for scattered thick secretions throughout both lungs without obstructing mucus plug. Removal of airway secretions did not improve airway pressures or dynamic hyperinflation.

After consultation and discussion with the chief of anesthesia, the patient was placed on an anesthesia ventilator and started on sevoflurane 1.5% in the ICU. Anesthesiology was available 24 hours a day, and the anesthesiologist rounded with the intensivist frequently for this patient. The anesthesia technician worked closely with respiratory therapy regarding ventilator setting and changing the anesthesia gas scavenging charcoal canister. Within 4 hours, her gas exchange normalized (Table). The patient’s ABG was pH 7.44, PaCO₂ 52 mm Hg, and PaO₂ 69 mm Hg on FiO₂ 0.4. On volume cycled ventilation with a rate of 12, flow rate of 40 L/min, and tidal volume 6 mL/kg/IBW, the PAP decreased to 41-cm H₂O.

Within 24 hours bronchospasm improved as evidenced by decreased airway pressures, resolution of wheezing, and decreased CO₂ retention. The sevoflurane was easily weaned over the next 48 hours by decreasing the dose by 25% every 12-hour shift without rebound bronchospasm. Airway pressures and ABGs were frequently monitored during the weaning process. The patient resumed conventional mechanical ventilation, cisatracurium was discontinued, and she underwent a percutaneous tracheostomy for critical illness polymyopathy. Her respiratory muscle strength recovered more robustly than anticipated. Prior to discharge to a skilled nursing facility for continued rehabilitation, she was removed from mechanical ventilation and decannulated.

Discussion

This case illustrates the successful treatment of a patient with extreme status asthmaticus given inhalational anesthesia as supportive care while the bronchospasm and status asthmaticus abated. This is an unusual treatment in an ominous situation. Inhalational anesthetics are potent bronchodilators and have been successfully used in the management of status asthmaticus refractory to conventional therapy.⁴ Inhalational anesthetics have been shown to decrease airway resistance, dynamic hyperinflation, and intrinsic PEEP.⁵ These agents result in rapid bronchodilation by relaxing the smooth muscle and are associated with early liberation from mechanical ventilation.^5,6 Although there are no guidelines regarding which inhalational agent is best, specific dosing, duration, or titration, case reports in the literature regarding the successful use of inhalational agents in life-threatening status asthmaticus exist.^2,5,7

Caveats regarding the use of inhalational anesthetics in status asthmaticus include proarrhythmias, severe hepatic and renal toxicity. Although isoflurane is less likely to cause arrhythmia, both isoflurane and sevoflurane can cause dose-dependent hypotension by peripheral vasodilatation.⁷ Ourpatient did not manifest any adverse effects.

Additional challenges regarding the use of inhalational anesthetics for status asthmaticus include differences in ventilators and occupational hazards.⁸ Anesthesia or operating room ventilators differ from ICU ventilators in flow and pressure capabilities.⁷ The anesthesia ventilator is not capable of generating inspiratory pressures sufficient to ventilate patients with severely elevated airway resistance. Thus, the decrease inspiratory flow that occurs with increasing airway pressure limits the tidal volume delivered and consequently the minute volume. Although newer anesthesia ventilators have increased flow capabilities, they require a fully trained staff.⁸

Potential occupational exposure to these volatile anesthetic gases occurs as patients being treated may exhale considerable amounts of volatile anesthetics.⁸ An anesthesia gas scavenging device, such as a charcoal canister, must be attached to the ventilator to capture the exhaled anesthetic gases and should be changed every 12 hours.⁸ Finally, there is a potential for rebound bronchospasm as the anesthetic agent is tapered.^6,7,9-11

Conclusion

Inhalational anesthetics are an option as rescue therapy for severe life-threatening asthma when all other therapies have failed. Use of inhalational anesthetics in status asthmaticus consists of case reports of which half are in children.^2,5,7 Our patient contributes to the literature of case reports regarding using sevoflurane in refractory status asthmaticus. A decision to choose them must be a collaborative team approach with anesthesiology, pulmonary/critical care medicine, respiratory therapy, and ICU nurses, and the risks and benefits should be discussed with decision-making family members. Since there are no specific guidelines for the use of inhalational agents in status asthmaticus, close attention to inspiratory flows, gas scavenging devices, and clinical response is required. Additionally, the team must be comfortable with the plan to use an anesthesia ventilator and trained on its limitations.

Asthma attacks account for 1.8 million emergency department (ED) visits each year in the US and for 10 deaths daily.¹ Management of asthma attacks includes administration of inhaled ß₂ adrenergic agonists, inhaled anticholinergic agents, IV magnesium sulfate, and corticosteroids.² Status asthmaticus is an intense acute exacerbation of asthma that does not respond to repeated treatments of bronchodilators and corticosteroids.³ It is a medical emergency requiring immediate recognition and treatment. The decision to intubate a patient with status asthmaticus is a clinical decision based on work of breathing, respiratory acidosis, and failure to respond to medical interventions.

In refractory cases of status asthmaticus, intubation and mechanical ventilation are undertaken to provide oxygenation and ventilation until the bronchospasm resolves. However, mechanical ventilation is associated with significant risks, including high end-inspiratory pressures, barotrauma, and volutrauma.⁴ Rescue therapies include muscle relaxation, infusion of ketamine (central acting nonopioid analgesic with bronchodilatory properties), heliox, and general anesthesia.^2,4 We report a case of a patient with life-threatening asthma and status asthmaticus treated with sevoflurane general anesthesia.

Case Presentation

A 55-year-old woman whose medical history was notable for asthma, psoriasis, hypothyroidism, tobacco, and alcohol abuse, and posttraumatic stress disorder (PTSD) presented to the ED. The patient had rarely sought medical attention and had no prior ED visits or hospitalizations in the electronic health record. Her home regimen included an albuterol inhaler used as needed. Her family reported that they had found her in distress in bed in a tripod position, unable to speak and struggling to breath.

Emergency medical services found the patient cyanotic, apneic, and pulseless. She received cardiopulmonary resuscitation for 30 seconds and 1-mg IV epinephrine, and spontaneous circulation returned. The patient arrived in the ED with an oral airway in place receiving bag valve mask ventilation. The patient expelled the oral airway. She was unable to speak due to dyspnea, exhibited persistent cyanosis, fatigue due to work of breathing, and failed to respond to nebulized albuterol/ipratropium bromide, IV methylprednisolone, and magnesium sulfate. The patient met criteria for acute severe asthma, or status asthmaticus. Thus, the patient received rapid sequence induction with rocuronium and ketamine and was intubated.

According to her family, the patient had no previous intensive care unit (ICU) admissions or prior intubations. Her only asthma medication was an albuterol inhaler as needed. The patient worked as a supervisor at a window blind manufacturing company. She lived alone, smoked 2 packs of cigarettes a day for more than 30 years, had no pets, drank unknown quantities of beer, wine, and hard liquor daily, and had smoked marijuana for several years.

The patient’s physical examination was notable for diffuse expiratory wheezes. Laboratory analysis revealed white blood cell count of 13.7 k/mcL, sodium 140 mmol/L, potassium 4.9 mmol/L, chloride 105 mmol/L, CO₂ 17 mmol/L, creatinine 0.98 mg/dL, troponin 0.03 ng/mL, lactate 7.2 mmol/L. Her chest X-ray showed hyperinflation but no focal opacities, pneumothorax, or pulmonary edema. Her endotracheal tube was in good position (Figure 1). A computed tomography pulmonary angiogram showed no pulmonary embolus or emphysema. There were atelectatic changes in the dependent portion of the right lower lobe, central bronchial wall thickening, and no stigmata of air trapping (Figure 2). An echocardiogram revealed a left ventricular ejection fraction of 45%, normal right ventricle and right ventricular size and function with an estimated right ventricular systolic pressure of 40 mm Hg.

The patient was admitted to the ICU and started on continuous infusion cisatracurium for paralysis and deep sedation to improve ventilatory synchrony and decrease auto positive end-expiratory pressure (PEEP). Mechanical ventilation was initiated with volume-cycled assist control ventilation, 6 mL/kg/ideal body weight (IBW) at 5-cm H₂O PEEP, and 1 minute ventilation of 10 liters. The patient had severe air trapping and high airway pressures. The dynamic PEEP was 22-cm H₂O (normal PEEP of 5-cm H₂O), peak airway pressure (PAP) 41-cm H₂O, and plateau pressure 31-cm H₂O. In addition, the arterial blood gas (ABG) showed severe hypercapnic respiratory acidosis without significant hypoxemia with pH 7.15, PaCO₂ 90 mm Hg, and PaO₂ 150 mm Hg.

Pressure controlled ventilation was attempted unsuccessfully due to high airway resistance. Ultimately, the patient was set on volume control with low tidal volume, 6 mL/kg/IBW, high flow 90 L/min, PEEP 0 cm of H₂O, and a low respiratory rate of 10 to achieve an inspiratory to expiratory (I:E) ratio of 1:7. Managing the ventilator to avoid dynamic hyperinflation and auto-PEEP, she remained relatively stable and improved.

By day 4 the patient’s ventilator was set to volume assist control with respiratory rate of 16, tidal volume, 6 mL/kg/IBW, PEEP 5-cm H₂O with auto PEEP of 3-cm H₂O, and fraction of inspired ABG O₂ (FiO₂) 0.35 with PAP of 46-cm H₂O and plateau pressure of 17-cm H₂O. The ABG was pH 7.32, PaCO₂ 65 mm Hg, and PaO₂ 74 mm Hg. However, on hospital day 5, she developed worsening PAP 60 to 77-cm H₂O, plateau pressures 17-cm H₂O, and a dynamic PEEP 16-cm H₂O and was unresponsive to ventilator maneuvers to lower airway pressures and improve ventilation.

The patient had been receiving continuous albuterol and ipratropium nebulizer treatments. Ketamine infusion was considered fraught with potential for a dissociative reaction due to the patient’s significant PTSD. The patient’s family requested avoidance of ketamine infusion since the patient was paralyzed and psychiatric effects could not be monitored. Heliox 80/20 mixture was considered; however, it is incompatible with the ventilator that was being used since it could not account for the density of the helium gas flow in the tidal volumes. Extracorporeal membrane oxygenation (ECMO) was not available at our facility, and the patient was not a candidate for the regional ECMO center.

On hospital day 8, the patient developed worsening respiratory acidosis. The patient’s PAP increased to > 77-cm H₂O, and her ABG revealed pH 7.22, PaCO₂ 90 mm Hg, and PaO₂ 77 mm Hg with FiO₂ 0.4. A chest X-ray demonstrated a new left lower lobe infiltrate. Fiber optic bronchoscopy was notable for scattered thick secretions throughout both lungs without obstructing mucus plug. Removal of airway secretions did not improve airway pressures or dynamic hyperinflation.

After consultation and discussion with the chief of anesthesia, the patient was placed on an anesthesia ventilator and started on sevoflurane 1.5% in the ICU. Anesthesiology was available 24 hours a day, and the anesthesiologist rounded with the intensivist frequently for this patient. The anesthesia technician worked closely with respiratory therapy regarding ventilator setting and changing the anesthesia gas scavenging charcoal canister. Within 4 hours, her gas exchange normalized (Table). The patient’s ABG was pH 7.44, PaCO₂ 52 mm Hg, and PaO₂ 69 mm Hg on FiO₂ 0.4. On volume cycled ventilation with a rate of 12, flow rate of 40 L/min, and tidal volume 6 mL/kg/IBW, the PAP decreased to 41-cm H₂O.

Within 24 hours bronchospasm improved as evidenced by decreased airway pressures, resolution of wheezing, and decreased CO₂ retention. The sevoflurane was easily weaned over the next 48 hours by decreasing the dose by 25% every 12-hour shift without rebound bronchospasm. Airway pressures and ABGs were frequently monitored during the weaning process. The patient resumed conventional mechanical ventilation, cisatracurium was discontinued, and she underwent a percutaneous tracheostomy for critical illness polymyopathy. Her respiratory muscle strength recovered more robustly than anticipated. Prior to discharge to a skilled nursing facility for continued rehabilitation, she was removed from mechanical ventilation and decannulated.

Discussion

This case illustrates the successful treatment of a patient with extreme status asthmaticus given inhalational anesthesia as supportive care while the bronchospasm and status asthmaticus abated. This is an unusual treatment in an ominous situation. Inhalational anesthetics are potent bronchodilators and have been successfully used in the management of status asthmaticus refractory to conventional therapy.⁴ Inhalational anesthetics have been shown to decrease airway resistance, dynamic hyperinflation, and intrinsic PEEP.⁵ These agents result in rapid bronchodilation by relaxing the smooth muscle and are associated with early liberation from mechanical ventilation.^5,6 Although there are no guidelines regarding which inhalational agent is best, specific dosing, duration, or titration, case reports in the literature regarding the successful use of inhalational agents in life-threatening status asthmaticus exist.^2,5,7

Caveats regarding the use of inhalational anesthetics in status asthmaticus include proarrhythmias, severe hepatic and renal toxicity. Although isoflurane is less likely to cause arrhythmia, both isoflurane and sevoflurane can cause dose-dependent hypotension by peripheral vasodilatation.⁷ Ourpatient did not manifest any adverse effects.

Additional challenges regarding the use of inhalational anesthetics for status asthmaticus include differences in ventilators and occupational hazards.⁸ Anesthesia or operating room ventilators differ from ICU ventilators in flow and pressure capabilities.⁷ The anesthesia ventilator is not capable of generating inspiratory pressures sufficient to ventilate patients with severely elevated airway resistance. Thus, the decrease inspiratory flow that occurs with increasing airway pressure limits the tidal volume delivered and consequently the minute volume. Although newer anesthesia ventilators have increased flow capabilities, they require a fully trained staff.⁸

Potential occupational exposure to these volatile anesthetic gases occurs as patients being treated may exhale considerable amounts of volatile anesthetics.⁸ An anesthesia gas scavenging device, such as a charcoal canister, must be attached to the ventilator to capture the exhaled anesthetic gases and should be changed every 12 hours.⁸ Finally, there is a potential for rebound bronchospasm as the anesthetic agent is tapered.^6,7,9-11

Conclusion

Inhalational anesthetics are an option as rescue therapy for severe life-threatening asthma when all other therapies have failed. Use of inhalational anesthetics in status asthmaticus consists of case reports of which half are in children.^2,5,7 Our patient contributes to the literature of case reports regarding using sevoflurane in refractory status asthmaticus. A decision to choose them must be a collaborative team approach with anesthesiology, pulmonary/critical care medicine, respiratory therapy, and ICU nurses, and the risks and benefits should be discussed with decision-making family members. Since there are no specific guidelines for the use of inhalational agents in status asthmaticus, close attention to inspiratory flows, gas scavenging devices, and clinical response is required. Additionally, the team must be comfortable with the plan to use an anesthesia ventilator and trained on its limitations.

Asthma attacks account for 1.8 million emergency department (ED) visits each year in the US and for 10 deaths daily.¹ Management of asthma attacks includes administration of inhaled ß₂ adrenergic agonists, inhaled anticholinergic agents, IV magnesium sulfate, and corticosteroids.² Status asthmaticus is an intense acute exacerbation of asthma that does not respond to repeated treatments of bronchodilators and corticosteroids.³ It is a medical emergency requiring immediate recognition and treatment. The decision to intubate a patient with status asthmaticus is a clinical decision based on work of breathing, respiratory acidosis, and failure to respond to medical interventions.

In refractory cases of status asthmaticus, intubation and mechanical ventilation are undertaken to provide oxygenation and ventilation until the bronchospasm resolves. However, mechanical ventilation is associated with significant risks, including high end-inspiratory pressures, barotrauma, and volutrauma.⁴ Rescue therapies include muscle relaxation, infusion of ketamine (central acting nonopioid analgesic with bronchodilatory properties), heliox, and general anesthesia.^2,4 We report a case of a patient with life-threatening asthma and status asthmaticus treated with sevoflurane general anesthesia.

Case Presentation

A 55-year-old woman whose medical history was notable for asthma, psoriasis, hypothyroidism, tobacco, and alcohol abuse, and posttraumatic stress disorder (PTSD) presented to the ED. The patient had rarely sought medical attention and had no prior ED visits or hospitalizations in the electronic health record. Her home regimen included an albuterol inhaler used as needed. Her family reported that they had found her in distress in bed in a tripod position, unable to speak and struggling to breath.

Emergency medical services found the patient cyanotic, apneic, and pulseless. She received cardiopulmonary resuscitation for 30 seconds and 1-mg IV epinephrine, and spontaneous circulation returned. The patient arrived in the ED with an oral airway in place receiving bag valve mask ventilation. The patient expelled the oral airway. She was unable to speak due to dyspnea, exhibited persistent cyanosis, fatigue due to work of breathing, and failed to respond to nebulized albuterol/ipratropium bromide, IV methylprednisolone, and magnesium sulfate. The patient met criteria for acute severe asthma, or status asthmaticus. Thus, the patient received rapid sequence induction with rocuronium and ketamine and was intubated.

According to her family, the patient had no previous intensive care unit (ICU) admissions or prior intubations. Her only asthma medication was an albuterol inhaler as needed. The patient worked as a supervisor at a window blind manufacturing company. She lived alone, smoked 2 packs of cigarettes a day for more than 30 years, had no pets, drank unknown quantities of beer, wine, and hard liquor daily, and had smoked marijuana for several years.

The patient’s physical examination was notable for diffuse expiratory wheezes. Laboratory analysis revealed white blood cell count of 13.7 k/mcL, sodium 140 mmol/L, potassium 4.9 mmol/L, chloride 105 mmol/L, CO₂ 17 mmol/L, creatinine 0.98 mg/dL, troponin 0.03 ng/mL, lactate 7.2 mmol/L. Her chest X-ray showed hyperinflation but no focal opacities, pneumothorax, or pulmonary edema. Her endotracheal tube was in good position (Figure 1). A computed tomography pulmonary angiogram showed no pulmonary embolus or emphysema. There were atelectatic changes in the dependent portion of the right lower lobe, central bronchial wall thickening, and no stigmata of air trapping (Figure 2). An echocardiogram revealed a left ventricular ejection fraction of 45%, normal right ventricle and right ventricular size and function with an estimated right ventricular systolic pressure of 40 mm Hg.

The patient was admitted to the ICU and started on continuous infusion cisatracurium for paralysis and deep sedation to improve ventilatory synchrony and decrease auto positive end-expiratory pressure (PEEP). Mechanical ventilation was initiated with volume-cycled assist control ventilation, 6 mL/kg/ideal body weight (IBW) at 5-cm H₂O PEEP, and 1 minute ventilation of 10 liters. The patient had severe air trapping and high airway pressures. The dynamic PEEP was 22-cm H₂O (normal PEEP of 5-cm H₂O), peak airway pressure (PAP) 41-cm H₂O, and plateau pressure 31-cm H₂O. In addition, the arterial blood gas (ABG) showed severe hypercapnic respiratory acidosis without significant hypoxemia with pH 7.15, PaCO₂ 90 mm Hg, and PaO₂ 150 mm Hg.

Pressure controlled ventilation was attempted unsuccessfully due to high airway resistance. Ultimately, the patient was set on volume control with low tidal volume, 6 mL/kg/IBW, high flow 90 L/min, PEEP 0 cm of H₂O, and a low respiratory rate of 10 to achieve an inspiratory to expiratory (I:E) ratio of 1:7. Managing the ventilator to avoid dynamic hyperinflation and auto-PEEP, she remained relatively stable and improved.

By day 4 the patient’s ventilator was set to volume assist control with respiratory rate of 16, tidal volume, 6 mL/kg/IBW, PEEP 5-cm H₂O with auto PEEP of 3-cm H₂O, and fraction of inspired ABG O₂ (FiO₂) 0.35 with PAP of 46-cm H₂O and plateau pressure of 17-cm H₂O. The ABG was pH 7.32, PaCO₂ 65 mm Hg, and PaO₂ 74 mm Hg. However, on hospital day 5, she developed worsening PAP 60 to 77-cm H₂O, plateau pressures 17-cm H₂O, and a dynamic PEEP 16-cm H₂O and was unresponsive to ventilator maneuvers to lower airway pressures and improve ventilation.

The patient had been receiving continuous albuterol and ipratropium nebulizer treatments. Ketamine infusion was considered fraught with potential for a dissociative reaction due to the patient’s significant PTSD. The patient’s family requested avoidance of ketamine infusion since the patient was paralyzed and psychiatric effects could not be monitored. Heliox 80/20 mixture was considered; however, it is incompatible with the ventilator that was being used since it could not account for the density of the helium gas flow in the tidal volumes. Extracorporeal membrane oxygenation (ECMO) was not available at our facility, and the patient was not a candidate for the regional ECMO center.

On hospital day 8, the patient developed worsening respiratory acidosis. The patient’s PAP increased to > 77-cm H₂O, and her ABG revealed pH 7.22, PaCO₂ 90 mm Hg, and PaO₂ 77 mm Hg with FiO₂ 0.4. A chest X-ray demonstrated a new left lower lobe infiltrate. Fiber optic bronchoscopy was notable for scattered thick secretions throughout both lungs without obstructing mucus plug. Removal of airway secretions did not improve airway pressures or dynamic hyperinflation.

After consultation and discussion with the chief of anesthesia, the patient was placed on an anesthesia ventilator and started on sevoflurane 1.5% in the ICU. Anesthesiology was available 24 hours a day, and the anesthesiologist rounded with the intensivist frequently for this patient. The anesthesia technician worked closely with respiratory therapy regarding ventilator setting and changing the anesthesia gas scavenging charcoal canister. Within 4 hours, her gas exchange normalized (Table). The patient’s ABG was pH 7.44, PaCO₂ 52 mm Hg, and PaO₂ 69 mm Hg on FiO₂ 0.4. On volume cycled ventilation with a rate of 12, flow rate of 40 L/min, and tidal volume 6 mL/kg/IBW, the PAP decreased to 41-cm H₂O.

Within 24 hours bronchospasm improved as evidenced by decreased airway pressures, resolution of wheezing, and decreased CO₂ retention. The sevoflurane was easily weaned over the next 48 hours by decreasing the dose by 25% every 12-hour shift without rebound bronchospasm. Airway pressures and ABGs were frequently monitored during the weaning process. The patient resumed conventional mechanical ventilation, cisatracurium was discontinued, and she underwent a percutaneous tracheostomy for critical illness polymyopathy. Her respiratory muscle strength recovered more robustly than anticipated. Prior to discharge to a skilled nursing facility for continued rehabilitation, she was removed from mechanical ventilation and decannulated.

Discussion

This case illustrates the successful treatment of a patient with extreme status asthmaticus given inhalational anesthesia as supportive care while the bronchospasm and status asthmaticus abated. This is an unusual treatment in an ominous situation. Inhalational anesthetics are potent bronchodilators and have been successfully used in the management of status asthmaticus refractory to conventional therapy.⁴ Inhalational anesthetics have been shown to decrease airway resistance, dynamic hyperinflation, and intrinsic PEEP.⁵ These agents result in rapid bronchodilation by relaxing the smooth muscle and are associated with early liberation from mechanical ventilation.^5,6 Although there are no guidelines regarding which inhalational agent is best, specific dosing, duration, or titration, case reports in the literature regarding the successful use of inhalational agents in life-threatening status asthmaticus exist.^2,5,7

Caveats regarding the use of inhalational anesthetics in status asthmaticus include proarrhythmias, severe hepatic and renal toxicity. Although isoflurane is less likely to cause arrhythmia, both isoflurane and sevoflurane can cause dose-dependent hypotension by peripheral vasodilatation.⁷ Ourpatient did not manifest any adverse effects.

Additional challenges regarding the use of inhalational anesthetics for status asthmaticus include differences in ventilators and occupational hazards.⁸ Anesthesia or operating room ventilators differ from ICU ventilators in flow and pressure capabilities.⁷ The anesthesia ventilator is not capable of generating inspiratory pressures sufficient to ventilate patients with severely elevated airway resistance. Thus, the decrease inspiratory flow that occurs with increasing airway pressure limits the tidal volume delivered and consequently the minute volume. Although newer anesthesia ventilators have increased flow capabilities, they require a fully trained staff.⁸

Potential occupational exposure to these volatile anesthetic gases occurs as patients being treated may exhale considerable amounts of volatile anesthetics.⁸ An anesthesia gas scavenging device, such as a charcoal canister, must be attached to the ventilator to capture the exhaled anesthetic gases and should be changed every 12 hours.⁸ Finally, there is a potential for rebound bronchospasm as the anesthetic agent is tapered.^6,7,9-11

Conclusion

Inhalational anesthetics are an option as rescue therapy for severe life-threatening asthma when all other therapies have failed. Use of inhalational anesthetics in status asthmaticus consists of case reports of which half are in children.^2,5,7 Our patient contributes to the literature of case reports regarding using sevoflurane in refractory status asthmaticus. A decision to choose them must be a collaborative team approach with anesthesiology, pulmonary/critical care medicine, respiratory therapy, and ICU nurses, and the risks and benefits should be discussed with decision-making family members. Since there are no specific guidelines for the use of inhalational agents in status asthmaticus, close attention to inspiratory flows, gas scavenging devices, and clinical response is required. Additionally, the team must be comfortable with the plan to use an anesthesia ventilator and trained on its limitations.

In the current health care environment, hospitals are constantly challenged to improve quality metrics and deliver better health care outcomes. One means to achieving quality improvement is through the use of order sets, groups of related orders that a health care provider (HCP) can place with either a few keystrokes or mouse clicks.¹

Historically, design of order sets has largely focused on clicking checkboxes containing evidence-based practices. According to Bates and colleagues and the Institute for Safe Medication Practices, incorporating evidence-based medicine (EBM) into order sets is not by itself sufficient.^2,3Execution of proper design coupled with simplicity and provider efficiency is paramount to HCP buy-in, increased likelihood of order set adherence, and to potentially better outcomes.

In this article, we outline advancements in order set design. These improvements increase provider efficiency and ease of use; incorporate human factors engineering (HFE); apply failure mode and effects analysis; and include EBM.

Methods

An inpatient nicotine replacement therapy (NRT) order was developed as part of a multifaceted solution to improve tobacco cessation care at the James A. Haley Veterans’ Hospital (JAHVH) in Tampa, Florida, a complexity level 1a facility. This NRT order set used the 4-step order set design framework the authors’ developed (for additional information about the NRT order set, contact the authors). We distinguish order set design technique between 2 different inpatient NRT order sets. The first order set in the comparison (Figure 1) is an inpatient NRT order set of unknown origin—it is common for US Department of Veterans Affairs (VA) medical facilities to share order sets and other resources. The second order set (Figure 2) is an inpatient NRT order set we designed using our 4-step process for comparison in this article. No institutional review board approval was required as this work met criteria for operational improvement activities exempt from ethics review.

Justin Iannello, DO, MBA, was the team leader and developer of the 4-step order set design technique. The intervention team consisted of 4 internal medicine physicians with expertise in quality improvement and patient safety: 1 certified professional in patient safety and certified as a Lean Six Sigma Black Belt; 2 physicians certified as Lean Six Sigma Black Belts; and 1 physician certified as a Lean Six Sigma Green Belt. Two inpatient clinical pharmacists and 1 quality management specialist also were involved in its development.

Development of a new NRT order set was felt to be an integral part of the tobacco cessation care delivery process. An NRT order set perceived by users as value-added required a solution that merged EBM with standardization and applied quality improvement principles. The result was an approach to order set design that focused on 4 key questions: Is the order set efficient and easy to use/navigate? Is human factors engineering incorporated? Is failure mode and effects analysis applied? Are evidence-based practices included?

Ease of Use and Navigation

Implementing an order set that is efficient and easy to use or navigate seems straightforward but can be difficult to execute. Figure 1 shows many detailed options consisting of different combinations of nicotine patches, lozenges, and gum. Also included are oral tobacco cessation options (bupropion and varenicline). Although more options may seem better, confusion about appropriate medication selection can occur.

According to Heath and Heath, too many options can result in lack of action.⁴ For example, Heath and Heath discuss a food store that offered 6 free samples of different jams on one day and 24 jams the following day. The customers who sampled 6 different types of jam were 10 times more likely to buy jam. The authors concluded that the more options available, the more difficulty a potential buyer has in deciding on a course of action.⁴

In clinical situations where a HCP is using an order set, the number of options can mean the difference between use vs avoidance if the choices are overwhelming. HCPs process layers of detail every day when creating differential diagnoses and treatment plans. While that level of detail is necessary clinically, that same level of detail included in orders sets can create challenges for HCPs.

Figure 2 advances the order set in Figure 1 by providing a simpler and cleaner design, so HCPs can more easily review and process the information. This order set design minimizes the number of options available to help users make the right decision, focusing on value for the appropriate setting and audience. In other words, order sets should not be a “one size fits all” approach.

Order sets should be tailored to the appropriate clinical setting (eg, inpatient acute care, outpatient clinic setting, etc) and HCP (eg, hospitalist, tobacco cessation specialist, etc). We are comparing NRT order sets designed for HCPs who do not routinely prescribe oral tobacco cessation products in the inpatient setting. When possible, autogenerated bundle orders should also be used according to evidence-based recommendations (such as nicotine patch tapers) for ease of use and further simplification of order sets.

Finally, usability testing known as “evaluating a product or service by testing it with representative users” helps further refine an order set.⁵Usability testing should be applied during all phases of order set development with end user(s) as it helps identify problems with order set design prior to implementation. By applying usability testing, the order set becomes more meaningful and valued by the user.

Human Factors Engineering

HFE is “the study of all the factors that make it easier to do the work in the right way.”⁶ HFE seeks to identify, align, and apply processes for people and the world within which they live and work to promote safe and efficient practices, especially in relation to the technology and physical design features in their work environment.⁶

The average American adult makes about 35,000 decisions per day.⁷ Thus, there is potential for error at any moment. Design that does not take HFE into account can be dangerous. For example, when tube feed and IV line connectors look similar and are compatible, patients may inadvertently receive food administered directly into their bloodstream.⁸

HFE can and should be applied to order sets. Everything from the look, feel, and verbiage of an order set affects potential outcomes. For example, consider the impact even seemingly minor modifications can have on outcomes simply by guiding users in a different way: Figure 1 provides NRT options based on cigarette use per day, whereas Figure 2 conveys pack use per day in relation to the equivalent number of cigarettes used daily. These differences may seem small; however, it helps guide users to the right choice when considering that health care providers have been historically trained on social history gathering that emphasizes packs per day and pack-years.

Failure Mode and Effects Analysis

Failure mode and effects analysis (FMEA) is “a structured way to identify and address potential problems, or failures and their resulting effects on the system or process before an adverse event occurs.”⁹ The benefit of an order set must be weighed against the risk during development. FMEA should be applied during order set design to assess and limit risk just as with any other clinical care process.

FMEA examines both level of risk and frequency of risk occurrence associated with a new proposed process. For example, let’s evaluate an order set designed for pain control after surgery that consists of multiple high-risk opioids along with antihistamine medications for as-needed itch relief (a non-life-threatening adverse event (AE) of opioids well known by the medical community). An interdisciplinary FMEA team consisting of subject matter experts may examine how the process should flow in step-by-step detail and then discuss the benefit of a process and risk for potential error. A FMEA team would then analyze what could go wrong with each part of the process and assign a level of risk and risk frequency for various steps in the process, and then decide that certain steps should be modified or eliminated. Perhaps after FMEA, a facility might conclude that the risk of serious complications is high when you combine opioid use with antihistamine medications. The facility could decide to remove antihistamine medications from an order set if it is determined that risks outweigh benefits. While a root cause analysis might identify the cause of an AE after order set use, these situations can be prevented with FMEA.

When applying FMEA to Figure 1, while bupropion is known as an evidence-based oral tobacco cessation option, there is the possibility that bupropion could be inadvertently prescribed from the order set in a hospitalized patient with alcohol withdrawal and withdrawal seizure history. These potentially dangerous situations can be avoided with FMEA. Thus, although bupropion may be evidence-based for NRT, decisions regarding order set design using EBM alone are insufficient.

The practitioner must consider possible unintended consequences within order sets and target treatment options to the appropriate setting and audience. Although Figure 1 may appear to be more inclusive, the interdisciplinary committee designing the inpatient NRT order set felt there was heightened risk with introducing bupropion in Figure 1 and decided the risk would be lowered by removing bupropion from the redesigned NRT order set (Figure 2). In addition to the goal of balancing availability of NRT options with acceptable risk, Figure 2 also focused on building an NRT order set most applicable to the inpatient setting.

Including Evidence-Based Practices

EBM has become a routine part of clinical decision making. Therefore, including EBM in order set design is vital. EBM for NRT has demonstrated that combination therapy is more effective than is monotherapy to help tobacco users quit. Incremental doses of NRT are recommended for patients who use tobacco more frequently.¹⁰

As shown in Figures 1 and 2, both order set designs incorporate EBM for NRT. Although the importance of implementing EBM is evident, critical factors, such as HFE and FMEA make a difference with well-designed order sets.

Results

The 4-step order set design technique was used during development of an inpatient NRT order set at the JAHVH. Results for the inpatient Joint Commission Tobacco Treatment Measures were obtained from the Veterans Health Administration quality metric reporting system known as Strategic Analytics for Improvement and Learning (SAIL). SAIL performance measure outcomes, which include the inpatient Joint Commission Tobacco Treatment Measures, are derived from chart reviews conducted by the External Peer Review Program. Outcomes demonstrated that TOB-2 and TOB-3 (2 inpatient Joint Commission Tobacco Treatment Measures) known as tob20 and tob40, respectively, within SAIL improved by more than 300% after development of an NRT order set using the 4-step order set design framework along with implementation of a multifaceted tobacco cessation care delivery system at JAHVH.

Discussion

While the overall tobacco cessation care delivery system contributed to improved outcomes with the inpatient Joint Commission Tobacco Treatment Measures at JAHVH, the NRT order set was a cornerstone of the design. Although using our order set design technique does not necessarily guarantee successful outcomes, we believe using the 4-step order set design process increases the value of order sets and has potential to improve quality outcomes.

Limitations

Although improved outcomes following implementation of our NRT order set suggest correlation, causation cannot be proven. Also while the NRT order set is believed to have helped tremendously with outcomes, the entire tobacco cessation care delivery system at JAHVH contributed to the results. In addition, the inpatient Joint Commission Tobacco Treatment Measures help improve processes for tobacco cessation care. However, we are uncertain whether the results of our improvement efforts helped patients stop tobacco use. Further studies are needed to determine impact on population health. Finally, our results were based on improvement work done at a single center. Further studies are necessary to see whether results are reproducible.

Conclusion

There was significant improvement with the inpatient Joint Commission Tobacco Treatment Measures outcomes following development of a tobacco cessation care delivery system that included design of an inpatient NRT order set using a 4-step process we developed. This 4-step structure includes emphasis on efficiency and ease of use; human factors engineering; failure mode and effects analysis; and incorporation of evidence-based medicine (Box.) Postimplementation results showed improvement of the inpatient Joint Commission Tobacco Treatment Measures by greater than 3-fold at a single hospital.

The next steps for this initiative include testing the 4-step order set design process in multiple clinical settings to determine the effectiveness of this approach in other areas of clinical care.

In the current health care environment, hospitals are constantly challenged to improve quality metrics and deliver better health care outcomes. One means to achieving quality improvement is through the use of order sets, groups of related orders that a health care provider (HCP) can place with either a few keystrokes or mouse clicks.¹

Historically, design of order sets has largely focused on clicking checkboxes containing evidence-based practices. According to Bates and colleagues and the Institute for Safe Medication Practices, incorporating evidence-based medicine (EBM) into order sets is not by itself sufficient.^2,3Execution of proper design coupled with simplicity and provider efficiency is paramount to HCP buy-in, increased likelihood of order set adherence, and to potentially better outcomes.

In this article, we outline advancements in order set design. These improvements increase provider efficiency and ease of use; incorporate human factors engineering (HFE); apply failure mode and effects analysis; and include EBM.

Methods

An inpatient nicotine replacement therapy (NRT) order was developed as part of a multifaceted solution to improve tobacco cessation care at the James A. Haley Veterans’ Hospital (JAHVH) in Tampa, Florida, a complexity level 1a facility. This NRT order set used the 4-step order set design framework the authors’ developed (for additional information about the NRT order set, contact the authors). We distinguish order set design technique between 2 different inpatient NRT order sets. The first order set in the comparison (Figure 1) is an inpatient NRT order set of unknown origin—it is common for US Department of Veterans Affairs (VA) medical facilities to share order sets and other resources. The second order set (Figure 2) is an inpatient NRT order set we designed using our 4-step process for comparison in this article. No institutional review board approval was required as this work met criteria for operational improvement activities exempt from ethics review.

Justin Iannello, DO, MBA, was the team leader and developer of the 4-step order set design technique. The intervention team consisted of 4 internal medicine physicians with expertise in quality improvement and patient safety: 1 certified professional in patient safety and certified as a Lean Six Sigma Black Belt; 2 physicians certified as Lean Six Sigma Black Belts; and 1 physician certified as a Lean Six Sigma Green Belt. Two inpatient clinical pharmacists and 1 quality management specialist also were involved in its development.

Development of a new NRT order set was felt to be an integral part of the tobacco cessation care delivery process. An NRT order set perceived by users as value-added required a solution that merged EBM with standardization and applied quality improvement principles. The result was an approach to order set design that focused on 4 key questions: Is the order set efficient and easy to use/navigate? Is human factors engineering incorporated? Is failure mode and effects analysis applied? Are evidence-based practices included?

Ease of Use and Navigation

Implementing an order set that is efficient and easy to use or navigate seems straightforward but can be difficult to execute. Figure 1 shows many detailed options consisting of different combinations of nicotine patches, lozenges, and gum. Also included are oral tobacco cessation options (bupropion and varenicline). Although more options may seem better, confusion about appropriate medication selection can occur.

According to Heath and Heath, too many options can result in lack of action.⁴ For example, Heath and Heath discuss a food store that offered 6 free samples of different jams on one day and 24 jams the following day. The customers who sampled 6 different types of jam were 10 times more likely to buy jam. The authors concluded that the more options available, the more difficulty a potential buyer has in deciding on a course of action.⁴

In clinical situations where a HCP is using an order set, the number of options can mean the difference between use vs avoidance if the choices are overwhelming. HCPs process layers of detail every day when creating differential diagnoses and treatment plans. While that level of detail is necessary clinically, that same level of detail included in orders sets can create challenges for HCPs.

Figure 2 advances the order set in Figure 1 by providing a simpler and cleaner design, so HCPs can more easily review and process the information. This order set design minimizes the number of options available to help users make the right decision, focusing on value for the appropriate setting and audience. In other words, order sets should not be a “one size fits all” approach.

Order sets should be tailored to the appropriate clinical setting (eg, inpatient acute care, outpatient clinic setting, etc) and HCP (eg, hospitalist, tobacco cessation specialist, etc). We are comparing NRT order sets designed for HCPs who do not routinely prescribe oral tobacco cessation products in the inpatient setting. When possible, autogenerated bundle orders should also be used according to evidence-based recommendations (such as nicotine patch tapers) for ease of use and further simplification of order sets.

Finally, usability testing known as “evaluating a product or service by testing it with representative users” helps further refine an order set.⁵Usability testing should be applied during all phases of order set development with end user(s) as it helps identify problems with order set design prior to implementation. By applying usability testing, the order set becomes more meaningful and valued by the user.

Human Factors Engineering

HFE is “the study of all the factors that make it easier to do the work in the right way.”⁶ HFE seeks to identify, align, and apply processes for people and the world within which they live and work to promote safe and efficient practices, especially in relation to the technology and physical design features in their work environment.⁶

The average American adult makes about 35,000 decisions per day.⁷ Thus, there is potential for error at any moment. Design that does not take HFE into account can be dangerous. For example, when tube feed and IV line connectors look similar and are compatible, patients may inadvertently receive food administered directly into their bloodstream.⁸

HFE can and should be applied to order sets. Everything from the look, feel, and verbiage of an order set affects potential outcomes. For example, consider the impact even seemingly minor modifications can have on outcomes simply by guiding users in a different way: Figure 1 provides NRT options based on cigarette use per day, whereas Figure 2 conveys pack use per day in relation to the equivalent number of cigarettes used daily. These differences may seem small; however, it helps guide users to the right choice when considering that health care providers have been historically trained on social history gathering that emphasizes packs per day and pack-years.

Failure Mode and Effects Analysis

Failure mode and effects analysis (FMEA) is “a structured way to identify and address potential problems, or failures and their resulting effects on the system or process before an adverse event occurs.”⁹ The benefit of an order set must be weighed against the risk during development. FMEA should be applied during order set design to assess and limit risk just as with any other clinical care process.

FMEA examines both level of risk and frequency of risk occurrence associated with a new proposed process. For example, let’s evaluate an order set designed for pain control after surgery that consists of multiple high-risk opioids along with antihistamine medications for as-needed itch relief (a non-life-threatening adverse event (AE) of opioids well known by the medical community). An interdisciplinary FMEA team consisting of subject matter experts may examine how the process should flow in step-by-step detail and then discuss the benefit of a process and risk for potential error. A FMEA team would then analyze what could go wrong with each part of the process and assign a level of risk and risk frequency for various steps in the process, and then decide that certain steps should be modified or eliminated. Perhaps after FMEA, a facility might conclude that the risk of serious complications is high when you combine opioid use with antihistamine medications. The facility could decide to remove antihistamine medications from an order set if it is determined that risks outweigh benefits. While a root cause analysis might identify the cause of an AE after order set use, these situations can be prevented with FMEA.

When applying FMEA to Figure 1, while bupropion is known as an evidence-based oral tobacco cessation option, there is the possibility that bupropion could be inadvertently prescribed from the order set in a hospitalized patient with alcohol withdrawal and withdrawal seizure history. These potentially dangerous situations can be avoided with FMEA. Thus, although bupropion may be evidence-based for NRT, decisions regarding order set design using EBM alone are insufficient.

The practitioner must consider possible unintended consequences within order sets and target treatment options to the appropriate setting and audience. Although Figure 1 may appear to be more inclusive, the interdisciplinary committee designing the inpatient NRT order set felt there was heightened risk with introducing bupropion in Figure 1 and decided the risk would be lowered by removing bupropion from the redesigned NRT order set (Figure 2). In addition to the goal of balancing availability of NRT options with acceptable risk, Figure 2 also focused on building an NRT order set most applicable to the inpatient setting.

Including Evidence-Based Practices

EBM has become a routine part of clinical decision making. Therefore, including EBM in order set design is vital. EBM for NRT has demonstrated that combination therapy is more effective than is monotherapy to help tobacco users quit. Incremental doses of NRT are recommended for patients who use tobacco more frequently.¹⁰

As shown in Figures 1 and 2, both order set designs incorporate EBM for NRT. Although the importance of implementing EBM is evident, critical factors, such as HFE and FMEA make a difference with well-designed order sets.

Results

The 4-step order set design technique was used during development of an inpatient NRT order set at the JAHVH. Results for the inpatient Joint Commission Tobacco Treatment Measures were obtained from the Veterans Health Administration quality metric reporting system known as Strategic Analytics for Improvement and Learning (SAIL). SAIL performance measure outcomes, which include the inpatient Joint Commission Tobacco Treatment Measures, are derived from chart reviews conducted by the External Peer Review Program. Outcomes demonstrated that TOB-2 and TOB-3 (2 inpatient Joint Commission Tobacco Treatment Measures) known as tob20 and tob40, respectively, within SAIL improved by more than 300% after development of an NRT order set using the 4-step order set design framework along with implementation of a multifaceted tobacco cessation care delivery system at JAHVH.

Discussion

While the overall tobacco cessation care delivery system contributed to improved outcomes with the inpatient Joint Commission Tobacco Treatment Measures at JAHVH, the NRT order set was a cornerstone of the design. Although using our order set design technique does not necessarily guarantee successful outcomes, we believe using the 4-step order set design process increases the value of order sets and has potential to improve quality outcomes.

Limitations

Although improved outcomes following implementation of our NRT order set suggest correlation, causation cannot be proven. Also while the NRT order set is believed to have helped tremendously with outcomes, the entire tobacco cessation care delivery system at JAHVH contributed to the results. In addition, the inpatient Joint Commission Tobacco Treatment Measures help improve processes for tobacco cessation care. However, we are uncertain whether the results of our improvement efforts helped patients stop tobacco use. Further studies are needed to determine impact on population health. Finally, our results were based on improvement work done at a single center. Further studies are necessary to see whether results are reproducible.

Conclusion

There was significant improvement with the inpatient Joint Commission Tobacco Treatment Measures outcomes following development of a tobacco cessation care delivery system that included design of an inpatient NRT order set using a 4-step process we developed. This 4-step structure includes emphasis on efficiency and ease of use; human factors engineering; failure mode and effects analysis; and incorporation of evidence-based medicine (Box.) Postimplementation results showed improvement of the inpatient Joint Commission Tobacco Treatment Measures by greater than 3-fold at a single hospital.

The next steps for this initiative include testing the 4-step order set design process in multiple clinical settings to determine the effectiveness of this approach in other areas of clinical care.

In the current health care environment, hospitals are constantly challenged to improve quality metrics and deliver better health care outcomes. One means to achieving quality improvement is through the use of order sets, groups of related orders that a health care provider (HCP) can place with either a few keystrokes or mouse clicks.¹

Historically, design of order sets has largely focused on clicking checkboxes containing evidence-based practices. According to Bates and colleagues and the Institute for Safe Medication Practices, incorporating evidence-based medicine (EBM) into order sets is not by itself sufficient.^2,3Execution of proper design coupled with simplicity and provider efficiency is paramount to HCP buy-in, increased likelihood of order set adherence, and to potentially better outcomes.

In this article, we outline advancements in order set design. These improvements increase provider efficiency and ease of use; incorporate human factors engineering (HFE); apply failure mode and effects analysis; and include EBM.

Methods

An inpatient nicotine replacement therapy (NRT) order was developed as part of a multifaceted solution to improve tobacco cessation care at the James A. Haley Veterans’ Hospital (JAHVH) in Tampa, Florida, a complexity level 1a facility. This NRT order set used the 4-step order set design framework the authors’ developed (for additional information about the NRT order set, contact the authors). We distinguish order set design technique between 2 different inpatient NRT order sets. The first order set in the comparison (Figure 1) is an inpatient NRT order set of unknown origin—it is common for US Department of Veterans Affairs (VA) medical facilities to share order sets and other resources. The second order set (Figure 2) is an inpatient NRT order set we designed using our 4-step process for comparison in this article. No institutional review board approval was required as this work met criteria for operational improvement activities exempt from ethics review.

Justin Iannello, DO, MBA, was the team leader and developer of the 4-step order set design technique. The intervention team consisted of 4 internal medicine physicians with expertise in quality improvement and patient safety: 1 certified professional in patient safety and certified as a Lean Six Sigma Black Belt; 2 physicians certified as Lean Six Sigma Black Belts; and 1 physician certified as a Lean Six Sigma Green Belt. Two inpatient clinical pharmacists and 1 quality management specialist also were involved in its development.

Development of a new NRT order set was felt to be an integral part of the tobacco cessation care delivery process. An NRT order set perceived by users as value-added required a solution that merged EBM with standardization and applied quality improvement principles. The result was an approach to order set design that focused on 4 key questions: Is the order set efficient and easy to use/navigate? Is human factors engineering incorporated? Is failure mode and effects analysis applied? Are evidence-based practices included?

Ease of Use and Navigation

Implementing an order set that is efficient and easy to use or navigate seems straightforward but can be difficult to execute. Figure 1 shows many detailed options consisting of different combinations of nicotine patches, lozenges, and gum. Also included are oral tobacco cessation options (bupropion and varenicline). Although more options may seem better, confusion about appropriate medication selection can occur.

According to Heath and Heath, too many options can result in lack of action.⁴ For example, Heath and Heath discuss a food store that offered 6 free samples of different jams on one day and 24 jams the following day. The customers who sampled 6 different types of jam were 10 times more likely to buy jam. The authors concluded that the more options available, the more difficulty a potential buyer has in deciding on a course of action.⁴

In clinical situations where a HCP is using an order set, the number of options can mean the difference between use vs avoidance if the choices are overwhelming. HCPs process layers of detail every day when creating differential diagnoses and treatment plans. While that level of detail is necessary clinically, that same level of detail included in orders sets can create challenges for HCPs.

Figure 2 advances the order set in Figure 1 by providing a simpler and cleaner design, so HCPs can more easily review and process the information. This order set design minimizes the number of options available to help users make the right decision, focusing on value for the appropriate setting and audience. In other words, order sets should not be a “one size fits all” approach.

Order sets should be tailored to the appropriate clinical setting (eg, inpatient acute care, outpatient clinic setting, etc) and HCP (eg, hospitalist, tobacco cessation specialist, etc). We are comparing NRT order sets designed for HCPs who do not routinely prescribe oral tobacco cessation products in the inpatient setting. When possible, autogenerated bundle orders should also be used according to evidence-based recommendations (such as nicotine patch tapers) for ease of use and further simplification of order sets.

Finally, usability testing known as “evaluating a product or service by testing it with representative users” helps further refine an order set.⁵Usability testing should be applied during all phases of order set development with end user(s) as it helps identify problems with order set design prior to implementation. By applying usability testing, the order set becomes more meaningful and valued by the user.

Human Factors Engineering

HFE is “the study of all the factors that make it easier to do the work in the right way.”⁶ HFE seeks to identify, align, and apply processes for people and the world within which they live and work to promote safe and efficient practices, especially in relation to the technology and physical design features in their work environment.⁶

The average American adult makes about 35,000 decisions per day.⁷ Thus, there is potential for error at any moment. Design that does not take HFE into account can be dangerous. For example, when tube feed and IV line connectors look similar and are compatible, patients may inadvertently receive food administered directly into their bloodstream.⁸

HFE can and should be applied to order sets. Everything from the look, feel, and verbiage of an order set affects potential outcomes. For example, consider the impact even seemingly minor modifications can have on outcomes simply by guiding users in a different way: Figure 1 provides NRT options based on cigarette use per day, whereas Figure 2 conveys pack use per day in relation to the equivalent number of cigarettes used daily. These differences may seem small; however, it helps guide users to the right choice when considering that health care providers have been historically trained on social history gathering that emphasizes packs per day and pack-years.

Failure Mode and Effects Analysis

Failure mode and effects analysis (FMEA) is “a structured way to identify and address potential problems, or failures and their resulting effects on the system or process before an adverse event occurs.”⁹ The benefit of an order set must be weighed against the risk during development. FMEA should be applied during order set design to assess and limit risk just as with any other clinical care process.

FMEA examines both level of risk and frequency of risk occurrence associated with a new proposed process. For example, let’s evaluate an order set designed for pain control after surgery that consists of multiple high-risk opioids along with antihistamine medications for as-needed itch relief (a non-life-threatening adverse event (AE) of opioids well known by the medical community). An interdisciplinary FMEA team consisting of subject matter experts may examine how the process should flow in step-by-step detail and then discuss the benefit of a process and risk for potential error. A FMEA team would then analyze what could go wrong with each part of the process and assign a level of risk and risk frequency for various steps in the process, and then decide that certain steps should be modified or eliminated. Perhaps after FMEA, a facility might conclude that the risk of serious complications is high when you combine opioid use with antihistamine medications. The facility could decide to remove antihistamine medications from an order set if it is determined that risks outweigh benefits. While a root cause analysis might identify the cause of an AE after order set use, these situations can be prevented with FMEA.

When applying FMEA to Figure 1, while bupropion is known as an evidence-based oral tobacco cessation option, there is the possibility that bupropion could be inadvertently prescribed from the order set in a hospitalized patient with alcohol withdrawal and withdrawal seizure history. These potentially dangerous situations can be avoided with FMEA. Thus, although bupropion may be evidence-based for NRT, decisions regarding order set design using EBM alone are insufficient.

The practitioner must consider possible unintended consequences within order sets and target treatment options to the appropriate setting and audience. Although Figure 1 may appear to be more inclusive, the interdisciplinary committee designing the inpatient NRT order set felt there was heightened risk with introducing bupropion in Figure 1 and decided the risk would be lowered by removing bupropion from the redesigned NRT order set (Figure 2). In addition to the goal of balancing availability of NRT options with acceptable risk, Figure 2 also focused on building an NRT order set most applicable to the inpatient setting.

Including Evidence-Based Practices

EBM has become a routine part of clinical decision making. Therefore, including EBM in order set design is vital. EBM for NRT has demonstrated that combination therapy is more effective than is monotherapy to help tobacco users quit. Incremental doses of NRT are recommended for patients who use tobacco more frequently.¹⁰

As shown in Figures 1 and 2, both order set designs incorporate EBM for NRT. Although the importance of implementing EBM is evident, critical factors, such as HFE and FMEA make a difference with well-designed order sets.

Results

The 4-step order set design technique was used during development of an inpatient NRT order set at the JAHVH. Results for the inpatient Joint Commission Tobacco Treatment Measures were obtained from the Veterans Health Administration quality metric reporting system known as Strategic Analytics for Improvement and Learning (SAIL). SAIL performance measure outcomes, which include the inpatient Joint Commission Tobacco Treatment Measures, are derived from chart reviews conducted by the External Peer Review Program. Outcomes demonstrated that TOB-2 and TOB-3 (2 inpatient Joint Commission Tobacco Treatment Measures) known as tob20 and tob40, respectively, within SAIL improved by more than 300% after development of an NRT order set using the 4-step order set design framework along with implementation of a multifaceted tobacco cessation care delivery system at JAHVH.

Discussion

While the overall tobacco cessation care delivery system contributed to improved outcomes with the inpatient Joint Commission Tobacco Treatment Measures at JAHVH, the NRT order set was a cornerstone of the design. Although using our order set design technique does not necessarily guarantee successful outcomes, we believe using the 4-step order set design process increases the value of order sets and has potential to improve quality outcomes.

Limitations

Although improved outcomes following implementation of our NRT order set suggest correlation, causation cannot be proven. Also while the NRT order set is believed to have helped tremendously with outcomes, the entire tobacco cessation care delivery system at JAHVH contributed to the results. In addition, the inpatient Joint Commission Tobacco Treatment Measures help improve processes for tobacco cessation care. However, we are uncertain whether the results of our improvement efforts helped patients stop tobacco use. Further studies are needed to determine impact on population health. Finally, our results were based on improvement work done at a single center. Further studies are necessary to see whether results are reproducible.

Conclusion

There was significant improvement with the inpatient Joint Commission Tobacco Treatment Measures outcomes following development of a tobacco cessation care delivery system that included design of an inpatient NRT order set using a 4-step process we developed. This 4-step structure includes emphasis on efficiency and ease of use; human factors engineering; failure mode and effects analysis; and incorporation of evidence-based medicine (Box.) Postimplementation results showed improvement of the inpatient Joint Commission Tobacco Treatment Measures by greater than 3-fold at a single hospital.

The next steps for this initiative include testing the 4-step order set design process in multiple clinical settings to determine the effectiveness of this approach in other areas of clinical care.

"Extreme remedies are very appropriate for extreme diseases"
- Hippocrates Aphorisms

On March 5, 2019, the US Food and Drug Administration (FDA) approved a nasal spray formulation of the drug ketamine, an old anesthetic that has been put to a new use over the past 10 years as therapy for treatment-resistant severe depression. Ketamine, known on the street as Special K, has long been known to cause dissociation, hallucinations, and other hallucinogenic effects. In many randomized controlled trials, subanesthetic doses administered intravenously have demonstrated rapid and often dramatic relief of depressive symptoms.

Neuroscientists have heralded ketamine as the paradigm of the glutamatergic modifying drug class, which represents the first real breakthrough in the pharmaceutical treatment of depression in decades.¹ There have been 2 major pharmacologic limitations attached to this promising new treatment: the IV form of the drug and the short duration of its antidepressant effect. Pharmaceutical companies and neuroscientists predictably have been engaging in a fast and furious race to successfully overcome these obstacles, hoping to win fame and fortune and bring hope and help to the millions of patients who have failed to fully respond to or been unable to tolerate existing therapies for mood disorders.²

When the FDA approved Spravato (esketamine), a nasal administration of ketamine, many people hoped that researchers had succeeded in overcoming these barriers. The risks of serious adverse events (AEs) as well as the potential for abuse and diversion led the FDA to limit prescriptions under a Risk Evaluation and Mitigation Strategy (REMS).³ Patients self-administer the nasal spray but only in a certified medical facility under the observation of a health care practitioner. Patients also must agree to remain on site for 2 hours after administration of the drug to ensure their safety. The FDA recommends the drug be given twice a week for 4 weeks along with a conventional monoamine-acting antidepressant.When the US Department of Veterans Affairs (VA) cleared the way for use of esketamine, less than 2 weeks after the FDA approval, it also launched a series of controversies over how to use the drug in its massive health care system, which is the subject of this editorial. On March 19, 2019, the VA announced that VA practitioners would be able to prescribe the nasal spray for patients who were determined to have treatment-resistant depression but only after appropriate clinical assessment and in accordance with their patients’ preferences.

A number of controversies have emerged surrounding the VA adoption of esketamine, including its cost/benefit/risk ratio and who should be able to access the medication. Each of these issues has onion layers of political, regulatory, and ethical concerns that can only be superficially noted here and warrant fuller unpeeling. In June The New York Times featured a story alleging that in response to the tragic tide of ever-increasing veteran suicides, the VA sanctioned esketamine prescribing despite its cost and the serious questions experts raised about the data the FDA cited to establish its safety and efficacy. Although the cost to the VA of Spravato is unclear, it is much higher than generic IV ketamine.⁴

The access controversy is almost the ethical inverse of the first. In June 2019, a Veterans Health Administration advisory panel voted against allowing general use of esketamine, limiting it to individual cases of patients who are preapproved and have failed 2 antidepressant trials. Esketamine will not be on the VA formulary for widespread use. Congressional and public advocacy groups have noted that the formulary decision came in the wake of ongoing attention to the role of the pharmaceutical industry in the VA’s rapid adoption of the drug.^5,6 For the thousands of veterans for whom the data show conventional antidepressants even in combination with other psychotropic medications and evidence-based psychotherapies resulted in AEs or only partial remission of depression symptoms, the VA’s restriction will likely seem unfair and even uncaring.⁷

As a practicing VA psychiatrist, I know firsthand how desperately we need new, more effective, and better-tolerated treatments for severe unipolar and bipolar depression. Although I have not prescribed ketamine or esketamine, several of my most respected colleagues do. I have seen patients with chronic, severe, depression respond and even recover in ways that seem just a little short of miraculous when compared with other therapies. Yet as a longtime student of the history of psychiatry, I have also seen that often the treatments that initially seem so auspicious, in time, turn out to have a dark side. Families, communities, the country, VA, and the US Department of Defense and its practitioners in and out of mental health cannot in any moral universe abide by the fact that 20 plus men and women who served take their lives every day.⁸

As the epigraph to this column notes, we must often try radical therapies for grave cases in drastic crises. Yet we must also in making serious public health decisions fraught with unseen consequences take all due and considered diligence that we do not violate the even more fundamental dictum of the Hippocratic School, “at least do not harm.” That means trying to balance safety and availability while VA conducts its own research in a precarious way that leaves almost no stakeholder completely happy.

"Extreme remedies are very appropriate for extreme diseases"
- Hippocrates Aphorisms

On March 5, 2019, the US Food and Drug Administration (FDA) approved a nasal spray formulation of the drug ketamine, an old anesthetic that has been put to a new use over the past 10 years as therapy for treatment-resistant severe depression. Ketamine, known on the street as Special K, has long been known to cause dissociation, hallucinations, and other hallucinogenic effects. In many randomized controlled trials, subanesthetic doses administered intravenously have demonstrated rapid and often dramatic relief of depressive symptoms.

Neuroscientists have heralded ketamine as the paradigm of the glutamatergic modifying drug class, which represents the first real breakthrough in the pharmaceutical treatment of depression in decades.¹ There have been 2 major pharmacologic limitations attached to this promising new treatment: the IV form of the drug and the short duration of its antidepressant effect. Pharmaceutical companies and neuroscientists predictably have been engaging in a fast and furious race to successfully overcome these obstacles, hoping to win fame and fortune and bring hope and help to the millions of patients who have failed to fully respond to or been unable to tolerate existing therapies for mood disorders.²

When the FDA approved Spravato (esketamine), a nasal administration of ketamine, many people hoped that researchers had succeeded in overcoming these barriers. The risks of serious adverse events (AEs) as well as the potential for abuse and diversion led the FDA to limit prescriptions under a Risk Evaluation and Mitigation Strategy (REMS).³ Patients self-administer the nasal spray but only in a certified medical facility under the observation of a health care practitioner. Patients also must agree to remain on site for 2 hours after administration of the drug to ensure their safety. The FDA recommends the drug be given twice a week for 4 weeks along with a conventional monoamine-acting antidepressant.When the US Department of Veterans Affairs (VA) cleared the way for use of esketamine, less than 2 weeks after the FDA approval, it also launched a series of controversies over how to use the drug in its massive health care system, which is the subject of this editorial. On March 19, 2019, the VA announced that VA practitioners would be able to prescribe the nasal spray for patients who were determined to have treatment-resistant depression but only after appropriate clinical assessment and in accordance with their patients’ preferences.

A number of controversies have emerged surrounding the VA adoption of esketamine, including its cost/benefit/risk ratio and who should be able to access the medication. Each of these issues has onion layers of political, regulatory, and ethical concerns that can only be superficially noted here and warrant fuller unpeeling. In June The New York Times featured a story alleging that in response to the tragic tide of ever-increasing veteran suicides, the VA sanctioned esketamine prescribing despite its cost and the serious questions experts raised about the data the FDA cited to establish its safety and efficacy. Although the cost to the VA of Spravato is unclear, it is much higher than generic IV ketamine.⁴

The access controversy is almost the ethical inverse of the first. In June 2019, a Veterans Health Administration advisory panel voted against allowing general use of esketamine, limiting it to individual cases of patients who are preapproved and have failed 2 antidepressant trials. Esketamine will not be on the VA formulary for widespread use. Congressional and public advocacy groups have noted that the formulary decision came in the wake of ongoing attention to the role of the pharmaceutical industry in the VA’s rapid adoption of the drug.^5,6 For the thousands of veterans for whom the data show conventional antidepressants even in combination with other psychotropic medications and evidence-based psychotherapies resulted in AEs or only partial remission of depression symptoms, the VA’s restriction will likely seem unfair and even uncaring.⁷

As a practicing VA psychiatrist, I know firsthand how desperately we need new, more effective, and better-tolerated treatments for severe unipolar and bipolar depression. Although I have not prescribed ketamine or esketamine, several of my most respected colleagues do. I have seen patients with chronic, severe, depression respond and even recover in ways that seem just a little short of miraculous when compared with other therapies. Yet as a longtime student of the history of psychiatry, I have also seen that often the treatments that initially seem so auspicious, in time, turn out to have a dark side. Families, communities, the country, VA, and the US Department of Defense and its practitioners in and out of mental health cannot in any moral universe abide by the fact that 20 plus men and women who served take their lives every day.⁸

As the epigraph to this column notes, we must often try radical therapies for grave cases in drastic crises. Yet we must also in making serious public health decisions fraught with unseen consequences take all due and considered diligence that we do not violate the even more fundamental dictum of the Hippocratic School, “at least do not harm.” That means trying to balance safety and availability while VA conducts its own research in a precarious way that leaves almost no stakeholder completely happy.

"Extreme remedies are very appropriate for extreme diseases"
- Hippocrates Aphorisms

On March 5, 2019, the US Food and Drug Administration (FDA) approved a nasal spray formulation of the drug ketamine, an old anesthetic that has been put to a new use over the past 10 years as therapy for treatment-resistant severe depression. Ketamine, known on the street as Special K, has long been known to cause dissociation, hallucinations, and other hallucinogenic effects. In many randomized controlled trials, subanesthetic doses administered intravenously have demonstrated rapid and often dramatic relief of depressive symptoms.

Neuroscientists have heralded ketamine as the paradigm of the glutamatergic modifying drug class, which represents the first real breakthrough in the pharmaceutical treatment of depression in decades.¹ There have been 2 major pharmacologic limitations attached to this promising new treatment: the IV form of the drug and the short duration of its antidepressant effect. Pharmaceutical companies and neuroscientists predictably have been engaging in a fast and furious race to successfully overcome these obstacles, hoping to win fame and fortune and bring hope and help to the millions of patients who have failed to fully respond to or been unable to tolerate existing therapies for mood disorders.²

When the FDA approved Spravato (esketamine), a nasal administration of ketamine, many people hoped that researchers had succeeded in overcoming these barriers. The risks of serious adverse events (AEs) as well as the potential for abuse and diversion led the FDA to limit prescriptions under a Risk Evaluation and Mitigation Strategy (REMS).³ Patients self-administer the nasal spray but only in a certified medical facility under the observation of a health care practitioner. Patients also must agree to remain on site for 2 hours after administration of the drug to ensure their safety. The FDA recommends the drug be given twice a week for 4 weeks along with a conventional monoamine-acting antidepressant.When the US Department of Veterans Affairs (VA) cleared the way for use of esketamine, less than 2 weeks after the FDA approval, it also launched a series of controversies over how to use the drug in its massive health care system, which is the subject of this editorial. On March 19, 2019, the VA announced that VA practitioners would be able to prescribe the nasal spray for patients who were determined to have treatment-resistant depression but only after appropriate clinical assessment and in accordance with their patients’ preferences.

A number of controversies have emerged surrounding the VA adoption of esketamine, including its cost/benefit/risk ratio and who should be able to access the medication. Each of these issues has onion layers of political, regulatory, and ethical concerns that can only be superficially noted here and warrant fuller unpeeling. In June The New York Times featured a story alleging that in response to the tragic tide of ever-increasing veteran suicides, the VA sanctioned esketamine prescribing despite its cost and the serious questions experts raised about the data the FDA cited to establish its safety and efficacy. Although the cost to the VA of Spravato is unclear, it is much higher than generic IV ketamine.⁴

The access controversy is almost the ethical inverse of the first. In June 2019, a Veterans Health Administration advisory panel voted against allowing general use of esketamine, limiting it to individual cases of patients who are preapproved and have failed 2 antidepressant trials. Esketamine will not be on the VA formulary for widespread use. Congressional and public advocacy groups have noted that the formulary decision came in the wake of ongoing attention to the role of the pharmaceutical industry in the VA’s rapid adoption of the drug.^5,6 For the thousands of veterans for whom the data show conventional antidepressants even in combination with other psychotropic medications and evidence-based psychotherapies resulted in AEs or only partial remission of depression symptoms, the VA’s restriction will likely seem unfair and even uncaring.⁷

As a practicing VA psychiatrist, I know firsthand how desperately we need new, more effective, and better-tolerated treatments for severe unipolar and bipolar depression. Although I have not prescribed ketamine or esketamine, several of my most respected colleagues do. I have seen patients with chronic, severe, depression respond and even recover in ways that seem just a little short of miraculous when compared with other therapies. Yet as a longtime student of the history of psychiatry, I have also seen that often the treatments that initially seem so auspicious, in time, turn out to have a dark side. Families, communities, the country, VA, and the US Department of Defense and its practitioners in and out of mental health cannot in any moral universe abide by the fact that 20 plus men and women who served take their lives every day.⁸

As the epigraph to this column notes, we must often try radical therapies for grave cases in drastic crises. Yet we must also in making serious public health decisions fraught with unseen consequences take all due and considered diligence that we do not violate the even more fundamental dictum of the Hippocratic School, “at least do not harm.” That means trying to balance safety and availability while VA conducts its own research in a precarious way that leaves almost no stakeholder completely happy.

Artificial intelligence (AI), first described in 1956, encompasses the field of computer science in which machines are trained to learn from experience. The term was popularized by the 1956 Dartmouth College Summer Research Project on Artificial Intelligence.¹ The field of AI is rapidly growing and has the potential to affect many aspects of our lives. The emerging importance of AI is demonstrated by a February 2019 executive order that launched the American AI Initiative, allocating resources and funding for AI development.² The executive order stresses the potential impact of AI in the health care field, including its potential utility to diagnose disease. Federal agencies were directed to invest in AI research and development to promote rapid breakthroughs in AI technology that may impact multiple areas of society.

Machine learning (ML), a subset of AI, was defined in 1959 by Arthur Samuel and is achieved by employing mathematic models to compute sample data sets.³ Originating from statistical linear models, neural networks were conceived to accomplish these tasks.⁴ These pioneering scientific achievements led to recent developments of deep neural networks. These models are developed to recognize patterns and achieve complex computational tasks within a matter of minutes, often far exceeding human ability.⁵ ML can increase efficiency with decreased computation time, high precision, and recall when compared with that of human decision making.⁶

ML has the potential for numerous applications in the health care field.^7-9 One promising application is in the field of anatomic pathology. ML allows representative images to be used to train a computer to recognize patterns from labeled photographs. Based on a set of images selected to represent a specific tissue or disease process, the computer can be trained to evaluate and recognize new and unique images from patients and render a diagnosis.¹⁰ Prior to modern ML models, users would have to import many thousands of training images to produce algorithms that could recognize patterns with high accuracy. Modern ML algorithms allow for a model known as transfer learning, such that far fewer images are required for training.^11-13

Two novel ML platforms available for public use are offered through Google (Mountain View, CA) and Apple (Cupertino, CA).^14,15 They each offer a user-friendly interface with minimal experience required in computer science. Google AutoML uses ML via cloud services to store and retrieve data with ease. No coding knowledge is required. The Apple Create ML Module provides computer-based ML, requiring only a few lines of code.

The Veterans Health Administration (VHA) is the largest single health care system in the US, and nearly 50 000 cancer cases are diagnosed at the VHA annually.¹⁶ Cancers of the lung and colon are among the most common sources of invasive cancer and are the 2 most common causes of cancer deaths in America.¹⁶ We have previously reported using Apple ML in detecting non-small cell lung cancers (NSCLCs), including adenocarcinomas and squamous cell carcinomas (SCCs); and colon cancers with accuracy.^17,18 In the present study, we expand on these findings by comparing Apple and Google ML platforms in a variety of common pathologic scenarios in veteran patients. Using limited training data, both programs are compared for precision and recall in differentiating conditions involving lung and colon pathology.

In the first 4 experiments, we evaluated the ability of the platforms to differentiate normal lung tissue from cancerous lung tissue, to distinguish lung adenocarcinoma from SCC, and to differentiate colon adenocarcinoma from normal colon tissue. Next, cases of colon adenocarcinoma were assessed to determine whether the presence or absence of the KRAS proto-oncogene could be determined histologically using the AI platforms. KRAS is found in a variety of cancers, including about 40% of colon adenocarcinomas.¹⁹ For colon cancers, the presence or absence of the mutation in KRAS has important implications for patients as it determines whether the tumor will respond to specific chemotherapy agents.²⁰ The presence of the KRAS gene is currently determined by complex molecular testing of tumor tissue.²¹ However, we assessed the potential of ML to determine whether the mutation is present by computerized morphologic analysis alone. Our last experiment examined the ability of the Apple and Google platforms to differentiate between adenocarcinomas of lung origin vs colon origin. This has potential utility in determining the site of origin of metastatic carcinoma.²²

Methods

Fifty cases of lung SCC, 50 cases of lung adenocarcinoma, and 50 cases of colon adenocarcinoma were randomly retrieved from our molecular database. Twenty-five colon adenocarcinoma cases were positive for mutation in KRAS, while 25 cases were negative for mutation in KRAS. Seven hundred fifty total images of lung tissue (250 benign lung tissue, 250 lung adenocarcinomas, and 250 lung SCCs) and 500 total images of colon tissue (250 benign colon tissue and 250 colon adenocarcinoma) were obtained using a Leica Microscope MC190 HD Camera (Wetzlar, Germany) connected to an Olympus BX41 microscope (Center Valley, PA) and the Leica Acquire 9072 software for Apple computers. All the images were captured at a resolution of 1024 x 768 pixels using a 60x dry objective. Lung tissue images were captured and saved on a 2012 Apple MacBook Pro computer, and colon images were captured and saved on a 2011 Apple iMac computer. Both computers were running macOS v10.13.

Creating Image Classifier Models Using Apple Create ML

Apple Create ML is a suite of products that use various tools to create and train custom ML models on Apple computers.¹⁵ The suite contains many features, including image classification to train a ML model to classify images, natural language processing to classify natural language text, and tabular data to train models that deal with labeling information or estimating new quantities. We used Create ML Image Classification to create image classifier models for our project (Appendix A).

Creating ML Modules Using Google Cloud AutoML Vision Beta

Google Cloud AutoML is a suite of machine learning products, including AutoML Vision, AutoML Natural Language and AutoML Translation.¹⁴ All Cloud AutoML machine learning products were in beta version at the time of experimentation. We used Cloud AutoML Vision beta to create ML modules for our project. Unlike Apple Create ML, which is run on a local Apple computer, the Google Cloud AutoML is run online using a Google Cloud account. There are no minimum specifications requirements for the local computer since it is using the cloud-based architecture (Appendix B).

Experiment 1

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect and subclassify NSCLC based on the histopathologic images. We created 3 classes of images (250 images each): benign lung tissue, lung adenocarcinoma, and lung SCC.

Experiment 2

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between normal lung tissue and NSCLC histopathologic images with 50/50 mixture of lung adenocarcinoma and lung SCC. We created 2 classes of images (250 images each): benign lung tissue and lung NSCLC.

Experiment 3

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and lung SCC histopathologic images. We created 2 classes of images (250 images each): adenocarcinoma and SCC.

Experiment 4

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect colon cancer histopathologic images regardless of mutation in KRAS status. We created 2 classes of images (250 images each): benign colon tissue and colon adenocarcinoma.

Experiment 5

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between colon adenocarcinoma with mutations in KRAS and colon adenocarcinoma without the mutation in KRAS histopathologic images. We created 2 classes of images (125 images each): colon adenocarcinoma cases with mutation in KRAS and colon adenocarcinoma cases without the mutation in KRAS.

Experiment 6

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and colon adenocarcinoma histopathologic images. We created 2 classes of images (250 images each): colon adenocarcinoma lung adenocarcinoma.

Results

Twelve machine learning models were created in 6 experiments using the Apple Create ML and the Google AutoML (Table). To investigate recall and precision differences between the Apple and the Google ML algorithms, we performed 2-tailed distribution, paired t tests. No statistically significant differences were found (P = .52 for recall and .60 for precision).

Overall, each model performed well in distinguishing between normal and neoplastic tissue for both lung and colon cancers. In subclassifying NSCLC into adenocarcinoma and SCC, the models were shown to have high levels of precision and recall. The models also were successful in distinguishing between lung and colonic origin of adenocarcinoma (Figures 1-4). However, both systems had trouble discerning colon adenocarcinoma with mutations in KRAS from adenocarcinoma without mutations in KRAS.

Discussion

Image classifier models using ML algorithms hold a promising future to revolutionize the health care field. ML products, such as those modules offered by Apple and Google, are easy to use and have a simple graphic user interface to allow individuals to train models to perform humanlike tasks in real time. In our experiments, we compared multiple algorithms to determine their ability to differentiate and subclassify histopathologic images with high precision and recall using common scenarios in treating veteran patients.

Analysis of the results revealed high precision and recall values illustrating the models’ ability to differentiate and detect benign lung tissue from lung SCC and lung adenocarcinoma in ML model 1, benign lung from NSCLC carcinoma in ML model 2, and benign colon from colonic adenocarcinoma in ML model 4. In ML model 3 and 6, both ML algorithms performed at a high level to differentiate lung SCC from lung adenocarcinoma and lung adenocarcinoma from colonic adenocarcinoma, respectively. Of note, ML model 5 had the lowest precision and recall values across both algorithms demonstrating the models’ limited utility in predicting molecular profiles, such as mutations in KRAS as tested here. This is not surprising as pathologists currently require complex molecular tests to detect mutations in KRAS reliably in colon cancer.

Both modules require minimal programming experience and are easy to use. In our comparison, we demonstrated critical distinguishing characteristics that differentiate the 2 products.

Apple Create ML image classifier is available for use on local Mac computers that use Xcode version 10 and macOS 10.14 or later, with just 3 lines of code required to perform computations. Although this product is limited to Apple computers, it is free to use, and images are stored on the computer hard drive. Of unique significance on the Apple system platform, images can be augmented to alter their appearance to enhance model training. For example, imported images can be cropped, rotated, blurred, and flipped, in order to optimize the model’s training abilities to recognize test images and perform pattern recognition. This feature is not as readily available on the Google platform. Apple Create ML Image classifier’s default training set consists of 75% of total imported images with 5% of the total images being randomly used as a validation set. The remaining 20% of images comprise the testing set. The module’s computational analysis to train the model is achieved in about 2 minutes on average. The score threshold is set at 50% and cannot be manipulated for each image class as in Google AutoML Vision.

Google AutoML Vision is open and can be accessed from many devices. It stores images on remote Google servers but requires computing fees after a $300 credit for 12 months. On AutoML Vision, random 80% of the total images are used in the training set, 10% are used in the validation set, and 10% are used in the testing set. It is important to highlight the different percentages used in the default settings on the respective modules. The time to train the Google AutoML Vision with default computational power is longer on average than Apple Create ML, with about 8 minutes required to train the machine learning module. However, it is possible to choose more computational power for an additional fee and decrease module training time. The user will receive e-mail alerts when the computer time begins and is completed. The computation time is calculated by subtracting the time of the initial e-mail from the final e-mail.

Based on our calculations, we determined there was no significant difference between the 2 machine learning algorithms tested at the default settings with recall and precision values obtained. These findings demonstrate the promise of using a ML algorithm to assist in the performance of human tasks and behaviors, specifically the diagnosis of histopathologic images. These results have numerous potential uses in clinical medicine. ML algorithms have been successfully applied to diagnostic and prognostic endeavors in pathology,^23-28 dermatology,^29-31 ophthalmology,³² cardiology,³³ and radiology.^34-36

Pathologists often use additional tests, such as special staining of tissues or molecular tests, to assist with accurate classification of tumors. ML platforms offer the potential of an additional tool for pathologists to use along with human microscopic interpretation.^37,38 In addition, the number of pathologists in the US is dramatically decreasing, and many other countries have marked physician shortages, especially in fields of specialized training such as pathology.^39-42 These models could readily assist physicians in underserved countries and impact shortages of pathologists elsewhere by providing more specific diagnoses in an expedited manner.⁴³

Finally, although we have explored the application of these platforms in common cancer scenarios, great potential exists to use similar techniques in the detection of other conditions. These include the potential for classification and risk assessment of precancerous lesions, infectious processes in tissue (eg, detection of tuberculosis or malaria),^24,44 inflammatory conditions (eg, arthritis subtypes, gout),⁴⁵ blood disorders (eg, abnormal blood cell morphology),⁴⁶ and many others. The potential of these technologies to improve health care delivery to veteran patients seems to be limited only by the imagination of the user.⁴⁷

Regarding the limited effectiveness in determining the presence or absence of mutations in KRAS for colon adenocarcinoma, it is mentioned that currently pathologists rely on complex molecular tests to detect the mutations at the DNA level.²¹ It is possible that the use of more extensive training data sets may improve recall and precision in cases such as these and warrants further study. Our experiments were limited to the stipulations placed by the free trial software agreements; no costs were expended to use the algorithms, though an Apple computer was required.

Conclusion

We have demonstrated the successful application of 2 readily available ML platforms in providing diagnostic guidance in differentiation between common cancer conditions in veteran patient populations. Although both platforms performed very well with no statistically significant differences in results, some distinctions are worth noting. Apple Create ML can be used on local computers but is limited to an Apple operating system. Google AutoML is not platform-specific but runs only via Google Cloud with associated computational fees. Using these readily available models, we demonstrated the vast potential of AI in diagnostic pathology. The application of AI to clinical medicine remains in the very early stages. The VA is uniquely poised to provide leadership as AI technologies will continue to dramatically change the future of health care, both in veteran and nonveteran patients nationwide.

Acknowledgments

The authors thank Paul Borkowski for his constructive criticism and proofreading of this manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.

Artificial intelligence (AI), first described in 1956, encompasses the field of computer science in which machines are trained to learn from experience. The term was popularized by the 1956 Dartmouth College Summer Research Project on Artificial Intelligence.¹ The field of AI is rapidly growing and has the potential to affect many aspects of our lives. The emerging importance of AI is demonstrated by a February 2019 executive order that launched the American AI Initiative, allocating resources and funding for AI development.² The executive order stresses the potential impact of AI in the health care field, including its potential utility to diagnose disease. Federal agencies were directed to invest in AI research and development to promote rapid breakthroughs in AI technology that may impact multiple areas of society.

Machine learning (ML), a subset of AI, was defined in 1959 by Arthur Samuel and is achieved by employing mathematic models to compute sample data sets.³ Originating from statistical linear models, neural networks were conceived to accomplish these tasks.⁴ These pioneering scientific achievements led to recent developments of deep neural networks. These models are developed to recognize patterns and achieve complex computational tasks within a matter of minutes, often far exceeding human ability.⁵ ML can increase efficiency with decreased computation time, high precision, and recall when compared with that of human decision making.⁶

ML has the potential for numerous applications in the health care field.^7-9 One promising application is in the field of anatomic pathology. ML allows representative images to be used to train a computer to recognize patterns from labeled photographs. Based on a set of images selected to represent a specific tissue or disease process, the computer can be trained to evaluate and recognize new and unique images from patients and render a diagnosis.¹⁰ Prior to modern ML models, users would have to import many thousands of training images to produce algorithms that could recognize patterns with high accuracy. Modern ML algorithms allow for a model known as transfer learning, such that far fewer images are required for training.^11-13

Two novel ML platforms available for public use are offered through Google (Mountain View, CA) and Apple (Cupertino, CA).^14,15 They each offer a user-friendly interface with minimal experience required in computer science. Google AutoML uses ML via cloud services to store and retrieve data with ease. No coding knowledge is required. The Apple Create ML Module provides computer-based ML, requiring only a few lines of code.

The Veterans Health Administration (VHA) is the largest single health care system in the US, and nearly 50 000 cancer cases are diagnosed at the VHA annually.¹⁶ Cancers of the lung and colon are among the most common sources of invasive cancer and are the 2 most common causes of cancer deaths in America.¹⁶ We have previously reported using Apple ML in detecting non-small cell lung cancers (NSCLCs), including adenocarcinomas and squamous cell carcinomas (SCCs); and colon cancers with accuracy.^17,18 In the present study, we expand on these findings by comparing Apple and Google ML platforms in a variety of common pathologic scenarios in veteran patients. Using limited training data, both programs are compared for precision and recall in differentiating conditions involving lung and colon pathology.

In the first 4 experiments, we evaluated the ability of the platforms to differentiate normal lung tissue from cancerous lung tissue, to distinguish lung adenocarcinoma from SCC, and to differentiate colon adenocarcinoma from normal colon tissue. Next, cases of colon adenocarcinoma were assessed to determine whether the presence or absence of the KRAS proto-oncogene could be determined histologically using the AI platforms. KRAS is found in a variety of cancers, including about 40% of colon adenocarcinomas.¹⁹ For colon cancers, the presence or absence of the mutation in KRAS has important implications for patients as it determines whether the tumor will respond to specific chemotherapy agents.²⁰ The presence of the KRAS gene is currently determined by complex molecular testing of tumor tissue.²¹ However, we assessed the potential of ML to determine whether the mutation is present by computerized morphologic analysis alone. Our last experiment examined the ability of the Apple and Google platforms to differentiate between adenocarcinomas of lung origin vs colon origin. This has potential utility in determining the site of origin of metastatic carcinoma.²²

Methods

Fifty cases of lung SCC, 50 cases of lung adenocarcinoma, and 50 cases of colon adenocarcinoma were randomly retrieved from our molecular database. Twenty-five colon adenocarcinoma cases were positive for mutation in KRAS, while 25 cases were negative for mutation in KRAS. Seven hundred fifty total images of lung tissue (250 benign lung tissue, 250 lung adenocarcinomas, and 250 lung SCCs) and 500 total images of colon tissue (250 benign colon tissue and 250 colon adenocarcinoma) were obtained using a Leica Microscope MC190 HD Camera (Wetzlar, Germany) connected to an Olympus BX41 microscope (Center Valley, PA) and the Leica Acquire 9072 software for Apple computers. All the images were captured at a resolution of 1024 x 768 pixels using a 60x dry objective. Lung tissue images were captured and saved on a 2012 Apple MacBook Pro computer, and colon images were captured and saved on a 2011 Apple iMac computer. Both computers were running macOS v10.13.

Creating Image Classifier Models Using Apple Create ML

Apple Create ML is a suite of products that use various tools to create and train custom ML models on Apple computers.¹⁵ The suite contains many features, including image classification to train a ML model to classify images, natural language processing to classify natural language text, and tabular data to train models that deal with labeling information or estimating new quantities. We used Create ML Image Classification to create image classifier models for our project (Appendix A).

Creating ML Modules Using Google Cloud AutoML Vision Beta

Google Cloud AutoML is a suite of machine learning products, including AutoML Vision, AutoML Natural Language and AutoML Translation.¹⁴ All Cloud AutoML machine learning products were in beta version at the time of experimentation. We used Cloud AutoML Vision beta to create ML modules for our project. Unlike Apple Create ML, which is run on a local Apple computer, the Google Cloud AutoML is run online using a Google Cloud account. There are no minimum specifications requirements for the local computer since it is using the cloud-based architecture (Appendix B).

Experiment 1

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect and subclassify NSCLC based on the histopathologic images. We created 3 classes of images (250 images each): benign lung tissue, lung adenocarcinoma, and lung SCC.

Experiment 2

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between normal lung tissue and NSCLC histopathologic images with 50/50 mixture of lung adenocarcinoma and lung SCC. We created 2 classes of images (250 images each): benign lung tissue and lung NSCLC.

Experiment 3

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and lung SCC histopathologic images. We created 2 classes of images (250 images each): adenocarcinoma and SCC.

Experiment 4

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect colon cancer histopathologic images regardless of mutation in KRAS status. We created 2 classes of images (250 images each): benign colon tissue and colon adenocarcinoma.

Experiment 5

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between colon adenocarcinoma with mutations in KRAS and colon adenocarcinoma without the mutation in KRAS histopathologic images. We created 2 classes of images (125 images each): colon adenocarcinoma cases with mutation in KRAS and colon adenocarcinoma cases without the mutation in KRAS.

Experiment 6

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and colon adenocarcinoma histopathologic images. We created 2 classes of images (250 images each): colon adenocarcinoma lung adenocarcinoma.

Results

Twelve machine learning models were created in 6 experiments using the Apple Create ML and the Google AutoML (Table). To investigate recall and precision differences between the Apple and the Google ML algorithms, we performed 2-tailed distribution, paired t tests. No statistically significant differences were found (P = .52 for recall and .60 for precision).

Overall, each model performed well in distinguishing between normal and neoplastic tissue for both lung and colon cancers. In subclassifying NSCLC into adenocarcinoma and SCC, the models were shown to have high levels of precision and recall. The models also were successful in distinguishing between lung and colonic origin of adenocarcinoma (Figures 1-4). However, both systems had trouble discerning colon adenocarcinoma with mutations in KRAS from adenocarcinoma without mutations in KRAS.

Discussion

Image classifier models using ML algorithms hold a promising future to revolutionize the health care field. ML products, such as those modules offered by Apple and Google, are easy to use and have a simple graphic user interface to allow individuals to train models to perform humanlike tasks in real time. In our experiments, we compared multiple algorithms to determine their ability to differentiate and subclassify histopathologic images with high precision and recall using common scenarios in treating veteran patients.

Analysis of the results revealed high precision and recall values illustrating the models’ ability to differentiate and detect benign lung tissue from lung SCC and lung adenocarcinoma in ML model 1, benign lung from NSCLC carcinoma in ML model 2, and benign colon from colonic adenocarcinoma in ML model 4. In ML model 3 and 6, both ML algorithms performed at a high level to differentiate lung SCC from lung adenocarcinoma and lung adenocarcinoma from colonic adenocarcinoma, respectively. Of note, ML model 5 had the lowest precision and recall values across both algorithms demonstrating the models’ limited utility in predicting molecular profiles, such as mutations in KRAS as tested here. This is not surprising as pathologists currently require complex molecular tests to detect mutations in KRAS reliably in colon cancer.

Both modules require minimal programming experience and are easy to use. In our comparison, we demonstrated critical distinguishing characteristics that differentiate the 2 products.

Apple Create ML image classifier is available for use on local Mac computers that use Xcode version 10 and macOS 10.14 or later, with just 3 lines of code required to perform computations. Although this product is limited to Apple computers, it is free to use, and images are stored on the computer hard drive. Of unique significance on the Apple system platform, images can be augmented to alter their appearance to enhance model training. For example, imported images can be cropped, rotated, blurred, and flipped, in order to optimize the model’s training abilities to recognize test images and perform pattern recognition. This feature is not as readily available on the Google platform. Apple Create ML Image classifier’s default training set consists of 75% of total imported images with 5% of the total images being randomly used as a validation set. The remaining 20% of images comprise the testing set. The module’s computational analysis to train the model is achieved in about 2 minutes on average. The score threshold is set at 50% and cannot be manipulated for each image class as in Google AutoML Vision.

Google AutoML Vision is open and can be accessed from many devices. It stores images on remote Google servers but requires computing fees after a $300 credit for 12 months. On AutoML Vision, random 80% of the total images are used in the training set, 10% are used in the validation set, and 10% are used in the testing set. It is important to highlight the different percentages used in the default settings on the respective modules. The time to train the Google AutoML Vision with default computational power is longer on average than Apple Create ML, with about 8 minutes required to train the machine learning module. However, it is possible to choose more computational power for an additional fee and decrease module training time. The user will receive e-mail alerts when the computer time begins and is completed. The computation time is calculated by subtracting the time of the initial e-mail from the final e-mail.

Based on our calculations, we determined there was no significant difference between the 2 machine learning algorithms tested at the default settings with recall and precision values obtained. These findings demonstrate the promise of using a ML algorithm to assist in the performance of human tasks and behaviors, specifically the diagnosis of histopathologic images. These results have numerous potential uses in clinical medicine. ML algorithms have been successfully applied to diagnostic and prognostic endeavors in pathology,^23-28 dermatology,^29-31 ophthalmology,³² cardiology,³³ and radiology.^34-36

Pathologists often use additional tests, such as special staining of tissues or molecular tests, to assist with accurate classification of tumors. ML platforms offer the potential of an additional tool for pathologists to use along with human microscopic interpretation.^37,38 In addition, the number of pathologists in the US is dramatically decreasing, and many other countries have marked physician shortages, especially in fields of specialized training such as pathology.^39-42 These models could readily assist physicians in underserved countries and impact shortages of pathologists elsewhere by providing more specific diagnoses in an expedited manner.⁴³

Finally, although we have explored the application of these platforms in common cancer scenarios, great potential exists to use similar techniques in the detection of other conditions. These include the potential for classification and risk assessment of precancerous lesions, infectious processes in tissue (eg, detection of tuberculosis or malaria),^24,44 inflammatory conditions (eg, arthritis subtypes, gout),⁴⁵ blood disorders (eg, abnormal blood cell morphology),⁴⁶ and many others. The potential of these technologies to improve health care delivery to veteran patients seems to be limited only by the imagination of the user.⁴⁷

Regarding the limited effectiveness in determining the presence or absence of mutations in KRAS for colon adenocarcinoma, it is mentioned that currently pathologists rely on complex molecular tests to detect the mutations at the DNA level.²¹ It is possible that the use of more extensive training data sets may improve recall and precision in cases such as these and warrants further study. Our experiments were limited to the stipulations placed by the free trial software agreements; no costs were expended to use the algorithms, though an Apple computer was required.

Conclusion

We have demonstrated the successful application of 2 readily available ML platforms in providing diagnostic guidance in differentiation between common cancer conditions in veteran patient populations. Although both platforms performed very well with no statistically significant differences in results, some distinctions are worth noting. Apple Create ML can be used on local computers but is limited to an Apple operating system. Google AutoML is not platform-specific but runs only via Google Cloud with associated computational fees. Using these readily available models, we demonstrated the vast potential of AI in diagnostic pathology. The application of AI to clinical medicine remains in the very early stages. The VA is uniquely poised to provide leadership as AI technologies will continue to dramatically change the future of health care, both in veteran and nonveteran patients nationwide.

Acknowledgments

The authors thank Paul Borkowski for his constructive criticism and proofreading of this manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.

Artificial intelligence (AI), first described in 1956, encompasses the field of computer science in which machines are trained to learn from experience. The term was popularized by the 1956 Dartmouth College Summer Research Project on Artificial Intelligence.¹ The field of AI is rapidly growing and has the potential to affect many aspects of our lives. The emerging importance of AI is demonstrated by a February 2019 executive order that launched the American AI Initiative, allocating resources and funding for AI development.² The executive order stresses the potential impact of AI in the health care field, including its potential utility to diagnose disease. Federal agencies were directed to invest in AI research and development to promote rapid breakthroughs in AI technology that may impact multiple areas of society.

Machine learning (ML), a subset of AI, was defined in 1959 by Arthur Samuel and is achieved by employing mathematic models to compute sample data sets.³ Originating from statistical linear models, neural networks were conceived to accomplish these tasks.⁴ These pioneering scientific achievements led to recent developments of deep neural networks. These models are developed to recognize patterns and achieve complex computational tasks within a matter of minutes, often far exceeding human ability.⁵ ML can increase efficiency with decreased computation time, high precision, and recall when compared with that of human decision making.⁶

ML has the potential for numerous applications in the health care field.^7-9 One promising application is in the field of anatomic pathology. ML allows representative images to be used to train a computer to recognize patterns from labeled photographs. Based on a set of images selected to represent a specific tissue or disease process, the computer can be trained to evaluate and recognize new and unique images from patients and render a diagnosis.¹⁰ Prior to modern ML models, users would have to import many thousands of training images to produce algorithms that could recognize patterns with high accuracy. Modern ML algorithms allow for a model known as transfer learning, such that far fewer images are required for training.^11-13

Two novel ML platforms available for public use are offered through Google (Mountain View, CA) and Apple (Cupertino, CA).^14,15 They each offer a user-friendly interface with minimal experience required in computer science. Google AutoML uses ML via cloud services to store and retrieve data with ease. No coding knowledge is required. The Apple Create ML Module provides computer-based ML, requiring only a few lines of code.

The Veterans Health Administration (VHA) is the largest single health care system in the US, and nearly 50 000 cancer cases are diagnosed at the VHA annually.¹⁶ Cancers of the lung and colon are among the most common sources of invasive cancer and are the 2 most common causes of cancer deaths in America.¹⁶ We have previously reported using Apple ML in detecting non-small cell lung cancers (NSCLCs), including adenocarcinomas and squamous cell carcinomas (SCCs); and colon cancers with accuracy.^17,18 In the present study, we expand on these findings by comparing Apple and Google ML platforms in a variety of common pathologic scenarios in veteran patients. Using limited training data, both programs are compared for precision and recall in differentiating conditions involving lung and colon pathology.

In the first 4 experiments, we evaluated the ability of the platforms to differentiate normal lung tissue from cancerous lung tissue, to distinguish lung adenocarcinoma from SCC, and to differentiate colon adenocarcinoma from normal colon tissue. Next, cases of colon adenocarcinoma were assessed to determine whether the presence or absence of the KRAS proto-oncogene could be determined histologically using the AI platforms. KRAS is found in a variety of cancers, including about 40% of colon adenocarcinomas.¹⁹ For colon cancers, the presence or absence of the mutation in KRAS has important implications for patients as it determines whether the tumor will respond to specific chemotherapy agents.²⁰ The presence of the KRAS gene is currently determined by complex molecular testing of tumor tissue.²¹ However, we assessed the potential of ML to determine whether the mutation is present by computerized morphologic analysis alone. Our last experiment examined the ability of the Apple and Google platforms to differentiate between adenocarcinomas of lung origin vs colon origin. This has potential utility in determining the site of origin of metastatic carcinoma.²²

Methods

Fifty cases of lung SCC, 50 cases of lung adenocarcinoma, and 50 cases of colon adenocarcinoma were randomly retrieved from our molecular database. Twenty-five colon adenocarcinoma cases were positive for mutation in KRAS, while 25 cases were negative for mutation in KRAS. Seven hundred fifty total images of lung tissue (250 benign lung tissue, 250 lung adenocarcinomas, and 250 lung SCCs) and 500 total images of colon tissue (250 benign colon tissue and 250 colon adenocarcinoma) were obtained using a Leica Microscope MC190 HD Camera (Wetzlar, Germany) connected to an Olympus BX41 microscope (Center Valley, PA) and the Leica Acquire 9072 software for Apple computers. All the images were captured at a resolution of 1024 x 768 pixels using a 60x dry objective. Lung tissue images were captured and saved on a 2012 Apple MacBook Pro computer, and colon images were captured and saved on a 2011 Apple iMac computer. Both computers were running macOS v10.13.

Creating Image Classifier Models Using Apple Create ML

Apple Create ML is a suite of products that use various tools to create and train custom ML models on Apple computers.¹⁵ The suite contains many features, including image classification to train a ML model to classify images, natural language processing to classify natural language text, and tabular data to train models that deal with labeling information or estimating new quantities. We used Create ML Image Classification to create image classifier models for our project (Appendix A).

Creating ML Modules Using Google Cloud AutoML Vision Beta

Google Cloud AutoML is a suite of machine learning products, including AutoML Vision, AutoML Natural Language and AutoML Translation.¹⁴ All Cloud AutoML machine learning products were in beta version at the time of experimentation. We used Cloud AutoML Vision beta to create ML modules for our project. Unlike Apple Create ML, which is run on a local Apple computer, the Google Cloud AutoML is run online using a Google Cloud account. There are no minimum specifications requirements for the local computer since it is using the cloud-based architecture (Appendix B).

Experiment 1

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect and subclassify NSCLC based on the histopathologic images. We created 3 classes of images (250 images each): benign lung tissue, lung adenocarcinoma, and lung SCC.

Experiment 2

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between normal lung tissue and NSCLC histopathologic images with 50/50 mixture of lung adenocarcinoma and lung SCC. We created 2 classes of images (250 images each): benign lung tissue and lung NSCLC.

Experiment 3

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and lung SCC histopathologic images. We created 2 classes of images (250 images each): adenocarcinoma and SCC.

Experiment 4

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect colon cancer histopathologic images regardless of mutation in KRAS status. We created 2 classes of images (250 images each): benign colon tissue and colon adenocarcinoma.

Experiment 5

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between colon adenocarcinoma with mutations in KRAS and colon adenocarcinoma without the mutation in KRAS histopathologic images. We created 2 classes of images (125 images each): colon adenocarcinoma cases with mutation in KRAS and colon adenocarcinoma cases without the mutation in KRAS.

Experiment 6

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and colon adenocarcinoma histopathologic images. We created 2 classes of images (250 images each): colon adenocarcinoma lung adenocarcinoma.

Results

Twelve machine learning models were created in 6 experiments using the Apple Create ML and the Google AutoML (Table). To investigate recall and precision differences between the Apple and the Google ML algorithms, we performed 2-tailed distribution, paired t tests. No statistically significant differences were found (P = .52 for recall and .60 for precision).

Overall, each model performed well in distinguishing between normal and neoplastic tissue for both lung and colon cancers. In subclassifying NSCLC into adenocarcinoma and SCC, the models were shown to have high levels of precision and recall. The models also were successful in distinguishing between lung and colonic origin of adenocarcinoma (Figures 1-4). However, both systems had trouble discerning colon adenocarcinoma with mutations in KRAS from adenocarcinoma without mutations in KRAS.

Discussion

Image classifier models using ML algorithms hold a promising future to revolutionize the health care field. ML products, such as those modules offered by Apple and Google, are easy to use and have a simple graphic user interface to allow individuals to train models to perform humanlike tasks in real time. In our experiments, we compared multiple algorithms to determine their ability to differentiate and subclassify histopathologic images with high precision and recall using common scenarios in treating veteran patients.

Analysis of the results revealed high precision and recall values illustrating the models’ ability to differentiate and detect benign lung tissue from lung SCC and lung adenocarcinoma in ML model 1, benign lung from NSCLC carcinoma in ML model 2, and benign colon from colonic adenocarcinoma in ML model 4. In ML model 3 and 6, both ML algorithms performed at a high level to differentiate lung SCC from lung adenocarcinoma and lung adenocarcinoma from colonic adenocarcinoma, respectively. Of note, ML model 5 had the lowest precision and recall values across both algorithms demonstrating the models’ limited utility in predicting molecular profiles, such as mutations in KRAS as tested here. This is not surprising as pathologists currently require complex molecular tests to detect mutations in KRAS reliably in colon cancer.

Both modules require minimal programming experience and are easy to use. In our comparison, we demonstrated critical distinguishing characteristics that differentiate the 2 products.

Apple Create ML image classifier is available for use on local Mac computers that use Xcode version 10 and macOS 10.14 or later, with just 3 lines of code required to perform computations. Although this product is limited to Apple computers, it is free to use, and images are stored on the computer hard drive. Of unique significance on the Apple system platform, images can be augmented to alter their appearance to enhance model training. For example, imported images can be cropped, rotated, blurred, and flipped, in order to optimize the model’s training abilities to recognize test images and perform pattern recognition. This feature is not as readily available on the Google platform. Apple Create ML Image classifier’s default training set consists of 75% of total imported images with 5% of the total images being randomly used as a validation set. The remaining 20% of images comprise the testing set. The module’s computational analysis to train the model is achieved in about 2 minutes on average. The score threshold is set at 50% and cannot be manipulated for each image class as in Google AutoML Vision.

Google AutoML Vision is open and can be accessed from many devices. It stores images on remote Google servers but requires computing fees after a $300 credit for 12 months. On AutoML Vision, random 80% of the total images are used in the training set, 10% are used in the validation set, and 10% are used in the testing set. It is important to highlight the different percentages used in the default settings on the respective modules. The time to train the Google AutoML Vision with default computational power is longer on average than Apple Create ML, with about 8 minutes required to train the machine learning module. However, it is possible to choose more computational power for an additional fee and decrease module training time. The user will receive e-mail alerts when the computer time begins and is completed. The computation time is calculated by subtracting the time of the initial e-mail from the final e-mail.

Based on our calculations, we determined there was no significant difference between the 2 machine learning algorithms tested at the default settings with recall and precision values obtained. These findings demonstrate the promise of using a ML algorithm to assist in the performance of human tasks and behaviors, specifically the diagnosis of histopathologic images. These results have numerous potential uses in clinical medicine. ML algorithms have been successfully applied to diagnostic and prognostic endeavors in pathology,^23-28 dermatology,^29-31 ophthalmology,³² cardiology,³³ and radiology.^34-36

Pathologists often use additional tests, such as special staining of tissues or molecular tests, to assist with accurate classification of tumors. ML platforms offer the potential of an additional tool for pathologists to use along with human microscopic interpretation.^37,38 In addition, the number of pathologists in the US is dramatically decreasing, and many other countries have marked physician shortages, especially in fields of specialized training such as pathology.^39-42 These models could readily assist physicians in underserved countries and impact shortages of pathologists elsewhere by providing more specific diagnoses in an expedited manner.⁴³

Finally, although we have explored the application of these platforms in common cancer scenarios, great potential exists to use similar techniques in the detection of other conditions. These include the potential for classification and risk assessment of precancerous lesions, infectious processes in tissue (eg, detection of tuberculosis or malaria),^24,44 inflammatory conditions (eg, arthritis subtypes, gout),⁴⁵ blood disorders (eg, abnormal blood cell morphology),⁴⁶ and many others. The potential of these technologies to improve health care delivery to veteran patients seems to be limited only by the imagination of the user.⁴⁷

Regarding the limited effectiveness in determining the presence or absence of mutations in KRAS for colon adenocarcinoma, it is mentioned that currently pathologists rely on complex molecular tests to detect the mutations at the DNA level.²¹ It is possible that the use of more extensive training data sets may improve recall and precision in cases such as these and warrants further study. Our experiments were limited to the stipulations placed by the free trial software agreements; no costs were expended to use the algorithms, though an Apple computer was required.

Conclusion

We have demonstrated the successful application of 2 readily available ML platforms in providing diagnostic guidance in differentiation between common cancer conditions in veteran patient populations. Although both platforms performed very well with no statistically significant differences in results, some distinctions are worth noting. Apple Create ML can be used on local computers but is limited to an Apple operating system. Google AutoML is not platform-specific but runs only via Google Cloud with associated computational fees. Using these readily available models, we demonstrated the vast potential of AI in diagnostic pathology. The application of AI to clinical medicine remains in the very early stages. The VA is uniquely poised to provide leadership as AI technologies will continue to dramatically change the future of health care, both in veteran and nonveteran patients nationwide.

Acknowledgments

The authors thank Paul Borkowski for his constructive criticism and proofreading of this manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.

MADRID – Two newly published but previously reported phase 3 trials associated triple therapy in a single inhaler with a 23% reduction (P = .008) in asthma exacerbations relative to a two-drug inhaler, but fresh data from a prespecified analysis presented at the annual congress of the European Respiratory Society has identified those patients most likely to benefit.

Ted Bosworth/MDedge News

“Six easily identifiable factors appear to be associated with the most prominent response to treatment and may help in the treatment step-up decision at the point of care,” reported Dave Singh, MD, professor in the division of infection, immunity, and respiratory medicine, University of Manchester (England).

The six factors associated with a lower relative risk (RR) of severe exacerbations were derived from the phase 3 TRIMARIN and TRIGGER trials. The primary results of these trials were presented several months ago at the 2019 American Thoracic Society (ATS) meeting, but the full data were published on the day that Dr. Singh spoke at the ERS.

To identify predictors of response, the pooled analysis of TRIMARIN and TRIGGER was prespecified. Both of these trials, which were similarly designed, compared a single inhaler of inhaled corticosteroids (ICS), long-acting beta agonist (LABA), and long-acting muscarinic antagonist (LAMA) to a single ICS/LABA inhaler.

On the basis of risk for severe exacerbations, greater protection from triple therapy relative to a conventional ICS/LABA inhaler was identified for those with a high degree of reversibility (defined as greater than 400 mL) relative to those with a lower degree (RR, 0.729; P = .024), those with a body mass index less than 25 kg/m² relative to a higher BMI (RR, 0.570; P = .005), those with only one exacerbation in the previous 12 months relative to those with more (RR, 0.731; P = .009), never-smokers relative to those with smoking history (RR, 0.764; P = .013), those younger than age 65 years relative to older (RR, 0.770; P = .17), and males relative to females (RR, 0.651; P = .009).

“This gives us six factors to consider when you are thinking about stepping up to triple therapy and are trying to determine which patients would benefit the most,” Dr. Singh said.

Both the TRIMARIN and the TRIGGER trials were double blind and placebo controlled. In both, the experimental arm was a single inhaler triple therapy of the ICS beclomethasone, the LABA formoterol, and the LAMA glycopyrronium. The control arm was a single inhaler combination of beclomethasone and formoterol. All inhalers were used twice daily.

TRIMARIN, with 171 participating sites in 16 countries, randomized 1,155 patients to the triple-drug inhaler with a moderate dose of ICS (100 mcg) or to the ICS/LABA inhaler. In TRIGGER, with 221 sites in 17 countries, 1,437 patients were randomized to one of three arms. Both the triple-drug inhaler arm and the ICS/LABA arm contained a higher dose of ICS (200 mcg) than in TRIMARIN. In an open-label third arm, patients also received the higher dose of ICS plus LABA and a second inhaler with tiotropium. The formoterol dose in all arms of both studies was 6 mcg.

As reported at the ATS and now published in the Lancet, the reduction in exacerbations on single inhaler triple therapy relative to ICS/LABA was significant when the data were pooled (even though the reduction in the TRIGGER study fell short of statistical significance). The median improvement in lung function for single inhaler triple therapy relative to ICS/LABA was significant in both TRIMARIN (57 mL; P = .008) and TRIGGER (73 mL; P = .0025).

In discussing the new pooled analysis of response predictors in TRIMARIN/TRIGGER, the ERS-invited discussant, Celeste M. Porsbjerg, MD, Bispebjerg Hospital, Copenhagen, expressed particular interest in reversibility. A positive reversibility test to salbutamol was an entry criterion for both trials, but Dr. Porsbjerg pointed out that a greater response in those with the highest reversibility suggests these patients have a phenotype in which bronchodilation is a more important driver of disease than is inflammation.

While conceding that this was possible, Dr. Singh cautioned that he considers these predictors of response to be “exploratory.” He believes that the TRIMARIN/TRIGGER studies were not designed to tease out the relative importance of mechanisms of asthma in response to the assigned therapies. However, he believes the response predictor analysis is a step in this direction, which might be valuable for better individualizing therapy.

The studies were funded by Chiesi Farmaceutici. Dr. Singh reports no potential conflicts of interest.

Virchow JC et al. Lancet. 2019 Sep 30. doi. org/10.1016/S0140-6736(19)32215-9.

MADRID – Two newly published but previously reported phase 3 trials associated triple therapy in a single inhaler with a 23% reduction (P = .008) in asthma exacerbations relative to a two-drug inhaler, but fresh data from a prespecified analysis presented at the annual congress of the European Respiratory Society has identified those patients most likely to benefit.

Ted Bosworth/MDedge News

“Six easily identifiable factors appear to be associated with the most prominent response to treatment and may help in the treatment step-up decision at the point of care,” reported Dave Singh, MD, professor in the division of infection, immunity, and respiratory medicine, University of Manchester (England).

The six factors associated with a lower relative risk (RR) of severe exacerbations were derived from the phase 3 TRIMARIN and TRIGGER trials. The primary results of these trials were presented several months ago at the 2019 American Thoracic Society (ATS) meeting, but the full data were published on the day that Dr. Singh spoke at the ERS.

To identify predictors of response, the pooled analysis of TRIMARIN and TRIGGER was prespecified. Both of these trials, which were similarly designed, compared a single inhaler of inhaled corticosteroids (ICS), long-acting beta agonist (LABA), and long-acting muscarinic antagonist (LAMA) to a single ICS/LABA inhaler.

On the basis of risk for severe exacerbations, greater protection from triple therapy relative to a conventional ICS/LABA inhaler was identified for those with a high degree of reversibility (defined as greater than 400 mL) relative to those with a lower degree (RR, 0.729; P = .024), those with a body mass index less than 25 kg/m² relative to a higher BMI (RR, 0.570; P = .005), those with only one exacerbation in the previous 12 months relative to those with more (RR, 0.731; P = .009), never-smokers relative to those with smoking history (RR, 0.764; P = .013), those younger than age 65 years relative to older (RR, 0.770; P = .17), and males relative to females (RR, 0.651; P = .009).

“This gives us six factors to consider when you are thinking about stepping up to triple therapy and are trying to determine which patients would benefit the most,” Dr. Singh said.

Both the TRIMARIN and the TRIGGER trials were double blind and placebo controlled. In both, the experimental arm was a single inhaler triple therapy of the ICS beclomethasone, the LABA formoterol, and the LAMA glycopyrronium. The control arm was a single inhaler combination of beclomethasone and formoterol. All inhalers were used twice daily.

TRIMARIN, with 171 participating sites in 16 countries, randomized 1,155 patients to the triple-drug inhaler with a moderate dose of ICS (100 mcg) or to the ICS/LABA inhaler. In TRIGGER, with 221 sites in 17 countries, 1,437 patients were randomized to one of three arms. Both the triple-drug inhaler arm and the ICS/LABA arm contained a higher dose of ICS (200 mcg) than in TRIMARIN. In an open-label third arm, patients also received the higher dose of ICS plus LABA and a second inhaler with tiotropium. The formoterol dose in all arms of both studies was 6 mcg.

As reported at the ATS and now published in the Lancet, the reduction in exacerbations on single inhaler triple therapy relative to ICS/LABA was significant when the data were pooled (even though the reduction in the TRIGGER study fell short of statistical significance). The median improvement in lung function for single inhaler triple therapy relative to ICS/LABA was significant in both TRIMARIN (57 mL; P = .008) and TRIGGER (73 mL; P = .0025).

In discussing the new pooled analysis of response predictors in TRIMARIN/TRIGGER, the ERS-invited discussant, Celeste M. Porsbjerg, MD, Bispebjerg Hospital, Copenhagen, expressed particular interest in reversibility. A positive reversibility test to salbutamol was an entry criterion for both trials, but Dr. Porsbjerg pointed out that a greater response in those with the highest reversibility suggests these patients have a phenotype in which bronchodilation is a more important driver of disease than is inflammation.

While conceding that this was possible, Dr. Singh cautioned that he considers these predictors of response to be “exploratory.” He believes that the TRIMARIN/TRIGGER studies were not designed to tease out the relative importance of mechanisms of asthma in response to the assigned therapies. However, he believes the response predictor analysis is a step in this direction, which might be valuable for better individualizing therapy.

The studies were funded by Chiesi Farmaceutici. Dr. Singh reports no potential conflicts of interest.

Virchow JC et al. Lancet. 2019 Sep 30. doi. org/10.1016/S0140-6736(19)32215-9.

MADRID – Two newly published but previously reported phase 3 trials associated triple therapy in a single inhaler with a 23% reduction (P = .008) in asthma exacerbations relative to a two-drug inhaler, but fresh data from a prespecified analysis presented at the annual congress of the European Respiratory Society has identified those patients most likely to benefit.

Ted Bosworth/MDedge News

“Six easily identifiable factors appear to be associated with the most prominent response to treatment and may help in the treatment step-up decision at the point of care,” reported Dave Singh, MD, professor in the division of infection, immunity, and respiratory medicine, University of Manchester (England).

The six factors associated with a lower relative risk (RR) of severe exacerbations were derived from the phase 3 TRIMARIN and TRIGGER trials. The primary results of these trials were presented several months ago at the 2019 American Thoracic Society (ATS) meeting, but the full data were published on the day that Dr. Singh spoke at the ERS.

To identify predictors of response, the pooled analysis of TRIMARIN and TRIGGER was prespecified. Both of these trials, which were similarly designed, compared a single inhaler of inhaled corticosteroids (ICS), long-acting beta agonist (LABA), and long-acting muscarinic antagonist (LAMA) to a single ICS/LABA inhaler.

On the basis of risk for severe exacerbations, greater protection from triple therapy relative to a conventional ICS/LABA inhaler was identified for those with a high degree of reversibility (defined as greater than 400 mL) relative to those with a lower degree (RR, 0.729; P = .024), those with a body mass index less than 25 kg/m² relative to a higher BMI (RR, 0.570; P = .005), those with only one exacerbation in the previous 12 months relative to those with more (RR, 0.731; P = .009), never-smokers relative to those with smoking history (RR, 0.764; P = .013), those younger than age 65 years relative to older (RR, 0.770; P = .17), and males relative to females (RR, 0.651; P = .009).

“This gives us six factors to consider when you are thinking about stepping up to triple therapy and are trying to determine which patients would benefit the most,” Dr. Singh said.

Both the TRIMARIN and the TRIGGER trials were double blind and placebo controlled. In both, the experimental arm was a single inhaler triple therapy of the ICS beclomethasone, the LABA formoterol, and the LAMA glycopyrronium. The control arm was a single inhaler combination of beclomethasone and formoterol. All inhalers were used twice daily.

TRIMARIN, with 171 participating sites in 16 countries, randomized 1,155 patients to the triple-drug inhaler with a moderate dose of ICS (100 mcg) or to the ICS/LABA inhaler. In TRIGGER, with 221 sites in 17 countries, 1,437 patients were randomized to one of three arms. Both the triple-drug inhaler arm and the ICS/LABA arm contained a higher dose of ICS (200 mcg) than in TRIMARIN. In an open-label third arm, patients also received the higher dose of ICS plus LABA and a second inhaler with tiotropium. The formoterol dose in all arms of both studies was 6 mcg.

As reported at the ATS and now published in the Lancet, the reduction in exacerbations on single inhaler triple therapy relative to ICS/LABA was significant when the data were pooled (even though the reduction in the TRIGGER study fell short of statistical significance). The median improvement in lung function for single inhaler triple therapy relative to ICS/LABA was significant in both TRIMARIN (57 mL; P = .008) and TRIGGER (73 mL; P = .0025).

In discussing the new pooled analysis of response predictors in TRIMARIN/TRIGGER, the ERS-invited discussant, Celeste M. Porsbjerg, MD, Bispebjerg Hospital, Copenhagen, expressed particular interest in reversibility. A positive reversibility test to salbutamol was an entry criterion for both trials, but Dr. Porsbjerg pointed out that a greater response in those with the highest reversibility suggests these patients have a phenotype in which bronchodilation is a more important driver of disease than is inflammation.

While conceding that this was possible, Dr. Singh cautioned that he considers these predictors of response to be “exploratory.” He believes that the TRIMARIN/TRIGGER studies were not designed to tease out the relative importance of mechanisms of asthma in response to the assigned therapies. However, he believes the response predictor analysis is a step in this direction, which might be valuable for better individualizing therapy.

The studies were funded by Chiesi Farmaceutici. Dr. Singh reports no potential conflicts of interest.

Virchow JC et al. Lancet. 2019 Sep 30. doi. org/10.1016/S0140-6736(19)32215-9.

Physician burnout has been labeled a public health crisis by the Harvard School of Public Health and other institutions.¹ A 2018 Physician’s Foundation survey found that 78% of physicians had symptoms of burnout,² which result from chronic workplace stress and include feeling depleted of energy or exhausted, mentally distanced from or cynical about one’s job, and problems getting one’s job done successfully.³ Among ObGyns, almost half (46%) report burnout.⁴ One-third of ObGyns responded on a recent Medscape Burnout Report that the computerization of practice is contributing to their burnout, and 54% said too many bureaucratic tasks, including charting, were adding to their burnout.⁵

Inefficient electronic medical records (EMRs) have been implicated as one reason for burnout, with improvements in efficiency cited as one of several potential resolutions to the problem. About 96% of hospitals have adopted EMRs today, compared with only 9% in 2008,⁶ and many physicians report recognizing value in the technology. For instance, 60% of participants in Stanford Medicine’s 2018 National Physician Poll said EMRs had led to improved patient care. At the same time, however, about as many (59%) said EMRs needed a “complete overhaul” and that the systems had detracted from their professional satisfaction (54%) as well as from their clinical effectiveness (49%).⁷

With this roundtable, we explore the concerns with hours spent on the EMR with several experts, and whether it is a problem that has been contributing to burnout among staff at their institutions. In addition, are there solutions that their institutions have implemented that they can share to help to cope with the problem?
 

OBG Management: ObGyns report that the computerization of practice and too many bureaucratic tasks, including charting, are contributing to burnout. Do you see this problem at your institution?

John J. Dougherty, MD, MBA: Yes, absolutely. There is not a day that goes by that I don’t hear about or experience “Epic Fails.” (We use Epic’s EMR product at our institution.) Too many clicks are needed to navigate even the simplest tasks—finding notes or results, documenting visits, and billing for services are all unnecessarily complex. In addition, we are being held accountable for achieving a long and growing list of “metrics” measures, education projects (HealthStream), and productivity goals. When do we have time to treat patients? And it is not just practicing physicians and clinicians. Our resident physicians spend an inordinate amount of time in front of the computer documenting, placing orders, and transferring patients using a system with a very inefficient user interface, to say the least.

Megan L. Evans, MD, MPH: I absolutely agree. Over the years, my institution has created a conglomerate of EMRs, requiring physicians across the hospital to be fluent in a multitude of systems. For example, you finish your clinic notes in one system, sign off on discharge summaries in another, and complete your operative notes in an entirely different system. As busy attendings, it is hard to keep ahead of all of these tasks, especially when the systems do not talk to one another. Fortunately, my hospital is changing our EMR to a single system within the next year. Until then, however, we will work in this piecemeal system.

Mark Woodland, MS, MD: EMR and computerization of medicine is the number 1 issue relating to dissatisfaction by ObGyn providers in our institution. Providers are earnest in their attempt to be compliant with EMR requirements, but the reality is that they are dealing with an automated system that does not have realistic expectations for management of results, follow-up tasks, and patient communications for a human provider. The actual charting, ordering of tests and consults, and communication between providers has been enhanced. However, the “in-basket” of tasks to be accomplished are extraordinary and much of it relies on the provider, which requires an inordinate amount of time. Additionally, while other members of the medical staff are stationary at a desk, physicians and other providers are not. They are mobile between inpatient units, labor and delivery, operating rooms, and emergency rooms. Time management does not always allow for providers to access computers from all of these areas to facilitate their managing the expectations of the EMR. This requires providers to access the EMR at off hours, extending their workload. Finally, the EMR is neither personal nor friendly. It is not designed with the clinician in mind, and it is not fun or engaging for a provider.

Continue to: OBG Management: What solutions have been instituted...

OBG Management: What solutions have been instituted in order to help physicians with data entry into the EMR?

Dr. Dougherty: When our institution compared EMR offerings, EMR companies put their best collective marketing feet forward. The general notion, at least with the Epic EMR, was that “you can customize Epic to your liking.” It did not take long for a bunch of motivated Epic users to create “smart” stuff (lists, phrases, and texts) in an effort to customize workflows and create fancy-looking electronic notes. Shortly thereafter, it was obvious that, as an institution, our reporting efforts kept coming up short—our reports lacked accuracy and meaning. Everyone was documenting in different ways and in different areas. Considering that reports are currently generated using (mostly) discrete data entries (data placed in specific fields within the EMR), it became obvious that our data entry paradigm needed to change. Therefore, standardization became the leading buzzword. Our institution recently initiated a project aimed at standardizing our workflows and documentation habits. In addition, we have incorporated a third-party information exchange product into our health system data aggregation and analysis workflow. Much more needs to be done, but it is a start.

Dr. Evans: At my institution, as a group, we have created templates for routine procedures and visits that also auto populate billing codes. I know that some departments have used scribes. From the hospital side, there has been improved access to the EMR from home. Some of my colleagues like this feature; however, others, like myself, believe this contributes to some of our burnout. I like to leave work at work. Having the ability to continue working at home is not a solution in my mind.

Dr. Woodland: At our institution, we have engaged our chaperones and medical assistants to help facilitate completion of the medical records during the office visit. Providers work with their assistants to accommodate documentation of history and physical findings while also listening to the provider as they are speaking in order to document patient care plans and orders. This saves the clinicians time in reviewing and editing the record as well as making sure the appropriate care plan is instituted. Our EMR provider recently has begun experimenting with personalization of color themes as well as pictures as part of the interface. Having said this, I still ask, “Why have medical professionals allowed non–clinical agencies and information technology groups to run this show?” It is also inconceivable to me that this unfunded mandate—that has increased cost, decreased clinical efficiency, and decreased clinician satisfaction—has not been addressed by national and international medical communities.

OBG Management: What changes do you feel your EMR system needs to undergo?

Dr. Woodland: I feel that we need to appropriately manage expectations of the EMR and the institution with relation to EMR and providers. By this I mean that we need to make the EMR more user-friendly and appropriate for different clinicians as well as patients. We also need to manage expectations of our patients. In a digital age where immediate contact is the norm, we need to address the issue that the EMR is not social media but rather a communication tool for routine contact and information transmission. Emergencies are not typically addressed well through the EMR platform; they are better handled with a more appropriate communication interface.

Dr. Dougherty: I feel that the biggest change needed is a competent, simple, and standard user-interface. Our old charting methods were great on a number of levels. For instance, if I wanted to add an order, I flipped to the ”Orders” tab and entered an order. If I needed to document a note, I flipped to the “Notes” tab and started writing, etc. Obviously, manual charting had its downsides—like trying to decipher handwriting art! EMRs could easily adopt the stuff that worked from our old methods of documentation, while leveraging the advantages that computerized workflows can bring to practitioners, including efficient transfer of records, meaningful reporting, simple electronic ordering, and interprofessional communication portals.

Dr. Evans: Our systems need to better communicate with one another. I am in an academic practice, and I should be able to see labs, consultant notes, imaging, all in one spot to improve efficiency and ease with patient visits. Minimizing clicks would be helpful as well. I try to write as much as I can while in the room with a patient to avoid after-hours note writing, but it takes away from my interaction with each patient.

Continue to: OBG Management: With an aim toward alleviating burnout...

OBG Management: With an aim toward alleviating burnout, are there any tips you can offer your colleagues on interfacing with the EMR?

Dr. Evans: When I first started as a new attending, it would take me hours to finish my notes, partly because of the level of detail I would write in my history of present illness (HPI) and assessment and plan. One great piece of advice I received was to be satisfied with good notes, not perfect notes. I worked to consolidate my thoughts and use preconstructed phrases/paragraphs on common problems I saw. This saved time to focus on other aspects of my academic job.

Dr. Dougherty: We need to refocus on the patient first, and mold our systems to meet that priority. Much too often, we have our backs to the patients or spend too much time interfacing with our EMR systems, and our patients are not happy about it (as many surveys have demonstrated). More importantly, a renewed focus on patient care, not EMR care, would allow our practitioners to do what they signed up for—treating patients. In the meantime, I would suggest that practitioners stay away from EMR gimmicks and go back to old-style documentation practices (like those established by the Centers for Medicare and Medicaid Services in 1997 and 1998), and ask the IT folks to help with molding the EMR systems to meet your own standards, not the standards established by EMR companies. I am also very hopeful that the consumer will drive most of the health care-related data collection in the near future, thereby marginalizing the current generation of EMR systems.

Dr. Woodland: I would add that providers need to manage the EMR and not let the EMR manage them. Set up task reminders at point times to handle results and communications from the EMR and set up time in your schedule where you can facilitate meeting these tasks. When providers are out on vacation, make sure to have an out-of-office reminder built into their EMR so that patients and others know timing of potential responses. Try to make the EMR as enjoyable as possible and focus on the good points of the EMR, such as legibility, order verification, safety, and documentation.
 

OBG Management: Do you feel that the EMR has led to improved patient care?

Dr. Evans: Yes and no. Yes, in that it can be much easier to follow a patient’s health care history from other provider notes or prior surgeries. Information is searchable and legible. If an EMR is built correctly, it can save time for providers, through smart phrases and templates, and it can help providers with proper billing codes and documentation requirements. No, in that it can take away from important patient interaction. We are required to see more patients in less time all while using, at times, a cumbersome EMR system.

Dr. Woodland: This is a tricky question because the EMR has both positive and negative attributes. Certainly, the legibility and order verification has improved, but the ease of accessing information in the EMR has changed. Additionally, there has been a drastic increase in provider dissatisfaction that has not been addressed. Provider dissatisfaction can lead to problems in patient care. If there was a clear-cut increased value for the cost, I do not think the EMR would be such a huge focus of negative attention. Providers need to take back control of their EMR and their profession so that they can utilize the EMR as the tool it was supposed to be and not the dissatisfier that it has become.

Dr. Dougherty: I do not believe patient care has been improved by EMR systems, for all of the reasons we have discussed, and then some. But there is an enormous amount of potential, if we get the interface between humans and EMR systems right!

Physician burnout has been labeled a public health crisis by the Harvard School of Public Health and other institutions.¹ A 2018 Physician’s Foundation survey found that 78% of physicians had symptoms of burnout,² which result from chronic workplace stress and include feeling depleted of energy or exhausted, mentally distanced from or cynical about one’s job, and problems getting one’s job done successfully.³ Among ObGyns, almost half (46%) report burnout.⁴ One-third of ObGyns responded on a recent Medscape Burnout Report that the computerization of practice is contributing to their burnout, and 54% said too many bureaucratic tasks, including charting, were adding to their burnout.⁵

Inefficient electronic medical records (EMRs) have been implicated as one reason for burnout, with improvements in efficiency cited as one of several potential resolutions to the problem. About 96% of hospitals have adopted EMRs today, compared with only 9% in 2008,⁶ and many physicians report recognizing value in the technology. For instance, 60% of participants in Stanford Medicine’s 2018 National Physician Poll said EMRs had led to improved patient care. At the same time, however, about as many (59%) said EMRs needed a “complete overhaul” and that the systems had detracted from their professional satisfaction (54%) as well as from their clinical effectiveness (49%).⁷

With this roundtable, we explore the concerns with hours spent on the EMR with several experts, and whether it is a problem that has been contributing to burnout among staff at their institutions. In addition, are there solutions that their institutions have implemented that they can share to help to cope with the problem?
 

OBG Management: ObGyns report that the computerization of practice and too many bureaucratic tasks, including charting, are contributing to burnout. Do you see this problem at your institution?

John J. Dougherty, MD, MBA: Yes, absolutely. There is not a day that goes by that I don’t hear about or experience “Epic Fails.” (We use Epic’s EMR product at our institution.) Too many clicks are needed to navigate even the simplest tasks—finding notes or results, documenting visits, and billing for services are all unnecessarily complex. In addition, we are being held accountable for achieving a long and growing list of “metrics” measures, education projects (HealthStream), and productivity goals. When do we have time to treat patients? And it is not just practicing physicians and clinicians. Our resident physicians spend an inordinate amount of time in front of the computer documenting, placing orders, and transferring patients using a system with a very inefficient user interface, to say the least.

Megan L. Evans, MD, MPH: I absolutely agree. Over the years, my institution has created a conglomerate of EMRs, requiring physicians across the hospital to be fluent in a multitude of systems. For example, you finish your clinic notes in one system, sign off on discharge summaries in another, and complete your operative notes in an entirely different system. As busy attendings, it is hard to keep ahead of all of these tasks, especially when the systems do not talk to one another. Fortunately, my hospital is changing our EMR to a single system within the next year. Until then, however, we will work in this piecemeal system.

Mark Woodland, MS, MD: EMR and computerization of medicine is the number 1 issue relating to dissatisfaction by ObGyn providers in our institution. Providers are earnest in their attempt to be compliant with EMR requirements, but the reality is that they are dealing with an automated system that does not have realistic expectations for management of results, follow-up tasks, and patient communications for a human provider. The actual charting, ordering of tests and consults, and communication between providers has been enhanced. However, the “in-basket” of tasks to be accomplished are extraordinary and much of it relies on the provider, which requires an inordinate amount of time. Additionally, while other members of the medical staff are stationary at a desk, physicians and other providers are not. They are mobile between inpatient units, labor and delivery, operating rooms, and emergency rooms. Time management does not always allow for providers to access computers from all of these areas to facilitate their managing the expectations of the EMR. This requires providers to access the EMR at off hours, extending their workload. Finally, the EMR is neither personal nor friendly. It is not designed with the clinician in mind, and it is not fun or engaging for a provider.

Continue to: OBG Management: What solutions have been instituted...

OBG Management: What solutions have been instituted in order to help physicians with data entry into the EMR?

Dr. Dougherty: When our institution compared EMR offerings, EMR companies put their best collective marketing feet forward. The general notion, at least with the Epic EMR, was that “you can customize Epic to your liking.” It did not take long for a bunch of motivated Epic users to create “smart” stuff (lists, phrases, and texts) in an effort to customize workflows and create fancy-looking electronic notes. Shortly thereafter, it was obvious that, as an institution, our reporting efforts kept coming up short—our reports lacked accuracy and meaning. Everyone was documenting in different ways and in different areas. Considering that reports are currently generated using (mostly) discrete data entries (data placed in specific fields within the EMR), it became obvious that our data entry paradigm needed to change. Therefore, standardization became the leading buzzword. Our institution recently initiated a project aimed at standardizing our workflows and documentation habits. In addition, we have incorporated a third-party information exchange product into our health system data aggregation and analysis workflow. Much more needs to be done, but it is a start.

Dr. Evans: At my institution, as a group, we have created templates for routine procedures and visits that also auto populate billing codes. I know that some departments have used scribes. From the hospital side, there has been improved access to the EMR from home. Some of my colleagues like this feature; however, others, like myself, believe this contributes to some of our burnout. I like to leave work at work. Having the ability to continue working at home is not a solution in my mind.

Dr. Woodland: At our institution, we have engaged our chaperones and medical assistants to help facilitate completion of the medical records during the office visit. Providers work with their assistants to accommodate documentation of history and physical findings while also listening to the provider as they are speaking in order to document patient care plans and orders. This saves the clinicians time in reviewing and editing the record as well as making sure the appropriate care plan is instituted. Our EMR provider recently has begun experimenting with personalization of color themes as well as pictures as part of the interface. Having said this, I still ask, “Why have medical professionals allowed non–clinical agencies and information technology groups to run this show?” It is also inconceivable to me that this unfunded mandate—that has increased cost, decreased clinical efficiency, and decreased clinician satisfaction—has not been addressed by national and international medical communities.

OBG Management: What changes do you feel your EMR system needs to undergo?

Dr. Woodland: I feel that we need to appropriately manage expectations of the EMR and the institution with relation to EMR and providers. By this I mean that we need to make the EMR more user-friendly and appropriate for different clinicians as well as patients. We also need to manage expectations of our patients. In a digital age where immediate contact is the norm, we need to address the issue that the EMR is not social media but rather a communication tool for routine contact and information transmission. Emergencies are not typically addressed well through the EMR platform; they are better handled with a more appropriate communication interface.

Dr. Dougherty: I feel that the biggest change needed is a competent, simple, and standard user-interface. Our old charting methods were great on a number of levels. For instance, if I wanted to add an order, I flipped to the ”Orders” tab and entered an order. If I needed to document a note, I flipped to the “Notes” tab and started writing, etc. Obviously, manual charting had its downsides—like trying to decipher handwriting art! EMRs could easily adopt the stuff that worked from our old methods of documentation, while leveraging the advantages that computerized workflows can bring to practitioners, including efficient transfer of records, meaningful reporting, simple electronic ordering, and interprofessional communication portals.

Dr. Evans: Our systems need to better communicate with one another. I am in an academic practice, and I should be able to see labs, consultant notes, imaging, all in one spot to improve efficiency and ease with patient visits. Minimizing clicks would be helpful as well. I try to write as much as I can while in the room with a patient to avoid after-hours note writing, but it takes away from my interaction with each patient.

Continue to: OBG Management: With an aim toward alleviating burnout...

OBG Management: With an aim toward alleviating burnout, are there any tips you can offer your colleagues on interfacing with the EMR?

Dr. Evans: When I first started as a new attending, it would take me hours to finish my notes, partly because of the level of detail I would write in my history of present illness (HPI) and assessment and plan. One great piece of advice I received was to be satisfied with good notes, not perfect notes. I worked to consolidate my thoughts and use preconstructed phrases/paragraphs on common problems I saw. This saved time to focus on other aspects of my academic job.

Dr. Dougherty: We need to refocus on the patient first, and mold our systems to meet that priority. Much too often, we have our backs to the patients or spend too much time interfacing with our EMR systems, and our patients are not happy about it (as many surveys have demonstrated). More importantly, a renewed focus on patient care, not EMR care, would allow our practitioners to do what they signed up for—treating patients. In the meantime, I would suggest that practitioners stay away from EMR gimmicks and go back to old-style documentation practices (like those established by the Centers for Medicare and Medicaid Services in 1997 and 1998), and ask the IT folks to help with molding the EMR systems to meet your own standards, not the standards established by EMR companies. I am also very hopeful that the consumer will drive most of the health care-related data collection in the near future, thereby marginalizing the current generation of EMR systems.

Dr. Woodland: I would add that providers need to manage the EMR and not let the EMR manage them. Set up task reminders at point times to handle results and communications from the EMR and set up time in your schedule where you can facilitate meeting these tasks. When providers are out on vacation, make sure to have an out-of-office reminder built into their EMR so that patients and others know timing of potential responses. Try to make the EMR as enjoyable as possible and focus on the good points of the EMR, such as legibility, order verification, safety, and documentation.
 

OBG Management: Do you feel that the EMR has led to improved patient care?

Dr. Evans: Yes and no. Yes, in that it can be much easier to follow a patient’s health care history from other provider notes or prior surgeries. Information is searchable and legible. If an EMR is built correctly, it can save time for providers, through smart phrases and templates, and it can help providers with proper billing codes and documentation requirements. No, in that it can take away from important patient interaction. We are required to see more patients in less time all while using, at times, a cumbersome EMR system.

Dr. Woodland: This is a tricky question because the EMR has both positive and negative attributes. Certainly, the legibility and order verification has improved, but the ease of accessing information in the EMR has changed. Additionally, there has been a drastic increase in provider dissatisfaction that has not been addressed. Provider dissatisfaction can lead to problems in patient care. If there was a clear-cut increased value for the cost, I do not think the EMR would be such a huge focus of negative attention. Providers need to take back control of their EMR and their profession so that they can utilize the EMR as the tool it was supposed to be and not the dissatisfier that it has become.

Dr. Dougherty: I do not believe patient care has been improved by EMR systems, for all of the reasons we have discussed, and then some. But there is an enormous amount of potential, if we get the interface between humans and EMR systems right!

Physician burnout has been labeled a public health crisis by the Harvard School of Public Health and other institutions.¹ A 2018 Physician’s Foundation survey found that 78% of physicians had symptoms of burnout,² which result from chronic workplace stress and include feeling depleted of energy or exhausted, mentally distanced from or cynical about one’s job, and problems getting one’s job done successfully.³ Among ObGyns, almost half (46%) report burnout.⁴ One-third of ObGyns responded on a recent Medscape Burnout Report that the computerization of practice is contributing to their burnout, and 54% said too many bureaucratic tasks, including charting, were adding to their burnout.⁵

Inefficient electronic medical records (EMRs) have been implicated as one reason for burnout, with improvements in efficiency cited as one of several potential resolutions to the problem. About 96% of hospitals have adopted EMRs today, compared with only 9% in 2008,⁶ and many physicians report recognizing value in the technology. For instance, 60% of participants in Stanford Medicine’s 2018 National Physician Poll said EMRs had led to improved patient care. At the same time, however, about as many (59%) said EMRs needed a “complete overhaul” and that the systems had detracted from their professional satisfaction (54%) as well as from their clinical effectiveness (49%).⁷

With this roundtable, we explore the concerns with hours spent on the EMR with several experts, and whether it is a problem that has been contributing to burnout among staff at their institutions. In addition, are there solutions that their institutions have implemented that they can share to help to cope with the problem?
 

OBG Management: ObGyns report that the computerization of practice and too many bureaucratic tasks, including charting, are contributing to burnout. Do you see this problem at your institution?

John J. Dougherty, MD, MBA: Yes, absolutely. There is not a day that goes by that I don’t hear about or experience “Epic Fails.” (We use Epic’s EMR product at our institution.) Too many clicks are needed to navigate even the simplest tasks—finding notes or results, documenting visits, and billing for services are all unnecessarily complex. In addition, we are being held accountable for achieving a long and growing list of “metrics” measures, education projects (HealthStream), and productivity goals. When do we have time to treat patients? And it is not just practicing physicians and clinicians. Our resident physicians spend an inordinate amount of time in front of the computer documenting, placing orders, and transferring patients using a system with a very inefficient user interface, to say the least.

Megan L. Evans, MD, MPH: I absolutely agree. Over the years, my institution has created a conglomerate of EMRs, requiring physicians across the hospital to be fluent in a multitude of systems. For example, you finish your clinic notes in one system, sign off on discharge summaries in another, and complete your operative notes in an entirely different system. As busy attendings, it is hard to keep ahead of all of these tasks, especially when the systems do not talk to one another. Fortunately, my hospital is changing our EMR to a single system within the next year. Until then, however, we will work in this piecemeal system.

Mark Woodland, MS, MD: EMR and computerization of medicine is the number 1 issue relating to dissatisfaction by ObGyn providers in our institution. Providers are earnest in their attempt to be compliant with EMR requirements, but the reality is that they are dealing with an automated system that does not have realistic expectations for management of results, follow-up tasks, and patient communications for a human provider. The actual charting, ordering of tests and consults, and communication between providers has been enhanced. However, the “in-basket” of tasks to be accomplished are extraordinary and much of it relies on the provider, which requires an inordinate amount of time. Additionally, while other members of the medical staff are stationary at a desk, physicians and other providers are not. They are mobile between inpatient units, labor and delivery, operating rooms, and emergency rooms. Time management does not always allow for providers to access computers from all of these areas to facilitate their managing the expectations of the EMR. This requires providers to access the EMR at off hours, extending their workload. Finally, the EMR is neither personal nor friendly. It is not designed with the clinician in mind, and it is not fun or engaging for a provider.

Continue to: OBG Management: What solutions have been instituted...

OBG Management: What solutions have been instituted in order to help physicians with data entry into the EMR?

Dr. Dougherty: When our institution compared EMR offerings, EMR companies put their best collective marketing feet forward. The general notion, at least with the Epic EMR, was that “you can customize Epic to your liking.” It did not take long for a bunch of motivated Epic users to create “smart” stuff (lists, phrases, and texts) in an effort to customize workflows and create fancy-looking electronic notes. Shortly thereafter, it was obvious that, as an institution, our reporting efforts kept coming up short—our reports lacked accuracy and meaning. Everyone was documenting in different ways and in different areas. Considering that reports are currently generated using (mostly) discrete data entries (data placed in specific fields within the EMR), it became obvious that our data entry paradigm needed to change. Therefore, standardization became the leading buzzword. Our institution recently initiated a project aimed at standardizing our workflows and documentation habits. In addition, we have incorporated a third-party information exchange product into our health system data aggregation and analysis workflow. Much more needs to be done, but it is a start.

Dr. Evans: At my institution, as a group, we have created templates for routine procedures and visits that also auto populate billing codes. I know that some departments have used scribes. From the hospital side, there has been improved access to the EMR from home. Some of my colleagues like this feature; however, others, like myself, believe this contributes to some of our burnout. I like to leave work at work. Having the ability to continue working at home is not a solution in my mind.

Dr. Woodland: At our institution, we have engaged our chaperones and medical assistants to help facilitate completion of the medical records during the office visit. Providers work with their assistants to accommodate documentation of history and physical findings while also listening to the provider as they are speaking in order to document patient care plans and orders. This saves the clinicians time in reviewing and editing the record as well as making sure the appropriate care plan is instituted. Our EMR provider recently has begun experimenting with personalization of color themes as well as pictures as part of the interface. Having said this, I still ask, “Why have medical professionals allowed non–clinical agencies and information technology groups to run this show?” It is also inconceivable to me that this unfunded mandate—that has increased cost, decreased clinical efficiency, and decreased clinician satisfaction—has not been addressed by national and international medical communities.

OBG Management: What changes do you feel your EMR system needs to undergo?

Dr. Woodland: I feel that we need to appropriately manage expectations of the EMR and the institution with relation to EMR and providers. By this I mean that we need to make the EMR more user-friendly and appropriate for different clinicians as well as patients. We also need to manage expectations of our patients. In a digital age where immediate contact is the norm, we need to address the issue that the EMR is not social media but rather a communication tool for routine contact and information transmission. Emergencies are not typically addressed well through the EMR platform; they are better handled with a more appropriate communication interface.

Dr. Dougherty: I feel that the biggest change needed is a competent, simple, and standard user-interface. Our old charting methods were great on a number of levels. For instance, if I wanted to add an order, I flipped to the ”Orders” tab and entered an order. If I needed to document a note, I flipped to the “Notes” tab and started writing, etc. Obviously, manual charting had its downsides—like trying to decipher handwriting art! EMRs could easily adopt the stuff that worked from our old methods of documentation, while leveraging the advantages that computerized workflows can bring to practitioners, including efficient transfer of records, meaningful reporting, simple electronic ordering, and interprofessional communication portals.

Dr. Evans: Our systems need to better communicate with one another. I am in an academic practice, and I should be able to see labs, consultant notes, imaging, all in one spot to improve efficiency and ease with patient visits. Minimizing clicks would be helpful as well. I try to write as much as I can while in the room with a patient to avoid after-hours note writing, but it takes away from my interaction with each patient.

Continue to: OBG Management: With an aim toward alleviating burnout...

OBG Management: With an aim toward alleviating burnout, are there any tips you can offer your colleagues on interfacing with the EMR?

Dr. Evans: When I first started as a new attending, it would take me hours to finish my notes, partly because of the level of detail I would write in my history of present illness (HPI) and assessment and plan. One great piece of advice I received was to be satisfied with good notes, not perfect notes. I worked to consolidate my thoughts and use preconstructed phrases/paragraphs on common problems I saw. This saved time to focus on other aspects of my academic job.

Dr. Dougherty: We need to refocus on the patient first, and mold our systems to meet that priority. Much too often, we have our backs to the patients or spend too much time interfacing with our EMR systems, and our patients are not happy about it (as many surveys have demonstrated). More importantly, a renewed focus on patient care, not EMR care, would allow our practitioners to do what they signed up for—treating patients. In the meantime, I would suggest that practitioners stay away from EMR gimmicks and go back to old-style documentation practices (like those established by the Centers for Medicare and Medicaid Services in 1997 and 1998), and ask the IT folks to help with molding the EMR systems to meet your own standards, not the standards established by EMR companies. I am also very hopeful that the consumer will drive most of the health care-related data collection in the near future, thereby marginalizing the current generation of EMR systems.

Dr. Woodland: I would add that providers need to manage the EMR and not let the EMR manage them. Set up task reminders at point times to handle results and communications from the EMR and set up time in your schedule where you can facilitate meeting these tasks. When providers are out on vacation, make sure to have an out-of-office reminder built into their EMR so that patients and others know timing of potential responses. Try to make the EMR as enjoyable as possible and focus on the good points of the EMR, such as legibility, order verification, safety, and documentation.
 

OBG Management: Do you feel that the EMR has led to improved patient care?

Dr. Evans: Yes and no. Yes, in that it can be much easier to follow a patient’s health care history from other provider notes or prior surgeries. Information is searchable and legible. If an EMR is built correctly, it can save time for providers, through smart phrases and templates, and it can help providers with proper billing codes and documentation requirements. No, in that it can take away from important patient interaction. We are required to see more patients in less time all while using, at times, a cumbersome EMR system.

Dr. Woodland: This is a tricky question because the EMR has both positive and negative attributes. Certainly, the legibility and order verification has improved, but the ease of accessing information in the EMR has changed. Additionally, there has been a drastic increase in provider dissatisfaction that has not been addressed. Provider dissatisfaction can lead to problems in patient care. If there was a clear-cut increased value for the cost, I do not think the EMR would be such a huge focus of negative attention. Providers need to take back control of their EMR and their profession so that they can utilize the EMR as the tool it was supposed to be and not the dissatisfier that it has become.

Dr. Dougherty: I do not believe patient care has been improved by EMR systems, for all of the reasons we have discussed, and then some. But there is an enormous amount of potential, if we get the interface between humans and EMR systems right!

MENSTRUAL PAIN RELIEF THROUGH MICRO-PULSES

Livia, by iPulse Medical Ltd, is a US Food and Drug Administration (FDA) approved, drug-free option to treat menstrual pain through the transmission of electrical pulses. Electrodes are placed on the body at the source of menstrual pain and send a frequency to the nerves to reduce pain. Livia was designed based on the principles of the “gate control” theory of pain, says iPulse Medical. When the nerves are stimulated by the device’s electrodes, the nerve gate is closed, preventing pain signals from being received or felt in the brain.

The device can be worn in public or at home and allows the user to adjust the frequency of the electrical signal to correspond with her pain intensity. According to iPulse Medical, there are no adverse effects and the user will not build up a tolerance; however, the device should not be worn if the user has a pacemaker or is undergoing fertility treatment.

FOR MORE INFORMATION, VISIT: https://mylivia.com/

EXPAREL FOR CESAREAN DELIVERY

Pacira BioSciences an-nounced completion of their Phase 4 study of Exparel (bupivacaine lipsome injectable suspension), a local analgesic given to patients undergoing planned cesarean delivery (CD), aimed at reducing postsurgical pain and total opioid consumption through the first 72 hours postsurgery. Exparel is administered through transversus abdominis plane field block.

Pacira’s multicenter, randomized, double-blind study of 186 patients showed that those receiving Exparel plus bupivacaine HCl had a 52% reduction in total opioid consumption and significantly lower pain scores through the first 72 hours after CD, compared with those receiving only bupivacaine HCl. The most common adverse effects are itching and nausea. Exparel should not be used for patients under the age of 18 and should be used cautiously in patients with hepatic disease.

FOR MORE INFORMATION, VISIT: https://www.exparel.com/

M GENITALIUM ASSAY DETECTS THE STI

Hologic’s Aptima^® Mycoplasma genitalium assay is the first FDA-cleared diagnostic test for this sexually transmitted infection (STI), which has been identified by the Centers for Disease Control and Prevention as an emerging public health threat. The assay is an in vitro nucleic acid amplification test that can be used to verify swab or urine samples from women and men. In published studies, the ribosomal RNA-based assay displayed greater sensitivity than lab-developed or CE-marked DNA-based tests. Early detection is important, Hologic asserts, because M genitalium is increasing in prevalence among higher-risk populations; however, it is not well known and often misdiagnosed, leading to incorrect treatment as well as risk for transmission and recurrence.

Hologic cites several studies that have shown M genitalium can be asymptomatic; however, it also can be associated with nongonococcal urethritis in men and cervicitis in women, as well as increased risk for pelvic inflammatory disease, preterm birth, spontaneous abortion, and infertility. A high percentage of infected people have an antibiotic-resistant strain, demonstrating a need for early detection and screening.

FOR MORE INFORMATION, VISIT: https://www.hologic.com

NEW ART OPTION

FOR MORE INFORMATION, VISIT: https://www.ferringusa.com

MENSTRUAL PAIN RELIEF THROUGH MICRO-PULSES

Livia, by iPulse Medical Ltd, is a US Food and Drug Administration (FDA) approved, drug-free option to treat menstrual pain through the transmission of electrical pulses. Electrodes are placed on the body at the source of menstrual pain and send a frequency to the nerves to reduce pain. Livia was designed based on the principles of the “gate control” theory of pain, says iPulse Medical. When the nerves are stimulated by the device’s electrodes, the nerve gate is closed, preventing pain signals from being received or felt in the brain.

The device can be worn in public or at home and allows the user to adjust the frequency of the electrical signal to correspond with her pain intensity. According to iPulse Medical, there are no adverse effects and the user will not build up a tolerance; however, the device should not be worn if the user has a pacemaker or is undergoing fertility treatment.

FOR MORE INFORMATION, VISIT: https://mylivia.com/

EXPAREL FOR CESAREAN DELIVERY

Pacira BioSciences an-nounced completion of their Phase 4 study of Exparel (bupivacaine lipsome injectable suspension), a local analgesic given to patients undergoing planned cesarean delivery (CD), aimed at reducing postsurgical pain and total opioid consumption through the first 72 hours postsurgery. Exparel is administered through transversus abdominis plane field block.

Pacira’s multicenter, randomized, double-blind study of 186 patients showed that those receiving Exparel plus bupivacaine HCl had a 52% reduction in total opioid consumption and significantly lower pain scores through the first 72 hours after CD, compared with those receiving only bupivacaine HCl. The most common adverse effects are itching and nausea. Exparel should not be used for patients under the age of 18 and should be used cautiously in patients with hepatic disease.

FOR MORE INFORMATION, VISIT: https://www.exparel.com/

M GENITALIUM ASSAY DETECTS THE STI

Hologic’s Aptima^® Mycoplasma genitalium assay is the first FDA-cleared diagnostic test for this sexually transmitted infection (STI), which has been identified by the Centers for Disease Control and Prevention as an emerging public health threat. The assay is an in vitro nucleic acid amplification test that can be used to verify swab or urine samples from women and men. In published studies, the ribosomal RNA-based assay displayed greater sensitivity than lab-developed or CE-marked DNA-based tests. Early detection is important, Hologic asserts, because M genitalium is increasing in prevalence among higher-risk populations; however, it is not well known and often misdiagnosed, leading to incorrect treatment as well as risk for transmission and recurrence.

Hologic cites several studies that have shown M genitalium can be asymptomatic; however, it also can be associated with nongonococcal urethritis in men and cervicitis in women, as well as increased risk for pelvic inflammatory disease, preterm birth, spontaneous abortion, and infertility. A high percentage of infected people have an antibiotic-resistant strain, demonstrating a need for early detection and screening.

FOR MORE INFORMATION, VISIT: https://www.hologic.com

NEW ART OPTION

FOR MORE INFORMATION, VISIT: https://www.ferringusa.com

MENSTRUAL PAIN RELIEF THROUGH MICRO-PULSES

Livia, by iPulse Medical Ltd, is a US Food and Drug Administration (FDA) approved, drug-free option to treat menstrual pain through the transmission of electrical pulses. Electrodes are placed on the body at the source of menstrual pain and send a frequency to the nerves to reduce pain. Livia was designed based on the principles of the “gate control” theory of pain, says iPulse Medical. When the nerves are stimulated by the device’s electrodes, the nerve gate is closed, preventing pain signals from being received or felt in the brain.

The device can be worn in public or at home and allows the user to adjust the frequency of the electrical signal to correspond with her pain intensity. According to iPulse Medical, there are no adverse effects and the user will not build up a tolerance; however, the device should not be worn if the user has a pacemaker or is undergoing fertility treatment.

FOR MORE INFORMATION, VISIT: https://mylivia.com/

EXPAREL FOR CESAREAN DELIVERY

Pacira BioSciences an-nounced completion of their Phase 4 study of Exparel (bupivacaine lipsome injectable suspension), a local analgesic given to patients undergoing planned cesarean delivery (CD), aimed at reducing postsurgical pain and total opioid consumption through the first 72 hours postsurgery. Exparel is administered through transversus abdominis plane field block.

Pacira’s multicenter, randomized, double-blind study of 186 patients showed that those receiving Exparel plus bupivacaine HCl had a 52% reduction in total opioid consumption and significantly lower pain scores through the first 72 hours after CD, compared with those receiving only bupivacaine HCl. The most common adverse effects are itching and nausea. Exparel should not be used for patients under the age of 18 and should be used cautiously in patients with hepatic disease.

FOR MORE INFORMATION, VISIT: https://www.exparel.com/

M GENITALIUM ASSAY DETECTS THE STI

Hologic’s Aptima^® Mycoplasma genitalium assay is the first FDA-cleared diagnostic test for this sexually transmitted infection (STI), which has been identified by the Centers for Disease Control and Prevention as an emerging public health threat. The assay is an in vitro nucleic acid amplification test that can be used to verify swab or urine samples from women and men. In published studies, the ribosomal RNA-based assay displayed greater sensitivity than lab-developed or CE-marked DNA-based tests. Early detection is important, Hologic asserts, because M genitalium is increasing in prevalence among higher-risk populations; however, it is not well known and often misdiagnosed, leading to incorrect treatment as well as risk for transmission and recurrence.

Hologic cites several studies that have shown M genitalium can be asymptomatic; however, it also can be associated with nongonococcal urethritis in men and cervicitis in women, as well as increased risk for pelvic inflammatory disease, preterm birth, spontaneous abortion, and infertility. A high percentage of infected people have an antibiotic-resistant strain, demonstrating a need for early detection and screening.

FOR MORE INFORMATION, VISIT: https://www.hologic.com

NEW ART OPTION

FOR MORE INFORMATION, VISIT: https://www.ferringusa.com