Hospital Medicine

The HHS has announced more than $50 million in funding for 75 health centers in 23 states, Puerto Rico, and the Federated States of Micronesia.

One in 13 people nationwide depend on a Health Resources and Services Administration (HRSA)-funded health center for preventive and primary health care needs. Among the special populations served are nearly 2 million homeless patients, 910,172 agricultural workers, and 305,520 veterans.

Health centers are community based and patient directed, delivering comprehensive, culturally competent primary care. They also often link to pharmacy, mental health, substance abuse, and oral health services in areas where economic, geographic, or cultural barriers limit access to affordable health care services.

Although the health centers serve patients who are often sicker and more at risk than is the general population, the quality of care “equals and often surpasses” that provided by other primary care providers, HRSA says. For example, > 93% of HRSA-funded health centers met or exceeded at least 1 goal of Healthy People 2020 for clinical performance in 2015. And > 68% of health centers are recognized by national accrediting organizations as Patient-Centered Medical Homes, an advanced model of team-based primary care.

The health centers, which started 50 years ago with just 2, have expanded to > 9,800 clinic sites. Between 2008 -2015, HRSA-supported centers increased by 27%, and the number of patients increased by 42% to more than 7 million more patients. In 2015 alone, HRSA funded nearly 430 new center sites. Health centers already provide care to  more than 24 million people; this new funding will extend care to about 240,000 additional patients.

The HHS has announced more than $50 million in funding for 75 health centers in 23 states, Puerto Rico, and the Federated States of Micronesia.

One in 13 people nationwide depend on a Health Resources and Services Administration (HRSA)-funded health center for preventive and primary health care needs. Among the special populations served are nearly 2 million homeless patients, 910,172 agricultural workers, and 305,520 veterans.

Health centers are community based and patient directed, delivering comprehensive, culturally competent primary care. They also often link to pharmacy, mental health, substance abuse, and oral health services in areas where economic, geographic, or cultural barriers limit access to affordable health care services.

Although the health centers serve patients who are often sicker and more at risk than is the general population, the quality of care “equals and often surpasses” that provided by other primary care providers, HRSA says. For example, > 93% of HRSA-funded health centers met or exceeded at least 1 goal of Healthy People 2020 for clinical performance in 2015. And > 68% of health centers are recognized by national accrediting organizations as Patient-Centered Medical Homes, an advanced model of team-based primary care.

The health centers, which started 50 years ago with just 2, have expanded to > 9,800 clinic sites. Between 2008 -2015, HRSA-supported centers increased by 27%, and the number of patients increased by 42% to more than 7 million more patients. In 2015 alone, HRSA funded nearly 430 new center sites. Health centers already provide care to  more than 24 million people; this new funding will extend care to about 240,000 additional patients.

The HHS has announced more than $50 million in funding for 75 health centers in 23 states, Puerto Rico, and the Federated States of Micronesia.

One in 13 people nationwide depend on a Health Resources and Services Administration (HRSA)-funded health center for preventive and primary health care needs. Among the special populations served are nearly 2 million homeless patients, 910,172 agricultural workers, and 305,520 veterans.

Health centers are community based and patient directed, delivering comprehensive, culturally competent primary care. They also often link to pharmacy, mental health, substance abuse, and oral health services in areas where economic, geographic, or cultural barriers limit access to affordable health care services.

Although the health centers serve patients who are often sicker and more at risk than is the general population, the quality of care “equals and often surpasses” that provided by other primary care providers, HRSA says. For example, > 93% of HRSA-funded health centers met or exceeded at least 1 goal of Healthy People 2020 for clinical performance in 2015. And > 68% of health centers are recognized by national accrediting organizations as Patient-Centered Medical Homes, an advanced model of team-based primary care.

The health centers, which started 50 years ago with just 2, have expanded to > 9,800 clinic sites. Between 2008 -2015, HRSA-supported centers increased by 27%, and the number of patients increased by 42% to more than 7 million more patients. In 2015 alone, HRSA funded nearly 430 new center sites. Health centers already provide care to  more than 24 million people; this new funding will extend care to about 240,000 additional patients.

Americans who live in rural areas are more likely than their urban counterparts are to die of the 5 leading causes of death, according to a CDC study of data from the National Vital Statistics System.

In 2014, 25,000 rural residents died of heart disease, 19,000 of cancer, 12,000 of unintentional injuries, 11,000 of chronic lower respiratory disease, and 4,000 of stroke. The study also found that unintentional injury deaths were about 50% higher in rural areas than in urban areas, partly due to a greater risk of death in vehicle crashes and of opioid overdoses. The problem is compounded by the fact that the distance between health care facilities and trauma centers can make rapid access to specialized health care difficult.

The study researchers say several factors could influence the rural-urban gap. For instance, many of the deaths are associated with sociodemographic differences. Rural residents tend to be older, poorer, and sicker with limited physical activity due to chronic conditions. But that “striking gap” in health can be closed, says CDC Director Tom Frieden, MD, MPH, by better understanding and addressing the health threats that put rural Americans at risk.

CDC suggests, for instance, that health care providers in rural areas:

• Screen patients for high blood pressure and make control a quality improvement goal;
• Increase cancer prevention and early detection—for example, by participating in state-level comprehensive control coalitions, which focus on prevention, education, screening, access, support, and overall good health;
• Encourage physical activity and healthy eating to reduce obesity;
• Encourage patients to stop smoking;
• Promote vehicle safety (rural residents are less likely to use seatbelts); and
• Engage in safe prescribing of opioids for pain, and use nonpharmacologic therapies

The report and a companion commentary are part of a new rural health series in CDC’s Morbidity and Mortality Weekly Report. The Health Resources and Services Administration, which houses the Federal Office of Rural Health Policy, will collaborate with the CDC on the series and help promote the findings and recommendations to rural communities.

Americans who live in rural areas are more likely than their urban counterparts are to die of the 5 leading causes of death, according to a CDC study of data from the National Vital Statistics System.

In 2014, 25,000 rural residents died of heart disease, 19,000 of cancer, 12,000 of unintentional injuries, 11,000 of chronic lower respiratory disease, and 4,000 of stroke. The study also found that unintentional injury deaths were about 50% higher in rural areas than in urban areas, partly due to a greater risk of death in vehicle crashes and of opioid overdoses. The problem is compounded by the fact that the distance between health care facilities and trauma centers can make rapid access to specialized health care difficult.

The study researchers say several factors could influence the rural-urban gap. For instance, many of the deaths are associated with sociodemographic differences. Rural residents tend to be older, poorer, and sicker with limited physical activity due to chronic conditions. But that “striking gap” in health can be closed, says CDC Director Tom Frieden, MD, MPH, by better understanding and addressing the health threats that put rural Americans at risk.

CDC suggests, for instance, that health care providers in rural areas:

• Screen patients for high blood pressure and make control a quality improvement goal;
• Increase cancer prevention and early detection—for example, by participating in state-level comprehensive control coalitions, which focus on prevention, education, screening, access, support, and overall good health;
• Encourage physical activity and healthy eating to reduce obesity;
• Encourage patients to stop smoking;
• Promote vehicle safety (rural residents are less likely to use seatbelts); and
• Engage in safe prescribing of opioids for pain, and use nonpharmacologic therapies

The report and a companion commentary are part of a new rural health series in CDC’s Morbidity and Mortality Weekly Report. The Health Resources and Services Administration, which houses the Federal Office of Rural Health Policy, will collaborate with the CDC on the series and help promote the findings and recommendations to rural communities.

Americans who live in rural areas are more likely than their urban counterparts are to die of the 5 leading causes of death, according to a CDC study of data from the National Vital Statistics System.

In 2014, 25,000 rural residents died of heart disease, 19,000 of cancer, 12,000 of unintentional injuries, 11,000 of chronic lower respiratory disease, and 4,000 of stroke. The study also found that unintentional injury deaths were about 50% higher in rural areas than in urban areas, partly due to a greater risk of death in vehicle crashes and of opioid overdoses. The problem is compounded by the fact that the distance between health care facilities and trauma centers can make rapid access to specialized health care difficult.

The study researchers say several factors could influence the rural-urban gap. For instance, many of the deaths are associated with sociodemographic differences. Rural residents tend to be older, poorer, and sicker with limited physical activity due to chronic conditions. But that “striking gap” in health can be closed, says CDC Director Tom Frieden, MD, MPH, by better understanding and addressing the health threats that put rural Americans at risk.

CDC suggests, for instance, that health care providers in rural areas:

• Screen patients for high blood pressure and make control a quality improvement goal;
• Increase cancer prevention and early detection—for example, by participating in state-level comprehensive control coalitions, which focus on prevention, education, screening, access, support, and overall good health;
• Encourage physical activity and healthy eating to reduce obesity;
• Encourage patients to stop smoking;
• Promote vehicle safety (rural residents are less likely to use seatbelts); and
• Engage in safe prescribing of opioids for pain, and use nonpharmacologic therapies

The report and a companion commentary are part of a new rural health series in CDC’s Morbidity and Mortality Weekly Report. The Health Resources and Services Administration, which houses the Federal Office of Rural Health Policy, will collaborate with the CDC on the series and help promote the findings and recommendations to rural communities.

Ginsenosides are pharmacologically active components of ginseng, which often is used to relieve coughs and colds. They also have been found to have antineoplastic, antioxidant, antimicrobial, and antifungal properties; other studies suggest neuroprotective properties as well. Ginsenosides may act against coxsackievirus B3, enterovirus 71, human rhinovirus 3, and hemagglutinating virus of Japan (HVJ) infection. But do they have an antiviral effect on influenza?

Related: A New Kind of Flu Drug

Researchers from University Health Network & Shantou University Medical College and Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, both in China, and University of Toronto in Canada conducted a study in mice of the anti-influenza properties of ginseng and ginseng-derived compounds both in vitro and in vivo. They found that ginsenosides exerted “strong antiviral activity” to 2009 pandemic H1N1 virus. Ginsenoside protected the animals from infection and lowered viral titers in their lungs.

Sugars were the key to the effectiveness of the ginsenosides, which are composed of a steroid skeleton with various sugar groups attached. The researchers note that previous studies have shown that ginsenosides’ anticancer activity and antioxidant activity are related to the type and position of sugar moieties.

Related: How Common is Flu Without Fever?

The pilot experiment did not have negative or toxic effects on the animals or in cell proliferation in vitro, thus “defining the nontoxic nature and therapeutic value of these compounds,” the researchers say. They also point out that in phase 2 randomized clinical trials in children, oral consumption of ginseng extract as an alternative influenza treatment did not result in severe adverse effects. They suggest that their findings could spur other research into a novel antiviral drug for influenza.

Source:

Dong W, Farooqui A, Leon AJ, Kelvin DJ. PloS One. 2017;12(2):e0171936.
doi: 10.1371/journal.pone.0171936.

Ginsenosides are pharmacologically active components of ginseng, which often is used to relieve coughs and colds. They also have been found to have antineoplastic, antioxidant, antimicrobial, and antifungal properties; other studies suggest neuroprotective properties as well. Ginsenosides may act against coxsackievirus B3, enterovirus 71, human rhinovirus 3, and hemagglutinating virus of Japan (HVJ) infection. But do they have an antiviral effect on influenza?

Related: A New Kind of Flu Drug

Researchers from University Health Network & Shantou University Medical College and Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, both in China, and University of Toronto in Canada conducted a study in mice of the anti-influenza properties of ginseng and ginseng-derived compounds both in vitro and in vivo. They found that ginsenosides exerted “strong antiviral activity” to 2009 pandemic H1N1 virus. Ginsenoside protected the animals from infection and lowered viral titers in their lungs.

Sugars were the key to the effectiveness of the ginsenosides, which are composed of a steroid skeleton with various sugar groups attached. The researchers note that previous studies have shown that ginsenosides’ anticancer activity and antioxidant activity are related to the type and position of sugar moieties.

Related: How Common is Flu Without Fever?

The pilot experiment did not have negative or toxic effects on the animals or in cell proliferation in vitro, thus “defining the nontoxic nature and therapeutic value of these compounds,” the researchers say. They also point out that in phase 2 randomized clinical trials in children, oral consumption of ginseng extract as an alternative influenza treatment did not result in severe adverse effects. They suggest that their findings could spur other research into a novel antiviral drug for influenza.

Source:

Dong W, Farooqui A, Leon AJ, Kelvin DJ. PloS One. 2017;12(2):e0171936.
doi: 10.1371/journal.pone.0171936.

Ginsenosides are pharmacologically active components of ginseng, which often is used to relieve coughs and colds. They also have been found to have antineoplastic, antioxidant, antimicrobial, and antifungal properties; other studies suggest neuroprotective properties as well. Ginsenosides may act against coxsackievirus B3, enterovirus 71, human rhinovirus 3, and hemagglutinating virus of Japan (HVJ) infection. But do they have an antiviral effect on influenza?

Related: A New Kind of Flu Drug

Researchers from University Health Network & Shantou University Medical College and Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, both in China, and University of Toronto in Canada conducted a study in mice of the anti-influenza properties of ginseng and ginseng-derived compounds both in vitro and in vivo. They found that ginsenosides exerted “strong antiviral activity” to 2009 pandemic H1N1 virus. Ginsenoside protected the animals from infection and lowered viral titers in their lungs.

Sugars were the key to the effectiveness of the ginsenosides, which are composed of a steroid skeleton with various sugar groups attached. The researchers note that previous studies have shown that ginsenosides’ anticancer activity and antioxidant activity are related to the type and position of sugar moieties.

Related: How Common is Flu Without Fever?

The pilot experiment did not have negative or toxic effects on the animals or in cell proliferation in vitro, thus “defining the nontoxic nature and therapeutic value of these compounds,” the researchers say. They also point out that in phase 2 randomized clinical trials in children, oral consumption of ginseng extract as an alternative influenza treatment did not result in severe adverse effects. They suggest that their findings could spur other research into a novel antiviral drug for influenza.

Source:

Dong W, Farooqui A, Leon AJ, Kelvin DJ. PloS One. 2017;12(2):e0171936.
doi: 10.1371/journal.pone.0171936.

Nearly half of a group of homeless and formerly homeless veterans reported experiencing food insecurity, according to VA researchers. More than one-quarter of those said they’d averaged only 1 meal a day.

Researchers screened 270 new patients who enrolled in 1 of 6 VA primary care clinics. Screening began with a single question: “In the past month, were there times when the food for you just did not last, and there was no money to buy more?” Patients who answered yes were then asked where they got their food, how many meals per day they ate, whether they prepared their meals, whether they received food stamps, whether they had diabetes, and whether they had symptoms of hypoglycemia.

Of the respondents, 63% were living in their own apartment, and 26% were in a transitional housing program where they were responsible for some of their meals. Of the patients who reported food insecurity, 87% prepared their meals, with half relying on food they bought, 23% on food from soup kitchens and food pantries, 15% from shelters, 19% from family and friends. About half (47%) were receiving food stamps.

One-fifth of the patients had diabetes or prediabetes, and 44% reported hypoglycemia symptoms when without food. The researchers point out that the consequences of food insecurity are “significant and potentially life threatening.” They cite another study that found risk for hospital admissions for hypoglycemia rose 27% in the last week of the month among low-income populations, typically when food stamps and supplies at food pantries ran low or were exhausted.

The study revealed that asking about only food insecurity was not enough, the researchers say. “The additional context provided by the follow-up questions and the breadth of different responses underscored that the needs of these patients extend beyond those available from 1 health care provider or 1 health care discipline.”

Both patients and health care providers endorsed the screening program. One staff member, for instance, called the program a good rapport builder. No team found the questions burdensome, the researchers say. In fact,4  teams said the follow-up questions highlighted the complexity of issues underlying food insecurity and the need for a well-integrated, multidisciplinary approach to the problem.

Nearly half of a group of homeless and formerly homeless veterans reported experiencing food insecurity, according to VA researchers. More than one-quarter of those said they’d averaged only 1 meal a day.

Researchers screened 270 new patients who enrolled in 1 of 6 VA primary care clinics. Screening began with a single question: “In the past month, were there times when the food for you just did not last, and there was no money to buy more?” Patients who answered yes were then asked where they got their food, how many meals per day they ate, whether they prepared their meals, whether they received food stamps, whether they had diabetes, and whether they had symptoms of hypoglycemia.

Of the respondents, 63% were living in their own apartment, and 26% were in a transitional housing program where they were responsible for some of their meals. Of the patients who reported food insecurity, 87% prepared their meals, with half relying on food they bought, 23% on food from soup kitchens and food pantries, 15% from shelters, 19% from family and friends. About half (47%) were receiving food stamps.

One-fifth of the patients had diabetes or prediabetes, and 44% reported hypoglycemia symptoms when without food. The researchers point out that the consequences of food insecurity are “significant and potentially life threatening.” They cite another study that found risk for hospital admissions for hypoglycemia rose 27% in the last week of the month among low-income populations, typically when food stamps and supplies at food pantries ran low or were exhausted.

The study revealed that asking about only food insecurity was not enough, the researchers say. “The additional context provided by the follow-up questions and the breadth of different responses underscored that the needs of these patients extend beyond those available from 1 health care provider or 1 health care discipline.”

Both patients and health care providers endorsed the screening program. One staff member, for instance, called the program a good rapport builder. No team found the questions burdensome, the researchers say. In fact,4  teams said the follow-up questions highlighted the complexity of issues underlying food insecurity and the need for a well-integrated, multidisciplinary approach to the problem.

Nearly half of a group of homeless and formerly homeless veterans reported experiencing food insecurity, according to VA researchers. More than one-quarter of those said they’d averaged only 1 meal a day.

Researchers screened 270 new patients who enrolled in 1 of 6 VA primary care clinics. Screening began with a single question: “In the past month, were there times when the food for you just did not last, and there was no money to buy more?” Patients who answered yes were then asked where they got their food, how many meals per day they ate, whether they prepared their meals, whether they received food stamps, whether they had diabetes, and whether they had symptoms of hypoglycemia.

Of the respondents, 63% were living in their own apartment, and 26% were in a transitional housing program where they were responsible for some of their meals. Of the patients who reported food insecurity, 87% prepared their meals, with half relying on food they bought, 23% on food from soup kitchens and food pantries, 15% from shelters, 19% from family and friends. About half (47%) were receiving food stamps.

One-fifth of the patients had diabetes or prediabetes, and 44% reported hypoglycemia symptoms when without food. The researchers point out that the consequences of food insecurity are “significant and potentially life threatening.” They cite another study that found risk for hospital admissions for hypoglycemia rose 27% in the last week of the month among low-income populations, typically when food stamps and supplies at food pantries ran low or were exhausted.

The study revealed that asking about only food insecurity was not enough, the researchers say. “The additional context provided by the follow-up questions and the breadth of different responses underscored that the needs of these patients extend beyond those available from 1 health care provider or 1 health care discipline.”

Both patients and health care providers endorsed the screening program. One staff member, for instance, called the program a good rapport builder. No team found the questions burdensome, the researchers say. In fact,4  teams said the follow-up questions highlighted the complexity of issues underlying food insecurity and the need for a well-integrated, multidisciplinary approach to the problem.

The number of older Americans is growing, but the number of those with hearing loss is declining, according to data from the National Health and Nutrition Examination Survey. Researchers compared 2 time periods (1999-2004 and 2011-2012) and found overall annual prevalence of hearing loss dropped from 16% to 14% in 1999-2004, to 28 million adults, then dropped further to 27.7 million in 2011-2012.

Age was the greatest predictor of hearing loss; people in the oldest age group surveyed (aged 60 to 69) had the most loss. Although not included in the study, people aged ≥ 70 years have the highest prevalence of hearing loss of any age group, the authors say. Men of all ages were twice as likely as women to have hearing loss. Non-Hispanic white adults were more likely to have hearing loss than were adults in other ethnic groups. Non-Hispanic black adults had the lowest risk.

The researchers don’t know why hearing loss is becoming less prevalent but suggest reasons include fewer manufacturing jobs, increased use of hearing protectors, less smoking, and better medical care to manage risk factors associated with hearing loss. They did find that lower education level and heavy use of firearms were associated with hearing loss.

“Our findings show a promising trend of better hearing among adults that spans more than half a century,” says Howard Hoffman, MA, first author on the paper and director of the National Institute on Deafness and Other Communication Disorders Epidemiology and Statistics Program. “The decline in hearing loss rates among adults aged < 70 years suggests that age-related hearing loss may be delayed until later in life. This is good news because for those who do develop hearing loss, they will have experienced more quality years of life with better hearing than earlier generations."

The number of older Americans is growing, but the number of those with hearing loss is declining, according to data from the National Health and Nutrition Examination Survey. Researchers compared 2 time periods (1999-2004 and 2011-2012) and found overall annual prevalence of hearing loss dropped from 16% to 14% in 1999-2004, to 28 million adults, then dropped further to 27.7 million in 2011-2012.

Age was the greatest predictor of hearing loss; people in the oldest age group surveyed (aged 60 to 69) had the most loss. Although not included in the study, people aged ≥ 70 years have the highest prevalence of hearing loss of any age group, the authors say. Men of all ages were twice as likely as women to have hearing loss. Non-Hispanic white adults were more likely to have hearing loss than were adults in other ethnic groups. Non-Hispanic black adults had the lowest risk.

The researchers don’t know why hearing loss is becoming less prevalent but suggest reasons include fewer manufacturing jobs, increased use of hearing protectors, less smoking, and better medical care to manage risk factors associated with hearing loss. They did find that lower education level and heavy use of firearms were associated with hearing loss.

“Our findings show a promising trend of better hearing among adults that spans more than half a century,” says Howard Hoffman, MA, first author on the paper and director of the National Institute on Deafness and Other Communication Disorders Epidemiology and Statistics Program. “The decline in hearing loss rates among adults aged < 70 years suggests that age-related hearing loss may be delayed until later in life. This is good news because for those who do develop hearing loss, they will have experienced more quality years of life with better hearing than earlier generations."

The number of older Americans is growing, but the number of those with hearing loss is declining, according to data from the National Health and Nutrition Examination Survey. Researchers compared 2 time periods (1999-2004 and 2011-2012) and found overall annual prevalence of hearing loss dropped from 16% to 14% in 1999-2004, to 28 million adults, then dropped further to 27.7 million in 2011-2012.

Age was the greatest predictor of hearing loss; people in the oldest age group surveyed (aged 60 to 69) had the most loss. Although not included in the study, people aged ≥ 70 years have the highest prevalence of hearing loss of any age group, the authors say. Men of all ages were twice as likely as women to have hearing loss. Non-Hispanic white adults were more likely to have hearing loss than were adults in other ethnic groups. Non-Hispanic black adults had the lowest risk.

The researchers don’t know why hearing loss is becoming less prevalent but suggest reasons include fewer manufacturing jobs, increased use of hearing protectors, less smoking, and better medical care to manage risk factors associated with hearing loss. They did find that lower education level and heavy use of firearms were associated with hearing loss.

“Our findings show a promising trend of better hearing among adults that spans more than half a century,” says Howard Hoffman, MA, first author on the paper and director of the National Institute on Deafness and Other Communication Disorders Epidemiology and Statistics Program. “The decline in hearing loss rates among adults aged < 70 years suggests that age-related hearing loss may be delayed until later in life. This is good news because for those who do develop hearing loss, they will have experienced more quality years of life with better hearing than earlier generations."

Geriatric syndromes are multifactorial health conditions that affect older people and include dementia, delirium, impaired mobility, falls, frailty, poor nutrition, weight loss, incontinence, and difficulties with activities of daily living.¹ These syndromes are highly prevalent among older patients admitted to acute-care hospitals^2,3 and often add complexity to the clinical status of hospitalized older adults with multiple comorbid conditions.⁴ In the English National Health Service (NHS), the proportion of older people admitted to acute-care hospitals with geriatric syndromes has increased dramatically.⁵

The recognition and management of geriatric syndromes by hospitalists requires specific knowledge and skill sets.⁶ However, geriatricians are a scarce resource in many settings, including the NHS. A challenge for service evaluation and research is the generally poor capture of information about geriatric syndromes compared to specific comorbidities in discharge summaries and hospital coding.⁷ Steps are being taken in the NHS to address this issue, and in 2013 our center started the routine collection of data on clinical frailty, history of dementia (HoD) and acute confusional state (ACS) in all patients 75 years or older admitted nonelectively to the hospital.⁸The presence of geriatric syndromes in older inpatients is an important driver of adverse outcomes, particularly length of stay (LOS) and admission to institutional care.⁹ However, acute illness severity (AIS) is also an important determinant of poor outcomes in the inpatient population and may drive disproportionate changes in health status in the most vulnerable.¹⁰ Research studies with geriatric syndromes in acute settings have not been able to simultaneously consider AIS.¹¹ In addition, comorbidity is not always associated with an increased number of geriatric syndromes.¹²

We aimed to study the association of geriatric syndromes such as frailty, HoD and ACS that are measured in routine clinical care with hospital outcomes (prolonged LOS, inpatient mortality, delayed discharge, institutionalization, and 30-day readmission), while controlling for demographics (age, gender), AIS, comorbidity, and discharging specialty (general medicine, geriatric medicine, surgery).

Study Design and Setting

This retrospective observational study was conducted in a large tertiary university hospital in England with 1000 acute beds receiving more than 102,000 visits to the emergency department (ED) and admitting over 73,000 patients per year; among the latter, more than 12,000 are 75 years and older.

Sample

We analyzed all first nonelective inpatient episodes (ie, from ED admission to discharge) of people 75 years and older (all specialties) between the October 26, 2014 and the October 26, 2015. Data were obtained via the hospital’s information systems following the implementation of a new electronic patient record on October 26, 2014.

Patients’ Characteristics

The following anonymized variables were extracted:

• Age and gender
• AIS information is routinely collected in our ED using a Modified Early Warning Score (ED-MEWS). The components and scoring of ED-MEWS are shown in Table 1. Where more than 1 ED-MEWS was collected, the highest was used in the analyses.
• Charlson Comorbidity Index (CCI, without age adjustment).¹³ The CCI is based on the discharge diagnoses, as coded according to WHO International Classification of Diseases, v 10 (ICD-10). The CCI was calculated retrospectively and would have not been available to clinicians early during the patients’ admission.
• Clinical Frailty Scale (CFS). The scoring of CFS is based on a global assessment of patients’ comorbidity symptoms, and their level of physical activity and dependency on activities of daily living, estimated to reflect the status immediately before the onset of the acute illness leading to hospitalization. The possible scores are: 1 (very fit), 2 (well), 3 (managing well), 4 (vulnerable), 5 (mildly frail), 6 (moderately frail), 7 (severely frail), 8 (very severely frail), and 9 (terminally ill) ().¹⁴ The use of the CFS in admissions of people 75 years and older was introduced in our center in 2013 under a local Commissioning for Quality and Innovation (CQUIN) scheme.⁸ The CQUIN required that all patients 75 years and older admitted to the hospital, via the ED, be screened for frailty using the CFS within 72 hours of admission. The admitting doctor usually scores the CFS on the electronic admission record, but it can also be completed by ED nurses or by nursing or therapy staff from the trust-wide Specialist Advice for the Frail Elderly team. Training on CFS scoring is provided to staff at a hiring orientation and at regular educational meetings. Permission to use CFS for clinical purposes was obtained from the principal investigator at Geriatric Medicine Research, Dalhousie University, Halifax, Canada.
• Cognitive variables were collected early during the admission in patients 75 years and older, thanks to a parallel local CQUIN scheme. The cognitive CQUIN variables are screening variables, not gold standard. The admission clerking is designed to clinically classify patients within 72 hours of admission into the following 3 mutually exclusive categories:

○ Known HoD (in the database: no = 0; yes = 1)

○ ACS, without HoD (in the database: no = 0; yes = 1)

○ Neither HoD nor ACS

• The cognitive CQUIN assessment does not intend to diagnose dementia in those who are not known to have it, but tries to separate the dementias that general practitioners (GPs) know from hospital-identified acute cognitive concerns that GPs may need to assess or investigate after discharge. The latter may include delirium and/or undiagnosed dementia.
• In our routine hospital practice, the initial cognitive assessment is performed by a clinician in the following fashion: if the patient is known to have dementia (ie, based on clinical history and/or chart review), the clinician selects the “known history of dementia” option in the admission navigator, and no further cognitive screening is conducted. If the patient has no known dementia, the clinician administers the 4-item Abbreviated Mental Test (AMT4): (1) age, (2) date of birth, (3) place, and (4) year, with impaired cognition indicated by an AMT4 of less than 4 and triggering the selection of “ACS without known HoD” option. If the AMT4 is normal, the clinician selects the “neither HoD nor ACS” option.
• Due to the service evaluation nature of our work, these measures could not be assessed for reliability within the electronic medical records system (eg, regarding sensitivity and specificity against a gold standard or inter-rater reliability).
• Discharged from geriatric medicine (no = 0; yes = 1). Every year, our hospital admits over 12,000 patients 75 years and older, of which 25% are managed by the Department of Medicine for the Elderly (DME). The DME specialist bed base consists of 5 core wards, which specialize in ward-based comprehensive geriatric assessment (CGA) and are supported by dedicated nursing, physiotherapy, occupational therapy, and social work teams, as well as by readily available input from speech and language therapy, clinical nutrition, psychogeriatric, pharmacy and palliative care teams. Formal multidisciplinary team meetings occur at least twice weekly. A sixth specialist DME ward with a more acute perspective has been operational for 7 years; this ward was renamed the Frailty and Acute Medicine for the Elderly (FAME) ward in 2014 and has daily multidisciplinary team meetings. Although admission to FAME is through the ED, admission to core DME wards can occur from FAME (ie, within-DME transfer), via the ED, or from other inpatient specialty areas if older patients are perceived to be in high need of CGA after screening by the Specialist Advice for the Frail Elderly team. An audit in our center showed that up to 20% of patients discharged by DME were not initially admitted by DME, underscoring the significant role of core specialist DME wards in absorbing complex cases, especially from the general medical wards.⁸
• Discharged from general medicine (no = 0; yes = 1). In our setting, virtually all patients discharged by general medicine were first admitted by general medicine.⁸
• Discharged by a surgical specialty (no = 0; yes = 1)

Hospital Outcomes

The following anonymized variables were identified:

• LOS (days). Prolonged LOS was defined as 10 or more days (no = 0; yes = 1)
• Inpatient mortality (no = 0; yes = 1)
• Delayed discharge (no = 0; yes = 1). This was defined as the total LOS being at least 1 day longer than the LOS up to the last recorded clinically fit date. This date is used in NHS hospitals to indicate that the acute medical episode has finished and discharge-planning arrangements (often via social care providers) can commence.
• Institutionalization (no = 0; yes = 1). This was defined as the discharge destination being a care home, when a care home was not the usual place of residence.
• 30-day readmission (no = 0; yes = 1)

Statistical Analyses

Anonymized data were analyzed with IBM SPSS Statistics (v 22, Armonk, New York) software. Descriptive statistics were given as count (with percentage) or mean (with standard deviation.

To avoid potential problems with multicollinearity in the multivariate regression models, the correlations among the predictor variables were checked using a correlation matrix of 2-sided Spearman’s rho correlation coefficients. Correlations of 0.50 or more were considered large.^15,16

Because all outcomes in the study were binary, multivariate binary logistic regression models were computed. In these models, the odds ratio (OR) reflects the effect size of each predictor; 95% confidence intervals (CI) were requested for each OR. Predictors with P < 0.01 were considered as statistically significant. The classification performance of each logistic regression model was assessed calculating its area under the curve (AUC). 

Sensitivity analyses were conducted after imputing missing data (SPSS multiple imputation procedure) and after fitting interaction terms between geriatric syndromes and discharge by geriatric medicine.

The initial database contained 12,282 nonelective admission and discharge episodes (all specialties) of patients 75 years and older between October 26, 2014 and October 26, 2015. Among those, 8202 (66.8%) were first episodes. Table 2 shows the sample descriptives, and Table 3 shows the breakdown of geriatric syndromes (single and multiple) in the total sample (n = 8282), including missing frailty data.

In the correlation matrix of 2-sided Spearman’s rho correlation coefficients, no correlations with large-effect size were found to suggest issues with multicollinearity; the largest correlation coefficients were between age and CFS (rho = 0.35), HoD and CFS (rho = 0.32), and CCI and CFS (rho = 0.26).

The results of the multivariate regression models are shown in Table 4. The best performing models were the ones for inpatient mortality (AUC = 0.80), followed by institutionalization (AUC = 0.76), and prolonged LOS (AUC = 0.71). After full adjustment, clinical frailty was an independent predictor of prolonged LOS, inpatient mortality, delayed discharge, and institutionalization. HoD was an independent predictor of prolonged LOS, delayed discharge, and institutionalization; and ACS was an independent predictor of prolonged LOS, delayed discharge, institutionalization, and 30-day readmission (Table 4). Results did not significantly change in sensitivity analyses conducted after multiple imputation of missing data and after inclusion of interaction terms (see Supplemental Table 1 and Supplemental Table 2).

Our aim was to study the association of geriatric syndromes (measured in routine clinical care) with hospital outcomes. We found that geriatric syndromes such as clinical frailty, HoD, and ACS were strong independent predictors. Concerning prolonged LOS, delayed discharge, and institutionalization, geriatric syndromes had ORs that were greater than those of traditionally measured factors such as demographics, comorbidity and acute illness severity. Our findings add to the body of knowledge in this area because we accounted for the latter effects. Our experience shows that metrics on geriatric syndromes can be successfully collected in the routine hospital setting and add clear value to the prediction of operational outcomes. This may encourage other hospitals to do the same.

Our findings are consistent with suggestions that accounting for chronic conditions alone may be less informative than also accounting for the co-occurrence of geriatric syndromes.¹⁷ The focus of CFS is on the pre-admission level of physical activity and dependency on activities of daily living, and poorer scores may confer vulnerability to adverse outcomes due to reduced physiological reserve and ability to withstand acute stressors.¹⁸ Other studies have also found CFS to be a good predictor of inpatient outcomes,^19-22 and it has been recommended as a possible means to identify vulnerable older adults in acute-care settings.²³

Acknowledgments

The authors wish to thank all members of the acute teams in our hospital, without which this initiative would have not been possible. Licensed access to the NHS Foundation Trust’s information systems is also gratefully acknowledged.

Disclosure

The authors report no financial conflicts of interest.

Geriatric syndromes are multifactorial health conditions that affect older people and include dementia, delirium, impaired mobility, falls, frailty, poor nutrition, weight loss, incontinence, and difficulties with activities of daily living.¹ These syndromes are highly prevalent among older patients admitted to acute-care hospitals^2,3 and often add complexity to the clinical status of hospitalized older adults with multiple comorbid conditions.⁴ In the English National Health Service (NHS), the proportion of older people admitted to acute-care hospitals with geriatric syndromes has increased dramatically.⁵

The recognition and management of geriatric syndromes by hospitalists requires specific knowledge and skill sets.⁶ However, geriatricians are a scarce resource in many settings, including the NHS. A challenge for service evaluation and research is the generally poor capture of information about geriatric syndromes compared to specific comorbidities in discharge summaries and hospital coding.⁷ Steps are being taken in the NHS to address this issue, and in 2013 our center started the routine collection of data on clinical frailty, history of dementia (HoD) and acute confusional state (ACS) in all patients 75 years or older admitted nonelectively to the hospital.⁸The presence of geriatric syndromes in older inpatients is an important driver of adverse outcomes, particularly length of stay (LOS) and admission to institutional care.⁹ However, acute illness severity (AIS) is also an important determinant of poor outcomes in the inpatient population and may drive disproportionate changes in health status in the most vulnerable.¹⁰ Research studies with geriatric syndromes in acute settings have not been able to simultaneously consider AIS.¹¹ In addition, comorbidity is not always associated with an increased number of geriatric syndromes.¹²

We aimed to study the association of geriatric syndromes such as frailty, HoD and ACS that are measured in routine clinical care with hospital outcomes (prolonged LOS, inpatient mortality, delayed discharge, institutionalization, and 30-day readmission), while controlling for demographics (age, gender), AIS, comorbidity, and discharging specialty (general medicine, geriatric medicine, surgery).

Study Design and Setting

This retrospective observational study was conducted in a large tertiary university hospital in England with 1000 acute beds receiving more than 102,000 visits to the emergency department (ED) and admitting over 73,000 patients per year; among the latter, more than 12,000 are 75 years and older.

Sample

We analyzed all first nonelective inpatient episodes (ie, from ED admission to discharge) of people 75 years and older (all specialties) between the October 26, 2014 and the October 26, 2015. Data were obtained via the hospital’s information systems following the implementation of a new electronic patient record on October 26, 2014.

Patients’ Characteristics

The following anonymized variables were extracted:

• Age and gender
• AIS information is routinely collected in our ED using a Modified Early Warning Score (ED-MEWS). The components and scoring of ED-MEWS are shown in Table 1. Where more than 1 ED-MEWS was collected, the highest was used in the analyses.
• Charlson Comorbidity Index (CCI, without age adjustment).¹³ The CCI is based on the discharge diagnoses, as coded according to WHO International Classification of Diseases, v 10 (ICD-10). The CCI was calculated retrospectively and would have not been available to clinicians early during the patients’ admission.
• Clinical Frailty Scale (CFS). The scoring of CFS is based on a global assessment of patients’ comorbidity symptoms, and their level of physical activity and dependency on activities of daily living, estimated to reflect the status immediately before the onset of the acute illness leading to hospitalization. The possible scores are: 1 (very fit), 2 (well), 3 (managing well), 4 (vulnerable), 5 (mildly frail), 6 (moderately frail), 7 (severely frail), 8 (very severely frail), and 9 (terminally ill) ().¹⁴ The use of the CFS in admissions of people 75 years and older was introduced in our center in 2013 under a local Commissioning for Quality and Innovation (CQUIN) scheme.⁸ The CQUIN required that all patients 75 years and older admitted to the hospital, via the ED, be screened for frailty using the CFS within 72 hours of admission. The admitting doctor usually scores the CFS on the electronic admission record, but it can also be completed by ED nurses or by nursing or therapy staff from the trust-wide Specialist Advice for the Frail Elderly team. Training on CFS scoring is provided to staff at a hiring orientation and at regular educational meetings. Permission to use CFS for clinical purposes was obtained from the principal investigator at Geriatric Medicine Research, Dalhousie University, Halifax, Canada.
• Cognitive variables were collected early during the admission in patients 75 years and older, thanks to a parallel local CQUIN scheme. The cognitive CQUIN variables are screening variables, not gold standard. The admission clerking is designed to clinically classify patients within 72 hours of admission into the following 3 mutually exclusive categories:

○ Known HoD (in the database: no = 0; yes = 1)

○ ACS, without HoD (in the database: no = 0; yes = 1)

○ Neither HoD nor ACS

• The cognitive CQUIN assessment does not intend to diagnose dementia in those who are not known to have it, but tries to separate the dementias that general practitioners (GPs) know from hospital-identified acute cognitive concerns that GPs may need to assess or investigate after discharge. The latter may include delirium and/or undiagnosed dementia.
• In our routine hospital practice, the initial cognitive assessment is performed by a clinician in the following fashion: if the patient is known to have dementia (ie, based on clinical history and/or chart review), the clinician selects the “known history of dementia” option in the admission navigator, and no further cognitive screening is conducted. If the patient has no known dementia, the clinician administers the 4-item Abbreviated Mental Test (AMT4): (1) age, (2) date of birth, (3) place, and (4) year, with impaired cognition indicated by an AMT4 of less than 4 and triggering the selection of “ACS without known HoD” option. If the AMT4 is normal, the clinician selects the “neither HoD nor ACS” option.
• Due to the service evaluation nature of our work, these measures could not be assessed for reliability within the electronic medical records system (eg, regarding sensitivity and specificity against a gold standard or inter-rater reliability).
• Discharged from geriatric medicine (no = 0; yes = 1). Every year, our hospital admits over 12,000 patients 75 years and older, of which 25% are managed by the Department of Medicine for the Elderly (DME). The DME specialist bed base consists of 5 core wards, which specialize in ward-based comprehensive geriatric assessment (CGA) and are supported by dedicated nursing, physiotherapy, occupational therapy, and social work teams, as well as by readily available input from speech and language therapy, clinical nutrition, psychogeriatric, pharmacy and palliative care teams. Formal multidisciplinary team meetings occur at least twice weekly. A sixth specialist DME ward with a more acute perspective has been operational for 7 years; this ward was renamed the Frailty and Acute Medicine for the Elderly (FAME) ward in 2014 and has daily multidisciplinary team meetings. Although admission to FAME is through the ED, admission to core DME wards can occur from FAME (ie, within-DME transfer), via the ED, or from other inpatient specialty areas if older patients are perceived to be in high need of CGA after screening by the Specialist Advice for the Frail Elderly team. An audit in our center showed that up to 20% of patients discharged by DME were not initially admitted by DME, underscoring the significant role of core specialist DME wards in absorbing complex cases, especially from the general medical wards.⁸
• Discharged from general medicine (no = 0; yes = 1). In our setting, virtually all patients discharged by general medicine were first admitted by general medicine.⁸
• Discharged by a surgical specialty (no = 0; yes = 1)

Hospital Outcomes

The following anonymized variables were identified:

• LOS (days). Prolonged LOS was defined as 10 or more days (no = 0; yes = 1)
• Inpatient mortality (no = 0; yes = 1)
• Delayed discharge (no = 0; yes = 1). This was defined as the total LOS being at least 1 day longer than the LOS up to the last recorded clinically fit date. This date is used in NHS hospitals to indicate that the acute medical episode has finished and discharge-planning arrangements (often via social care providers) can commence.
• Institutionalization (no = 0; yes = 1). This was defined as the discharge destination being a care home, when a care home was not the usual place of residence.
• 30-day readmission (no = 0; yes = 1)

Statistical Analyses

Anonymized data were analyzed with IBM SPSS Statistics (v 22, Armonk, New York) software. Descriptive statistics were given as count (with percentage) or mean (with standard deviation.

To avoid potential problems with multicollinearity in the multivariate regression models, the correlations among the predictor variables were checked using a correlation matrix of 2-sided Spearman’s rho correlation coefficients. Correlations of 0.50 or more were considered large.^15,16

Because all outcomes in the study were binary, multivariate binary logistic regression models were computed. In these models, the odds ratio (OR) reflects the effect size of each predictor; 95% confidence intervals (CI) were requested for each OR. Predictors with P < 0.01 were considered as statistically significant. The classification performance of each logistic regression model was assessed calculating its area under the curve (AUC). 

Sensitivity analyses were conducted after imputing missing data (SPSS multiple imputation procedure) and after fitting interaction terms between geriatric syndromes and discharge by geriatric medicine.

The initial database contained 12,282 nonelective admission and discharge episodes (all specialties) of patients 75 years and older between October 26, 2014 and October 26, 2015. Among those, 8202 (66.8%) were first episodes. Table 2 shows the sample descriptives, and Table 3 shows the breakdown of geriatric syndromes (single and multiple) in the total sample (n = 8282), including missing frailty data.

In the correlation matrix of 2-sided Spearman’s rho correlation coefficients, no correlations with large-effect size were found to suggest issues with multicollinearity; the largest correlation coefficients were between age and CFS (rho = 0.35), HoD and CFS (rho = 0.32), and CCI and CFS (rho = 0.26).

The results of the multivariate regression models are shown in Table 4. The best performing models were the ones for inpatient mortality (AUC = 0.80), followed by institutionalization (AUC = 0.76), and prolonged LOS (AUC = 0.71). After full adjustment, clinical frailty was an independent predictor of prolonged LOS, inpatient mortality, delayed discharge, and institutionalization. HoD was an independent predictor of prolonged LOS, delayed discharge, and institutionalization; and ACS was an independent predictor of prolonged LOS, delayed discharge, institutionalization, and 30-day readmission (Table 4). Results did not significantly change in sensitivity analyses conducted after multiple imputation of missing data and after inclusion of interaction terms (see Supplemental Table 1 and Supplemental Table 2).

Our aim was to study the association of geriatric syndromes (measured in routine clinical care) with hospital outcomes. We found that geriatric syndromes such as clinical frailty, HoD, and ACS were strong independent predictors. Concerning prolonged LOS, delayed discharge, and institutionalization, geriatric syndromes had ORs that were greater than those of traditionally measured factors such as demographics, comorbidity and acute illness severity. Our findings add to the body of knowledge in this area because we accounted for the latter effects. Our experience shows that metrics on geriatric syndromes can be successfully collected in the routine hospital setting and add clear value to the prediction of operational outcomes. This may encourage other hospitals to do the same.

Our findings are consistent with suggestions that accounting for chronic conditions alone may be less informative than also accounting for the co-occurrence of geriatric syndromes.¹⁷ The focus of CFS is on the pre-admission level of physical activity and dependency on activities of daily living, and poorer scores may confer vulnerability to adverse outcomes due to reduced physiological reserve and ability to withstand acute stressors.¹⁸ Other studies have also found CFS to be a good predictor of inpatient outcomes,^19-22 and it has been recommended as a possible means to identify vulnerable older adults in acute-care settings.²³

Acknowledgments

The authors wish to thank all members of the acute teams in our hospital, without which this initiative would have not been possible. Licensed access to the NHS Foundation Trust’s information systems is also gratefully acknowledged.

Disclosure

The authors report no financial conflicts of interest.

Geriatric syndromes are multifactorial health conditions that affect older people and include dementia, delirium, impaired mobility, falls, frailty, poor nutrition, weight loss, incontinence, and difficulties with activities of daily living.¹ These syndromes are highly prevalent among older patients admitted to acute-care hospitals^2,3 and often add complexity to the clinical status of hospitalized older adults with multiple comorbid conditions.⁴ In the English National Health Service (NHS), the proportion of older people admitted to acute-care hospitals with geriatric syndromes has increased dramatically.⁵

The recognition and management of geriatric syndromes by hospitalists requires specific knowledge and skill sets.⁶ However, geriatricians are a scarce resource in many settings, including the NHS. A challenge for service evaluation and research is the generally poor capture of information about geriatric syndromes compared to specific comorbidities in discharge summaries and hospital coding.⁷ Steps are being taken in the NHS to address this issue, and in 2013 our center started the routine collection of data on clinical frailty, history of dementia (HoD) and acute confusional state (ACS) in all patients 75 years or older admitted nonelectively to the hospital.⁸The presence of geriatric syndromes in older inpatients is an important driver of adverse outcomes, particularly length of stay (LOS) and admission to institutional care.⁹ However, acute illness severity (AIS) is also an important determinant of poor outcomes in the inpatient population and may drive disproportionate changes in health status in the most vulnerable.¹⁰ Research studies with geriatric syndromes in acute settings have not been able to simultaneously consider AIS.¹¹ In addition, comorbidity is not always associated with an increased number of geriatric syndromes.¹²

We aimed to study the association of geriatric syndromes such as frailty, HoD and ACS that are measured in routine clinical care with hospital outcomes (prolonged LOS, inpatient mortality, delayed discharge, institutionalization, and 30-day readmission), while controlling for demographics (age, gender), AIS, comorbidity, and discharging specialty (general medicine, geriatric medicine, surgery).

Study Design and Setting

This retrospective observational study was conducted in a large tertiary university hospital in England with 1000 acute beds receiving more than 102,000 visits to the emergency department (ED) and admitting over 73,000 patients per year; among the latter, more than 12,000 are 75 years and older.

Sample

We analyzed all first nonelective inpatient episodes (ie, from ED admission to discharge) of people 75 years and older (all specialties) between the October 26, 2014 and the October 26, 2015. Data were obtained via the hospital’s information systems following the implementation of a new electronic patient record on October 26, 2014.

Patients’ Characteristics

The following anonymized variables were extracted:

• Age and gender
• AIS information is routinely collected in our ED using a Modified Early Warning Score (ED-MEWS). The components and scoring of ED-MEWS are shown in Table 1. Where more than 1 ED-MEWS was collected, the highest was used in the analyses.
• Charlson Comorbidity Index (CCI, without age adjustment).¹³ The CCI is based on the discharge diagnoses, as coded according to WHO International Classification of Diseases, v 10 (ICD-10). The CCI was calculated retrospectively and would have not been available to clinicians early during the patients’ admission.
• Clinical Frailty Scale (CFS). The scoring of CFS is based on a global assessment of patients’ comorbidity symptoms, and their level of physical activity and dependency on activities of daily living, estimated to reflect the status immediately before the onset of the acute illness leading to hospitalization. The possible scores are: 1 (very fit), 2 (well), 3 (managing well), 4 (vulnerable), 5 (mildly frail), 6 (moderately frail), 7 (severely frail), 8 (very severely frail), and 9 (terminally ill) ().¹⁴ The use of the CFS in admissions of people 75 years and older was introduced in our center in 2013 under a local Commissioning for Quality and Innovation (CQUIN) scheme.⁸ The CQUIN required that all patients 75 years and older admitted to the hospital, via the ED, be screened for frailty using the CFS within 72 hours of admission. The admitting doctor usually scores the CFS on the electronic admission record, but it can also be completed by ED nurses or by nursing or therapy staff from the trust-wide Specialist Advice for the Frail Elderly team. Training on CFS scoring is provided to staff at a hiring orientation and at regular educational meetings. Permission to use CFS for clinical purposes was obtained from the principal investigator at Geriatric Medicine Research, Dalhousie University, Halifax, Canada.
• Cognitive variables were collected early during the admission in patients 75 years and older, thanks to a parallel local CQUIN scheme. The cognitive CQUIN variables are screening variables, not gold standard. The admission clerking is designed to clinically classify patients within 72 hours of admission into the following 3 mutually exclusive categories:

○ Known HoD (in the database: no = 0; yes = 1)

○ ACS, without HoD (in the database: no = 0; yes = 1)

○ Neither HoD nor ACS

• The cognitive CQUIN assessment does not intend to diagnose dementia in those who are not known to have it, but tries to separate the dementias that general practitioners (GPs) know from hospital-identified acute cognitive concerns that GPs may need to assess or investigate after discharge. The latter may include delirium and/or undiagnosed dementia.
• In our routine hospital practice, the initial cognitive assessment is performed by a clinician in the following fashion: if the patient is known to have dementia (ie, based on clinical history and/or chart review), the clinician selects the “known history of dementia” option in the admission navigator, and no further cognitive screening is conducted. If the patient has no known dementia, the clinician administers the 4-item Abbreviated Mental Test (AMT4): (1) age, (2) date of birth, (3) place, and (4) year, with impaired cognition indicated by an AMT4 of less than 4 and triggering the selection of “ACS without known HoD” option. If the AMT4 is normal, the clinician selects the “neither HoD nor ACS” option.
• Due to the service evaluation nature of our work, these measures could not be assessed for reliability within the electronic medical records system (eg, regarding sensitivity and specificity against a gold standard or inter-rater reliability).
• Discharged from geriatric medicine (no = 0; yes = 1). Every year, our hospital admits over 12,000 patients 75 years and older, of which 25% are managed by the Department of Medicine for the Elderly (DME). The DME specialist bed base consists of 5 core wards, which specialize in ward-based comprehensive geriatric assessment (CGA) and are supported by dedicated nursing, physiotherapy, occupational therapy, and social work teams, as well as by readily available input from speech and language therapy, clinical nutrition, psychogeriatric, pharmacy and palliative care teams. Formal multidisciplinary team meetings occur at least twice weekly. A sixth specialist DME ward with a more acute perspective has been operational for 7 years; this ward was renamed the Frailty and Acute Medicine for the Elderly (FAME) ward in 2014 and has daily multidisciplinary team meetings. Although admission to FAME is through the ED, admission to core DME wards can occur from FAME (ie, within-DME transfer), via the ED, or from other inpatient specialty areas if older patients are perceived to be in high need of CGA after screening by the Specialist Advice for the Frail Elderly team. An audit in our center showed that up to 20% of patients discharged by DME were not initially admitted by DME, underscoring the significant role of core specialist DME wards in absorbing complex cases, especially from the general medical wards.⁸
• Discharged from general medicine (no = 0; yes = 1). In our setting, virtually all patients discharged by general medicine were first admitted by general medicine.⁸
• Discharged by a surgical specialty (no = 0; yes = 1)

Hospital Outcomes

The following anonymized variables were identified:

• LOS (days). Prolonged LOS was defined as 10 or more days (no = 0; yes = 1)
• Inpatient mortality (no = 0; yes = 1)
• Delayed discharge (no = 0; yes = 1). This was defined as the total LOS being at least 1 day longer than the LOS up to the last recorded clinically fit date. This date is used in NHS hospitals to indicate that the acute medical episode has finished and discharge-planning arrangements (often via social care providers) can commence.
• Institutionalization (no = 0; yes = 1). This was defined as the discharge destination being a care home, when a care home was not the usual place of residence.
• 30-day readmission (no = 0; yes = 1)

Statistical Analyses

Anonymized data were analyzed with IBM SPSS Statistics (v 22, Armonk, New York) software. Descriptive statistics were given as count (with percentage) or mean (with standard deviation.

To avoid potential problems with multicollinearity in the multivariate regression models, the correlations among the predictor variables were checked using a correlation matrix of 2-sided Spearman’s rho correlation coefficients. Correlations of 0.50 or more were considered large.^15,16

Because all outcomes in the study were binary, multivariate binary logistic regression models were computed. In these models, the odds ratio (OR) reflects the effect size of each predictor; 95% confidence intervals (CI) were requested for each OR. Predictors with P < 0.01 were considered as statistically significant. The classification performance of each logistic regression model was assessed calculating its area under the curve (AUC). 

Sensitivity analyses were conducted after imputing missing data (SPSS multiple imputation procedure) and after fitting interaction terms between geriatric syndromes and discharge by geriatric medicine.

The initial database contained 12,282 nonelective admission and discharge episodes (all specialties) of patients 75 years and older between October 26, 2014 and October 26, 2015. Among those, 8202 (66.8%) were first episodes. Table 2 shows the sample descriptives, and Table 3 shows the breakdown of geriatric syndromes (single and multiple) in the total sample (n = 8282), including missing frailty data.

In the correlation matrix of 2-sided Spearman’s rho correlation coefficients, no correlations with large-effect size were found to suggest issues with multicollinearity; the largest correlation coefficients were between age and CFS (rho = 0.35), HoD and CFS (rho = 0.32), and CCI and CFS (rho = 0.26).

The results of the multivariate regression models are shown in Table 4. The best performing models were the ones for inpatient mortality (AUC = 0.80), followed by institutionalization (AUC = 0.76), and prolonged LOS (AUC = 0.71). After full adjustment, clinical frailty was an independent predictor of prolonged LOS, inpatient mortality, delayed discharge, and institutionalization. HoD was an independent predictor of prolonged LOS, delayed discharge, and institutionalization; and ACS was an independent predictor of prolonged LOS, delayed discharge, institutionalization, and 30-day readmission (Table 4). Results did not significantly change in sensitivity analyses conducted after multiple imputation of missing data and after inclusion of interaction terms (see Supplemental Table 1 and Supplemental Table 2).

Our aim was to study the association of geriatric syndromes (measured in routine clinical care) with hospital outcomes. We found that geriatric syndromes such as clinical frailty, HoD, and ACS were strong independent predictors. Concerning prolonged LOS, delayed discharge, and institutionalization, geriatric syndromes had ORs that were greater than those of traditionally measured factors such as demographics, comorbidity and acute illness severity. Our findings add to the body of knowledge in this area because we accounted for the latter effects. Our experience shows that metrics on geriatric syndromes can be successfully collected in the routine hospital setting and add clear value to the prediction of operational outcomes. This may encourage other hospitals to do the same.

Our findings are consistent with suggestions that accounting for chronic conditions alone may be less informative than also accounting for the co-occurrence of geriatric syndromes.¹⁷ The focus of CFS is on the pre-admission level of physical activity and dependency on activities of daily living, and poorer scores may confer vulnerability to adverse outcomes due to reduced physiological reserve and ability to withstand acute stressors.¹⁸ Other studies have also found CFS to be a good predictor of inpatient outcomes,^19-22 and it has been recommended as a possible means to identify vulnerable older adults in acute-care settings.²³

Acknowledgments

The authors wish to thank all members of the acute teams in our hospital, without which this initiative would have not been possible. Licensed access to the NHS Foundation Trust’s information systems is also gratefully acknowledged.

Disclosure

The authors report no financial conflicts of interest.

Interest in mHealth—the use of mobile communication devices for clinical and public health—has exploded among clinicians and researchers for its potential to efficiently improve patient health. Recent studies have used mHealth’s asynchronous receptive and expressive communication functions in interventions targeted to managing care transitions and hospital readmissions.^1-3 We also recently published on improved readmission risk assessments using postdischarge measures of patient reported outcomes, which could be collected through mobile devices. ⁴ But persistent disparities in access to⁵ and engagement with⁶ smartphones may threaten validity and equity when mHealth strategies do not fully address its own limitations.

Disparities introduced by uneven access to technology are well known, but the rapid, albeit belated, adoption of mobile devices by racial minority groups in the United States has allowed authors of recent thoughtful publications to recast mHealth as itself offering solutions to the disparities’ problem.^7,8 Others have cautioned the emergence of disparities along domains other than race, such as low literacy and limited English proficiency (LEP).⁹ In this paper, we assessed the impact of inadequate reading health literacy (IRHL) and LEP on factors related to access and engagement with mHealth. We conducted our study among urban low-income adults in whom IRHL and LEP are common.

We surveyed patients in a large public safety-net health system serving 132 municipalities, including the city of Chicago, in northeastern Illinois. In 2015, nearly 90% of patients were racial-ethnic minorities with more than one-third insured by Medicaid and another one-third uninsured. We sampled adult inpatients and outpatients separately by nonselectively approaching patients in November 2015 to complete an in-person questionnaire in a 464-bed hospital and in 2 primary-care clinics. All inpatients occupied a nonisolation room in a general medical-surgical ward that had been sampled for data collection for that day in 9-day cycles with 8 other similar units. All outpatients in the clinic waiting areas were approached on consecutive days until a predetermined recruitment target was met. Each participant was surveyed once in his/her preferred language (English or Spanish), was 18 years and older, consented verbally, and received no compensation. Sample size provided 80% power to detect a device ownership rate of 50% in an evenly allocated low literacy population compared to a reference rate of 66% assuming a 2-sided α of 0.05 using the Fisher exact test.

The 18-item questionnaire was informed by constructs addressed in the 2015 Pew Research Center smartphone survey.¹⁰ However, in addition to device ownership, we inquired about device capabilities, service-plan details, service interruptions due to difficulty paying bills in the previous year, home-Internet access, an active e-mail account, and self-assigned demographics. Self-reported reading health literacy,¹¹ more directly measured than e-health literacy, was screened using a parsimonious instrument validated as a dichotomized measure.¹² Instruments in English and Spanish were tested for appropriate and comprehensible word choices and syntax through pilot testing. We inferred LEP among patients preferring to complete the survey in Spanish based on our familiarity with the population. We defined any Internet access as having a mobile data-service plan or having home-Internet access. In addition, we inquired about primary insurance provider and offered Medicaid patients an informational brochure about the federal Lifeline Program (https://www.fcc.gov/lifeline) that subsidizes text-messaging-enabled cellular telephone service for low-income patients. Notably, we assessed engagement by asking about the extent of patients’ interest in “new ways of communicating with your doctor, clinic, or pharmacy using” text, e-mail, or mobile apps with a 5-level response scale ranging from “not at all interested” to “very interested”.

Participant characteristics were confirmed to be similar to the Cook County Health and Hospitals System patient population in 2015 with regards to age, gender, and race/ethnicity. We calculated unadjusted and adjusted odds ratios for IRHL and LEP’s association with each dependent measure of access (to smartphone, Internet, or e-mail) and engagement (using text messaging, e-mail, or mobile apps) controlling for age, gender, primary payer, recruitment location, IRHL, and LEP. Because we oversampled inpatients, we estimated sampling-weight-adjusted proportions and 95% confidence intervals (CI) of the entire CCHHS patient population with access to smartphone, data/text plan, non-prepaid plan, and service interruptions using STATA v13 (StataCorp LP, College Station Texas). The project received a waiver upon review by the local Institutional Review Board.

Participation rate was 65% (302/464). Differences in patients by site are shown in Table 1. IRHL was more frequent and LEP less frequent among hospitalized patients. As shown in Table 2, patients with IRHL were less likely to have any Internet access, to have an active e-mail account, and to be interested in using e-mail for healthcare communications. Patients with LEP were less likely than English speakers to be interested in using mobile apps. Inpatients were less likely than outpatients to be interested in text messaging for healthcare communications.

The estimated proportion (95% CI) of the health system’s patients owning a text-enabled mobile device was 87% (75%-94%) and an Internet-enabled mobile device was 64% (47%-78%). The proportion with no data service interruptions in the previous year was 40% (31%-50%).

In this cross-section of urban low-income adult patients, IRHL and LEP were factors associated with potential disparities introduced by mHealth. Even as access to smartphones becomes ubiquitous, lagging access to Internet and e-mail among low literacy patients, and low levels of technology engagement for healthcare communications among patients with IRHL or LEP, underscore concerns about equity in health systems’ adoption of mHealth strategies. Hospitalized patients were found to have diminished engagement with mHealth independent of IRHL and LEP.

Regarding engagement, significantly fewer patients with IRHL or LEP were interested in using technology for healthcare communications. Our finding suggests that health disparities already associated with these conditions¹³ may not be reduced by mobile device outreach alone and may even be worsened by it. Touch screens, audio-enabled questionnaires, and language translation engines are innovations that may be helpful to mitigate IRHL and LEP, but evidence is scarce. Privacy and security concerns, and lack of experience with technology, may also lower engagement. A contemporaneous study found lower apps’ usage among Latinos, also suggesting that language concordance between apps, their source, and targeted users is important.¹⁴ Low-tech solutions involving mobile telephone or even lower tech in-person communications targeted to the estimated 26% of the US population with low literacy¹⁵ and 20% with LEP¹⁶ may be practical stopgap measures. Even as disparities in access to technology across race-ethnicity are diminishing,¹⁰ equity across poverty levels, low levels of education, cultural norms, and disabilities may be more challenging to overcome. Our assessment indicates that large exclusions of a safety-net population in 2015 are a legitimate concern in communication strategies that rely too heavily on mHealth. These findings underscore the CONSORT-EHEALTH recommendation that investigators report web-based recruitment strategies and data-collection methods comprehensively.¹⁷

Regarding access, our estimates suggest that historical disparities in smartphone ownership are diminishing, but access to Internet capabilities may still be lower among the urban poor compared to the nation as a whole. The Pew Research Center found that 64% of Americans owned a smartphone in 2015 (respondents defined smartphone).¹⁰ In comparison, 87% (95% CI, 75%, 94%) of our study participants owned a text-enabled mobile device and 64% (47%, 78%) owned an Internet-enabled mobile device. However, the 40% (31%, 50%) of our safety-net population with an uninterrupted data plan over the previous year may be lower than the 50% of Americans reporting uninterrupted data plans over their lifetime.¹⁰ The impact of expense-related data plan interruptions is magnified by the 40% of our study population—compared to 15% of Americans—who are dependent on mobile devices for Internet access.¹⁰ The association between Internet connectivity and literacy evokes multiple bidirectional pathways yet to be elucidated. But if mHealth can reduce health disparities, closing the gap in device ownership is only a partial accomplishment, and future work also needs to expand Internet connectivity to allow literacy-enhancing and literacy-naïve technologies to flourish.

This study has limitations. Our study population was a consecutive sample and participation rate was less than 100%. However, we recruited participants into the study the way we may also have approached patients to introduce mHealth options in our clinical settings. Our sampling method proved adequate for our primary goal to explain differences in technology access and engagement using regression analysis. Although our patient population may not directly generalize to many healthcare systems, including other safety-net systems serving regions with variable technology uptake,¹⁸ our findings reflect the capacities and the preferences of the most disadvantaged segments of urban populations. We systematically excluded LEP non-Spanish speakers, but they consisted of less than 5% of inpatients and no outpatients. We did not assess current technology use. Finally, as discussed earlier, access and use of new technologies change rapidly and frequent updates are necessary.

mHealth is a promising tool because it may increase healthcare access, improve care quality, and promote research. All these potential benefits will be obtained with accompanying efforts to reduce healthcare disparities, especially where some technologies themselves are exclusionary.⁹ As research of mHealth methods grows, support for patients with IRHL and LEP are still necessary to simultaneously advance our shared goal for equity.

Disclosures

The authors report no financial conflicts of interest.

Interest in mHealth—the use of mobile communication devices for clinical and public health—has exploded among clinicians and researchers for its potential to efficiently improve patient health. Recent studies have used mHealth’s asynchronous receptive and expressive communication functions in interventions targeted to managing care transitions and hospital readmissions.^1-3 We also recently published on improved readmission risk assessments using postdischarge measures of patient reported outcomes, which could be collected through mobile devices. ⁴ But persistent disparities in access to⁵ and engagement with⁶ smartphones may threaten validity and equity when mHealth strategies do not fully address its own limitations.

Disparities introduced by uneven access to technology are well known, but the rapid, albeit belated, adoption of mobile devices by racial minority groups in the United States has allowed authors of recent thoughtful publications to recast mHealth as itself offering solutions to the disparities’ problem.^7,8 Others have cautioned the emergence of disparities along domains other than race, such as low literacy and limited English proficiency (LEP).⁹ In this paper, we assessed the impact of inadequate reading health literacy (IRHL) and LEP on factors related to access and engagement with mHealth. We conducted our study among urban low-income adults in whom IRHL and LEP are common.

We surveyed patients in a large public safety-net health system serving 132 municipalities, including the city of Chicago, in northeastern Illinois. In 2015, nearly 90% of patients were racial-ethnic minorities with more than one-third insured by Medicaid and another one-third uninsured. We sampled adult inpatients and outpatients separately by nonselectively approaching patients in November 2015 to complete an in-person questionnaire in a 464-bed hospital and in 2 primary-care clinics. All inpatients occupied a nonisolation room in a general medical-surgical ward that had been sampled for data collection for that day in 9-day cycles with 8 other similar units. All outpatients in the clinic waiting areas were approached on consecutive days until a predetermined recruitment target was met. Each participant was surveyed once in his/her preferred language (English or Spanish), was 18 years and older, consented verbally, and received no compensation. Sample size provided 80% power to detect a device ownership rate of 50% in an evenly allocated low literacy population compared to a reference rate of 66% assuming a 2-sided α of 0.05 using the Fisher exact test.

The 18-item questionnaire was informed by constructs addressed in the 2015 Pew Research Center smartphone survey.¹⁰ However, in addition to device ownership, we inquired about device capabilities, service-plan details, service interruptions due to difficulty paying bills in the previous year, home-Internet access, an active e-mail account, and self-assigned demographics. Self-reported reading health literacy,¹¹ more directly measured than e-health literacy, was screened using a parsimonious instrument validated as a dichotomized measure.¹² Instruments in English and Spanish were tested for appropriate and comprehensible word choices and syntax through pilot testing. We inferred LEP among patients preferring to complete the survey in Spanish based on our familiarity with the population. We defined any Internet access as having a mobile data-service plan or having home-Internet access. In addition, we inquired about primary insurance provider and offered Medicaid patients an informational brochure about the federal Lifeline Program (https://www.fcc.gov/lifeline) that subsidizes text-messaging-enabled cellular telephone service for low-income patients. Notably, we assessed engagement by asking about the extent of patients’ interest in “new ways of communicating with your doctor, clinic, or pharmacy using” text, e-mail, or mobile apps with a 5-level response scale ranging from “not at all interested” to “very interested”.

Participant characteristics were confirmed to be similar to the Cook County Health and Hospitals System patient population in 2015 with regards to age, gender, and race/ethnicity. We calculated unadjusted and adjusted odds ratios for IRHL and LEP’s association with each dependent measure of access (to smartphone, Internet, or e-mail) and engagement (using text messaging, e-mail, or mobile apps) controlling for age, gender, primary payer, recruitment location, IRHL, and LEP. Because we oversampled inpatients, we estimated sampling-weight-adjusted proportions and 95% confidence intervals (CI) of the entire CCHHS patient population with access to smartphone, data/text plan, non-prepaid plan, and service interruptions using STATA v13 (StataCorp LP, College Station Texas). The project received a waiver upon review by the local Institutional Review Board.

Participation rate was 65% (302/464). Differences in patients by site are shown in Table 1. IRHL was more frequent and LEP less frequent among hospitalized patients. As shown in Table 2, patients with IRHL were less likely to have any Internet access, to have an active e-mail account, and to be interested in using e-mail for healthcare communications. Patients with LEP were less likely than English speakers to be interested in using mobile apps. Inpatients were less likely than outpatients to be interested in text messaging for healthcare communications.

The estimated proportion (95% CI) of the health system’s patients owning a text-enabled mobile device was 87% (75%-94%) and an Internet-enabled mobile device was 64% (47%-78%). The proportion with no data service interruptions in the previous year was 40% (31%-50%).

In this cross-section of urban low-income adult patients, IRHL and LEP were factors associated with potential disparities introduced by mHealth. Even as access to smartphones becomes ubiquitous, lagging access to Internet and e-mail among low literacy patients, and low levels of technology engagement for healthcare communications among patients with IRHL or LEP, underscore concerns about equity in health systems’ adoption of mHealth strategies. Hospitalized patients were found to have diminished engagement with mHealth independent of IRHL and LEP.

Regarding engagement, significantly fewer patients with IRHL or LEP were interested in using technology for healthcare communications. Our finding suggests that health disparities already associated with these conditions¹³ may not be reduced by mobile device outreach alone and may even be worsened by it. Touch screens, audio-enabled questionnaires, and language translation engines are innovations that may be helpful to mitigate IRHL and LEP, but evidence is scarce. Privacy and security concerns, and lack of experience with technology, may also lower engagement. A contemporaneous study found lower apps’ usage among Latinos, also suggesting that language concordance between apps, their source, and targeted users is important.¹⁴ Low-tech solutions involving mobile telephone or even lower tech in-person communications targeted to the estimated 26% of the US population with low literacy¹⁵ and 20% with LEP¹⁶ may be practical stopgap measures. Even as disparities in access to technology across race-ethnicity are diminishing,¹⁰ equity across poverty levels, low levels of education, cultural norms, and disabilities may be more challenging to overcome. Our assessment indicates that large exclusions of a safety-net population in 2015 are a legitimate concern in communication strategies that rely too heavily on mHealth. These findings underscore the CONSORT-EHEALTH recommendation that investigators report web-based recruitment strategies and data-collection methods comprehensively.¹⁷

Regarding access, our estimates suggest that historical disparities in smartphone ownership are diminishing, but access to Internet capabilities may still be lower among the urban poor compared to the nation as a whole. The Pew Research Center found that 64% of Americans owned a smartphone in 2015 (respondents defined smartphone).¹⁰ In comparison, 87% (95% CI, 75%, 94%) of our study participants owned a text-enabled mobile device and 64% (47%, 78%) owned an Internet-enabled mobile device. However, the 40% (31%, 50%) of our safety-net population with an uninterrupted data plan over the previous year may be lower than the 50% of Americans reporting uninterrupted data plans over their lifetime.¹⁰ The impact of expense-related data plan interruptions is magnified by the 40% of our study population—compared to 15% of Americans—who are dependent on mobile devices for Internet access.¹⁰ The association between Internet connectivity and literacy evokes multiple bidirectional pathways yet to be elucidated. But if mHealth can reduce health disparities, closing the gap in device ownership is only a partial accomplishment, and future work also needs to expand Internet connectivity to allow literacy-enhancing and literacy-naïve technologies to flourish.

This study has limitations. Our study population was a consecutive sample and participation rate was less than 100%. However, we recruited participants into the study the way we may also have approached patients to introduce mHealth options in our clinical settings. Our sampling method proved adequate for our primary goal to explain differences in technology access and engagement using regression analysis. Although our patient population may not directly generalize to many healthcare systems, including other safety-net systems serving regions with variable technology uptake,¹⁸ our findings reflect the capacities and the preferences of the most disadvantaged segments of urban populations. We systematically excluded LEP non-Spanish speakers, but they consisted of less than 5% of inpatients and no outpatients. We did not assess current technology use. Finally, as discussed earlier, access and use of new technologies change rapidly and frequent updates are necessary.

mHealth is a promising tool because it may increase healthcare access, improve care quality, and promote research. All these potential benefits will be obtained with accompanying efforts to reduce healthcare disparities, especially where some technologies themselves are exclusionary.⁹ As research of mHealth methods grows, support for patients with IRHL and LEP are still necessary to simultaneously advance our shared goal for equity.

Disclosures

The authors report no financial conflicts of interest.

Interest in mHealth—the use of mobile communication devices for clinical and public health—has exploded among clinicians and researchers for its potential to efficiently improve patient health. Recent studies have used mHealth’s asynchronous receptive and expressive communication functions in interventions targeted to managing care transitions and hospital readmissions.^1-3 We also recently published on improved readmission risk assessments using postdischarge measures of patient reported outcomes, which could be collected through mobile devices. ⁴ But persistent disparities in access to⁵ and engagement with⁶ smartphones may threaten validity and equity when mHealth strategies do not fully address its own limitations.

Disparities introduced by uneven access to technology are well known, but the rapid, albeit belated, adoption of mobile devices by racial minority groups in the United States has allowed authors of recent thoughtful publications to recast mHealth as itself offering solutions to the disparities’ problem.^7,8 Others have cautioned the emergence of disparities along domains other than race, such as low literacy and limited English proficiency (LEP).⁹ In this paper, we assessed the impact of inadequate reading health literacy (IRHL) and LEP on factors related to access and engagement with mHealth. We conducted our study among urban low-income adults in whom IRHL and LEP are common.

We surveyed patients in a large public safety-net health system serving 132 municipalities, including the city of Chicago, in northeastern Illinois. In 2015, nearly 90% of patients were racial-ethnic minorities with more than one-third insured by Medicaid and another one-third uninsured. We sampled adult inpatients and outpatients separately by nonselectively approaching patients in November 2015 to complete an in-person questionnaire in a 464-bed hospital and in 2 primary-care clinics. All inpatients occupied a nonisolation room in a general medical-surgical ward that had been sampled for data collection for that day in 9-day cycles with 8 other similar units. All outpatients in the clinic waiting areas were approached on consecutive days until a predetermined recruitment target was met. Each participant was surveyed once in his/her preferred language (English or Spanish), was 18 years and older, consented verbally, and received no compensation. Sample size provided 80% power to detect a device ownership rate of 50% in an evenly allocated low literacy population compared to a reference rate of 66% assuming a 2-sided α of 0.05 using the Fisher exact test.

The 18-item questionnaire was informed by constructs addressed in the 2015 Pew Research Center smartphone survey.¹⁰ However, in addition to device ownership, we inquired about device capabilities, service-plan details, service interruptions due to difficulty paying bills in the previous year, home-Internet access, an active e-mail account, and self-assigned demographics. Self-reported reading health literacy,¹¹ more directly measured than e-health literacy, was screened using a parsimonious instrument validated as a dichotomized measure.¹² Instruments in English and Spanish were tested for appropriate and comprehensible word choices and syntax through pilot testing. We inferred LEP among patients preferring to complete the survey in Spanish based on our familiarity with the population. We defined any Internet access as having a mobile data-service plan or having home-Internet access. In addition, we inquired about primary insurance provider and offered Medicaid patients an informational brochure about the federal Lifeline Program (https://www.fcc.gov/lifeline) that subsidizes text-messaging-enabled cellular telephone service for low-income patients. Notably, we assessed engagement by asking about the extent of patients’ interest in “new ways of communicating with your doctor, clinic, or pharmacy using” text, e-mail, or mobile apps with a 5-level response scale ranging from “not at all interested” to “very interested”.

Participant characteristics were confirmed to be similar to the Cook County Health and Hospitals System patient population in 2015 with regards to age, gender, and race/ethnicity. We calculated unadjusted and adjusted odds ratios for IRHL and LEP’s association with each dependent measure of access (to smartphone, Internet, or e-mail) and engagement (using text messaging, e-mail, or mobile apps) controlling for age, gender, primary payer, recruitment location, IRHL, and LEP. Because we oversampled inpatients, we estimated sampling-weight-adjusted proportions and 95% confidence intervals (CI) of the entire CCHHS patient population with access to smartphone, data/text plan, non-prepaid plan, and service interruptions using STATA v13 (StataCorp LP, College Station Texas). The project received a waiver upon review by the local Institutional Review Board.

Participation rate was 65% (302/464). Differences in patients by site are shown in Table 1. IRHL was more frequent and LEP less frequent among hospitalized patients. As shown in Table 2, patients with IRHL were less likely to have any Internet access, to have an active e-mail account, and to be interested in using e-mail for healthcare communications. Patients with LEP were less likely than English speakers to be interested in using mobile apps. Inpatients were less likely than outpatients to be interested in text messaging for healthcare communications.

The estimated proportion (95% CI) of the health system’s patients owning a text-enabled mobile device was 87% (75%-94%) and an Internet-enabled mobile device was 64% (47%-78%). The proportion with no data service interruptions in the previous year was 40% (31%-50%).

In this cross-section of urban low-income adult patients, IRHL and LEP were factors associated with potential disparities introduced by mHealth. Even as access to smartphones becomes ubiquitous, lagging access to Internet and e-mail among low literacy patients, and low levels of technology engagement for healthcare communications among patients with IRHL or LEP, underscore concerns about equity in health systems’ adoption of mHealth strategies. Hospitalized patients were found to have diminished engagement with mHealth independent of IRHL and LEP.

Regarding engagement, significantly fewer patients with IRHL or LEP were interested in using technology for healthcare communications. Our finding suggests that health disparities already associated with these conditions¹³ may not be reduced by mobile device outreach alone and may even be worsened by it. Touch screens, audio-enabled questionnaires, and language translation engines are innovations that may be helpful to mitigate IRHL and LEP, but evidence is scarce. Privacy and security concerns, and lack of experience with technology, may also lower engagement. A contemporaneous study found lower apps’ usage among Latinos, also suggesting that language concordance between apps, their source, and targeted users is important.¹⁴ Low-tech solutions involving mobile telephone or even lower tech in-person communications targeted to the estimated 26% of the US population with low literacy¹⁵ and 20% with LEP¹⁶ may be practical stopgap measures. Even as disparities in access to technology across race-ethnicity are diminishing,¹⁰ equity across poverty levels, low levels of education, cultural norms, and disabilities may be more challenging to overcome. Our assessment indicates that large exclusions of a safety-net population in 2015 are a legitimate concern in communication strategies that rely too heavily on mHealth. These findings underscore the CONSORT-EHEALTH recommendation that investigators report web-based recruitment strategies and data-collection methods comprehensively.¹⁷

Regarding access, our estimates suggest that historical disparities in smartphone ownership are diminishing, but access to Internet capabilities may still be lower among the urban poor compared to the nation as a whole. The Pew Research Center found that 64% of Americans owned a smartphone in 2015 (respondents defined smartphone).¹⁰ In comparison, 87% (95% CI, 75%, 94%) of our study participants owned a text-enabled mobile device and 64% (47%, 78%) owned an Internet-enabled mobile device. However, the 40% (31%, 50%) of our safety-net population with an uninterrupted data plan over the previous year may be lower than the 50% of Americans reporting uninterrupted data plans over their lifetime.¹⁰ The impact of expense-related data plan interruptions is magnified by the 40% of our study population—compared to 15% of Americans—who are dependent on mobile devices for Internet access.¹⁰ The association between Internet connectivity and literacy evokes multiple bidirectional pathways yet to be elucidated. But if mHealth can reduce health disparities, closing the gap in device ownership is only a partial accomplishment, and future work also needs to expand Internet connectivity to allow literacy-enhancing and literacy-naïve technologies to flourish.

This study has limitations. Our study population was a consecutive sample and participation rate was less than 100%. However, we recruited participants into the study the way we may also have approached patients to introduce mHealth options in our clinical settings. Our sampling method proved adequate for our primary goal to explain differences in technology access and engagement using regression analysis. Although our patient population may not directly generalize to many healthcare systems, including other safety-net systems serving regions with variable technology uptake,¹⁸ our findings reflect the capacities and the preferences of the most disadvantaged segments of urban populations. We systematically excluded LEP non-Spanish speakers, but they consisted of less than 5% of inpatients and no outpatients. We did not assess current technology use. Finally, as discussed earlier, access and use of new technologies change rapidly and frequent updates are necessary.

mHealth is a promising tool because it may increase healthcare access, improve care quality, and promote research. All these potential benefits will be obtained with accompanying efforts to reduce healthcare disparities, especially where some technologies themselves are exclusionary.⁹ As research of mHealth methods grows, support for patients with IRHL and LEP are still necessary to simultaneously advance our shared goal for equity.

Disclosures

The authors report no financial conflicts of interest.

Each year, approximately one-third of all hospitalized medical and surgical patients in the United States (about 12 million patients) are exposed to heparin products for the prevention or treatment of thromboembolism.¹ Although generally safe, heparin can trigger an immune response in which platelet factor 4–heparin complexes set off an antibody-mediated cascade that can result in heparin-induced thrombocytopenia (HIT) and paradoxical arterial and venous thromboses, or heparin-induced thrombocytopenia with thrombosis (HITT). The incidence of HIT appears to be significantly higher with the more immunogenic unfractionated heparin (UFH) (2%-3% if treated for ≥5 days) than with low-molecular-weight heparin (LMWH) (0.2%-0.6%)² and is significantly higher in postoperative patients (1%-5%) than in medical patients.³ Older patients and female patients, especially those who undergo surgery, are thought to be at higher risk.⁴ Progression from HIT to HITT can occur in up to 50% of surgical patients,⁵ and HITT can significantly increase mortality.⁴

In the United States, LMWH use has increased 5-fold since 2000—an increase attributed to the 2010 release of generic enoxaparin.⁶ As US hospitals switch from UFH to LMWH with its significantly lower risk of HIT, up-to-date HIT incidence data may help physicians and payers better understand the impact of the disorder on mortality and hospital length of stay (LOS) for medical patients and subsets of surgical patients and subsequently direct screening efforts to those at highest risk. Therefore, in the present study, we used national data to determine the latest incidence and economic implications of HIT overall and for high-risk surgical groups.

In this study, we analyzed data from the Nationwide Inpatient Sample (NIS) database, part of the Healthcare Cost and Utilization Project (HCUP) of the Agency for Healthcare Research and Quality (AHRQ). The period studied was 2009-2011. We used International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 289.84, introduced in 2009, to identify patients who were at least 18 years old and had a primary or secondary diagnosis of HIT. Validated Clinical Classifications Software (CCS) was used to identify those who underwent cardiac, vascular, or orthopedic surgery, and ICD-9-CM codes for various thromboses were used to identify those with HITT (Supplemental Figure, Supplemental Table 1). Baseline patient and hospital characteristics were compared using the Pearson’s Chi-square test for categorical variables and the Student t test for continuous variables (2-sided P < 0.05 for statistical significance) (Table 1). We calculated the incidence of HIT overall and for the 3 surgical subgroups and compared the cohorts on their mean hospital LOS, mean hospital charge, and in-hospital mortality (Table 2).

Statistical analysis was performed with Stata Version 13.1 (Stata Corp, College Station, TX). Survey commands were used to account for the complex survey design in NIS. Reading Health System’s Institutional Review Board determined that our study protocol was exempt.

RESULTS

Of 98,636,364 hospitalizations, 72,515 (0.07%) involved HIT. There were no significant differences in the annual incidence of HIT during the study period (0.06% in 2009, 0.05% in 2010, 0.06% in 2011).

Patients with HIT were older than patients without HIT (mean age, 65.3 vs 57.3 years; P < 0.001). HIT was slightly more common in men overall (OR, 1.48; 95% CI, 1.46-1.51), but subgroup analyses revealed women had higher rates of HIT after cardiac surgery (OR, 1.41; 95% CI, 1.26-1.58) and vascular surgery (OR, 1.42; 95% CI, 1.29-1.57), though not after orthopedic surgery (OR, 1.06; 95% CI, 0.89-1.26). The majority of HIT cases were in urban teaching hospitals (56.23%) and in large hospitals, those with at least 325 beds (69.26%). There was no difference in mean age between patients with HITT and patients with HIT without thrombosis (65.46 vs 65.14 years; P = 0.32). Although the incidence of HITT did not differ by hospital location or teaching status, HITT cases were more common in hospitals with at least 325 beds (71.81%).

Regarding HIT, the death rate was 4-fold higher for patients with the disorder (9.63%) than for those without it (2.19%); hospital LOS and costs were significantly higher, too (Table 2). In addition, in-hospital mortality was higher (P < 0.001) for patients with HITT (12.28%) than for patients with HIT without thrombosis (8.24%); HITT patients’ hospital LOS and costs were higher as well. In patients who had cardiac, vascular, or orthopedic surgery, development of HIT was also associated with significantly higher in-hospital mortality, mean hospital LOS, and mean hospital charge. In patients with HITT, deep vein thrombosis (DVT) and pulmonary embolism represented the majority of reported cases (Supplemental Table 2). However, in patients who had cardiac surgery, acute arterial thromboses of coronary and cerebral vessels were more common.

DISCUSSION

In this national database survey, the overall incidence of HIT during the study period 2009-2011 was 0.07%, or 1 in 1350 hospitalized patients. Although earlier studies reported rates as high as 5% for high-risk subgroups of surgical patients,⁷ our data are more in line with more recently reported rates: about 0.02% for hospital admissions⁸ and from less than 0.1% to 0.4% for patients who received heparin.⁹ Older studies, which predominantly involved postoperative patients and were conducted when UFH often was the first-line heparin product used, may account for higher rates relative to ours. Of the 3 types of surgeries we evaluated, cardiac surgery had the highest HIT rate (0.5%), consistent with other studies.⁴ The higher HIT/HITT rates found for larger urban hospitals in our study might be attributable to increased awareness and testing, availability of hematology consultation, and higher risk of heparin use in this setting, where patients are sicker and cases and procedures more complicated.

Age was an important determinant of HIT risk in our study and in similar large-database series.⁴ Whether increased UFH use in the elderly (because of age or kidney disease) was a causative factor in this finding is unknown. In our study, although men and women had a nearly equal incidence of HIT, women had a significantly higher risk of HIT after both cardiac surgery and vascular surgery. Immune-mediated mechanisms that are more common in females may play a causative role in these settings.¹⁰

Our study results showed HIT associated with increased hospital LOS and an almost 4-fold increase in inpatient mortality and costs. The increased economic burden in HIT cases may be driven by the diagnostic work-up cost and expensive alternative anticoagulation.^11,12 Similarly, compared with HIT without thrombosis, HITT was associated with significantly increased hospital LOS (3.7 days), total hospital charge ($64,279) and mortality (49% increase, to 12.2% from 8.2%), consistent with prior studies.¹³ In addition, 34.1% (24,704) of our HIT patients developed at least 1 thrombotic complication, with venous thromboses more common than arterial thromboses, as previously reported.¹³ Lower extremity DVT was the most common thrombosis in orthopedic and vascular surgery. However, in cardiac surgery, acute coronary occlusion was the most common thrombotic complication. We postulate that the difference stems from the increased propensity of HIT-related thrombosis to occur in areas of vascular injury.¹⁴

The strengths of our study include its large size, which increases the generalizability of its results and avoids the biases inherent in small, single-center studies. As with any administrative dataset, the NIS may include coding errors related to underdiagnosis and overdiagnosis (eg, a HIT/HITT diagnosis carried forward from prior episodes). In our study, we inferred the HITT diagnosis in HIT cases with a vascular complication, but we could have missed HIT cases that had not been coded for vascular complications, and we could have overassociated vascular complications that had predated HIT and been treated with heparin. Although HIT and HITT were associated with worse clinical outcomes and increased hospital LOS, it is possible patients who were hospitalized longer had more opportunities for heparin use, and this exposure led to HIT or HITT. The lack of details regarding prior heparin use, including type of heparin (UFH or LMWH), prevented us from inferring the actual risks of individual heparin products.

In conclusion, in cardiac, vascular, and orthopedic surgery, HIT and especially HITT can significantly increase hospital LOS, inpatient costs, and mortality. Lower extremity DVT and acute coronary artery occlusion are the most common thrombotic complications in these cases. HIT screening strategies that incorporate platelet counts are recommended only in patients at highest risk (>1%), according to the most recent American College of Chest Physicians guidelines, but this recommendation was made on the basis of the high cost of alternative anticoagulants. Given our more recent data regarding the very high costs of HIT and especially HITT, screening strategies with platelet counts may prove more cost-effective. Recent genome-wide studies that found higher rates of HIT in patients with T-cell death–associated gene 8 (TDAG8) may help explain sex differences in postoperative patients and identify patients at highest risk so alternative anticoagulants can be used.¹⁵

 

 

Disclosures

This study was funded by the Reading Health System (grant RHS0010). Dr. Bhatt is supported by the Physician-Scientist Training Program (grant 2015-2016), College of Medicine, University of Nebraska Medical Center. The other authors report no financial conflicts of interest.

 

Each year, approximately one-third of all hospitalized medical and surgical patients in the United States (about 12 million patients) are exposed to heparin products for the prevention or treatment of thromboembolism.¹ Although generally safe, heparin can trigger an immune response in which platelet factor 4–heparin complexes set off an antibody-mediated cascade that can result in heparin-induced thrombocytopenia (HIT) and paradoxical arterial and venous thromboses, or heparin-induced thrombocytopenia with thrombosis (HITT). The incidence of HIT appears to be significantly higher with the more immunogenic unfractionated heparin (UFH) (2%-3% if treated for ≥5 days) than with low-molecular-weight heparin (LMWH) (0.2%-0.6%)² and is significantly higher in postoperative patients (1%-5%) than in medical patients.³ Older patients and female patients, especially those who undergo surgery, are thought to be at higher risk.⁴ Progression from HIT to HITT can occur in up to 50% of surgical patients,⁵ and HITT can significantly increase mortality.⁴

In the United States, LMWH use has increased 5-fold since 2000—an increase attributed to the 2010 release of generic enoxaparin.⁶ As US hospitals switch from UFH to LMWH with its significantly lower risk of HIT, up-to-date HIT incidence data may help physicians and payers better understand the impact of the disorder on mortality and hospital length of stay (LOS) for medical patients and subsets of surgical patients and subsequently direct screening efforts to those at highest risk. Therefore, in the present study, we used national data to determine the latest incidence and economic implications of HIT overall and for high-risk surgical groups.

In this study, we analyzed data from the Nationwide Inpatient Sample (NIS) database, part of the Healthcare Cost and Utilization Project (HCUP) of the Agency for Healthcare Research and Quality (AHRQ). The period studied was 2009-2011. We used International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 289.84, introduced in 2009, to identify patients who were at least 18 years old and had a primary or secondary diagnosis of HIT. Validated Clinical Classifications Software (CCS) was used to identify those who underwent cardiac, vascular, or orthopedic surgery, and ICD-9-CM codes for various thromboses were used to identify those with HITT (Supplemental Figure, Supplemental Table 1). Baseline patient and hospital characteristics were compared using the Pearson’s Chi-square test for categorical variables and the Student t test for continuous variables (2-sided P < 0.05 for statistical significance) (Table 1). We calculated the incidence of HIT overall and for the 3 surgical subgroups and compared the cohorts on their mean hospital LOS, mean hospital charge, and in-hospital mortality (Table 2).

Statistical analysis was performed with Stata Version 13.1 (Stata Corp, College Station, TX). Survey commands were used to account for the complex survey design in NIS. Reading Health System’s Institutional Review Board determined that our study protocol was exempt.

RESULTS

Of 98,636,364 hospitalizations, 72,515 (0.07%) involved HIT. There were no significant differences in the annual incidence of HIT during the study period (0.06% in 2009, 0.05% in 2010, 0.06% in 2011).

Patients with HIT were older than patients without HIT (mean age, 65.3 vs 57.3 years; P < 0.001). HIT was slightly more common in men overall (OR, 1.48; 95% CI, 1.46-1.51), but subgroup analyses revealed women had higher rates of HIT after cardiac surgery (OR, 1.41; 95% CI, 1.26-1.58) and vascular surgery (OR, 1.42; 95% CI, 1.29-1.57), though not after orthopedic surgery (OR, 1.06; 95% CI, 0.89-1.26). The majority of HIT cases were in urban teaching hospitals (56.23%) and in large hospitals, those with at least 325 beds (69.26%). There was no difference in mean age between patients with HITT and patients with HIT without thrombosis (65.46 vs 65.14 years; P = 0.32). Although the incidence of HITT did not differ by hospital location or teaching status, HITT cases were more common in hospitals with at least 325 beds (71.81%).

Regarding HIT, the death rate was 4-fold higher for patients with the disorder (9.63%) than for those without it (2.19%); hospital LOS and costs were significantly higher, too (Table 2). In addition, in-hospital mortality was higher (P < 0.001) for patients with HITT (12.28%) than for patients with HIT without thrombosis (8.24%); HITT patients’ hospital LOS and costs were higher as well. In patients who had cardiac, vascular, or orthopedic surgery, development of HIT was also associated with significantly higher in-hospital mortality, mean hospital LOS, and mean hospital charge. In patients with HITT, deep vein thrombosis (DVT) and pulmonary embolism represented the majority of reported cases (Supplemental Table 2). However, in patients who had cardiac surgery, acute arterial thromboses of coronary and cerebral vessels were more common.

DISCUSSION

In this national database survey, the overall incidence of HIT during the study period 2009-2011 was 0.07%, or 1 in 1350 hospitalized patients. Although earlier studies reported rates as high as 5% for high-risk subgroups of surgical patients,⁷ our data are more in line with more recently reported rates: about 0.02% for hospital admissions⁸ and from less than 0.1% to 0.4% for patients who received heparin.⁹ Older studies, which predominantly involved postoperative patients and were conducted when UFH often was the first-line heparin product used, may account for higher rates relative to ours. Of the 3 types of surgeries we evaluated, cardiac surgery had the highest HIT rate (0.5%), consistent with other studies.⁴ The higher HIT/HITT rates found for larger urban hospitals in our study might be attributable to increased awareness and testing, availability of hematology consultation, and higher risk of heparin use in this setting, where patients are sicker and cases and procedures more complicated.

Age was an important determinant of HIT risk in our study and in similar large-database series.⁴ Whether increased UFH use in the elderly (because of age or kidney disease) was a causative factor in this finding is unknown. In our study, although men and women had a nearly equal incidence of HIT, women had a significantly higher risk of HIT after both cardiac surgery and vascular surgery. Immune-mediated mechanisms that are more common in females may play a causative role in these settings.¹⁰

Our study results showed HIT associated with increased hospital LOS and an almost 4-fold increase in inpatient mortality and costs. The increased economic burden in HIT cases may be driven by the diagnostic work-up cost and expensive alternative anticoagulation.^11,12 Similarly, compared with HIT without thrombosis, HITT was associated with significantly increased hospital LOS (3.7 days), total hospital charge ($64,279) and mortality (49% increase, to 12.2% from 8.2%), consistent with prior studies.¹³ In addition, 34.1% (24,704) of our HIT patients developed at least 1 thrombotic complication, with venous thromboses more common than arterial thromboses, as previously reported.¹³ Lower extremity DVT was the most common thrombosis in orthopedic and vascular surgery. However, in cardiac surgery, acute coronary occlusion was the most common thrombotic complication. We postulate that the difference stems from the increased propensity of HIT-related thrombosis to occur in areas of vascular injury.¹⁴

The strengths of our study include its large size, which increases the generalizability of its results and avoids the biases inherent in small, single-center studies. As with any administrative dataset, the NIS may include coding errors related to underdiagnosis and overdiagnosis (eg, a HIT/HITT diagnosis carried forward from prior episodes). In our study, we inferred the HITT diagnosis in HIT cases with a vascular complication, but we could have missed HIT cases that had not been coded for vascular complications, and we could have overassociated vascular complications that had predated HIT and been treated with heparin. Although HIT and HITT were associated with worse clinical outcomes and increased hospital LOS, it is possible patients who were hospitalized longer had more opportunities for heparin use, and this exposure led to HIT or HITT. The lack of details regarding prior heparin use, including type of heparin (UFH or LMWH), prevented us from inferring the actual risks of individual heparin products.

In conclusion, in cardiac, vascular, and orthopedic surgery, HIT and especially HITT can significantly increase hospital LOS, inpatient costs, and mortality. Lower extremity DVT and acute coronary artery occlusion are the most common thrombotic complications in these cases. HIT screening strategies that incorporate platelet counts are recommended only in patients at highest risk (>1%), according to the most recent American College of Chest Physicians guidelines, but this recommendation was made on the basis of the high cost of alternative anticoagulants. Given our more recent data regarding the very high costs of HIT and especially HITT, screening strategies with platelet counts may prove more cost-effective. Recent genome-wide studies that found higher rates of HIT in patients with T-cell death–associated gene 8 (TDAG8) may help explain sex differences in postoperative patients and identify patients at highest risk so alternative anticoagulants can be used.¹⁵

 

 

Disclosures

This study was funded by the Reading Health System (grant RHS0010). Dr. Bhatt is supported by the Physician-Scientist Training Program (grant 2015-2016), College of Medicine, University of Nebraska Medical Center. The other authors report no financial conflicts of interest.

 

Each year, approximately one-third of all hospitalized medical and surgical patients in the United States (about 12 million patients) are exposed to heparin products for the prevention or treatment of thromboembolism.¹ Although generally safe, heparin can trigger an immune response in which platelet factor 4–heparin complexes set off an antibody-mediated cascade that can result in heparin-induced thrombocytopenia (HIT) and paradoxical arterial and venous thromboses, or heparin-induced thrombocytopenia with thrombosis (HITT). The incidence of HIT appears to be significantly higher with the more immunogenic unfractionated heparin (UFH) (2%-3% if treated for ≥5 days) than with low-molecular-weight heparin (LMWH) (0.2%-0.6%)² and is significantly higher in postoperative patients (1%-5%) than in medical patients.³ Older patients and female patients, especially those who undergo surgery, are thought to be at higher risk.⁴ Progression from HIT to HITT can occur in up to 50% of surgical patients,⁵ and HITT can significantly increase mortality.⁴

In the United States, LMWH use has increased 5-fold since 2000—an increase attributed to the 2010 release of generic enoxaparin.⁶ As US hospitals switch from UFH to LMWH with its significantly lower risk of HIT, up-to-date HIT incidence data may help physicians and payers better understand the impact of the disorder on mortality and hospital length of stay (LOS) for medical patients and subsets of surgical patients and subsequently direct screening efforts to those at highest risk. Therefore, in the present study, we used national data to determine the latest incidence and economic implications of HIT overall and for high-risk surgical groups.

In this study, we analyzed data from the Nationwide Inpatient Sample (NIS) database, part of the Healthcare Cost and Utilization Project (HCUP) of the Agency for Healthcare Research and Quality (AHRQ). The period studied was 2009-2011. We used International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 289.84, introduced in 2009, to identify patients who were at least 18 years old and had a primary or secondary diagnosis of HIT. Validated Clinical Classifications Software (CCS) was used to identify those who underwent cardiac, vascular, or orthopedic surgery, and ICD-9-CM codes for various thromboses were used to identify those with HITT (Supplemental Figure, Supplemental Table 1). Baseline patient and hospital characteristics were compared using the Pearson’s Chi-square test for categorical variables and the Student t test for continuous variables (2-sided P < 0.05 for statistical significance) (Table 1). We calculated the incidence of HIT overall and for the 3 surgical subgroups and compared the cohorts on their mean hospital LOS, mean hospital charge, and in-hospital mortality (Table 2).

Statistical analysis was performed with Stata Version 13.1 (Stata Corp, College Station, TX). Survey commands were used to account for the complex survey design in NIS. Reading Health System’s Institutional Review Board determined that our study protocol was exempt.

RESULTS

Of 98,636,364 hospitalizations, 72,515 (0.07%) involved HIT. There were no significant differences in the annual incidence of HIT during the study period (0.06% in 2009, 0.05% in 2010, 0.06% in 2011).

Patients with HIT were older than patients without HIT (mean age, 65.3 vs 57.3 years; P < 0.001). HIT was slightly more common in men overall (OR, 1.48; 95% CI, 1.46-1.51), but subgroup analyses revealed women had higher rates of HIT after cardiac surgery (OR, 1.41; 95% CI, 1.26-1.58) and vascular surgery (OR, 1.42; 95% CI, 1.29-1.57), though not after orthopedic surgery (OR, 1.06; 95% CI, 0.89-1.26). The majority of HIT cases were in urban teaching hospitals (56.23%) and in large hospitals, those with at least 325 beds (69.26%). There was no difference in mean age between patients with HITT and patients with HIT without thrombosis (65.46 vs 65.14 years; P = 0.32). Although the incidence of HITT did not differ by hospital location or teaching status, HITT cases were more common in hospitals with at least 325 beds (71.81%).

Regarding HIT, the death rate was 4-fold higher for patients with the disorder (9.63%) than for those without it (2.19%); hospital LOS and costs were significantly higher, too (Table 2). In addition, in-hospital mortality was higher (P < 0.001) for patients with HITT (12.28%) than for patients with HIT without thrombosis (8.24%); HITT patients’ hospital LOS and costs were higher as well. In patients who had cardiac, vascular, or orthopedic surgery, development of HIT was also associated with significantly higher in-hospital mortality, mean hospital LOS, and mean hospital charge. In patients with HITT, deep vein thrombosis (DVT) and pulmonary embolism represented the majority of reported cases (Supplemental Table 2). However, in patients who had cardiac surgery, acute arterial thromboses of coronary and cerebral vessels were more common.

DISCUSSION

In this national database survey, the overall incidence of HIT during the study period 2009-2011 was 0.07%, or 1 in 1350 hospitalized patients. Although earlier studies reported rates as high as 5% for high-risk subgroups of surgical patients,⁷ our data are more in line with more recently reported rates: about 0.02% for hospital admissions⁸ and from less than 0.1% to 0.4% for patients who received heparin.⁹ Older studies, which predominantly involved postoperative patients and were conducted when UFH often was the first-line heparin product used, may account for higher rates relative to ours. Of the 3 types of surgeries we evaluated, cardiac surgery had the highest HIT rate (0.5%), consistent with other studies.⁴ The higher HIT/HITT rates found for larger urban hospitals in our study might be attributable to increased awareness and testing, availability of hematology consultation, and higher risk of heparin use in this setting, where patients are sicker and cases and procedures more complicated.

Age was an important determinant of HIT risk in our study and in similar large-database series.⁴ Whether increased UFH use in the elderly (because of age or kidney disease) was a causative factor in this finding is unknown. In our study, although men and women had a nearly equal incidence of HIT, women had a significantly higher risk of HIT after both cardiac surgery and vascular surgery. Immune-mediated mechanisms that are more common in females may play a causative role in these settings.¹⁰

Our study results showed HIT associated with increased hospital LOS and an almost 4-fold increase in inpatient mortality and costs. The increased economic burden in HIT cases may be driven by the diagnostic work-up cost and expensive alternative anticoagulation.^11,12 Similarly, compared with HIT without thrombosis, HITT was associated with significantly increased hospital LOS (3.7 days), total hospital charge ($64,279) and mortality (49% increase, to 12.2% from 8.2%), consistent with prior studies.¹³ In addition, 34.1% (24,704) of our HIT patients developed at least 1 thrombotic complication, with venous thromboses more common than arterial thromboses, as previously reported.¹³ Lower extremity DVT was the most common thrombosis in orthopedic and vascular surgery. However, in cardiac surgery, acute coronary occlusion was the most common thrombotic complication. We postulate that the difference stems from the increased propensity of HIT-related thrombosis to occur in areas of vascular injury.¹⁴

The strengths of our study include its large size, which increases the generalizability of its results and avoids the biases inherent in small, single-center studies. As with any administrative dataset, the NIS may include coding errors related to underdiagnosis and overdiagnosis (eg, a HIT/HITT diagnosis carried forward from prior episodes). In our study, we inferred the HITT diagnosis in HIT cases with a vascular complication, but we could have missed HIT cases that had not been coded for vascular complications, and we could have overassociated vascular complications that had predated HIT and been treated with heparin. Although HIT and HITT were associated with worse clinical outcomes and increased hospital LOS, it is possible patients who were hospitalized longer had more opportunities for heparin use, and this exposure led to HIT or HITT. The lack of details regarding prior heparin use, including type of heparin (UFH or LMWH), prevented us from inferring the actual risks of individual heparin products.

In conclusion, in cardiac, vascular, and orthopedic surgery, HIT and especially HITT can significantly increase hospital LOS, inpatient costs, and mortality. Lower extremity DVT and acute coronary artery occlusion are the most common thrombotic complications in these cases. HIT screening strategies that incorporate platelet counts are recommended only in patients at highest risk (>1%), according to the most recent American College of Chest Physicians guidelines, but this recommendation was made on the basis of the high cost of alternative anticoagulants. Given our more recent data regarding the very high costs of HIT and especially HITT, screening strategies with platelet counts may prove more cost-effective. Recent genome-wide studies that found higher rates of HIT in patients with T-cell death–associated gene 8 (TDAG8) may help explain sex differences in postoperative patients and identify patients at highest risk so alternative anticoagulants can be used.¹⁵

 

 

Disclosures

This study was funded by the Reading Health System (grant RHS0010). Dr. Bhatt is supported by the Physician-Scientist Training Program (grant 2015-2016), College of Medicine, University of Nebraska Medical Center. The other authors report no financial conflicts of interest.

 

The average American adult reads at an 8th-grade level.¹ Limited general literacy can affect health literacy, which is defined as the “degree to which individuals have the capacity to obtain, process and understand basic health information and services needed to make appropriate health decisions.”^2,3 Adults with limited health literacy are at risk for poorer outcomes, including overuse of the emergency department and lower adherence to preventive care recommendations.⁴

Children transitioning from hospital to home depend on their adult caregivers (and their caregivers’ health literacy) to carry out discharge instructions. During the immediate postdischarge period, complex care needs can involve new or changed medications, follow-up instructions, home care instructions, and suggestions regarding when and why to seek additional care.

The discharge education provided to patients in the hospital is often subpar because of lack of standardization and divided responsibility among providers.⁵ Communication of vital information to patients with low health literacy has been noted to be particularly poor,⁶ as many patient education materials are written at 10th-, 11th-, and 12th-grade reading levels.⁴ Evidence supports providing materials written at 6th-grade level or lower to increase comprehension.⁷ Several studies have evaluated the quality and readability of discharge instructions for hospitalized adults,^8,9 and one study found a link between poorly written instructions for adult patients and readmission risk.¹⁰ Less is known about readability in pediatrics, in which education may be more important for families of children most commonly hospitalized for acute illness.

We conducted a study to describe readability levels, understandability scores, and completeness of written instructions given to families at hospital discharge.

Study Design and Setting

In this study, we performed a cross-sectional review of discharge instructions within electronic health records at Cincinnati Children’s Hospital Medical Center (CCHMC). The study was reviewed and approved by CCHMC’s Institutional Review Board. Charts were randomly selected from all hospital medicine service discharges during two 3-month periods of high patient volume: January-March 2014 and January-March 2015.

CCHMC is a large urban academic referral center that is the sole provider of general, subspecialty, and critical pediatric inpatient care for a large geographical area. CCHMC, which has 600 beds, provides cares for many children who live in impoverished settings. Its hospital medicine service consists of 4 teams that care for approximately 7000 children hospitalized with general pediatric illnesses each year. Each team consists of 5 or 6 pediatric residents supervised by a hospital medicine attending.

Providers, most commonly pediatric interns, generate discharge instructions in electronic health records. In this nonautomated process, they use free-text or nonstandardized templates to create content. At discharge, instructions are printed as part of the postvisit summary, which includes updates on medications and scheduled follow-up appointments. Bedside nurses verbally review the instructions with families and provide printed copies for home use.

Data Collection and Analysis

A random sequence generator was used to select charts for review. Instructions written in a language other than English were excluded. Written discharge instructions and clinical information, including age, sex, primary diagnosis, insurance type, number of discharge medications, number of scheduled appointments at discharge, and hospital length of stay, were abstracted from electronic health records and anonymized before analysis. The primary outcomes assessed were discharge instruction readability, understandability, and completeness. Readability was calculated with Fry Readability Scale (FRS) scores,¹¹ which range from 1 to 17 and correspond to reading levels (score 1 = 1st-grade reading level). Health literacy experts have used the FRS to assess readability in health care environments.¹²

Understandability was measured with the Patient Education Materials Assessment Tool (PEMAT), a validated scoring system provided by the Agency for Healthcare Research and Quality.¹³ The PEMAT measures the understandability of print materials on a scale ranging from 0% to 100%. Higher scores indicate increased understandability, and scores under 70% indicate instructions are difficult to understand.

Although recent efforts have focused on the development of quality metrics for hospital-to-home transitions of pediatric patients,¹⁴ during our study there were no standard items to include in pediatric discharge instructions. Five criteria for completeness were determined by consensus of 3 pediatric hospital medicine faculty and were informed by qualitative results of work performed at our institution—work in which families noted challenges with information overload and a desire for pertinent and usable information that would enhance caregiver confidence and discharge preparedness.¹⁵ The criteria included statement of diagnosis, description of diagnosis, signs and symptoms indicative of the need for escalation of care (warning signs), the person caregivers should call if worried, and contact information for the primary care provider, subspecialist, and/or emergency department. Each set of discharge instructions was manually evaluated for completeness (presence of each individual component, number of components present, presence of all components). All charts were scored by the same investigator. A convenience sample of 20 charts was evaluated by a different investigator to ensure rating parameters were clear and classification was consistent (defined as perfect agreement). If the primary rater was undecided on a discharge instruction score, the secondary rater rated the instruction, and consensus was reached.

Means, medians, and ranges were calculated to enumerate the distribution of readability levels, understandability scores, and completeness of discharge instructions. Instructions were classified as readable if the FRS score was 6 or under, as understandable if the PEMAT score was under 70%, and as complete if all 5 criteria were satisfied. Descriptive statistics were generated for all demographic and clinical variables.

Of the study period’s 3819 discharges, 200 were randomly selected for review. Table 1 lists the demographic and clinical information of patients included in the analyses. Median FRS score was 10, indicating a 10th-grade reading level (interquartile range, 8-12; range, 1-13) (Table 2). Only 14 (7%) of 200 discharge instructions had a score of 6 or under. Median PEMAT understandability score was 73% (interquartile range, 64%-82%), and 36% of instructions had a PEMAT score under 70%. No instruction satisfied all 5 of the defined characteristics of complete discharge instructions (Table 2).

To our knowledge, this is the first study of the readability, understandability, and completeness of discharge instructions in a pediatric population. We found that the majority of discharge instruction readability levels were 10th grade or higher, that many instructions were difficult to understand, and that important information was missing from many instructions.

Discharge instruction readability levels were higher than the literacy level of many families in surrounding communities. The high school dropout rates in Cincinnati are staggering; they range from 22% to 64% in the 10 neighborhoods with the largest proportion of residents not completing high school.¹⁶ However, such findings are not unique to Cincinnati; low literacy is prevalent throughout the United States. Caregivers with limited literacy skills may struggle to navigate complex health systems, understand medical instructions and anticipatory guidance, perform child care and self-care tasks, and understand issues related to consent, medical authorization, and risk communication.¹⁷

Although readability is important, other factors also correlate with comprehension and execution of discharge tasks.¹⁸ Information must be understandable, or presented in a way that makes sense and can inform appropriate action. In many cases in our study, instructions were incomplete, despite previous investigators’ emphasizing caregivers’ desire and need for written instructions that are complete, informative, and inclusive of clearly outlined contingency plans.^15,19 In addition, families may differ in the level of support needed after discharge; standardizing elements and including families in the development of discharge instructions may improve communication.⁸

This study had several limitations. First, the discharge instructions randomly selected for review were all written during the winter months. As the census on the hospital medicine teams is particularly high during that time, authors with competing responsibilities may not have had enough time to write effective discharge instructions then. We selected the winter period in order to capture real-world instructions written during a busy clinical time, when providers care for a high volume of patients. Second, caregiver health literacy and English-language proficiency were not assessed, and information regarding caregivers’ race/ethnicity, educational attainment, and socioeconomic status was unavailable. Third, interrater agreement was not formally evaluated. Fourth, this was a single-center study with results that may not be generalizable.

In conclusion, discharge instructions for pediatric patients are often difficult to read and understand, and incomplete. Efforts to address these communication gaps—including educational initiatives for physician trainees focused on health literacy, and quality improvement work directed at standardization and creation of readable, understandable, and complete discharge instructions—are crucial in providing safe, high-value care. Researchers need to evaluate the relationship between discharge instruction quality and outcomes, including unplanned office visits, emergency department visits, and readmissions.

Disclosure

Nothing to report.

The average American adult reads at an 8th-grade level.¹ Limited general literacy can affect health literacy, which is defined as the “degree to which individuals have the capacity to obtain, process and understand basic health information and services needed to make appropriate health decisions.”^2,3 Adults with limited health literacy are at risk for poorer outcomes, including overuse of the emergency department and lower adherence to preventive care recommendations.⁴

Children transitioning from hospital to home depend on their adult caregivers (and their caregivers’ health literacy) to carry out discharge instructions. During the immediate postdischarge period, complex care needs can involve new or changed medications, follow-up instructions, home care instructions, and suggestions regarding when and why to seek additional care.

The discharge education provided to patients in the hospital is often subpar because of lack of standardization and divided responsibility among providers.⁵ Communication of vital information to patients with low health literacy has been noted to be particularly poor,⁶ as many patient education materials are written at 10th-, 11th-, and 12th-grade reading levels.⁴ Evidence supports providing materials written at 6th-grade level or lower to increase comprehension.⁷ Several studies have evaluated the quality and readability of discharge instructions for hospitalized adults,^8,9 and one study found a link between poorly written instructions for adult patients and readmission risk.¹⁰ Less is known about readability in pediatrics, in which education may be more important for families of children most commonly hospitalized for acute illness.

We conducted a study to describe readability levels, understandability scores, and completeness of written instructions given to families at hospital discharge.

Study Design and Setting

In this study, we performed a cross-sectional review of discharge instructions within electronic health records at Cincinnati Children’s Hospital Medical Center (CCHMC). The study was reviewed and approved by CCHMC’s Institutional Review Board. Charts were randomly selected from all hospital medicine service discharges during two 3-month periods of high patient volume: January-March 2014 and January-March 2015.

CCHMC is a large urban academic referral center that is the sole provider of general, subspecialty, and critical pediatric inpatient care for a large geographical area. CCHMC, which has 600 beds, provides cares for many children who live in impoverished settings. Its hospital medicine service consists of 4 teams that care for approximately 7000 children hospitalized with general pediatric illnesses each year. Each team consists of 5 or 6 pediatric residents supervised by a hospital medicine attending.

Providers, most commonly pediatric interns, generate discharge instructions in electronic health records. In this nonautomated process, they use free-text or nonstandardized templates to create content. At discharge, instructions are printed as part of the postvisit summary, which includes updates on medications and scheduled follow-up appointments. Bedside nurses verbally review the instructions with families and provide printed copies for home use.

Data Collection and Analysis

A random sequence generator was used to select charts for review. Instructions written in a language other than English were excluded. Written discharge instructions and clinical information, including age, sex, primary diagnosis, insurance type, number of discharge medications, number of scheduled appointments at discharge, and hospital length of stay, were abstracted from electronic health records and anonymized before analysis. The primary outcomes assessed were discharge instruction readability, understandability, and completeness. Readability was calculated with Fry Readability Scale (FRS) scores,¹¹ which range from 1 to 17 and correspond to reading levels (score 1 = 1st-grade reading level). Health literacy experts have used the FRS to assess readability in health care environments.¹²

Understandability was measured with the Patient Education Materials Assessment Tool (PEMAT), a validated scoring system provided by the Agency for Healthcare Research and Quality.¹³ The PEMAT measures the understandability of print materials on a scale ranging from 0% to 100%. Higher scores indicate increased understandability, and scores under 70% indicate instructions are difficult to understand.

Although recent efforts have focused on the development of quality metrics for hospital-to-home transitions of pediatric patients,¹⁴ during our study there were no standard items to include in pediatric discharge instructions. Five criteria for completeness were determined by consensus of 3 pediatric hospital medicine faculty and were informed by qualitative results of work performed at our institution—work in which families noted challenges with information overload and a desire for pertinent and usable information that would enhance caregiver confidence and discharge preparedness.¹⁵ The criteria included statement of diagnosis, description of diagnosis, signs and symptoms indicative of the need for escalation of care (warning signs), the person caregivers should call if worried, and contact information for the primary care provider, subspecialist, and/or emergency department. Each set of discharge instructions was manually evaluated for completeness (presence of each individual component, number of components present, presence of all components). All charts were scored by the same investigator. A convenience sample of 20 charts was evaluated by a different investigator to ensure rating parameters were clear and classification was consistent (defined as perfect agreement). If the primary rater was undecided on a discharge instruction score, the secondary rater rated the instruction, and consensus was reached.

Means, medians, and ranges were calculated to enumerate the distribution of readability levels, understandability scores, and completeness of discharge instructions. Instructions were classified as readable if the FRS score was 6 or under, as understandable if the PEMAT score was under 70%, and as complete if all 5 criteria were satisfied. Descriptive statistics were generated for all demographic and clinical variables.

Of the study period’s 3819 discharges, 200 were randomly selected for review. Table 1 lists the demographic and clinical information of patients included in the analyses. Median FRS score was 10, indicating a 10th-grade reading level (interquartile range, 8-12; range, 1-13) (Table 2). Only 14 (7%) of 200 discharge instructions had a score of 6 or under. Median PEMAT understandability score was 73% (interquartile range, 64%-82%), and 36% of instructions had a PEMAT score under 70%. No instruction satisfied all 5 of the defined characteristics of complete discharge instructions (Table 2).

To our knowledge, this is the first study of the readability, understandability, and completeness of discharge instructions in a pediatric population. We found that the majority of discharge instruction readability levels were 10th grade or higher, that many instructions were difficult to understand, and that important information was missing from many instructions.

Discharge instruction readability levels were higher than the literacy level of many families in surrounding communities. The high school dropout rates in Cincinnati are staggering; they range from 22% to 64% in the 10 neighborhoods with the largest proportion of residents not completing high school.¹⁶ However, such findings are not unique to Cincinnati; low literacy is prevalent throughout the United States. Caregivers with limited literacy skills may struggle to navigate complex health systems, understand medical instructions and anticipatory guidance, perform child care and self-care tasks, and understand issues related to consent, medical authorization, and risk communication.¹⁷

Although readability is important, other factors also correlate with comprehension and execution of discharge tasks.¹⁸ Information must be understandable, or presented in a way that makes sense and can inform appropriate action. In many cases in our study, instructions were incomplete, despite previous investigators’ emphasizing caregivers’ desire and need for written instructions that are complete, informative, and inclusive of clearly outlined contingency plans.^15,19 In addition, families may differ in the level of support needed after discharge; standardizing elements and including families in the development of discharge instructions may improve communication.⁸

This study had several limitations. First, the discharge instructions randomly selected for review were all written during the winter months. As the census on the hospital medicine teams is particularly high during that time, authors with competing responsibilities may not have had enough time to write effective discharge instructions then. We selected the winter period in order to capture real-world instructions written during a busy clinical time, when providers care for a high volume of patients. Second, caregiver health literacy and English-language proficiency were not assessed, and information regarding caregivers’ race/ethnicity, educational attainment, and socioeconomic status was unavailable. Third, interrater agreement was not formally evaluated. Fourth, this was a single-center study with results that may not be generalizable.

In conclusion, discharge instructions for pediatric patients are often difficult to read and understand, and incomplete. Efforts to address these communication gaps—including educational initiatives for physician trainees focused on health literacy, and quality improvement work directed at standardization and creation of readable, understandable, and complete discharge instructions—are crucial in providing safe, high-value care. Researchers need to evaluate the relationship between discharge instruction quality and outcomes, including unplanned office visits, emergency department visits, and readmissions.

Disclosure

Nothing to report.

The average American adult reads at an 8th-grade level.¹ Limited general literacy can affect health literacy, which is defined as the “degree to which individuals have the capacity to obtain, process and understand basic health information and services needed to make appropriate health decisions.”^2,3 Adults with limited health literacy are at risk for poorer outcomes, including overuse of the emergency department and lower adherence to preventive care recommendations.⁴

Children transitioning from hospital to home depend on their adult caregivers (and their caregivers’ health literacy) to carry out discharge instructions. During the immediate postdischarge period, complex care needs can involve new or changed medications, follow-up instructions, home care instructions, and suggestions regarding when and why to seek additional care.

The discharge education provided to patients in the hospital is often subpar because of lack of standardization and divided responsibility among providers.⁵ Communication of vital information to patients with low health literacy has been noted to be particularly poor,⁶ as many patient education materials are written at 10th-, 11th-, and 12th-grade reading levels.⁴ Evidence supports providing materials written at 6th-grade level or lower to increase comprehension.⁷ Several studies have evaluated the quality and readability of discharge instructions for hospitalized adults,^8,9 and one study found a link between poorly written instructions for adult patients and readmission risk.¹⁰ Less is known about readability in pediatrics, in which education may be more important for families of children most commonly hospitalized for acute illness.

We conducted a study to describe readability levels, understandability scores, and completeness of written instructions given to families at hospital discharge.

Study Design and Setting

In this study, we performed a cross-sectional review of discharge instructions within electronic health records at Cincinnati Children’s Hospital Medical Center (CCHMC). The study was reviewed and approved by CCHMC’s Institutional Review Board. Charts were randomly selected from all hospital medicine service discharges during two 3-month periods of high patient volume: January-March 2014 and January-March 2015.

CCHMC is a large urban academic referral center that is the sole provider of general, subspecialty, and critical pediatric inpatient care for a large geographical area. CCHMC, which has 600 beds, provides cares for many children who live in impoverished settings. Its hospital medicine service consists of 4 teams that care for approximately 7000 children hospitalized with general pediatric illnesses each year. Each team consists of 5 or 6 pediatric residents supervised by a hospital medicine attending.

Providers, most commonly pediatric interns, generate discharge instructions in electronic health records. In this nonautomated process, they use free-text or nonstandardized templates to create content. At discharge, instructions are printed as part of the postvisit summary, which includes updates on medications and scheduled follow-up appointments. Bedside nurses verbally review the instructions with families and provide printed copies for home use.

Data Collection and Analysis

A random sequence generator was used to select charts for review. Instructions written in a language other than English were excluded. Written discharge instructions and clinical information, including age, sex, primary diagnosis, insurance type, number of discharge medications, number of scheduled appointments at discharge, and hospital length of stay, were abstracted from electronic health records and anonymized before analysis. The primary outcomes assessed were discharge instruction readability, understandability, and completeness. Readability was calculated with Fry Readability Scale (FRS) scores,¹¹ which range from 1 to 17 and correspond to reading levels (score 1 = 1st-grade reading level). Health literacy experts have used the FRS to assess readability in health care environments.¹²

Understandability was measured with the Patient Education Materials Assessment Tool (PEMAT), a validated scoring system provided by the Agency for Healthcare Research and Quality.¹³ The PEMAT measures the understandability of print materials on a scale ranging from 0% to 100%. Higher scores indicate increased understandability, and scores under 70% indicate instructions are difficult to understand.

Although recent efforts have focused on the development of quality metrics for hospital-to-home transitions of pediatric patients,¹⁴ during our study there were no standard items to include in pediatric discharge instructions. Five criteria for completeness were determined by consensus of 3 pediatric hospital medicine faculty and were informed by qualitative results of work performed at our institution—work in which families noted challenges with information overload and a desire for pertinent and usable information that would enhance caregiver confidence and discharge preparedness.¹⁵ The criteria included statement of diagnosis, description of diagnosis, signs and symptoms indicative of the need for escalation of care (warning signs), the person caregivers should call if worried, and contact information for the primary care provider, subspecialist, and/or emergency department. Each set of discharge instructions was manually evaluated for completeness (presence of each individual component, number of components present, presence of all components). All charts were scored by the same investigator. A convenience sample of 20 charts was evaluated by a different investigator to ensure rating parameters were clear and classification was consistent (defined as perfect agreement). If the primary rater was undecided on a discharge instruction score, the secondary rater rated the instruction, and consensus was reached.

Means, medians, and ranges were calculated to enumerate the distribution of readability levels, understandability scores, and completeness of discharge instructions. Instructions were classified as readable if the FRS score was 6 or under, as understandable if the PEMAT score was under 70%, and as complete if all 5 criteria were satisfied. Descriptive statistics were generated for all demographic and clinical variables.

Of the study period’s 3819 discharges, 200 were randomly selected for review. Table 1 lists the demographic and clinical information of patients included in the analyses. Median FRS score was 10, indicating a 10th-grade reading level (interquartile range, 8-12; range, 1-13) (Table 2). Only 14 (7%) of 200 discharge instructions had a score of 6 or under. Median PEMAT understandability score was 73% (interquartile range, 64%-82%), and 36% of instructions had a PEMAT score under 70%. No instruction satisfied all 5 of the defined characteristics of complete discharge instructions (Table 2).

To our knowledge, this is the first study of the readability, understandability, and completeness of discharge instructions in a pediatric population. We found that the majority of discharge instruction readability levels were 10th grade or higher, that many instructions were difficult to understand, and that important information was missing from many instructions.

Discharge instruction readability levels were higher than the literacy level of many families in surrounding communities. The high school dropout rates in Cincinnati are staggering; they range from 22% to 64% in the 10 neighborhoods with the largest proportion of residents not completing high school.¹⁶ However, such findings are not unique to Cincinnati; low literacy is prevalent throughout the United States. Caregivers with limited literacy skills may struggle to navigate complex health systems, understand medical instructions and anticipatory guidance, perform child care and self-care tasks, and understand issues related to consent, medical authorization, and risk communication.¹⁷

Although readability is important, other factors also correlate with comprehension and execution of discharge tasks.¹⁸ Information must be understandable, or presented in a way that makes sense and can inform appropriate action. In many cases in our study, instructions were incomplete, despite previous investigators’ emphasizing caregivers’ desire and need for written instructions that are complete, informative, and inclusive of clearly outlined contingency plans.^15,19 In addition, families may differ in the level of support needed after discharge; standardizing elements and including families in the development of discharge instructions may improve communication.⁸

This study had several limitations. First, the discharge instructions randomly selected for review were all written during the winter months. As the census on the hospital medicine teams is particularly high during that time, authors with competing responsibilities may not have had enough time to write effective discharge instructions then. We selected the winter period in order to capture real-world instructions written during a busy clinical time, when providers care for a high volume of patients. Second, caregiver health literacy and English-language proficiency were not assessed, and information regarding caregivers’ race/ethnicity, educational attainment, and socioeconomic status was unavailable. Third, interrater agreement was not formally evaluated. Fourth, this was a single-center study with results that may not be generalizable.

In conclusion, discharge instructions for pediatric patients are often difficult to read and understand, and incomplete. Efforts to address these communication gaps—including educational initiatives for physician trainees focused on health literacy, and quality improvement work directed at standardization and creation of readable, understandable, and complete discharge instructions—are crucial in providing safe, high-value care. Researchers need to evaluate the relationship between discharge instruction quality and outcomes, including unplanned office visits, emergency department visits, and readmissions.

Disclosure

Nothing to report.

During internal medicine (IM) clerkships, course directors are responsible for ensuring that medical students attain basic competency in patient management through use of risk–benefit, cost–benefit, and evidence-based considerations.¹ However, the students’ primary teachers—IM residents and attendings—consistently role-model high-value care (HVC) perhaps only half the time.² The inconsistency may have a few sources, including unawareness of the costs of tests and treatments ordered and little formal training in HVC.^3-5 In addition, the environment at some academic institutions may reward learners for performing tests that may be unnecessary.⁶

We conducted a study to assess medical students’ perceptions of unnecessary testing and the adequacy–inadequacy of HVC instruction, as well as their observations of behavior that may hinder the practice of HVC during the IM clerkship.

When students completed their third-year IM clerkships at The Johns Hopkins University School of Medicine, the Icahn School of Medicine at Mount Sinai, the University of Alabama at Birmingham School of Medicine, and the Tulane University School of Medicine, we sent them a recruitment email asking them to complete an anonymous survey regarding their clerkship experiences with HVC. The clerkships’ directors, who collaborated on survey development, searched the literature to quantify behavior thought to decrease the practice of HVC. The survey was tested several times with different learners and faculty to increase response process validity.

The SurveyMonkey online platform was used to administer the survey. Students were given 1 week after the end of their clerkship to complete the survey. Data were collected for the period October 2013 to December 2014. Each student was offered a $10 gift certificate for survey completion. Each institution received exempt approval from its institutional review board.

Survey respondents were divided into those who perceived HVC education as adequate and those who perceived it as inadequate. Chi-square tests were performed with Stata Version 12 (College Station, TX) to determine whether a student’s perception of HVC education being adequate or inadequate was significantly associated with the other survey questions.

Of 577 eligible students, 307 (53%) completed the survey. About 83% of the respondents reported noticing the ordering of laboratory or radiologic tests they considered unnecessary, and a majority (81%) of those students noticed this activity at least once a week. Overall, 51% of the respondents thought their HVC education was inadequate. Significantly more of the students who perceived their HVC education as inadequate were uncomfortable bringing an unnecessary test to the attention of the ward team, rarely discussed costs, and rarely observed team members being praised for forgoing unnecessary tests (Table). Two significant associations were found: between institution attended and perceived adequacy–inadequacy of HVC education and between institution and frequency of cost discussions.

Most (78.5%) students thought an HVC curriculum should be added to the IM clerkship, and 34.5% thought the HVC curriculum should be incorporated into daily rounds. In regards to additions to the clerkship curriculum, most students wanted to round with phlebotomy (29%) or discuss costs of testing on patients (26%).

Students attributed the ordering of unnecessary tests and treatments to several factors: residents investigating “interesting diagnoses” (46%), teams practicing defensive medicine (43%), consultants making requests (40%), attendings investigating “interesting diagnoses” (27%), and patients making requests (8%).

About 51% of the students thought their HVC education was inadequate, and about 83% noticed unnecessary testing. Our study findings reaffirm those of a single-site study in which 93% of students noted unnecessary testing.⁷

In this study, many students perceived HVC education as inadequate and reported wanting HVC principles added to their training and an HVC curriculum incorporated into daily rounds. Students who perceived HVC education as inadequate were significantly less comfortable bringing an unnecessary test to the attention of the ward team and noticed less discussion about costs and less praise for avoiding unnecessary tests. One institution had a significantly higher proportion of students perceiving their HVC education as adequate and noticing more discussions about test costs. This institution was the only one that incorporated discussions about test costs into its curriculum during the study period—which may account for its students’ perceptions.

This study had a few limitations. First, as the survey was administered after the IM clerkships, students’ responses may have been subject to recall bias. We minimized this bias by allowing 1 week for survey completion. Second, given the 53% response rate, there may have been response bias. However, one institution’s demographics showed no significant differences between responders and nonresponders with respect to age, sex, ethnicity, or type of degree. Third, students’ understanding of what tests and treatments are necessary and unnecessary may be relatively underdeveloped, given their training level. One study found that medical students with minimal clinical experience were able to identify unnecessary tests and treatments, but this study has not been validated at other institutions.⁷

Efforts to increase HVC education and practice have focused on residents and attendings, but our study findings reaffirm that HVC training is much needed and wanted in undergraduate medical education. Studies are needed to test the effectiveness of HVC curricula in medical school and the ability of these curricula to give students the tools they need to practice HVC.

Disclosures

Dr. Pahwa received support from the Johns Hopkins Hospitalist Scholars Fund, and Dr. Cayea is supported by the Daniel and Jeanette Hendin Schapiro Geriatric Medical Education Center. The sponsors had no role in study design, methods, subject recruitment, data collection, data analysis, or manuscript preparation. The authors have no conflicts of interest to disclose.

During internal medicine (IM) clerkships, course directors are responsible for ensuring that medical students attain basic competency in patient management through use of risk–benefit, cost–benefit, and evidence-based considerations.¹ However, the students’ primary teachers—IM residents and attendings—consistently role-model high-value care (HVC) perhaps only half the time.² The inconsistency may have a few sources, including unawareness of the costs of tests and treatments ordered and little formal training in HVC.^3-5 In addition, the environment at some academic institutions may reward learners for performing tests that may be unnecessary.⁶

We conducted a study to assess medical students’ perceptions of unnecessary testing and the adequacy–inadequacy of HVC instruction, as well as their observations of behavior that may hinder the practice of HVC during the IM clerkship.

When students completed their third-year IM clerkships at The Johns Hopkins University School of Medicine, the Icahn School of Medicine at Mount Sinai, the University of Alabama at Birmingham School of Medicine, and the Tulane University School of Medicine, we sent them a recruitment email asking them to complete an anonymous survey regarding their clerkship experiences with HVC. The clerkships’ directors, who collaborated on survey development, searched the literature to quantify behavior thought to decrease the practice of HVC. The survey was tested several times with different learners and faculty to increase response process validity.

The SurveyMonkey online platform was used to administer the survey. Students were given 1 week after the end of their clerkship to complete the survey. Data were collected for the period October 2013 to December 2014. Each student was offered a $10 gift certificate for survey completion. Each institution received exempt approval from its institutional review board.

Survey respondents were divided into those who perceived HVC education as adequate and those who perceived it as inadequate. Chi-square tests were performed with Stata Version 12 (College Station, TX) to determine whether a student’s perception of HVC education being adequate or inadequate was significantly associated with the other survey questions.

Of 577 eligible students, 307 (53%) completed the survey. About 83% of the respondents reported noticing the ordering of laboratory or radiologic tests they considered unnecessary, and a majority (81%) of those students noticed this activity at least once a week. Overall, 51% of the respondents thought their HVC education was inadequate. Significantly more of the students who perceived their HVC education as inadequate were uncomfortable bringing an unnecessary test to the attention of the ward team, rarely discussed costs, and rarely observed team members being praised for forgoing unnecessary tests (Table). Two significant associations were found: between institution attended and perceived adequacy–inadequacy of HVC education and between institution and frequency of cost discussions.

Most (78.5%) students thought an HVC curriculum should be added to the IM clerkship, and 34.5% thought the HVC curriculum should be incorporated into daily rounds. In regards to additions to the clerkship curriculum, most students wanted to round with phlebotomy (29%) or discuss costs of testing on patients (26%).

Students attributed the ordering of unnecessary tests and treatments to several factors: residents investigating “interesting diagnoses” (46%), teams practicing defensive medicine (43%), consultants making requests (40%), attendings investigating “interesting diagnoses” (27%), and patients making requests (8%).

About 51% of the students thought their HVC education was inadequate, and about 83% noticed unnecessary testing. Our study findings reaffirm those of a single-site study in which 93% of students noted unnecessary testing.⁷

In this study, many students perceived HVC education as inadequate and reported wanting HVC principles added to their training and an HVC curriculum incorporated into daily rounds. Students who perceived HVC education as inadequate were significantly less comfortable bringing an unnecessary test to the attention of the ward team and noticed less discussion about costs and less praise for avoiding unnecessary tests. One institution had a significantly higher proportion of students perceiving their HVC education as adequate and noticing more discussions about test costs. This institution was the only one that incorporated discussions about test costs into its curriculum during the study period—which may account for its students’ perceptions.

This study had a few limitations. First, as the survey was administered after the IM clerkships, students’ responses may have been subject to recall bias. We minimized this bias by allowing 1 week for survey completion. Second, given the 53% response rate, there may have been response bias. However, one institution’s demographics showed no significant differences between responders and nonresponders with respect to age, sex, ethnicity, or type of degree. Third, students’ understanding of what tests and treatments are necessary and unnecessary may be relatively underdeveloped, given their training level. One study found that medical students with minimal clinical experience were able to identify unnecessary tests and treatments, but this study has not been validated at other institutions.⁷

Efforts to increase HVC education and practice have focused on residents and attendings, but our study findings reaffirm that HVC training is much needed and wanted in undergraduate medical education. Studies are needed to test the effectiveness of HVC curricula in medical school and the ability of these curricula to give students the tools they need to practice HVC.

Disclosures

Dr. Pahwa received support from the Johns Hopkins Hospitalist Scholars Fund, and Dr. Cayea is supported by the Daniel and Jeanette Hendin Schapiro Geriatric Medical Education Center. The sponsors had no role in study design, methods, subject recruitment, data collection, data analysis, or manuscript preparation. The authors have no conflicts of interest to disclose.

During internal medicine (IM) clerkships, course directors are responsible for ensuring that medical students attain basic competency in patient management through use of risk–benefit, cost–benefit, and evidence-based considerations.¹ However, the students’ primary teachers—IM residents and attendings—consistently role-model high-value care (HVC) perhaps only half the time.² The inconsistency may have a few sources, including unawareness of the costs of tests and treatments ordered and little formal training in HVC.^3-5 In addition, the environment at some academic institutions may reward learners for performing tests that may be unnecessary.⁶

We conducted a study to assess medical students’ perceptions of unnecessary testing and the adequacy–inadequacy of HVC instruction, as well as their observations of behavior that may hinder the practice of HVC during the IM clerkship.

When students completed their third-year IM clerkships at The Johns Hopkins University School of Medicine, the Icahn School of Medicine at Mount Sinai, the University of Alabama at Birmingham School of Medicine, and the Tulane University School of Medicine, we sent them a recruitment email asking them to complete an anonymous survey regarding their clerkship experiences with HVC. The clerkships’ directors, who collaborated on survey development, searched the literature to quantify behavior thought to decrease the practice of HVC. The survey was tested several times with different learners and faculty to increase response process validity.

The SurveyMonkey online platform was used to administer the survey. Students were given 1 week after the end of their clerkship to complete the survey. Data were collected for the period October 2013 to December 2014. Each student was offered a $10 gift certificate for survey completion. Each institution received exempt approval from its institutional review board.

Survey respondents were divided into those who perceived HVC education as adequate and those who perceived it as inadequate. Chi-square tests were performed with Stata Version 12 (College Station, TX) to determine whether a student’s perception of HVC education being adequate or inadequate was significantly associated with the other survey questions.

Of 577 eligible students, 307 (53%) completed the survey. About 83% of the respondents reported noticing the ordering of laboratory or radiologic tests they considered unnecessary, and a majority (81%) of those students noticed this activity at least once a week. Overall, 51% of the respondents thought their HVC education was inadequate. Significantly more of the students who perceived their HVC education as inadequate were uncomfortable bringing an unnecessary test to the attention of the ward team, rarely discussed costs, and rarely observed team members being praised for forgoing unnecessary tests (Table). Two significant associations were found: between institution attended and perceived adequacy–inadequacy of HVC education and between institution and frequency of cost discussions.

Most (78.5%) students thought an HVC curriculum should be added to the IM clerkship, and 34.5% thought the HVC curriculum should be incorporated into daily rounds. In regards to additions to the clerkship curriculum, most students wanted to round with phlebotomy (29%) or discuss costs of testing on patients (26%).

Students attributed the ordering of unnecessary tests and treatments to several factors: residents investigating “interesting diagnoses” (46%), teams practicing defensive medicine (43%), consultants making requests (40%), attendings investigating “interesting diagnoses” (27%), and patients making requests (8%).

About 51% of the students thought their HVC education was inadequate, and about 83% noticed unnecessary testing. Our study findings reaffirm those of a single-site study in which 93% of students noted unnecessary testing.⁷

In this study, many students perceived HVC education as inadequate and reported wanting HVC principles added to their training and an HVC curriculum incorporated into daily rounds. Students who perceived HVC education as inadequate were significantly less comfortable bringing an unnecessary test to the attention of the ward team and noticed less discussion about costs and less praise for avoiding unnecessary tests. One institution had a significantly higher proportion of students perceiving their HVC education as adequate and noticing more discussions about test costs. This institution was the only one that incorporated discussions about test costs into its curriculum during the study period—which may account for its students’ perceptions.

This study had a few limitations. First, as the survey was administered after the IM clerkships, students’ responses may have been subject to recall bias. We minimized this bias by allowing 1 week for survey completion. Second, given the 53% response rate, there may have been response bias. However, one institution’s demographics showed no significant differences between responders and nonresponders with respect to age, sex, ethnicity, or type of degree. Third, students’ understanding of what tests and treatments are necessary and unnecessary may be relatively underdeveloped, given their training level. One study found that medical students with minimal clinical experience were able to identify unnecessary tests and treatments, but this study has not been validated at other institutions.⁷

Efforts to increase HVC education and practice have focused on residents and attendings, but our study findings reaffirm that HVC training is much needed and wanted in undergraduate medical education. Studies are needed to test the effectiveness of HVC curricula in medical school and the ability of these curricula to give students the tools they need to practice HVC.

Disclosures

Dr. Pahwa received support from the Johns Hopkins Hospitalist Scholars Fund, and Dr. Cayea is supported by the Daniel and Jeanette Hendin Schapiro Geriatric Medical Education Center. The sponsors had no role in study design, methods, subject recruitment, data collection, data analysis, or manuscript preparation. The authors have no conflicts of interest to disclose.